Committee:

Dr. Ralph, Advisor

Dr. Cai, Chair

Dr. Pestourie

Committee:

Dr. Naeemi, Advisor

Dr. Krishna, Chair

Dr. Mukhopadhyay

Explore how expertise is evolving in the age of AI. Dr. Gichoya will share insights from four years of the HITI Lab Datathon, highlighting lessons on data science, training, and real-world applications of AI in healthcare.

BIO
Dr. Gichoya is the William and Kay Casarella Professor of Radiology at Emory University and leads the HITI (Healthcare AI Innovation and Translational Informatics) lab. Her work is centered around using data science to study health equity. Her group works in 4 areas - building diverse datasets for machine learning (for example the Emory Breast dataset); evaluating AI for bias and fairness; validating AI in the real world setting and training the next generation of data scientists (both clinical and technical students) through hive learning and village mentoring. She serves as the program director for radiology: AI trainee editorial board and the Emory University medical students machine learning elective. She has mentored over 60 students across the world (now successful faculty, post doc, PHD, and industry employees) from several institutions around the world. She has received several awards including the most influential radiology researcher in 2022 and is a 2023 Emerging Scholar at the National Academy of Medicine.

Committee:

Dr. Yorai Wardi, ECE, Chair, Advisor

Dr. Fumin Zhang, ECE

Dr. Matthieu Bloch, ECE

Dr. Maegan Tucker, ECE

Dr. Haomin Zhou, Math

Relax with complementary beverages and come paint a succulent planter with a plant to go with it - pots, plants, and paints provided! 

Spots are limited so sign up to reserve yours: https://gatech.universitytickets.com/w/event.aspx?id=2259

Committee:

Dr. Callie Hao, ECE, Chair, Advisor

Dr. Tushar Krishna, ECE

Dr. Hyesoon Kim, CoC

Dr. Ismail Bustany, AMD

Dr. Yuan Xie, HKUST

Committee:

Dr. John Cressler, ECE, Chair, Advisor

Dr. Alan Doolittle

Dr. Visvesh Sathe, ECE

Dr. Jiahui Yuan, Sandisk

Dr. Guofu Niu, Auburn

Committee:

Dr. Ghassan AlRegib, ECE, Chair, Advisor

Dr. Larry Heck, ECE

Dr. Eva Dyer, Upenn

Dr. Jesus Martinez, Oxy

Dr. Yao Xie, ISyE

Committee:

Dr. Matthieu Bloch, ECE, Chair, Advisor

Dr. Cristopher Rozell, ECE, Co-Advisor

Dr. Mark Davenport, ECE

Dr. Ashwin Pananjadi, ISyE

Dr. Justin Romberg, ECE

Dr. Yao Xie, ISyE

Patents provide valuable and cutting-edge information for innovators and researchers. This workshop will begin with a brief introduction to intellectual property and then will focus on patents. The presenter will demonstrate how to conduct preliminary patent searches using publicly accessible patent tools. Attendees will have an opportunity to participate in hands-on learning activities.

《你的名字》让人想起，名字背后有记忆与牵挂；

《千与千寻》提醒我们，名字也可能被拿走。

这一次，让我们从两部电影出发，一起思想在风向改变的时候，如何不忘记自己真正的名字

聚会安排

6:30-7:10PM 吃饭与交流（提供免费的中餐）

7:10-7:40PM 敬拜与报告

7:40-9:00PM 专题聚会

43 But now thus says the Lord,
he who created you, O Jacob,
    he who formed you, O Israel:
“Fear not, for I have redeemed you;
    I have called you by name, you are mine. ——Isaiah 43:1

Your Name reminds us that names carry memories and connections.

Spirited Away shows that names can be forgotten or taken away.

As we step into a world that often changes its direction and values, let’s pause and reflect together:

How can we remember who we really are— the ones called by Christ’s name, and stand firm in faith amid the shifting winds around us?

Schedule

6:30–7:10 PM – Dinner and Fellowship (Free Chinese dinner provided)

7:10–7:40 PM – Worship and Announcements

7:40–9:00 PM – Special Topic

We will also be providing additional information about sign-ups for STEM Connect.
Event will run from 6:30pm to 8:00pm at Instructional Center 211.

📍 Sign up now: bit.ly/AshaSpringRun2026

#RunForEducation #AshaForEducation #SpringRun #GeorgiaTech"

Advisor: Dr. Dimitri Mavris

Milestone: MS Thesis Proposal

Degree Program: Aerospace Engineering

Title: Surrogate Modeling for Low Thrust Earth to Moon Spiral Trajectory Design and Preliminary Mission Feasibility Assessment

Abstract: Transfers from Earth to the Moon are becoming increasingly attractive due to the need for a permanent base on the Moon for interplanetary missions and cargo deliveries. Low thrust electric propulsion offers substantial propellant savings for such a trajectory. However, designing such transfers is computationally expensive, and there is insufficient real data from real missions for study. Formal optimization techniques for multi revolution spiral trajectories often take hours per trajectory and require comprehensive manual initial guess calculations. This makes it very difficult to explore the design space iteratively in the preliminary mission design stage, where engineers need to evaluate hundreds to thousands of combinations of candidate parameters. Machine learning surrogate models, however, offer a promising approximation that can deliver mission feasibility predictions in milliseconds rather than hours after training. Despite its potential, the use of machine learning surrogates for low thrust spiral Earth to Moon trajectories has not been investigated due to the spectral bias nature of these trajectories. Therefore, several key questions remain unanswered: Which state representation makes the surrogate model trainable? When will a physics informed neural network behave better than a purely data driven one, and can a single trained model actually be used in preliminary mission design? This thesis addresses these gaps by using a heuristic trajectory dataset in modified equinoctial elements and systematically comparing purely data driven and physics informed neural networks. It also investigates methods to improve speed and accuracy, such as perigee sampling and architecture that applies Fourier features to oscillatory channels. Preliminary findings indicate that the MEE formulation decreases the effect of spectral bias and provides trainable data for the neural network. Perigee based sampling significantly improves accuracy and training time, as it also reduces the effect of spectral bias. On clean data, the physics informed neural network reduces accuracy, but under noise, it matches the accuracy of the purely data driven network while yielding far more physically consistent predictions. The results show that a neural network surrogate model can be used to investigate the design space in milliseconds for preliminary mission design. 

Date and time: 2026-05-08, 09:00 - 11:00 am

Location: Collaborative Visualization Environment (CoVE) Weber SST II

Committee:
Dr. Dimitri Mavris (advisor), School of Aerospace Engineering
Prof. Graeme Kennedy , School of Aerospace Engineering
Dr. Tristan Sarton du Jonchay , School of Aerospace Engineering

 

Advisor: Dr. Dimitri Mavris

Milestone: PhD Thesis Proposal

Degree Program: Aerospace Engineering

Title: Development of A Simulator Based Training Framework for Autorotation Safety Improvements

Abstract: Commercial aviation in the U.S has experienced decades of steadily improving safety standards, culminating in an extremely safe operation today. General Aviation (GA) has also seen safety improvements; however, accident rates remain significantly higher than those observed in commercial aviation. Within GA, powerplant failure accidents remain a major concern, ranking as the second highest cause of fatalities. In rotorcraft operations in particular, emergency procedures following engine failure—specifically autorotation—present pilots with challenging, high-workload situations characterized by limited time and constrained decision-making options. Autorotation-related mishaps account for a substantial portion of rotorcraft incidents, representing nearly one-third of all accidents. While pilot training is a critical requirement for improving safety outcomes, autorotation training itself carries inherent risk, with training-related accidents accounting for approximately 40% of all autorotation accidents. This combination of high training risk and the need for thorough skill development highlights the necessity for alternative training approaches. Simulator-based training programs offer a viable solution; however, to be effective, a structured simulator training framework must be developed. A critical component of such a framework is the provision of objective assessment criteria and integrated feedback systems to enable efficient training and ensure that performance targets are consistently achieved. This thesis proposes a data-driven approach to support the development of assessment criteria for each phase of autorotation, focusing on the evaluation of critical pilot performance indicators that are not adequately addressed in current training standards or that rely on inconsistent, or subjective judgment calls. Additionally, an integrated feedback mechanism is proposed to enhance training efficiency and reinforce correct pilot responses. Together these proposals enable the implementation of the autorotation training framework by providing the key missing components for proper assessment and feedback that are required for a successful simulator training program. 

Date and time: 2026-05-15, 2 PM

Location: CoVE Weber SST II

Committee:
Dr. Dimitri Mavris (advisor), School of Aerospace Engineering
Dr. Marilyn Smith, School of Aerospace Engineering
Dr. Daniel Schrage, School of Aerospace Engineering
Dr. Alexia Payan, School of Aerospace Engineering
Mr. Charles Johnson, FAA

 

Advisor: Dr. Adam Steinberg

Milestone: PhD Thesis Proposal

Degree Program: Aerospace Engineering

Title: Optimization of Lean Premixed Prevaporized Combustion Through Pilot-Main Interaction

Abstract: Lean-burn engines have demonstrated significant reductions in major pollutants, particularly nitrogen oxides (NOx) and non-volatile particulate matter (nvPM). Lean premixed prevaporized (LPP) combustors have seen growing interest in aeronautical combustion as the increased combustor inlet temperatures associated with future compact core and potential civil supersonic engines can improve vaporization of liquid fuels and reduce emissions. However, a drawback of LPP combustion is the susceptibility to lean blowoff. To improve stability of the combustor, a multi-element configuration featuring a richer, more stable pilot flame is often employed. Yet further understanding of the interaction between the pilot and leaner-burning main flames, along with the stability-emission tradeoff of that interaction, is not well understood. This work aims to advance understanding of pilot-main interactions through both a fundamental and a practical perspective, including optimization of a supersonic LPP combustor operating a flight relevant conditions. To conduct this research, an LPP combustor featuring a central partially-premixed pilot flame surrounded by four LPP main flames has been developed and fabricated within the Georgia Tech Combustion Lab. Initial testing of the combustor demonstrated reductions in NOx and nvPM over conventional aeronautical combustors. In-depth analysis revealed the blowoff propensity of the LPP main flames at a given global fuel-air ratio was insensitive to the pilot flame equivalence ratio; the added stability of the slightly richer main flames appeared to offset any reduced effectiveness of a leaner, potentially less stabilizing pilot flame. Reduction of the pilot flame equivalence ratio resulted in a decrease in NOx emissions, however with diminishing returns. Thus, this configuration of an LPP combustor can achieve reduced emissions with no impact to stability by operating with a leaner pilot. However, the overall stability limits of the combustor, particularly with respect to lean blowoff, were worse than similar geometries in literature. It is expected that these stability limits can be improved with further optimization of the pilot-main interaction. For fundamental study of pilot-main interactions, along with advancement of CH PLIF, a second simplified geometry combustor has been developed. The combustor features identical dome face geometry to the LPP combustor, with the exception of containing only a single main flame. Modification to the pilot design allows for variation of critical parameters, such as the geometric swirl number and swirler divergence angle. Future research includes a preliminary test campaign on the single main combustor. CH PLIF will be used to quantify the impact of critical pilot parameters on the stability of the main flame, along with providing insight into the driving physics of the interaction. Blowoff data is to be collected at atmospheric and elevated pressures to provide a robust test matrix for characterizing the impact of a pilot flame while enabling CH PLIF for future testing in the LPP combustor. Following the single main combustor testing, the full LPP combustor will again be operated at conditions akin to cruise of a civil supersonic combustor. CH PLIF will be leveraged to characterize turbulent flame structure and local extinction. Direct gaseous emission sampling will provide metrics for characterizing combustor performance and emissions. Blowoff limits will be identified through OH and CH PLIF. Multiple pilot configurations will be explored, and the impact on stability and emissions will be studied. Ultimately, understanding of pilot-main interactions will be advanced, enabling optimization of an LPP combustor.

Date and time: 2026-04-27, 1:00 PM

Location: MK-317

Committee:
Dr. Adam Steinberg (advisor), School of Aerospace Engineering
Dr. Jerry Seitzman, School of Aerospace Engineering
Dr. Benjamin Emerson, School of Aerospace Engineering
Dr. Conner Godbold, School of Aerospace Engineering
, 
, 

Featured Speakers

Sam Bolton, Keynote - Sam is a fourth-year Scheller Business student with a concentration in marketing and a minor in leadership and social justice. Sam will discuss how getting lost – whether literally and physically, or metaphorically lost in passions, hobbies, dreams - is a means to finding where you belong in the world, on campus, and most importantly, where you belong internally.

Mike Jones, Guest Speaker - Mike is an administrative professional with Georgia Tech Human Resources. Mike’s talk centers on the idea that presence is something we actively choose. Presence is a choice, and how you choose to show up can shape your opportunities, relationships, and sense of belonging.

Samiksha Digambar, Guest Speaker - Samiksha is a first-year undergraduate student in the College of Engineering. Samiksha reflects on her journey thus far on how to be present despite the fear of others’ judgment and self-doubt. Using her own personal stories, she describes how shifting the focus away from yourself and toward helping others has transformed the way she uses her voice.

Please RSVP on Engage if interested in attending!

All ceremonies will be live streamed and will also be available for viewing after the event.

General information can be found at commencement.gatech.edu.

Colleges will host individual Graduation Celebrations for master's students following the Institute ceremony. 

All ceremonies will be live streamed and will also be available for viewing after the event.

General information can be found at commencement.gatech.edu.

This ceremony will feature Bachelor's graduates from the College of Computing and the Ivan Allen College of Liberal Arts.

All ceremonies will be live streamed and will also be available for viewing after the event.

General information can be found at commencement.gatech.edu.

This ceremony will feature Bachelor's graduates in select College of Engineering majors: Mechanical Engineering (ME), Civil Engineering (CE), Nuclear and Radioactive Engineering (NRE), Electrical Engineering (EE), Aerospace Engineering (AE), Computer Engineering (CMPE), Environmental Engineering (ENVE), Industrial Engineering (IE), and Materals Science Engineering (MSE).

All ceremonies will be live streamed and will also be available for viewing after the event.

General information can be found at commencement.gatech.edu.

This ceremony will feature Bachelor's graduates from the College of Design, Scheller College of Business, College of Sciences, College of Lifetime Learning, and Select College of Engineering Majors (CHBE and BMED)

All ceremonies will be live streamed and will also be available for viewing after the event.

General information can be found at commencement.gatech.edu.

Title: Learning to Price with Limited Information

Date: May 5th, 2026

Time: 10:00 AM – 12:00 PM ET

Location: KACB 3100 (Zoom link)

Committee:

Prof. Sahil Singla (Advisor), School of Computer Science, Georgia Institute of Technology

Prof. Jan van den Brand, School of Computer Science, Georgia Institute of Technology

Prof. Santosh Vempala, School of Computer Science, Georgia Institute of Technology

Prof. Will Ma, Graduate School of Business and Data Science Institute, Columbia University

Prof. Rad Niazadeh, Booth School of Business, University of Chicago

Abstract:

Resource allocation is a central problem in theoretical computer science, operations research, and economics. In a resource allocation problem, a platform must design efficient algorithms to assign limited resources to participating agents under uncertainty, aiming to maximize either the total social welfare or the total revenue.

Among the various algorithms for resource allocation, pricing algorithms are one of the most common ways to distribute resources due to its simplicity. Theoretically, pricing algorithms can be modeled as a system in which a platform sets prices for resources, while participating agents with valuations drawn from underlying distributions make greedy decisions with respect to the prices. However, these distributions are typically unknown to the platform and must therefore be learned, either from limited offline data or through real-time interactions with agents.

Traditional approaches to learning in pricing algorithms often assume access to a large number of samples from each agent. In many real-world markets, however, the available information is sparse, noisy, or indirect. This raises a fundamental question: how can we learn effective pricing algorithms under such limited information constraints?

In this thesis, I address this question by studying the learnability of resource allocation algorithms under three highly restrictive information models:

1. Learning from a single sample, where the platform has access to only one sample from each agent’s distribution.

2. Learning from pricing queries, where the platform can only post prices and observe agents’ decisions as feedback.

3. Online learning with limited feedback, where the platform needs to adjust the algorithm dynamically with limited feedback.

I will demonstrate how pricing algorithms can be successfully learned under these frameworks. Together, these results provide a unified perspective on learning pricing algorithms under limited information, and show that strong performance guarantees are attainable even in highly information-constrained environments.

Date: Wednesday May 6th, 2026

Time: 12 – 2pm

Location: IC collaboration HUB, TSRB

Virtual (Zoom): https://gatech.zoom.us/j/92979046531

Cynthia M. Baseman

Ph.D. Student in Human-Centered Computing 

School of Interactive Computing 

Georgia Institute of Technology 

Personal Website

Committee:

Dr. Rosa I. Arriaga (Advisor) – Georgia Tech, School of Interactive Computing

Dr. Betsy DiSalvo – Georgia Tech, School of Interactive Computing

Dr. Thomas Ploetz – Georgia Tech, School of Interactive Computing

Dr. Naveena Karusala – Georgia Tech, School of Interactive Computing

Dr. André L. Brock – Georgia Tech, School of Literature, Media, and Communication

Abstract:

As the array of available health self-management technologies grows, so too does the list of their promised benefits. Most prior work on these technologies, however, has not centered those at a greater risk of health disparities. My dissertation engages a historically marginalized community that is underrepresented in the health technology literature: Black older adults with low income who are living with diabetes. In this thesis proposal, I present three prior studies that take a community-engaged approach to qualitative and design research. The first two studies characterize community perceptions of mobile and ubiquitous health technologies for diabetic foot self-management. The third study explores how medical mistrust should alter our approach to designing these technologies with community members. The proposed fourth study will explore how community members (both in city and rural contexts) engage in health information seeking and conceptualize the potential role of large language models for health. Through engagements with community members, my work characterizes their perspectives and lived experiences with health self-management technologies, generating higher-level insights into the more equitable and responsible design of these tools.

School of Civil and Environmental Engineering

Ph.D. Thesis Defense Announcement

Ion Exchange for the Capture of Aqueous Selenium Oxyanions

By Zhuoyi Zeng

Advisor:

Dr. Sotira Yiacoumi (CEE)

Committee Members: 

Dr. Costas Tsouris (CEE/Oak Ridge National Lab), Dr. Yongsheng Chen (CEE), Dr. Xing Xie (CEE), Dr. Radu Custelcean (Oak Ridge National Lab)

Date and Time:  Tuesday, May 5th, 2026.  10 am EST

Location: 

Room 4222, Price Gilbert Memorial Library or https://gatech.zoom.us/j/99747481518  

ABSTRACT
Selenium (Se) is an essential micronutrient but becomes toxic at elevated concentrations, posing significant risks to aquatic ecosystems and human health. Coal-fired power plant wastewaters are a primary source of Se contamination, and the U.S. Environmental Protection Agency has set a stringent discharge limit of 5 μg/L for freshwater systems. Removing Se from these wastewaters is technically challenging because Se exists at trace concentrations alongside extreme competing sulfate concentrations. Ion exchange using commercially available strong-base anion-exchange resins offers a promising approach, yet its deployment is hindered by fundamental knowledge gaps and practical challenges, including insufficient mechanistic understanding, limited evaluation under real industrial conditions, and the absence of effective regeneration protocols for complex matrices.In this context, the dissertation presented here develops and evaluates an ion-exchange-based process using the commercial resin AmberLite IRA-900 for effective and sustainable Se removal from aqueous solutions. The work progresses through three interconnected tasks. The first task establishes a comprehensive understanding of Se oxyanion equilibrium, kinetics, and competitive interactions on IRA-900, supported by the mechanistic equilibrium and kinetic models that describe the ion-exchange behavior. The second task advances the evaluation to continuous-flow fixed-bed operations across increasingly complex aqueous matrices, from simulated wastewaters to real industrial leachate, and explores an integrated treatment strategy combining crystallization pretreatment with ion exchange polishing. The third task addresses resin regeneration, developing and validating a novel two-step protocol that overcomes the limitations of conventional approaches in complex industrial matrices. Together, these tasks establish a comprehensive scientific framework that bridges fundamental ion-exchange principles with practical process development, advancing the knowledge base needed for the effective treatment of Se-contaminated industrial wastewaters.

Date: Monday, May 11, 2026

Time: 4:30–6:30 PM Eastern time (U.S.)

Location: Technology Square Research Building (TSRB) Room 334 (VIS Lab) 

Virtual Meeting (hybrid): https://gatech.zoom.us/j/6902674214?pwd=WXgrZ1RkOVlhaWhUci83R1h0UER1QT09

Sichen Jin

Ph.D. Candidate in Computer Science

School of Interactive Computing, College of Computing

Georgia Institute of Technology 

Committee

Dr. Clio Andris (Advisor) - School of Interactive Computing, School of City & Regional Planning, Georgia Institute of Technology

Dr. Alex Endert - School of Interactive Computing, Georgia Institute of Technology

Dr. John Stasko - School of Interactive Computing, Georgia Institute of Technology

Dr. Yalong Yang - School of Interactive Computing, Georgia Institute of Technology

Dr. Shrirang Abhyankar - Principal - AI & Optimization, The Electric Reliability Council of Texas

Abstract
Spatial social networks (SSNs) represent social relationships embedded in geographic space, where nodes and edges are associated with geographic locations. Spatial social network analysis (SSNA) requires methods from both social network analysis (SNA) and geographic information systems (GIS), with applications in domains such as criminal network investigation, geosocial science, epidemiology, transportation and mobility analysis, urban planning, and location-based services. However, existing tools and workflows remain fragmented: SNA often ignores the influence of underlying geographic context, while GIS tools lack support for modeling relational structures. As a result, analysts often rely on disconnected pipelines that hinder effective SSNA.

This dissertation addresses these challenges by investigating how visual analytics can bridge SNA and GIS for integrated SSNA through design studies, long-term empirical evaluation, and pedagogical implementation. These research goals and contributions are organized into four main thrusts:

(1) A visual analytics system for integrated SSNA. We present SNoMaN, an interactive visual analytic system that supports simultaneous exploration of network and geospatial contexts through coordinated views, interactions, and dynamically computed spatial–network metrics. The system enables users to examine how spatial distributions relate to network structures, such as identifying whether densely connected communities are geographically clustered or dispersed. It also provides interactive visualizations for exploring relationships between geographic metrics (e.g., Euclidean distance, spatial dispersion, and average connection distance) and network metrics (e.g., degree, betweenness and closeness centralities, network density, and shortest path length), as well as for identifying anomalies.

(2) Uncovering interdisciplinary needs and challenges. We conducted a workshop-based study using SNoMaN as a design probe with researchers from SNA and GIS backgrounds. The findings reveal both the value of visual analytics as a boundary object for cross-disciplinary collaboration and key sources of science friction, including differences in terminology, analytical expectations, and familiarity with spatial versus network concepts. These insights informed iterative refinement of the system.

(3) Empirical evaluations of integrated SSNA. Building on iterative refinements of the system, we evaluated SNoMaN through longitudinal case studies with 12 domain experts using real-world datasets across diverse application domains. Over multiple sessions spanning several months, the results demonstrate that SNoMaN effectively supports exploratory spatial data analysis, hypothesis generation, and insight discovery. At the same time, the study highlights limitations related to transparency and trust in computed metrics, suggesting the need for improved interpretability and interoperability with programmable tools.

(4) SSNA teaching modules using visual analytics. We developed a learner-centered SSNA teaching module built around SNoMaN, designed to lower barriers to interdisciplinary learning across SNA and GIS. The curriculum was implemented across eight universities in multiple disciplines and evaluated through instructor observations, student engagement, and learning outcomes, leading to insights for broader interdisciplinary education.

Overall, this work shows that integrating geographic and network perspectives through visual analytics enables new forms of reasoning about SSNs that are difficult to achieve with fragmented workflows. By combining design study, empirical evaluation, and pedagogical contributions, this dissertation contributed to the use of visual analytics for interdisciplinary SNA and GIS analysis and supports broader adoption of SSNA in both research and education.

Date: Monday, May 4th, 2026
Time: 2:00 pm – 3:30 pm ET
Location: Groseclose 303 and Teams 

Committee:
Dr. Alejandro Toriello (Advisor), H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Alan Erera, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Chelsea White, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Myunseok Cheon, Supply Chain Optimization Technologies, Amazon
Dr. Chiwei Yan, Department of Industrial Engineering and Operations Research, University of California, Berkeley

Abstract:
This thesis studies three classes of optimization problems that arise in on-demand transportation platforms and e-commerce supply chains. We develop scalable and practical algorithms for systems operating at large scale under uncertainty or with complex combinatorial structure.

Chapter 2 studies a dynamic non-bipartite stochastic matching problem, where nodes appear following a type-specific independent distribution and wait in the system for a given sojourn time. This problem is motivated by applications in ride-sharing and freight transportation marketplaces. We study the asymptotic properties of two widely used policies, batching and greedy, by analyzing a single-pair case and then converting to the general counterpart using a fluid relaxation and randomization. We also extend our model to an impatient setting in which each unmatched node leaves at the end of each period with a type-dependent probability. We show that the results for the two policies still hold under different assumptions about the nodes' patience. Finally, we conduct a computational study simulating freight transportation and ride-sharing marketplaces to assess the empirical effectiveness of the policies. Our results suggest that managers can achieve near-optimal performance by using greedy or batching policies, with only a reasonably small maximum waiting time guarantee, and even in the presence of potentially impatient nodes.

Chapter 3 studies a network design problem with service time guarantees at industrial scale, motivated by the parcel delivery industry. This tactical service network design problem determines primary paths and delivery schedules for packages to minimize transportation and handling costs while ensuring committed service times. To construct a solution for a real-world instance with over 1,000 nodes, one million arcs, and 40,000 commodities, we propose a recursive graph partitioning and batching method. This method partitions the network into smaller regions and solves the problem for each region, considering only commodities whose origin and destination are in the same region. For commodities crossing regions, we first determine an appropriate sub-network, then solve a restricted model on this sub-network. To handle the large number of commodities, we divide these into batches based on schedule slack (intuitively, how flexibly a commodity can be scheduled) and solve the problem sequentially over each batch. Finally, we reoptimize commodities in low-utilization trailers. We demonstrate the scalability and efficiency of our approach through computational studies on real-world instances from an industry partner. Our method finds high-quality solutions after several hours of computing time, while a commercial solver is unable to even build a model for a much smaller instance.

Chapter 4 studies an inventory distribution problem for e-commerce supply chains. Despite extensive research on e-commerce supply chain optimization, some operational constraints remain underexplored. One critical factor is case-breaking at warehouses, where goods shipped in large quantities (case-packs) are disaggregated into individual units to meet retailers’ preference for small, frequent replenishments. Limited processing capacity may prevent all cases from being broken upstream, making it important to decide which cases to disaggregate and how to distribute both individual units and intact cases. Motivated by this, we study a case-pack allocation problem with case-breaking capacity constraints. Assuming deterministic warehouse inventory and target inventory levels at retailers, we optimize a concave, nondecreasing surrogate objective capturing product urgency, current inventory, and expected demand. We formulate an integer program and propose a column generation algorithm with efficient pricing heuristics. Computational experiments on realistic instances show the method finds near-optimal solutions with an average optimality gap of 0.004\% in 136 minutes, outperforming a commercial solver both in solution quality and runtime. We also analyze how the inventory distribution plan changes under tighter capacity constraints and varying warehouse constraints.

Advisor: Prof. Kyriaki Kalaitzidou

will propose a doctoral thesis entitled,

Structural Insulated Panels with Adhesive-free Oriented Strand Boards and

Bio-based Thermal Insulation Core

On

Wednesday, May 13, 2026, at 10:00 a.m.

4211 Conference Room MRDC

and

Virtually via MS Teams or Zoom

Link: https://teams.microsoft.com/meet/283808438267064?p=W0aj0ooOXQb9FqlBGE

Meeting ID: 283 808 438 267 064

Passcode: TE3Wn2LK

Committee

• Prof. Kyriaki Kalaitzidou - School of Materials Science and Engineering (advisor)

• Prof. Valerie M. Thomas - H. Milton Stewart School of Industrial and Systems Engineering

• Prof. Karl Jacob - School of Materials Science and Engineering

• Prof. Carson Meredith - School of Chemical and Biomolecular Engineering

• Dr. Robert J. Moon - USDA Forest Products Laboratory

Abstract

Structural insulated panels (SIPs) are widely used in building applications because they provide an

efficient combination of structural support, low weight, and thermal insulation. Despite these

advantages, conventional SIPs typically rely on petroleum-based foam cores and wood composites bonded with formaldehyde-based adhesives, which raise environmental and health concerns. The development of sustainable alternatives is therefore important for reducing dependence on fossil derived materials and improving the environmental profile of structural building components. This research addresses that need through the development of bio-based SIPs composed of adhesive-free oriented strand board (OSB) facings and renewable insulation core materials. A major focus of this thesis is the fabrication of adhesive-free OSB by activating native lignin in wood strands through an alkaline-hydrothermal treatment. The process involves NaOH soaking, autoclaving, and hot pressing, and is intended to promote interfacial bonding without the addition of synthetic resin. Preliminary studies have demonstrated the feasibility of this approach, with optimized processing conditions producing bonding strengths comparable to formaldehyde-bonded controls. Chemical and surface characterization further suggest the formation of a lignin-rich interphase, supporting the hypothesis that mobilized native lignin contributes to adhesive-free bonding. These findings establish a strong foundation for using treated wood strands as sustainable structural facings in SIP systems. In parallel, this research investigates bio-based insulation core materials derived from natural fibers and renewable binders, including lignin-based systems. Particular attention is given to the influence of composition, density, and processing conditions on thermal conductivity, porosity, moisture response, and mechanical integrity. Understanding these relationships is essential for identifying core materials that provide both effective insulation and sufficient structural compatibility with the panel facings. The optimized core materials will then be integrated with adhesive-free OSB facings to fabricate complete SIP structures. Overall, this work seeks to establish a pathway toward formaldehyde-free, high-performance structural panels derived from renewable resources and to contribute to the broader development of safer, more sustainable materials for the construction industry.

Ph.D. Thesis Defense Announcement

POST-DISASTER DAMAGE ASSESSMENT USING DEEP LEARNING WITH

SURROGATE DATA

By HUILI HUANG

Advisor:

Dr. Max Mahdi Roozbahani (COC) & Dr. J. David Frost (CEE)

Committee Members:  Dr. B. Aditya Prakash (COC),

Dr. Duen Horng Chau (COC),

Dr. Nimisha Roy (COC)

Date and Time:  May, 5th, 2026. 11:00 AM EST

Location: SEB122/ https://teams.microsoft.com/meet/27868450097026?p=7JAII0s7X3YKKAgSyG   

ABSTRACT
Natural hazards such as earthquakes, hurricanes, floods, wildfires, and landslides continue to cause substantial human and economic losses worldwide. This dissertation addresses the cold-start problem in post-disaster damage assessment, where timely and reliable damage information is urgently needed in the early aftermath of a hazard event, yet event-specific labeled data and expert-verified assessments are often unavailable. To mitigate this challenge, this dissertation investigates the use of surrogate data, defined here as timely, opportunistic, or non-traditional observations that provide proxy evidence of damage, disruption, or recovery when authoritative assessments are delayed. Specifically, this work examines nighttime lights as a proxy for regional disruption and recovery, and ground-view imagery from social media and expert reconnaissance as a source of fine-grained, asset-level damage evidence. Building on these data streams, it develops benchmark datasets, scalable annotation protocols, and computational methods for damage assessment under limited supervision, heterogeneous observations, and domain shift, including ScaleNet for multi-scale representation learning, DASeg for weakly supervised damage localization using vision foundation models, and SeismoMind for zero-shot damage assessment using multimodal large language models. Overall, the findings show that surrogate data, combined with robust and generalizable deep learning frameworks, can substantially mitigate cold-start constraints and enable more timely, scalable, and actionable post-disaster damage assessment.

John Kos 

Ph.D. Student in Human-centered Computing  

School of Interactive Computing  

Georgia Institute of Technology  

Date: May 6th 2026 

Time: 2:00pm-4:00pm 

Location: CODA 1203 Five Points 

Teams Link 

Committee: 

Ashok Goel (Advisor) - School of Interactive Computing, Georgia Tech 

Chris Dede - Graduate School of Education, Harvard University 

Christopher MacLellan - School of Interactive Computing, Georgia Tech 

Christina Schwarz - College of Education, Michigan State University 

Sashank Varma - School of Interactive Computing, Georgia Tech  

Emily Weigel – School of Biological Sciences, Georgia Tech 

Abstract  

Adult learners are often asked to address open-ended problems that require critical thinking such as scientific inquiry. In scientific inquiry, scientists use inquiry-based modeling to develop hypotheses, evaluate the model based on data, revise the model, and evaluate the outcome of their hypotheses. Supporting inquiry education is challenging because it requires the learners to engage in both cognitive (thinking about a problem) and metacognitive (thinking about one’s thinking, high level planning, and reflection) processes. 

AI and education tools built to assist these processes have been an open line of research in the literature for two decades. A series of obstacles including modeling the learner’s thought process, establishing the necessary cognitive understanding, and properly encouraging active engagement with metacognitive thinking have presented challenges to tools seeking to support metacognitive thinking using traditional machine learning methods. Traditional machine learning techniques have only been modestly successful in inferring intent from learner modeling actions, and as a result AI agents have struggled with supporting metacognitive thinking. 

I propose that the advent of Generative AI, paired with learner self-explanations, allows for evaluation and guidance of the learner's metacognitive thinking. My research explores the use of this new paradigm to support the learner through two different metacognitive processes: inquiry and metamodeling. Using the interactive learning environment VERA as a platform, I design specialized LLM agents to enable these metacognitive processes in the context of modeling ecological systems. To additionally support these processes, an additional AI agent orchestration system is designed to evaluate user self-explanation and cognitively scaffold user inquiry using a handful of other AI agents. I plan to evaluate the above Agentic AI system with a set of pilot studies designed to measure the gains in learning outcomes from the metacognitive agents. Additionally, I will do an analysis of differing architectures for AI agent orchestration and selection, to demonstrate proper deployment of the correct agent based on the learner’s self-explanation. 

Written by Kitty Brazelton and Vaibu Mohan, Water Memory is based on a provided prompt of integrating AI into a creative work. The two were paired by the 96-Hour Opera Project team and the match was serendipitous.

Both composer Kitty Brazelton and librettist Vaibu Mohan shared the experience of witnessing the decline of a beloved older woman in their family due to dementia. For this creative team, Water Memory has become a deeply personal story about the power of memory, family, and connection.

Composer: Kitty Brazelton
Librettist: Vaibu Mohan
World Premiere Performance
Performance runtime: approximately 65 minutes—with no intermission

Date: Tuesday, May 5th, 2026

Time: 10:00 AM 

Location: TSRB 423

Virtual Link: https://teams.microsoft.com/meet/277536990612040?p=To1pMC8JMAJuDVMn4S

Evanns G. Morales-Cuadrado

Robotics PhD Student

School of Electrical and Computer Engineering

Georgia Institute of Technology

Committee:

Dr. Samuel Coogan (Advisor) - School of Electrical and Computer Engineering & School of Civil and Environmental Engineering, Georgia Institute of Technology

Dr. Shreyas Kousik - School of Mechanical Engineering, Georgia Institute of Technology

Dr. Kyriakos Vamvoudakis - School of Aerospace Engineering, Georgia Institute of Technology

Dr. Yorai Wardi - School of Electrical and Computer Engineering, Georgia Institute of Technology

Dr. Saman Zonouz- School of Cybersecurity and Privacy  & Electrical and Computer Engineering, Georgia Institute of Technology

Abstract: 

We propose a comprehensive program for safe autonomy in robotic systems, with a particular focus on aerial platforms. This program is organized across three complementary layers: secure high-level control, safe trajectory planning under uncertainty, and computationally efficient low-level tracking. Preliminary work establishes a lightweight Newton–Raphson-flow tracking controller with demonstrated alpha-stability on a miniature blimp, achieving competitive performance against PX4, feedback linearization, and nonlinear model predictive control across quadrotor and lighter-than-air platforms. A second line of work develops RTD-RAX, a reachability-based trajectory design and runtime assurance framework that combines offline parameterized reachable sets for rapid candidate generation with online mixed-monotone reachability verification and trajectory repair, enabling safe planning under bounded disturbances while avoiding conservative planning. Building on this foundation, the proposed work extends RTD-RAX to learned Gaussian process disturbance models, transitions prior numerical ground-vehicle results to hardware, and proposes extensions to aerial platforms with hardware validation. We also propose improved trajectory repair strategies, and extending RTD-RAX from reach-avoid missions to more complex STL missions by learning which control parameters map onto given STL specifications. We further propose a standalone learning-accelerated signal temporal logic framework that reduces reliance on repeated mixed-integer linear program solves while preserving safety guarantees. Additionally, this work proposes cyber-physical inoculation, in which fault signatures and certified backup controllers expand a resilient safe operating envelope against adversarial attacks. Together, these proposed contributions advance fast, efficient, and resilient autonomy for safety-critical cyber-physical systems.

 About the event: The April 29th event marks the next milestone in our collaborative journey and builds momentum from our convening last fall. This professionally facilitated event will kickstart interdisciplinary research ideas and pave a path for competitive formal funding submissions. $$ CAPHI will provide research support and funding for follow-up professional grant writing services to teams formed during our Spring event. $$ 

This gathering is about aligning technical strengths and building essential relationships among participants. By connecting on both the capabilities and interpersonal levels, and by engaging in open dialogue, sharing perspectives, and fostering collaboration, we set the stage for meaningful impact and ensure that our collective efforts are both visionary and actionable. We look forward to seeing you there!

• When: April 29th 2026, from 10:00 am to 3:45 pm
• Where: Dalney 180 Conference Room (926 Dalney Street NW, Atlanta GA 30318; Dalney 180 | Office of Sponsored Programs)
• Parking: W22 Dalney Parking Deck (Directions and Parking | Office of Sponsored Programs)
• Registration: Community & Population Health Initiative (CAPHI)  Spring 2026 Convening – Fill out form
• CAPHI Researcher Database: CAPHI Researcher Database – Fill out form

*Refreshments: 10:30 AM - 11:00 AM (ES&T L1175)

Speaker:  Ilias Diakonikolas, Professor, The University of Wisconsin–Madison

Abstract:

Robustness is a basic requirement for trustworthy machine learning, yet achieving it efficiently in high dimensions has long been a fundamental challenge. For decades, the prevailing view was that learning algorithms with strong robustness guarantees necessarily come with prohibitive computational cost, creating a sharp tension between statistical guarantees and algorithmic tractability. This talk describes a research program aimed at overcoming this barrier through an algorithmic theory of robust learning. 

 I will describe two interconnected threads within this research program. The first develops a unified framework for efficient robust high-dimensional estimation, including the first polynomial-time algorithms for several fundamental unsupervised learning tasks under adversarial corruption. The second studies supervised learning under noisy labels, with an emphasis on learning predictors with low-dimensional latent representations. I will conclude by discussing future directions, including robustness beyond worst-case corruption and the efficient learning of richer nonlinear representations.

Bio:

lias Diakonikolas is the Lubar Professor in the Department of Computer Sciences at UW Madison. He obtained a Diploma in electrical and computer engineering from the National Technical University of Athens and a Ph.D. in computer science from Columbia University where he was advised by Mihalis Yannakakis. Before moving to UW, he was an Andrew and Erna Viterbi Early Career Chair at USC and a faculty member at the University of Edinburgh. Prior to that, he was the Simons postdoctoral fellow in theoretical computer science at the University of California, Berkeley. His research is on the algorithmic foundations of massive data sets, in particular on designing efficient algorithms for fundamental problems in machine learning. He is a recipient of the ACM Grace Murray Hopper award, a Sloan Fellowship, an NSF CAREER Award, a Romnes Faculty Fellowship, a Google Faculty Research Award, a Marie Curie Fellowship, best paper awards at NeurIPS and COLT, the IBM Research Pat Goldberg Best Paper Award, and an honorable mention in the George Nicholson competition from the INFORMS society. Ilias wrote with Daniel Kane the textbook "Algorithmic High-dimensional Robust Statistics" published by Cambridge University Press.

About CAPHI

Visit https://communityhealth.research.gatech.edu/ to learn more.

The Community and Population Health Initiative is Georgia Tech’s nexus for research and innovation at the intersection of health, technology, environment, and society. Our initiative brings together researchers, practitioners, and communities to reimagine health systems, investigate environmental quality, harness data for better decisions, and create healthier, more resilient communities.

By collaborating with Georgia Tech’s Interdisciplinary Research Institutes (IRIs) and programs from six Colleges, we amplify impact — linking breakthroughs in sustainability, people-centered and urban design, bioengineering, and data science, with systems thinking to transform health and wellbeing locally and across the globe.

About the event 

The April 29th event marks the next milestone in our collaborative journey and builds momentum from our convening last fall. This professionally facilitated event will kickstart interdisciplinary research ideas and pave a path for competitive formal funding submissions. CAPHI will provide research support and funding for follow-up professional grant writing services to teams formed during our Spring event.

 Event Agenda

This gathering is about aligning technical strengths and building essential relationships among participants. By connecting on both the capabilities and interpersonal levels, and by engaging in open dialogue, sharing perspectives, and fostering collaboration, we set the stage for meaningful impact and ensure that our collective efforts are both visionary and actionable. We look forward to seeing you there!

GEORGIA INSTITUTE OF TECHNOLOGY 

Under the provisions of the regulations for the degree 

MASTER OF INDUSTRIAL DESIGN 

on

Friday, May 1, 2026

10:00 a.m. – 11:00 a.m. EST 

West Architecture 155

Teams Link: Mingqi Wang Thesis Defense | Meeting-Join | Microsoft Teams

Meeting ID: 294 048 181 186 72

Passcode: WV6Mj2YH

Mingqi Wang

will present a thesis defense entitled, 

"Designing for Breathing-Stroke Coordination Awareness and Training in Front Crawl Swimming"

 Advisor: 

Dr. Yixiao Wang, Georgia Tech School of Industrial Design 

Committee:  

Dr. Eunsook Kwon, Georgia Tech School of Industrial Design 

Dr. Alexander T. Adams, Georgia Tech School of Interactive Computing

Faculty and students are invited to attend this presentation. 

Abstract  

Breathing-stroke coordination in front crawl swimming is a skill most swimmers cannot directly perceive or correct in real time. Every breath requires precise lateral head rotation timed to body roll and the arm pull cycle, yet swimmers have no direct view of their own mechanics while in motion. Traditional coaching addresses this through delayed verbal feedback, and while commercial swimming technology has expanded, no existing product targets breathing-stroke coordination specifically.

This thesis investigates how technology can support swimmers' awareness of breathing-stroke coordination in front crawl, with a focus on recreational and developing swimmers. The research follows a two-phase design process. Phase 1 consisted of semi-structured interviews with 16 swimmers and coaches spanning beginner through elite levels, analyzed through thematic analysis to characterize how coordination is experienced across skill levels and to derive design guidelines. Phase 2 developed a virtual reality training prototype grounded in these guidelines, presenting synchronized hand trajectory visualizations and spatial audio cues to make the timing relationship between stroke mechanics and breathing windows explicitly visible. A feasibility pilot study with seven amateur swimmers was conducted to explore the prototype's usability and its potential as a coordination training tool.

Participants will be introduced to generative AI fundamentals, prompt engineering, and practical applications such as automated inventory systems, predictive maintenance, and route optimization. The workshop will also cover ethical AI use, good and bad uses of generative AI (GenAI), and rapidly emerging use cases. By the end of the session, participants will have a better understanding of how to begin applying AI to enhance supply chain processes and drive success within their organizations.

Event includes networking, presentation, light bites, beverages, and Q&A.

Featuring Chris Gaffney, Managing Director of the Supply Chain and Logistics Institute (SCL), Academic Program Director for Georgia Tech Professional Education, and Edenfield Executive-in-Residence and a Professor of the Practice in the H. Milton Stewart School of Industrial and Systems Engineering.

Register via Eventbrite to attend the event taking place at the Georgia Tech Atrium (147 Technology Parkway Northwest, Peachtree Corners, GA 30092), the first-of-its-kind professional, academic, and research innovation space within Curiosity Lab’s state-of-the-art Innovation Center.

This event is in-person. No RSVP is required.

Student Name: Xinqiang Rao

Thesis Title: Multiscale Computational Modeling of Defect-Controlled Degradation in Layered Cathode Materials

Thesis Advisor: Dr. Julia H. Yang

Thesis Co-Advisor: N/A

Committee Members: Dr. Nian Liu, ChBE; Dr. Andrew J. Medford, ChBE; Dr. Solomon Tolulope Oyakhire, ChBE; Dr. Seung Soon Jang, MSE

Date: 5/5/2026

Time: 1:00 to 3:00 PM ET

Location: Ford ES&T Conference Room 2229

Kaely Hall

Ph.D. Student in Human-centered Computing 

School of Interactive Computing 

Georgia Institute of Technology 

Date: Friday, May 8th, 2026

Time:  12-3pm

Location: Coda 1215 Midtown  

Teams Info: https://teams.microsoft.com/meet/2255456448249?p=rIzXwFXWGRftAe5rTE 

Meeting ID: 225 545 644 824 9

Passcode: X7zT7AR3

Committee 

Dr. Jennifer Kim (advisor) - School of Interactive Computing, Georgia Institute of Technology

Dr. Munmun de Choudhury - School of Interactive Computing, Georgia Institute of Technology

Dr. Nasibeh Farahani - Mayo Clinic Platform, Mayo Clinic

Dr. Andrea Parker - School of Interactive Computing, Georgia Institute of Technology

Dr. Vedant Das Swain - Tanden School of Engineering, New York University

Abstract 

Artificial intelligence (AI) systems are increasingly used to support how individuals construct self-representations in everyday and decision-critical contexts. For example, people use AI to draft professional materials such as cover letters and bios, or to articulate preferences in medical settings, such as birth plans that communicate values and priorities for care. In these contexts, AI use extends beyond task assistance and becomes part of the process through which individuals construct and communicate representations of themselves. However, these representations often reflect the assumptions, conventions, and statistical patterns embedded in AI systems, resulting in outputs that may not faithfully capture users’ situated experiences, self-understandings, or how they intend to be understood. This dissertation introduces representational alignment as a framework for understanding this challenge: the extent to which AI-mediated representations preserve and convey a person’s intended meaning across contexts. Misalignment arises when a person’s intended self-representation diverges from the representation constructed by an AI system, often due to generalized or decontextualized interpretations of user input.

In my completed work, I examine representational alignment in two stages: breakdown and repair. The first study analyzes how misalignment emerges in practice through neurodivergent job-seekers’ use of an LLM-powered career support chatbot, showing that systems frequently misinterpret implicit cues and impose normative language and assumptions. As a result, the chatbot’s “support” is often grounded in misaligned representations of users’ experiences and intentions. The second study examines how users work to restore alignment through interaction. I introduce bi-directional alignment as an interaction paradigm in which users and systems iteratively shape representations together, and investigate this concept through LL.me, a research probe that supports iterative refinement of AI-generated professional self-representations. Findings show that alignment develops through reflection, reinterpretation, and refinement, as users incorporate tacit contextual knowledge, personal values, and anticipated audience expectations.

My proposed work extends representational alignment to clinical settings, where individuals must construct self-representations through the articulation of expectations preferences without full visibility into how they will be interpreted or executed within care institutions. Birth planning is a particularly challenging case, as patients must express delivery preferences in advance of uncertain and evolving conditions, where those preferences may later be reinterpreted, constrained, or overridden during care. I propose AIDoula, an interactive system designed to support patients in articulating and refining birth preferences by making clinical constraints, such as standard delivery intervention protocols, more visible and enabling iterative revision. In this context, AI-generated representations must function as boundary objects, carrying meaning between patients and clinicians while remaining actionable in care settings. Through a two-phase evaluation with patients and clinicians, I will examine how individuals construct conditional and flexible representations under partial constraint visibility, and how these representations are interpreted and reshaped within clinical workflows. Collectively, this dissertation advances a framework for representational alignment in human–AI systems, contributing empirical evidence of misalignment, interactional approaches for supporting alignment in practice, and a system that demonstrates how representations can remain meaningful under uncertain and evolving constraints.

Doctor of Philosophy in Ocean Science and Engineering

In the School of Civil and Environmental Engineering

Savannah L. Howard

Will defend her dissertation

FUNDAMENTAL HYDROGEOMECHANICAL PROPERTIES OF CARBONATE FORMATIONS: IMPLICATIONS FOR OFFSHORE COMPRESSED AIR ENERGY STORAGE

May 5th, 2026 at 8 AM

Ford ES&T, Room 3243 (The Ocean Room)

Teams: https://teams.microsoft.com/meet/252549462205277?p=eDS8aBGnt00SviVjBt

Meeting ID: 252 549 462 205 277

Passcode: hx7Vz7zX

Thesis Advisors:

Dr. Sheng Dai

School of Civil and Environmental Engineering

Georgia Institute of Technology

Committee Members:

Dr. Philipp Braun

Navier Laboratory

Ecole des Ponts, Paris Tech (ENPC)

Dr. Kevin Haas

School of Civil and Environmental Engineering

Georgia Institute of Technology

Dr. Joseph Montoya

School of Biological Sciences

Georgia Institute of Technology

Dr. Zhigang Peng

School of Earth and Atmospheric Sciences

Georgia Institute of Technology

Summary:

The U.S. energy grid faces a critical storage challenge: renewable sources such as wind and solar generate electricity intermittently, while demand peaks occur independently of generation. Among available grid-scale storage technologies, Compressed Air Energy Storage (CAES) — which stores energy by compressing air underground and releases it through turbines on demand — offers the lowest levelized cost of storage of any current technology, surpassing batteries, pumped hydro, hydrogen, and thermal systems. Existing CAES plants (Huntorf, Germany; McIntosh, Alabama) use salt caverns, which are geographically restricted. Porous carbonate formations, distributed across the southeastern U.S. coastal zones where offshore wind and wave energy resources are abundant, offer a far more geographically widespread alternative — but their suitability as CAES reservoirs under realistic subsurface conditions has never been systematically characterized.

This dissertation presents a comprehensive laboratory characterization of three distinct carbonate formations - St. Maximin, Miami, and Hemingway. Experiments characterized the full suite of properties relevant to CAES viability, including mineralogy (XRD), density, total and accessible porosity, micro-CT pore imaging, permeability, ultrasonic wave speeds and elastic moduli, unconfined compressive strength and failure mode (via acoustic emissions), water retention curves, creep behavior, and cyclic loading response. Energy storage density was calculated directly from measured pore and mechanical properties and benchmarked against the two operating CAES plants. The results highlight that pore geometry and connectivity must be characterized jointly with porosity for meaningful CAES modeling; water retention and evaporation govern energy storage density; creep is saturation-dependent and operationally significant; and cyclic pressurization does not cause rapid structural and strength deterioration, but cumulative strain can lead to failure in long-term operation. Calculated energy storage densities for offshore carbonate CAES are competitive with, and in high-porosity cases exceed, those of existing salt cavern plants (~7.5–18 MJ/m³), demonstrating the technical feasibility of porous carbonate formations as CAES reservoirs. The work establishes the first systematic experimental dataset for air–water saturated carbonate reservoir media under CAES-relevant conditions, filling a critical gap in a field previously dominated by oil–water and CO₂ storage studies. Findings directly inform site selection criteria, reservoir performance modeling, and operational safety assessments for future offshore CAES development in the southeastern U.S. — a region that combines abundant offshore renewable energy resources with extensive shallow carbonate geology.

Date: Monday, April 27th, 2026

Time: 1:00–3:00 PM ET

Location: online [Teams link]

Kaige Xie

Ph.D. Student in Computer Science

School of Interactive Computing

Georgia Institute of Technology

Committee:

Dr. Pascal Van Hentenryck (advisor) - School of Industrial and Systems Engineering and School of Interactive Computing, Georgia Institute of Technology

Dr. Thomas Ploetz - School of Interactive Computing, Georgia Institute of Technology

Dr. Chao Zhang - School of Computational Science and Engineering, Georgia Institute of Technology

Abstract:

This dissertation investigates how to optimize natural language generation (NLG) systems built on large language models (LLMs) from a holistic, lifecycle-oriented perspective. While recent advances in LLMs have led to substantial gains across a wide range of NLG tasks, prior research has largely focused on improving benchmark performance, often overlooking the broader challenges that arise across model training, inference, evaluation, and deployment. This dissertation argues that such a performance-centric view is insufficient for real-world NLG systems, whose success depends not only on output quality but also on efficiency, reasoning capability, evaluation fidelity, and user trust. To address this gap, the dissertation introduces a unified framework centered on text sub-structures—semantically meaningful intermediate representations embedded in text—and studies how their recognition and strategic utilization can improve NLG systems throughout their full lifecycle. 

The dissertation develops this framework across four representative NLG tasks: dialogue summarization, story generation, action plan generation, and question answering. In dialogue summarization, it shows how dialogue skeletons can facilitate more effective few-shot learning and improve cross-task prompt transfer under limited supervision. In story generation, it demonstrates how outline-based planning structures can guide LLMs toward producing more coherent and engaging narratives. In action plan generation, it examines precondition-effect dependencies as a form of latent world knowledge that enables LLMs to better model action feasibility and environmental change. In question answering, it explores sub-questions as a versatile sub-structure for both fine-grained system evaluation and explanation generation, improving the assessment of open-ended retrieval-augmented generation systems and enhancing users’ ability to judge model reliability. Collectively, these studies show that text sub-structures provide a general and effective semantic scaffold for improving learning efficiency, inference-time planning and reasoning, evaluation robustness, and deployment-time user experience. 

Committee:

Dr. Ayazi, Advisor

Dr. Taylor, Chair

Dr. Ansari

Committee:

Dr. Gaylord, Advisor

Dr. Cai, Chair

Dr. Adibi

Date: Tuesday, April 28th, 2026
Time: 11:00 am – 1:00 pm EST
Location: Groseclose 226 (Georgia Freight Bureau Conference Room) and Zoom

Committee:
Dr. Santanu S. Dey (Advisor), H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Oktay Günlük, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Jean-Philippe P. Richard, Department of Industrial and Systems Engineering, University of Minnesota
Dr. Nick Sahinidis, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Yang Wang, School of Civil and Environmental Engineering, Georgia Institute of Technology

Abstract:
Quadratically constrained programs (QCQPs) model critical real-world problems across engineering, finance, logistics, and data science. For nonconvex QCQPs, finding globally optimal solutions is computationally intractable in general, and even small instances can be challenging for state-of-the-art optimization solvers. This thesis studies the design and analysis of global optimization algorithms and relaxation schemes for QCQPs that are both mathematically rigorous and computationally practical.

Solving QCQPs to global optimality relies on three key components: (i) finding high-quality feasible solutions, (ii) constructing tight relaxations, and (iii) selecting effective branching strategies. These are crucial for the spatial branch-and-bound, which is the dominant approach for solving QCQPs. This thesis contributes to each of these components.

The first part of the thesis addresses the problem of finding high-quality feasible solutions for an energy storage optimization problem whose operational constraints are bilinear. We develop a regularized formulation whose linear programming relaxation yields a feasible solution to the original problem. This approach enables solving previously challenging trilevel optimization problems modeling adversarial attacks on power grids.

The second part of the thesis focuses on building a tighter convex relaxation for bilinear bipartite programs using aggregation techniques to construct second-order cone representable sets. We characterize when aggregation methods yield the exact convex hull and provide illustrative examples. Computational experiments demonstrate that aggregation methods improve bounds over single-row relaxations and commercial solvers, reducing the optimality gap.

The third part of the thesis proposes a novel branching rule, namely extreme strong branching, which jointly optimizes both branching variable and point selection via binary search. This approach leverages objective value improvements to minimize branch-and-bound tree size while producing bound tightening as a byproduct. For certain instances, this technique produces substantially smaller branch-and-bound trees to reach optimality compared to alternative branching rules.

The last part of the thesis studies relaxation schemes in a unified framework, from data-independent to data-dependent approaches. We prove that bounded Lagrangian relaxation provides the strongest bound among data-independent methods under mild conditions. We further analyze data-dependent relaxations and establish a hierarchy of the relative strength of various relaxation methods. 

Collectively, these contributions bridge the gap between theoretical possibility and computational feasibility, expanding the range of QCQP instances that can be addressed effectively in practice.

Committee:

Dr. Chatterjee, Advisor

Dr. Hao, Chair

Dr. Mukhopadhyay

Advisor: Dr. Dimitri Mavris

Milestone: PhD Thesis Final Examination (Defense)

Degree Program: Aerospace Engineering

Title: An Approach to Developing Ontology-Based Epistemic Infrastructure for Early-Phase Architecting of Space Exploration Campaigns

Abstract: NASA's Artemis Program, an ambitious space exploration campaign (SEC) to explore the Moon and Mars, is expected to grow to over 50 highly-coupled major acquisitions. Yet for the past 35 years, NASA's acquisition practices have been designated "high-risk" by the GAO due to chronic cost overruns and schedule delays. A root cause analysis traces these outcomes to epistemic shortfalls in early-phase architecting: tacitness, ambiguity, manual data transfers, and inconsistencies in how architectural knowledge is represented, exchanged, and verified. To address these shortfalls, this dissertation introduces ontologies and semantic web technologies into early-phase campaign architecting to develop ontology-based epistemic infrastructure. A tailored methodology, SEC Ontology Development Methodology (SECODM), was formulated. Following it, a layered ontology ecosystem, SECO, was developed along with minimum viable infrastructure for development, verification, and deployment. An evaluation framework based on elegant systems theory was developed to assess these contributions. Three case studies demonstrated that the ontology and infrastructure address the identified root causes through formal knowledge representation, machine-readable interoperability, and automated consistency verification.

Date and time: 2026-05-04, 1pm ET

Location: CoVE

Committee:
Dr. Dimitri Mavris (advisor), School of Aerospace Engineering
Prof. Daniel Scrage, School of Aerospace Engineering
Prof. Thomas Gonzalez Roberts, School of Aerospace Engineering
Dr. Bradford Robertson, School of Aerospace Engineering
Dr. Michael Balchanos, School of Aerospace Engineering
Dr. Stephen Edwards, NASA MSFC

Advisor: Dr. Marilyn Smith

Milestone: PhD Thesis Proposal

Degree Program: Aerospace Engineering

Title: Reduced-Order Modeling for Aero-Structural Design of Advanced Air Mobility Vehicles

Abstract: In recent years, the field of Advanced Air Mobility (AAM)---previously referred to as Urban Air Mobility (UAM)---has made significant progress towards commercialization. These AAM vehicles have consumer-facing applications such as air taxi and Uncrewed Aerial Vehicle (UAV) delivery services, with many proposed missions occurring within urban settings. This is an environment characterized by aerodynamic drivers that are not present in the traditional flight analysis methods applied in open-air conditions, such as the importance of building wake flows and small-scale turbulence. The AAM industry is also presently facing the challenging economics of bringing financially stable low capacity flights to the market, meaning minimizing costs while maximizing safety are a large concern. With this in mind, a deeper understanding of sources of structural fatigue and their significance may be crucial to keeping inspection and repair costs low while maintaining confidence in vehicle and passenger safety. This thesis aims to elucidate the feasibility of reduced-order models as alternatives to costly mid- or high-fidelity computational tools for early design and analysis of AAM's. Specifically, aerodynamic flow models will be assesed for their sensitivity and applicability in urban environment applications. Addionally, the effects of multi-tiltrotor downwash on a wing will be evaluated with a number of solvers. The resulting structural loads will be compared to determine the bounds of the tradeoff between model fidelity and solution accuracy.

Date and time: 2026-04-29, 2:00pm

Location: Skiles 246

Committee:
Dr. Marilyn Smith (advisor), School of Aerospace Engineering
Dr. Juergen Rauleder, School of Aerospace Engineering
Dr. Graeme Kennedy, School of Aerospace Engineering
, 

 

• Artwork must be original and created by the submitter
• Maximum of two submissions per artist
• Register your art submission by July 6
• Deliver artwork (clearly labeled with name and contact info) to the IBB front desk
  • July 15–17, between 8 a.m.-5 p.m. each day
  • Attendance at the July 23 Show is encouraged, but not required

⭐ Awards - All winning pieces to be displayed on the IBB website!

• Fan Favorite
  Voted on by IBB Art Gallery visitors from July 20–23. Celebrating the piece that resonates most with the community.
• 🔬 IBB ArtSci Award
  Recognizing a standout work that powerfully captures the intersection of art and science.
• 🧠 BRAINiArts Award
  Honoring creative expression inspired by neuroscience. 

Sponsored by Georgia Tech's Institute for Bioengineering and Bioscience (IBB), and the Bioengineering and Bioscience Unified Graduate Students (BBUGS)

Register Online to Attend

Advisor: Dr. Timothy Lieuwen

Milestone: PhD Thesis Proposal

Degree Program: Aerospace Engineering

Title: Unsteady Wake Structures in Jets in Crossflow

Abstract: The jet in crossflow (JICF) is a fundamental, fluid mechanics-rich flow topology employed in various combustor architectures such as lean premixed combustors, staged combustors, as well as in film cooling technologies, owing to its excellent mixing properties. While on paper the implementation of this flow configuration is simple enough, the interaction between the two fluid streams results in a super three-dimensional, unsteady large scale vortical structures, namely: horseshoe vortices, shear layer vortices, counter-rotating vortex pairs, and the wake vortices. Out of these, the wake vortices are the least understood and have received limited attention. A large body of literature exists in characterizing the formation and development of the shear layer vortices (SLV) and the counter-rotating vortex pairs (CVP), and as such, control strategies are developed around their behavior in system level applications. Despite the bulk of the mixing is attributed to the SLVs and the CVP, the wake structures appear to add another layer of far field mixing from their tornado-like behavior and are the major focus of this work. Although experimental and computational studies have been performed to characterize the periodicity associated with the wake vortices, general consensus on their precise formation mechanism and source of vorticity is yet to be achieved. Moreover, despite the existence of tornado-like structures in the wake of the jet are widely acknowledged in the research community, the exact nature of their evolution is yet to be explained. In the context of a reacting jet in crossflow (staged combustors, for instance), presence of combustion has been shown to significantly alter the underlying hydrodynamics of the flow field due to additional physics like dilatation effects, baroclinic torque, and local viscosity changes, resulting in complex flame-flow interactions. Furthermore, combustor phenomena such as flame stabilization and Nox emissions have been shown to be highly sensitive to the background fluid mechanics of this flow field, necessitating a systematic investigation onto combustion effects. Additionally, since the evolution of these large scale flow structures occurs at their respective time and length scales, studies on their interactions require highly-resolved spatial and temporal datasets. Motivated by the above-mentioned gaps in literature and challenges on analysis, this proposal seeks to explore the characteristics of the wake structures and attempts to answer fundamental questions like: (1) What is the nature of flow separation mechanisms (low pressure-driven events vs CVP induction) and source of vorticity (crossflow boundary layer vs jet shear layer), (2) what is the effect of flame attachment location and the consequent local heat release distribution on wake vortices, (3) what are the unsteady features of the wake vortices (characteristic frequencies, symmetry, mode shapes) and how do they compare between non-reacting and reacting jet in crossflow datasets. These research questions are systematically investigated using high fidelity large eddy simulations. The computations will be performed over a range of fluid dynamic parameters characterizing the flow field and the dependencies of the topological features of wake structures on test parameters will be ascertained. 

Date and time: 2026-05-01, 9.00 AM - 11.00 AM

Location: MK-317

Committee:
Dr. Timothy Lieuwen (advisor), School of Aerospace Engineering
Dr. Ari Glezer, School of Mechanical Engineering
Dr. Joseph Oefelein, School of Aerospace Engineering
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Student Name: Jacob Alan DeWitt

Thesis Title: Mechanocatalytic Synthesis of Ammonia over Transition Metal Nitrides

Thesis Advisor: Dr. Carsten Sievers

Thesis Co-Advisor: N/A

Committee Members: Dr. William J. Koros (School of Chemical & Biomolecular Engineering), Dr. David W. Flaherty (School of Chemical & Biomolecular Engineering), Dr. Faisal M. Alamgir (School of Materials Science and Engineering), Dr. Justin Hargreaves (School of Chemistry - University of Glasgow)

Date: 05/01/2026

Time: 9:00 am EST

Location: RBI 114

He is Professor of Bioengineering and Materials Science at UC Berkeley and Faculty Scientist at Lawrence Berkeley National Laboratory. He is a Fellow of the Royal Society of Chemistry, the American Institute for Medical and Biological Engineering, the Materials Research Society and the American Association for the Advancement of Science. In addition to his research, he mentors and teaches students in the fields of biomaterials design, materials science, polymer science, and nanomedicine.

Faculty host: Felipe Quiroz

 

Special emphasis is given to Tissue Engineering and Regenerative Medicine and particular applications and more details are described on the lab web page.

Faculty host: Alexander Vlahos

 

This event will take place on Wednesday, April 29 from 12 - 1 p.m. at the Beth Cabrera Reflection Space and Multicultural Lounge (John Lewis Student Center). If interested in attending, please RSVP on Engage.

Atrium’s managing director, Stephen Harmon, will share how Atrium connects visitors with resources and networks and offers programs that provide access to opportunities beyond the Tech campus. You will also hear from Jonna Lee and Eric Sembrat of the College of Lifetime Learning research team about their work applying personas, AI, and digital credentials to better serve learners. Additionally, Curiosity Lab’s Executive Director, Emily Heintz, will discuss the four sectors it serves and how they support inventors and entrepreneurs.

The program will conclude with a social hour for connecting with fellow attendees and speakers. Light refreshments will be served.

Please register by Thursday, May 7.

Register now

This course will demonstrate how warehouse operations are a key enabler to a successful supply chain implementation and the starting point for a transformation. It is critical to manage safety, quality and efficiency. Learn to leverage the lean supply chain modifications to improve customer responsiveness and reduce operating costs and in doing so contributing to a supply chain that creates a competitive advantage for a company. To accomplish this goal, we must bring lean principles into the warehouse and distribution center.

Who Should Attend

Supply chain professionals, logistics professionals, material managers, production control managers, transportation managers, warehousing managers and purchasing managers

How You Will Benefit

Upon completion of this course, you will be able to:

• Isolate the key elements of lean thinking to be used in the warehouse
• Value stream map warehouse operations
• Utilize lean tools to reduce waste in the warehouse
• Create a warehouse operation based on visual management and real time problem solving
• Reduce inventories in warehouse operations
• Create collaboration between warehousing and other functional areas

Benefits:

• Reduce inventories in the warehouse
• Reduce warehousing costs by as much as 25%
• Implement lean in the warehouse
• Create logistics collaboration between warehousing and other functional areas

What is Covered

• Lean Warehouse Overview
• Supply Chain Implementation Framework
• Lean Storage Planning Approach
• Application of a Lean Storage Location Sizing Method
• JIT Implementation Approach
• How To Develop Standard Work Batches
• Generation of an Operational Diagram
• Creation of a Daily Operational Work Load Plan
• Development of a Progress Control Board

BioE Ph.D. Defense Presentation

Wed, May 6, 2026, 12:30 PM

Location: EBB CHOA Seminar Room

https://gatech.zoom.us/j/96972528891?pwd=wfP5hwf2uLElzalXRKe0aCtPJxomYc.1&from=addon

 Advisor: Todd Sulchek, Ph.D. (ME, Georgia Institute of Technology)

Committee:

Alexander Alexeev, Ph.D. (ME, Georgia Institute of Technology)

Scott Danielsen, Ph.D. (MSE, Georgia Institute of Technology)

David Myers, Ph.D. (BME, Georgia Institute of Technology)

Guillem Pratx, Ph.D. (Radiology and Medical Physics, Stanford University) 

Optimization of Forces, Loading Rates and Strain for Cytosolic Delivery using Mechanoporation  

Efficient intracellular delivery of cargo remains a central challenge in biomedical engineering to improve gene editing, cell therapy production, and cell diagnostics. The optimal delivery not only introduces cargo into cells but achieves cytosolic access while preserving cell viability and normal function. Existing approaches often fall short of this balance. Viral and carrier-mediated methods can promote uptake of molecularly encoded genes but are limited by endosomal entrapment and intracellular degradation. Electroporation can disrupt cell membranes but also disturbs normal cellular physiology. These limitations have motivated interest in microfluidic mechanoporation, in which cells are transiently permeabilized through controlled mechanical deformation. However, the mechanism of mechanoporation remains poorly defined and, as such, performance remains difficult to predict and standardize. To bridge the gap, this dissertation establishes a framework connecting device geometry, transient loading dynamics, and delivery outcomes. A key learning is that mechanoporation performance is demonstrated to be better explained by cell loading kinetics than by deformation magnitude alone. Systematic channel width and ridge angle variation demonstrates how tuning force rate, directionality, and loading history improves delivery efficiency, cell survival and experimental consistency. Integration of high-speed trajectory analysis, computational fluid dynamics (CFD), and multivariate modeling further reveals that steady-state descriptors such as strain magnitude are insufficient to explain mechanoporation outcomes, which instead, correlate strongly with strain rate and history-dependent hydrodynamic forces. Extending these insights to nanoparticle delivery, imaging analyses demonstrate that optimized device geometries enable deeper intracellular access and improved cargo retention relative to conventional incubation-based approaches. Together, these results provide a force-based framework for mechanoporation that links device design to biologically meaningful intracellular delivery.

BioE PhD Defense Presentation- Avi Gupta

Summarize

Paige, Laura T<laura.paige@bioengineering.gatech.edu>

 

​Paige, Laura T​

​Nelson, Teresa L;​announcements@grad.gatech.edu​

Avi Gupta

BioE Ph.D. Defense Presentation

Wed, May 6, 2026, 12:30 PM

Location: EBB CHOA Seminar Room

https://gatech.zoom.us/j/96972528891?pwd=wfP5hwf2uLElzalXRKe0aCtPJxomYc.1&from=addon

 

 Advisor: Todd Sulchek, Ph.D. (ME, Georgia Institute of Technology)

 

Committee:

Alexander Alexeev, Ph.D. (ME, Georgia Institute of Technology)

Scott Danielsen, Ph.D. (MSE, Georgia Institute of Technology)

David Myers, Ph.D. (BME, Georgia Institute of Technology)

Guillem Pratx, Ph.D. (Radiology and Medical Physics, Stanford University) 

 

Optimization of Forces, Loading Rates and Strain for Cytosolic Delivery using Mechanoporation  

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Bioengineering PhD Defense Presentation

April 29, 2026 (Wednesday)

10:00 AM

Location: Howey N201/202

Meeting link: https://gatech.zoom.us/j/91549406195?pwd=ob1cyXlVnDSpDTjJqx6hQBqzIhnbVz.1

Advisor:  

James C. Gumbart, Ph.D. (School of Physics, Georgia Institute of Technology)  

Committee:  

M. G. Finn, Ph.D. (School of Chemistry and Biochemistry, Georgia Institute of Technology)

Peter Kasson, Ph.D. (School of Chemistry and Biochemistry; Coulter Department of Biomedical Engineering, Georgia Institute of Technology)

Jeffrey Skolnick, Ph.D. (School of Biological Sciences, Georgia Institute of Technology)

Adegboyega Oyelere, Ph.D. (School of Chemistry and Biochemistry, Georgia Institute of Technology)

Modulating Hepatitis B Virus Capsid Assembly and Function through Pharmacological Intervention  

Hepatitis B virus (HBV) is a major global health burden that causes chronic liver disease. Its replication depends on the assembly of a protein capsid, making this process an important target for antiviral intervention. This work integrates molecular dynamics simulations and biophysical experiments to investigate how small molecules interact with and modulate the HBV core protein across different structural contexts. First, biophysics-guided approaches were used to identify ligands targeting the inter-dimer assembly interface, showing that small molecules can perturb capsid assembly through changes in inter-subunit interactions. Second, free-energy calculations were conducted to characterize the conformational landscape of a key assembly intermediate, providing insight into structural features that may influence assembly nucleation. Third, a distinct intra-dimer pocket was characterized using fragment-based mapping and pharmacophore modeling, enabling virtual screening and experimental validation of ligands with extended binding modes. Together, these results provide a framework for understanding how small molecules can modulate HBV capsid behavior and highlight new directions for antiviral design.

Title: Tensor-based Predictive Modeling and Control for High-dimensional Data

Advisor: Dr. Jonathan Rogers

Milestone: MS Thesis Proposal

Degree Program: Aerospace Engineering

Title: UAV Implementation of Optimal Path Planning for Radiological Source Localization in Cluttered Environments

Abstract: This proposal presents an algorithm to optimize a radiological search path by leveraging a path-length optimization method combined with a measure of total information gain. A constrained optimization problem is solved in which the goal is to select measurement locations that yield the best-conditioned estimation problem, with the constraint that the total travel time to visit all points must be less than a specified value. The effectiveness of search locations is measured by the stable rank of the kernel matrix, which quantifies the attenuation between each candidate source location and candidate measurement location. Measurement locations are selected by a greedy algorithm for maximum stable rank while the total travel time is minimized by solving the well-known traveling salesman problem. Simulation results of the algorithm demonstrate that the optimization improves the estimation of radiological source term parameters for a given time constraint. The continued work will focus on the implementation of the algorithm on an unmanned aerial vehicle (UAV). Modifications to the algorithm will be needed to account for different absorption mediums and isotopes. A lab-based experiment demonstrating a search for radiation point sources of different isotopes is being designed.

Date and time: 2026-04-24, 3:00-4:00

Location: Weber 200

Committee:
Dr. Jonathan Rogers (advisor), School of Aerospace Engineering
Dr. Lu Gan, School of Aerospace Engineering
Dr. Craig Bakker, Pacific Northwest National Laboratory
,

BioE Ph.D. Defense Presentation

Fri, May 1, 2026, 12:00 PM – 2:00 PM

Location: IBB 1128 (Suddath Seminar Room)

https://gatech.zoom.us/j/2361182737?pwd=a2N4aFVRZzlHcXUwazBDdGROMUhoUT09

Advisor:

Ahmet F. Coskun, Ph.D. (Biomedical Engineering, Georgia Institute of Technology)

Committee:

Peng Qiu, Ph.D. (Department of Biomedical Engineering, Georgia Institute of Technology)

Saurabh Sinha, Ph.D. (Department of Biomedical Engineering, Georgia Institute of Technology)

Rabindra Tirouvanziam, Ph.D. (Department of Biomedical Engineering, Georgia Institute of Technology)

Marcus Cicerone, Ph.D. (Department of Chemistry & Biochemistry, Georgia Institute of Technology)

Timeseries Spatial Omics of Immune Cell Signaling in Inflammation

Cystic fibrosis (CF) affects over 162,000 people worldwide. Although FDA-approved therapies have shown promising results, chronic inflammation and bacterial infections persist, causing tissue damage and worsening quality of life despite a large immune cell presence. This thesis investigates the spatiotemporal dynamics of NF-κB signaling during inflammation by developing and applying spatial omics tools at the single-cell level across multiple biological contexts.

We first introduce PRISMS, an open-sourced, automated multiplexing pipeline for spatial transcriptomic and proteomic imaging compatible with Nikon widefield and Cephla spinning disk confocal microscopy. Using PRISMS, we apply pSigOmics static fixation to profile over 100,000 mouse fibroblasts stimulated with TNFα and IL-1β, revealing a novel asymmetric protein-RNA (APR) relationship between p65 RNA and protein. Graph neural network classification and PHATe trajectory analysis further delineate distinct APR subpopulations among stimulated fibroblasts.

We next extend pSigOmics with programmable fixation via helical perfusion to create a continuous gradient of cell response, confirming the APR phenomenon with finer temporal resolution through cross-correlation, generalized additive model smoothing, and pseudotime analyses. Together, static and programmable fixation establish a comprehensive spatiotemporal framework for studying oscillatory translational regulation in single cells.

We then develop graph-based super-resolution protein-protein interaction (GSR-PPI) analysis to predict cancer drug responses from spatial PPI networks in lung adenocarcinoma cells and patient-derived NSCLC tissues, and introduce multiplexed iterative sequential PLA (iseqPLA) to visualize 3D NF-κB supercomplex dynamics. Upstream supercomplexes containing TRAF-5_TRADD and TRAF-5_TRAF-2 undergo rapid dissociation upon cytokine stimulation, inversely correlated with p65 nuclear translocation. Macrophage-fibroblast coculture experiments reveal that CF airway supernatant-exposed macrophages impart a hyperinflammatory phenotype to neighboring fibroblasts through paracrine NF-κB activation. We additionally validate our NF-κB gene panel using scGPT foundation models trained on multi-disease transcriptomic datasets, establishing a mechanistic link between protein-level supercomplex dynamics and transcriptional outputs.

These contributions demonstrate the power of combining spatial multiplexing, temporal reconstruction, and computational modeling to decode immune signaling at single-cell and subcellular resolution, with implications for understanding chronic inflammation in CF and identifying opportune windows for therapeutic intervention.

Date: Friday, May 1st, 2026

Time: 12:00 pm- 2:00 pm Eastern Time

Location: Microsoft Teams

Committee:

• Dr. Ece Erdogmus, Founding Dean, College of Architecture, Art, and Construction, Clemson University (co-advisor and co-committee chair)

• Dr. Eunhwa Yang, School of Building Construction, College of Design, Georgia Institute of Technology (co-advisor and co-committee chair)

• Dr. Jing Wen, School of Building Construction, College of Design, Georgia Institute of Technology

• Dr. Carol Colatrella,  School of Literature, Media, and Communication, Ivan Allen College of Liberal Arts, Georgia Institute of Technology

• Dr. Heidi Diefus-Dux,  Department of Biological Systems Engineering, College of Engineering, University of Nebraska–Lincoln

Title

Enhancing High School Counselors’ Knowledge and Confidence in Promoting Construction Management Careers: A Mixed Method Study

Abstract 

The U.S. construction industry faces a significant and escalating shortage of construction management (CM) professionals driven by an aging workforce, increasing project demand and complexity, and a limited pipeline of students entering CM degree programs. These challenges are further compounded by disconnects between industry workforce needs and the career guidance students receive in high school settings. Although CM offers stable and high-opportunity career pathways, many students remain unaware of the profession. High school counselors, who play a critical role in shaping students’ postsecondary trajectories, often report limited knowledge of CM and low confidence in advising students about CM career pathways. These advising gaps restrict students’ access to CM opportunities and contribute to long-term workforce shortages.

To address these gaps, this study investigates how targeted professional development influences high school counselors’ confidence in advising students about construction management careers. The professional development intervention incorporates structured CM instructional content alongside an experiential learning component using a virtual reality (VR) module derived from the NSF-funded Virtual/Augmented-Reality-Based Discipline Exploration Rotations (VADER-R) project. While VR-based tools have been explored for student recruitment, their application in counselor-focused professional development remains limited.

Grounded in Social Cognitive Career Theory (SCCT), this mixed-methods study focuses on counselor self-efficacy as the primary construct of interest. The study develops and validates a construction management–specific adaptation of the Career Counseling Self-Efficacy Scale (CCSES-CM) to measure counselors’ confidence in advising students about CM pathways. In the quantitative phase, a pre–post design is used to assess changes in counselors’ CM advising self-efficacy following participation in the professional development workshop. In the qualitative phase, semi-structured interviews are conducted with a sample of participants to further explore how the intervention influenced their confidence, perceptions, and advising intentions.

The anticipated contributions of this study are threefold. First, it provides a validated, discipline-specific instrument (CCSES-CM) for measuring counselor self-efficacy in construction management advising. Second, it offers empirical evidence on how targeted professional development can strengthen counselors’ confidence in promoting workforce-critical career pathways. Third, it presents a scalable and adaptable professional development model that can be implemented by school districts, higher education institutions, and workforce development organizations. By strengthening counselors’ capacity to communicate accurate, relevant CM career information, this research supports improved alignment between educational advising systems and the construction industry's workforce needs.

School of Psychology – Ph.D. Dissertation Defense Meeting

Date: Friday, May 1st, 2026

Time: 2:00 PM - 3:30 PM

Location: Na Liu's Dissertation Defense | Meeting-Join | Microsoft Teams

Dissertation Committee Chair/Advisor:
James Roberts, Ph.D. (Georgia Tech)

Dissertation Committee Members:
Audrey Leroux, Ph.D. (Georgia Tech)
Dingjing Shi, Ph.D. (Georgia Tech)
Mark Himmelstein, Ph.D. (Georgia Tech)
Hongli Li, Ph.D. (Georgia State University)

Title: Enhancing Precision in the Generalized Graded Unfolding Model (GGUM) Using Successive Interval Judgements Indicative of Item Location

Abstract: The Generalized Graded Unfolding Model (GGUM) is an ideal-point item response theory model suited to measuring non-cognitive constructs such as attitudes, emotions, and personality. A practical limitation of the GGUM is its requirement for large samples (N > 750) to achieve stable parameter recovery. This dissertation evaluated whether integrating collateral item location information from the Method of Successive Intervals (MSI) into the traditional GGUM could improve parameter recovery and reduce sample size requirements. A Monte Carlo simulation study crossed two sample sizes (N = 300, 750), two response formats (2- and 6-category), and two MSI–trait correlations (r = .50, .80) across 10 replications per cell. Parameter recovery for item discriminations (α), item locations (δ), person locations (θ), and response category thresholds (τ) was evaluated using root mean square deviation (RMSD), posterior standard deviation (PSD), and relative parameter bias (RPB). An empirical comparison was also conducted using attitude-toward-abortion data from 40 items administered to undergraduate participants at the Georgia Institute of Technology. Simulation results showed that estimation quality was driven primarily by sample size and response format, whereas model-related effects were uniformly small and did not favor the new model. In the empirical comparison, posterior summaries for α, θ, and τ were highly similar across models, and δ differences were broadly homogeneous across replications. Overall, adding MSI-based collateral information to the GGUM did not produce consistent or practically meaningful improvements in parameter recovery under the conditions examined.

Date: Wednesday, April 29, 2026

Time: 10:30 AM - 12:30 PM, Eastern time (U.S.)

Location: Coda C1115 Druid Hills 

Virtual Meeting (hybrid): https://gatech.zoom.us/j/91333564325

Gennie Mansi

HCC PhD Candidate

School of Interactive Computing, College of Computing 

Georgia Institute of Technology

Committee:

Dr. Mark Riedl (Advisor) - School of Interactive Computing, Georgia Institute of Technology

Mr. Benjamin Sundholm, JD - Tulane Law School, Tulane University

Dr. Naveena Karusala - School of Interactive Computing, Georgia Institute of Technology

Dr. Andrea Parker - School of Interactive Computing, Georgia Institute of Technology

Dr. Agata Rozga - College of Computing, Georgia Institute of Technology

Summary: 

As AI tools are incorporated into high-stakes decision-making environments, such as healthcare and education, people need to act meaningfully in response to AI outputs. This thesis advances actionability—how AI tools and their explanations enable pragmatic action by people in complex sociotechnical environments shaped by power dynamics. I make two central arguments: first, that we can improve people’s ability to act with AI tools by examining how their actions and information needs connect to the ways they care for themselves and others; and second, that understanding how laws and regulations  impact people’s ability to care with AI tools, we can inform the creation and use of AI tools to support people navigating uneven or unknown risks.

This work deepens our understanding of actionability in 5 parts. I discuss how I created a user-centered catalog of information and actions to re-orient the design and evaluation of AI tools around users' needs. Building on this foundation, I draw out the complexities of enabling actionability through an in-depth investigation of physicians' needs, first uncovering how care—including laws, regulations, and collective responsibility for patient well-being—shapes actionability in ways current AI tools’ designs often overlook. Informed by the ways care highlights laws and regulations as a contextual factor, I investigate how doctors' perceptions of potential errors connect to their legal concerns for AI tools. I show that doctors do not connect legal risks to AI tools’ capabilities, and I discuss how this gap may result in unintentional harm in the form of defensive medical practices. To address these gaps, I describe an assets-based, co-design process with lawyers that used visualizations to surface tacit legal knowledge and generate strategies for stakeholders to predict and manage legal risks, revealing how power dynamics shape actionability. Finally, I ground these findings in practice through an analysis of 31 U.S. legal cases, identifying how a complex web of stakeholders and widely deployed AI tools negatively impact patient care, and proposing paths forward through revised liability structures and tools that support legal recourse for patients.

Adrian Choi

Ph.D. Student in Human-Centered Computing (HCC) 

School of Interactive Computing 

Georgia Institute of Technology 

Date: Monday, May 11th, 2026

Time: 10:00 AM-1:00 PM EST 

Location: TSRB 217A or Join via Zoom

Committee 

Dr. Andrea G. Parker (Advisor) – School of Interactive Computing, Georgia Institute of Technology

Dr. Lynn Dombrowski – School of Interactive Computing, Georgia Institute of Technology

Dr. Neha Kumar – School of Interactive Computing, Georgia Institute of Technology

Dr. Brooke Foucault Welles – College of Arts, Media, and Design, Northeastern University

Dr. Christopher Le Dantec – College of Arts, Media, and Design and the Khoury College of Computer Sciences, Northeastern University

Abstract

Free spaces have historically operated as safe learning environments where marginalized youth engage in critical reflection to build civic capacity and mobilize as counter-publics to contest the broader public sphere. However, as youth coalesce around political issues using networked technologies like social media, the formation of these publics becomes actively mediated by commercial technologies. While these technologies allow counter-publics to scale their political discourse, their underlying infrastructure often erodes the conditions necessary for sustaining critical awareness and engaging in constructive deliberation. As such, there is a need to examine how to scaffold technologically-mediated environments that cultivate critically conscious publics in support of an inclusive deliberative democracy.

I present five studies analyzing how sociotechnical assemblages shape critically conscious youth publics. My completed work examines youths' critical consciousness when using social media, investigates how members of a youth empowerment program (YEP) were thwarted even after infrastructuring to sustain a free space, and maps the socio-material conditions necessary for forming healthy networked publics. My proposed work investigates how technology-mediated discourse shapes youths' critical consciousness by analyzing media narratives after police killed Eric Garner. Concurrently, I examine the role of civic data in infrastructuring critically conscious publics within participatory design, aligning public data with YEPs to probe youths' attachments to matters of concern.

This dissertation proposal aims to advance our knowledge of the infrastructuring work involved in scaffolding youths' critical consciousness. By empirically examining YEPs as free spaces alongside the circulation of counter-public discourse online, this work reveals how sociotechnical assemblages can either support or threaten critically conscious publics. Building on these insights, I discuss the precarious nature of infrastructuring these physical and digital spaces, considering the conditions necessary for sociotechnical infrastructures to align with critical pedagogy and support more inclusive democratic deliberation.

This online session counts as one contact hour towards the in-person training requirement of the RCR Compliance Policy. For more information about the RCR training requirements, refer to the Compliance Training Matrix.

Georgia Tech Students, Staff, Faculty, can pre-register to help ensure RCR credit here: https://gatech.geniussis.com/Registration.aspx?aid=1891

Abstract: The emergence of micro-, nano-, and molecularly-tailored multimaterial systems, driven by breakthroughs in additive manufacturing (AM), is redefining the boundaries of material innovation. Leveraging the precision and versatility of 3D and 4D printing, AM enables unprecedented local customisation of material architectures and properties. In this talk, I will present our multidisciplinary research journey, highlighting compliance-engineered multilayers for enhanced performance, morphology-optimized composites, and surface-engineered systems for functional adaptability. I will also discuss nature-inspired materials, including nacreous composites and camouflage systems that emulate biological complexity; high-performance self-sensing systems and 4D-printed adaptive structures; multiscale and hierarchical fibre composites and electrically conductive materials with advanced functionalities; and architected metamaterials featuring 2D and 3D lattices designed for energy absorption, autonomous sensing, failure mitigation, lightweighting, reversible shape transformation, energy storage, water pollutant detection, and thermal management. We demonstrate the ability to control material behaviour at the micro- and nano-scale in 3D and 4D frameworks, which enables precise tuning of strain, stress, and functionality, paving the way for unprecedented performance and fabrication methods. By integrating scalable AM techniques with the design of nano- and micro-architected hierarchical structures, our research is driving the creation of new material classes with extraordinary properties. These paradigm-shifting advancements are poised to transform fields such as defence, energy, transportation, automotive, electronics, and aerospace, shaping the future of material science and engineering.

Probing Charge Carriers in Mixed Ionic-Electronic Conducting Polymers

Abstract:

Conjugated polymers continue to emerge as next-generation electronic materials for mixed ionic-electronic conduction applications, ranging from biomedical sensing to energy storage. However, their development is hampered by a lack of rational design principles due to missing fundamental knowledge about how ion-charge interactions and dynamic polymer nanostructure influence charge transport and storage along polymer chains. In this talk, I will first discuss how we are exploiting the ultrafast dynamics of photoexcited charge carriers to provide details on their nanoscale environment and trapping behavior. Then I will show how in situ electronic and vibrational spectroscopy of polymer electrodes can be used to track their complex nanoscale dynamics during charging, revealing insights into nanostructures that support the formation of mobile carriers.

Bio:

Dr. Chris Grieco is an Assistant Professor of Chemistry at Auburn University, where his research group develops laser spectroscopy methods to probe charge carriers in conducting polymers used in electrochemical applications ranging from bioelectronics to batteries. Prior to Auburn, Chris earned a PhD in Chemistry at Penn State University where he worked with Prof. John Asbury studying how to improve exciton and charge carrier dynamics in organic molecules and polymers for solar cells. Chris then worked with Prof. Bern Kohler as a postdoctoral scholar at the Ohio State University, where he developed ultrafast transient absorption spectroscopy methods for probing the elusive structure and photochemistry of the eumelanin biopigment.

Recent Awards:

• NSF CAREER Award, 2025
• Norman Edmund Inspiration Award, Edmund Optics, 2024
• Ultrafast Education Award - Bronze, Edmund Optics, 2024
• Gerhard Closs Postdoctoral Award, Inter-American Photochemical Society, 2023

BIO
Jeffrey Hubbell is a pioneer in the field of immuno-modulatory materials whose research spans biology, chemistry, and engineering to generate novel solutions to common health problems. His work focuses on using biomaterials and engineered proteins to enhance the body’s own immune system to fight inflammatory and autoimmune disorders, allergies, and disease states like cancers. With more than 400 papers and 100 issued US patents, Hubbell uses biomaterials and protein engineering approaches to investigate topics in regenerative medicine and immunotherapeutics. In regenerative medicine, he develops both materials and protein therapeutics to address issues such as chronic wound healing in diabetes and bone repair after trauma. In immunotherapeutics, he pursues nanomaterials in vaccination for infectious disease and cancer as well as targeted therapy with RNA, protein-material conjugates for inverse vaccines to induce antigen-specific tolerance in autoimmunity and allergy, and in protein therapy to tip immune balances toward aggression in immuno-oncology and toward tolerance in inflammatory disease. His interests are both basic and translational, having founded or co-founded six biomedical companies based on his technology.

Before moving to New York University, Hubbell was at the University of Chicago in the Pritzker School of Molecular Engineering, where he helped build its strength in immunoengineering. Prior to that, he was on the faculty of the Swiss Federal Institute of Technology Lausanne (EPFL, where he served as founding Director of the Institute of Bioengineering and Dean of the School of Life Sciences), the Swiss Federal Institute of Technology Zurich (ETHZ) and University of Zurich, the California Institute of Technology, and the University of Texas in Austin. He was elected to the US National Academy of Engineering in 2010, the National Academy of Medicine in 2019, the American Academy of Arts and Sciences in 2021, and the National Academy of Sciences in 2023.