Water Memory (Jala Smriti) was awarded the Antinori Foundation Grand Prize in the 96-Hour Opera Project in June 2024.

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

*Accessible to students via Buzzcard

BIO
Marcela Maus grew up in New York, NY and attended Massachusetts Institute of Technology for her undergraduate degree in Biology and Literature. She then earned her MD, PhD from the University of Pennsylvania School of Medicine where she studied the biology of human T cell activation under the direction of her PhD mentor, Dr. Carl June. After completing her doctorate degrees, Marcela did a one-year postdoctoral fellowship with Dr. Kathy High at the Children’s Hospital of Philadelphia studying patient immune responses to liver-directed AAV-mediate gene transfer as a treatment for hemophilia. She then completed her clinical training in internal medicine, hematology, and oncology as a resident at the University of Pennsylvania Health System and a fellow at Memorial Sloan Kettering Cancer Center. While at Memorial Sloan Kettering, she worked in Michel Sadelain’s lab to engineer new genetically modified T cells to target the cancer-testis antigen NY-ESO-1 and received specialized training in Phase I developmental therapeutics. She was also awarded a K08 grant by the NIH to facilitate her transition to a faculty position.

Following her extensive training (11 years), Marcela returned to the University of Pennsylvania in 2012, where she was recruited by her former PhD mentor, to expand their research efforts in the development of CAR T cells for lymphoma, myeloma, and other cancers. While there, Marcela continued her efforts on preclinical development of CAR T cells and correlative studies of CAR T cell-related toxicities and resistance to therapies. They discovered and reported on the first cases of anaphylaxis to CAR T cells and off target cross-reactivity. She also led an entire arc of translational research, from preclinical testing to a first-in-human clinical trial of EGFRvIII CAR T cells for treatment of recurrent glioblastoma. Correlative data collected from this trial led to an understanding of CAR T resistant mechanisms that have led further refinement of these CAR T cells, which is one current area of focus in her current laboratory and has led to the development of another Phase I clinical trial for GBM patients.

Now, Dr. Maus is a Professor of Hematology and Oncology at Massachusetts General Hospital and Harvard Medical School, where she runs a laboratory focused on developing and improving CAR T cell therapy for cancer patients while also periodically attending for the bone marrow transplant service. Marcela is the Paula O-Keeffe Endowed Chair of the Massachusetts General Hospital Cancer Center and the Director of the Cellular Immunotherapy Program. She has won numerous awards for her transformative research and is achieving her goal of using science to determine the best way to treat patients by harnessing the power of the immune system to solve intractable problems like cancer. She also enjoys training clinicians and scientists and watching her mentees succeed and become independent scientists and collaborators.

Speaker: Brendan Juba, Associate Professor, Washington University

Abstract:

Domain-independent planning is a classical AI task. For a specified set of objects and predicates, with some initial valuation, an action model is given that describes a set of actions in terms of their preconditions and effects; that is, respectively, properties that the domain must satisfy for an action to execute (e.g., a conjunction), and the changes to the values of predicates that result from the execution. A goal property is also given in terms of the predicates and objects. The domain-independent planning task is then, given this initial valuation, action model, and goal, to output a straight-line program consisting of actions that results in the satisfaction of the goal.

Here, we focus on the task of learning an action model from example executions. We propose a formulation of this task in the spirit of distribution-free PAC learning, that permits an arbitrary domain-independent planner to safely use the action model we produce. Safety here means that we guarantee that any plan produced by the planner executes in the actual action model, i.e., the actual preconditions are satisfied at each step, and results in the satisfaction of the given goal in the actual action model. We show that there are polynomial-time algorithms for safe learning of action models expressible in the classical STRIPS formalism, for example. We will also show that many richer families of domains are not safely learnable.

 Based on joint works with Hai Son Le, Argaman Mordoch, and Roni Stern.

Bio:

Brendan Juba is an Associate Professor in the Department of Computer Science and Engineering at Washington University in St. Louis. His current research interests lie in theoretical approaches to artificial intelligence, founded on the theory of Algorithms and Computational Complexity.

Website: https://www.cse.wustl.edu/~bjuba/

School of Psychology – Ph.D. Dissertation Defense Meeting

Date: Tuesday, May 12th, 2026

Time: 12:00 - 1:30PM

Location: in-person - JS Coon 150

and virtually (https://gatech.zoom.us/my/huadong.xiong?pwd=u8JPjJbQwVLXhhf9RPItRJS7dcFEw4.1)

Dissertation Committee Chair/Advisor:

Robert Wilson, Ph.D. (Georgia Tech)

Dissertation Committee Members:

Dobromir Rahnev, Ph.D. (Georgia Tech)

Apurva Ratan Murty, Ph.D. (Georgia Tech)

Audrey Sederberg, Ph.D. (Georgia Tech)

Marcelo Mattar, Ph.D. (New York University)

Title: An Online Learning Perspective on Cognition

Abstract: How should an intelligent system adapt when the world changes faster than its underlying architecture can be redesigned? Classical symbolic theories have approached this question by describing cognition as computation over structured variables, rules, and formal operations, whereas connectionist theories have approached it through learning rules such as Hebbian plasticity, error-driven updating, and distributed representational change. In my Ph.D. work, I develop an online learning perspective that connects these traditions by treating cognition as the continual adjustment of internal strategies in response to feedback, uncertainty, and resource constraints. This project has three main parts. First, I mathematically show that several influential cognitive models can be re-expressed through a duality with online optimization: although these models differ in surface form and theoretical vocabulary, they can often be understood as optimizing implicit loss functions over internal representations or policies. This provides a way to relate symbolic and connectionist models without reducing one tradition to the other. Second, I apply this framework to human decision making, showing that both value-based and perceptual decisions can be interpreted as adaptive online optimization processes. Alongside this modeling work, I develop faster and more reliable software tools for parameter estimation, enabling more efficient tests of cognitive models at scale. Third, I extend the same perspective to large language models, using online optimization as a lens for studying in-context learning, metacognition, and neural constraints on adaptation. Inspired by questions from human cognition, I further investigate representation interference, Bayesian hypothesis generation, Bayesian evidence integration, and feature binding in LLMs. Together, my work suggests that online learning offers a useful computational perspective for understanding adaptive intelligence across cognitive models, human behavior, and artificial systems.

Date: Tuesday, May 5th, 2026

Time:1:30-3:30 pm EST

Location: Remote

Zoom:https://gatech.zoom.us/j/93623089632

Hao Kang
Ph.D. Student in Computer Science
School of Computer Science
Georgia Institute of Technology
https://haokang-timmy.github.io/

Committee members
Dr. Tushar Krishna (advisor) - School of Computer Science, Georgia Institute of Technology
Dr. Alexey Tumanov - School of Computer Science, Georgia Institute of Technology
Dr. Tuo Zhao - School of Computational Science and Engineer, Georgia Institute of Technology

Dr. Song Han  - EECS, Massachusetts Institute of Technology

Abstract

Large Language Model (LLM) systems are increasingly deployed as agents that interact with external tools, simulators, markets, games, and software environments. Unlike conventional LLM serving, where workloads are often modeled as independent requests optimized for static throughput or latency targets, agentic execution exposes dynamic environment states, heterogeneous resource lifecycles, and task-dependent latency--quality trade-offs. In this dissertation, we study how to make AI systems configurable for dynamic agentic execution. The central goal is to design system abstractions and mechanisms that expose the right configuration space across precision, scheduling, memory management, and tool-resource orchestration, so that deployment can be specialized to the characteristics of the task, model, and environment rather than relying on one-size-fits-all serving policies.

In the first direction, we study latency-sensitive agent decision tasks, where an agent's final reward is jointly determined by response quality and inference latency. We introduce two real-time evaluation environments: HFTBench, a high-frequency trading simulator, and StreetFighter, a competitive gaming environment. These benchmarks show that different environments prefer different operating points along the latency--quality trade-off: some tasks reward faster but less accurate actions, while others require both low latency and high decision quality. To address this, we propose FPX, a configurable mixed-precision inference framework that exposes fine-grained latency-quality control through model-size selection and layer-wise FP8/FP4 precision assignment. FPX allows the system to choose task-appropriate inference configurations and improves downstream reward over fixed-precision baselines. This direction demonstrates that agent efficiency should be measured not only by standalone model accuracy or raw throughput, but also by how system configurations affect final reward in dynamic environments.

The second direction studies long-horizon multi-turn agentic execution, where agents repeatedly alternate between LLM reasoning and external tool execution. Existing systems loosely compose an inference engine with a general-purpose tool orchestrator, causing request-level scheduling, KV-cache thrashing, cross-node memory imbalance, and unmanaged tool-resource lifecycles. We propose ThunderAgent, a program-aware agentic inference system that abstracts each workflow as an LLM Program spanning model invocations, KV-cache states, and tool environments. Built on this abstraction, ThunderAgent introduces program-aware scheduling and tool-resource management to configure execution across GPU memory, data-parallel nodes, and external environments. This enables high-throughput serving and reinforcement-learning rollouts while reducing recomputation and resource leakage.

Together, these directions argue that future AI infrastructure should not treat agentic workloads as static requests. Instead, systems should expose configurable execution mechanisms that can be specialized to the dynamic requirements of models, tasks, and environments, enabling efficient and robust execution for emerging agentic AI applications.

Date: May 4 2026 

Time: 12pm

Location: TSRB 217A 

Zoom : https://gatech.zoom.us/j/3788369736?omn=91316693376

Darley Sackitey 

Ph.D. Student in Human-centered Computing 

School of Interactive Computing 

Georgia Institute of Technology 

tinyurl.com/darleysackitey 

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. Carl DiSalvo - School of Interactive Computing, Georgia Institute of  Technology

Dr. Angela D.R. Smith - School of Information, University of Texas at Austin

Dr. Christina Harrington - Google Research

Abstract 

Care is everywhere and yet, much of it goes unseen. From neighbourhood lending networks to online communities exchanging small acts of kindness, the informal, everyday practices through which people support one another form the connective tissue of social life. Yet technology research has largely looked past this quiet labour, focusing instead on care in hospitals, social services, and moments of crisis.

This dissertation brings everyday care into focus. Through five studies spanning systematic review, participant observation, social network analysis, and participatory design, it examines how ordinary people organise, sustain, and scale mutual support, from tight-knit Black church communities to loosely connected strangers on Reddit. Together, these studies reveal how the texture of relationships, the design of platforms, and the pressures of crisis each shape who gives care, who receives it, and whether it lasts. 

The result is a richer, account of care as a continuous collective accomplishment that communities build and rebuild over time. 

Date: Tuesday, May 5th, 2026

Time: 11am - 1pm EST

Location: Tech Square Research Building Room 223

Virtual (Zoom): https://gatech.zoom.us/j/96614909235

Qiao Zhang

Ph.D. Student in Computer Science

School of Interactive Computing

College of Computing

Georgia Institute of Technology

Committee:

Dr. Christopher J. MacLellan (Advisor) – School of Interactive Computing, Georgia Institute of Technology

Dr. Ashok Goel – School of Interactive Computing, Georgia Institute of Technology

Dr. Mark Riedl – School of Interactive Computing, Georgia Institute of Technology

Dr. Erik Harpstead –  Human-Computer Interaction Institute, Carnegie Mellon University

Abstract

Human-AI Teaming (HAT) is an increasingly important area of research as AI systems are integrated into complex, collaborative environments. AI agents were developed and applied in multiple real-life cases, including chatbots and personal assistants for single, well-defined tasks, embodied agents that operate in a self-contained environment, and AI teammates that support collaborative reasoning and decision-making. Although games have been widely used for agent training and evaluation, much existing work has focused primarily on improving agent capabilities or benchmarking performance against human experts. Fewer studies explored human-AI interaction in game settings, positioning AI agents as teammates rather than competitors. Similarly, research on human-AI and human-robot collaboration often positions AI as an assistant rather than a teammate, limiting AI’s autonomy and agency compared to human partners. These limitations create research gaps and opportunities to identify tasks that require true collaboration, arrange team composition, understand teaming dynamics, and examine strategy alignment.

In this thesis, I use Dice Adventure, a customizable multiplayer cooperative game developed in-house, to investigate three key aspects of human-AI teaming: how team structure influences performance, how communication conventions emerge in human teams, and how humans adapt when collaborating with AI under varying degrees of strategy alignment. My work aims to generate insights that inform the design of more adaptive and human-compatible AI teammates.

Student Name: McKenna Clinch

Thesis Title: Ingestible Electroceutical Device for Satiety and Digestion Hormone Regulation

Thesis Advisor: Alex Abramson

Thesis Co-Advisor: N/A

Committee Members: Alex Abramson - Chemical and Biomolecular Engineering; Hang Lu - Chemical and Biomolecular Engineering; Michael Filler - Chemical and Biomolecular Engineering; Matthew Flavin - Electrical and Computer Engineering; Shanthi Srinivasan - Department of Medicine (Emory)

Date: 5/19/2026

Time: 1:00 PM

Location: MoSE 3201A

Student Name: Amisha Patel

Thesis Title: Improving the signal transduction of human GPCRs via yeast machinery.

Thesis Advisor: Dr. Pamela Peralta-Yahya

Thesis Co-Advisor: N/A

Committee Members: Dr. John Blazeck (School of Chemical and Biomolecular Engineering) Dr. JC Gumbart (School of Physics) Dr. Andrew McShan (School of Chemistry and Biochemistry) Dr. Corey Wilson (School of Chemical and Biomolecular Engineering)

Date: 5/5/2026

Time: 11:00 AM

Location: MoSE 3201A

From 9:00 PM–Midnight, the Student Center will be transformed with themed activities, games, and surprises to help you recharge between study sessions. Enjoy a photo booth, meet Darth Vader, relax with massages, test your knowledge with trivia, grab some food, and more.
Study strong—and May the Force be with you!

Bioengineering PhD Defense Presentation

May 5, Tuesday

11:00 AM

Location: MoSE 3201

Meeting link:  https://gatech.zoom.us/j/97364717127?pwd=U2kSZYbv6WuhxhmxJAi1hnKgy2pAFL.1&from=addon

Advisor:  

Dr. Pamela Peralta-Yahya (School of Chemistry and Biochemistry)

Committee:  

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

Dr. John Blazeck (School of Chemical and Biomolecular Engineering, Georgia Institute of Technology)

Dr. Andrew McShan (School of Chemistry and Biochemistry, Georgia Institute of Technology)

Dr. Corey Wilson (School of Chemical and Biomolecular Engineering, Georgia Institute of Technology)

Improving signal transduction of human GPCRs via yeast machinery.

G-Protein coupled receptors, or GPCRs, are a highly druggable class of human receptors. They constitute one of the largest and most versatile families of membrane protein that translate external stimuli into intracellular responses. This ability to detect a diverse set of ligands makes them an integral part of human pharmacology. About 30% of FDA-approved drugs target GPCR mediated pathways. They can be heterologously expressed in systems like Saccharomyces cerevisiae to develop a fast, engineerable high-throughput biosensors capable of detecting receptor-ligand interactions with applications in drug discovery and development. However, functional expression of human GPCRs in yeast, a non-native chassis, remains challenging often due to inefficient signal transduction via yeast machinery. This thesis addresses this challenge and expands the utility of yeast-based GPCR biosensors by improving GPCR-GPA1 coupling.

School of Psychology – Ph.D. Dissertation Proposal Meeting

Date: Wednesday, May 13th, 2026

Time: 12:00 - 2:00PM

Location: JS Coon 148 or online (https://teams.microsoft.com/meet/215526552621487?p=mdNo1jdq8KNDRi5KvS)

Dissertation Committee Chair/Advisor:

Bruce Walker, Ph.D. (Georgia Tech)

Dissertation Committee Members:

Richard Catrambone, Ph.D. (Georgia Tech)

Woon Ju Park, Ph.D. (Georgia Tech)

Ben Satterfield, Ph.D. (Georgia Tech)

Barbara Chaparro, Ph.D. (Embry-Riddle Aeronautical University)

Title: From Acceptance to Mastery: An Evaluation of Technology Mastery and the Validation of the General Technology Mastery Scale

Abstract: The goal of this proposed dissertation is to empirically investigate the concept of technology mastery, operationally defined here as an individual’s knowledge, attitudes, and ability regarding the use of a technology.  To begin, I conducted a series of interviews with a mix of community members and college undergraduates to better define the concept of “technology mastery,” determine what behaviors and traits are associated with the term, and finally discuss any barriers individuals face when attempting to gain skill with technology. The results of this study were analyzed through thematic analysis and produced roughly 40 themes, which were then used to create the General Technology Mastery Scale (GTMS) through a series of factor analysis techniques. I then sought to validate the GTMS by comparing it to another available scale of technology mastery, the Continuum of Assistive Technology Mastery (CATM). Additionally, I collected data on hypothesized covariates based on the literature (such as problem-solving, technology self-efficacy, general self-efficacy, technology acceptance, system usability, locus of control, motivation, and creativity). For the proposed Study 4, I plan to collect data at four timepoints on a sample of master's level speech language pathology students at the University of Georgia who are enrolled in CMSD 6650 (a course on Augmentative and Alternative Communication Devices). This combined body of work introduces the most comprehensive explanation of technology mastery and its correlates to date. Furthermore, it proposes a quick (if imperfect) way to measure technology mastery when collecting data on objective performance is not feasible

Master’s Thesis Defense Meeting

Data: Friday, May 8th, 2026

Time: 12:00PM – 2:00PM

Location: Room 148 and virtual, link here: https://gatech.zoom.us/j/92396114398?pwd=oyyqwzRKjsfkPD7WfrK0UKtsdH6hEi.1

Thesis Chair/Advisor:
Hsiao-Wen Liao, Ph.D. (Georgia Tech)

Thesis Committee Members:

Sashank Varma, Ph.D. (Georgia Tech)

Audrey Leroux, Ph.D. (Georgia Tech)

Title: Gist-Based Compensation for Age-Related Episodic Decline in Emotional Autobiographical Memory: A Natural Language Analysis of Life’s Highs and Lows 

Abstract:

Age-related declines in episodic specificity during autobiographical recall are well-established. Recent work, however, suggests that older adults' preserved semantic knowledge may scaffold episodic retrieval. Sheldon et al. (2024) found that semantic content diversity — the breadth of semantic content within a memory narrative — predicted greater episodic, but not non-episodic, detail in memories collected via the Autobiographical Interview, a task that constrains recall toward maximizing episodic specificity. Whether this pattern generalizes to retrieval contexts that foreground identity and personal meaning remains untested. This preregistered study examined whether semantic content diversity predicted episodic detail in the Life Story Interview, in which memories are situated within a personal life story. A total of 4576 memory narratives spanning five memory types (high points, low points, turning points, critical events, and health events) from 932 older adults (ages 62–75; 56.3% female) were analyzed using LLM-based detail scoring and topic modeling–derived semantic content diversity. Multilevel models showed that semantic content diversity predicted greater episodic detail. This relationship held at both the memory and individual levels, with between-individual effects of greater magnitude. Neither association was moderated by memory type. While high point memories contained more episodic detail than low point memories, consistent with socioemotional aging accounts, the two memory types did not differ in semantic content diversity. Contrary to Sheldon et al. (2024), semantic content diversity was found to positively predict non-episodic detail. Taken together, the current study findings suggest that semantic knowledge supports detailed life story memories in older adults. The influence of retrieval context in shaping the scaffolding role of semantic processing in autobiographical memory is discussed.

Thesis Title: “Leveraging Garcia Gonzalez-Derived Glycosylfurans and Dihydrofurans for the Synthesis of Polycyclic Heterocycles"

Wednesday, May 6th, 2026 at 9:00 AM

Location: EBB Krone (Children's Healthcare of Atlanta Seminar Room), or Join via Zoom: https://us04web.zoom.us/j/8616877040?pwd=ua4O7imYP0PN0ka3hMctAi8eE8oGpa.1&omn=79273539137

(Meeting ID: 861 687 7040; Passcode: U9amTQ)

Committee Members:

Prof. Stefan France (Advisor) - School of Chemistry and Biochemistry 

Prof. Christopher W. Jones (Advisor) - School of School of Chemical & Biomolecular Engineering

Prof. Angus P. Wilkinson  - School of Chemistry and Biochemistry 

Prof. Will R. Gutekunst - School of Chemistry and Biochemistry 

Prof. Joseph Sadighi - School of Chemistry and Biochemistry 

Thesis Title: “Advances in Enyne Metathesis for the Synthesis of Water-Soluble Monomers and Biomacromolecular Conjugation”

Wednesday, May 6th, 2026 at 11:30 AM

Location: Howey Physics (classroom L3) or join via Zoom:

https://us04web.zoom.us/j/79864242007?pwd=i9txHSGHr8pkoUpazx9w0AqP3kws5C.1

Meeting ID: 798 6424 2007

Passcode: 8GVh8C

Committee Members

Prof. Will R. Gutekunst (Advisor) – School of Chemistry and Biochemistry

Prof. M.G. Finn – School of Chemistry and Biochemistry

Prof. John R. Reynolds – School of Chemistry and Biochemistry

Prof. Christoph J. Fahrni – School of Chemistry and Biochemistry

Prof. Julie A. Champion – School of Chemical & Biomolecular Engineering 

Admission is free and open to the public.

___________________________________

View the concert program.

Watch the live stream on the School of Music's YouTube channel.

Visit the School of Music's website to view the schedule of concerts for this semester. 

Follow the School of Music on Facebook to keep up with our news and events.

Date: Tuesday, May 5th, 2026

Time: 1:00 - 3:00 PM ET

Location: Klaus 3126

Zoom: https://gatech.zoom.us/j/98919205550

Pulkit Gupta

Ph.D. Student

School of Computer Science
College of Computing
Georgia Institute of Technology

Committee:

Dr. Thomas M. Conte (Advisor) – School of Computer Science, Georgia Institute of Technology

Dr. Hyesoon Kim – School of Computer Science, Georgia Institute of Technology

Dr. Vivek Sarkar – School of Computer Science, Georgia Institute of Technology

Dr. Divya Mahajan – School of Computer Science, Georgia Institute of Technology

Dr. Peter Kogge – College of Engineering, University of Notre Dame

Abstract:

Superconducting digital logic families offer immense energy and power savings over conventional CMOS based electronics. However, superconducting circuits pose significant challenges to designing an architecture with high performance. This thesis proposes 2 architectures, SYNDRA and STRIDE, which attempt to provide high single threaded performance and high system level throughput respectively. SYNDRA (Static Synchronous Dataflow RISC Architecture) overcomes the challenges in conventional high-ILP architectures by offloading all scheduling efforts to a compiler through the Statically Assigned DataGraph Execution (SADGE) model, showing a peak IPC of 4 at 1GHz. STRIDE tackles the latencies of superconducting systems, by proposing a manythreaded system with 512 threads, a per-thread IPC of 0.08, but a system-level IPC of 4 at 5GHz. Together these architectures comprise a Superconducting Unit of Performance (SUP), which allows for both high single-threaded and high system-level throughput. Many SUPs can be composed through the use of a superconducting interconnect to enable supercomputer-scale systems with immense energy efficiency.

This seminar presents a synthesis of research focused on leveraging these data sources for infectious disease prediction, spanning from classical statistical approaches to modern AI architectures.

Key Areas of Discussion:

• Statistical Methodologies: The presentation covers a family of models that combine autoregressive time series structure with penalized regression on Google search data. This includes extensions to spatial-temporal modeling, multi-disease settings, and COVID-19 adaptation.
• AI Architectures: Recent work on attention-based transformer architectures for time series forecasting will be discussed, highlighting methods for multivariate dependency modeling, in-context learning, and efficient linear attention.
• Practical Application: Drawing from ongoing participation in the CDC FluSight forecasting initiative, the session compares the strengths and limitations of both paradigms and shares practical lessons learned from real-time deployment.

The talk aims to offer perspective on when and how statistical rigor and deep learning flexibility each contribute to reliable disease forecasting.

About the Speaker

Dr. Shihao Yang is a Harold E. Smalley Early Career Professor and Assistant Professor in the School of Industrial and Systems Engineering at Georgia Tech. He completed his PhD in statistics and post-doc in Biomedical Informatics at Harvard University. Dr. Yang’s research focuses on data science, with special interest in time series, dynamical systems, and applications in infectious disease transmission forecasting.

To attend, please register online via Zoom.

Speaker: Jun Ding
Professor of Neurosurgery, and Neurology & Neurological Sciences
Stanford University School of Medicine
 

Friday, May 8, 2026
12:00 pm (lunch will be provided)
CNR 1000, 1st floor
Claudia Nance Rollins Building, Rollins School of Public Health

Paid parking available:

Michael Street Parking Deck
550 Houston Mill Road, Atlanta 30322

1525 Parking Deck
1525 Clifton Road, Atlanta, 30322
 

When: 9:00 AM – 11 AM
Where: McIntire Conference Room (Whitaker Bldg. Rm 3115)
What:

• Open-door format: stay for the full two hours or drop in for 15–30 minutes — every bit counts!
• Enjoy a supportive, motivating space to focus on your writing — no pressure, just progress.
• Coffee, tea, and pastries are provided to keep you fueled.

Who is Welcome?
Anyone working in neuroscience, neurotechnology, or society-related research and writing. Undergraduates, graduate students, trainees, postdocs, faculty, and staff are all encouraged to join!

• 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)

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.

Register Here! Complimentary registration required for all attendees. 

Rapid-fire and poster presentation submission deadline April 29

Registration (without abstract submission) is open through May 14.

Conference Agenda

• 8:00 a.m. - Registration/Breakfast
• 9:00 a.m. - Opening Remarks
• 9:15 a.m. - Abigail Wojtowicz, Medical Director, US Medical Strategy, Takeda
• 10:00 a.m. - Tina Dorr, Partner, Barnes & Thornburg Law
• 10:30 a.m. - Poster Session
• 11:45 a.m. - Cherry Wan, Senior Manager, Chemical Science, Alcon
• 12:15 p.m. - Lunch
• 1:15 p.m. - Bruno Marques, Senior Vice President, Technical Operations, Morphocell Technologies
• 2:00 p.m. - Jianming Li, Senior Program Officer, Venture Well
• 3:00 p.m - Rapid Fire Presentations
• 4:00 p.m. - Career Panel Discussion
• 5:00 p.m. - Closing Remarks
• 5:10 p.m. - Reception and Award Ceremony
• 7:00 p.m. - Adjourn

• Alex Adams - Interactive Computing - "Point-of-care Technologies for Fetal-maternal Health Monitoring"
• Ghassan AlRegib - Electrical and Computer Engineering - "Accessible Healthcare through AI-augmented Decisions (AHeAD)"
• Jon Duke - Computational Science and Engineering, Interactive Computing - "AI for Rural Health"
• Facundo Fernandez - Chemistry and Biochemistry - "AI-driven Metabolic Biomarker Discovery from High-dimensional Cellular Data"
• Ameet Pinto - Civil and Environmental Engineering - "AI-enabled Real-time Microbial Monitoring to Advance Sustainable Biomanufacturing"
• B. Aditya Prakash - Computational Science and Engineering - "AI for Epidemiology and Public Health"
• Agata Rozga - Interactive Computing - "Autism and Aging: Pushing AI Beyond Snapshots in Time"
• Nicoleta Serban - Industrial and Systems Engineering - "AI-enabled Operations Research for Health Systems"
• Jeffrey Skolnick - Biological Sciences - "Closing the Loop from Genome to Disease to Therapeutics to Patients with AI"
• May Dongmei Wang - Biomedical Engineering - "Translating AI for Impact in Healthcare: Challenges and Opportunities"
  
  Co-Chairs:
  Andrés García - Executive Director, Institute for Bioengineering and Bioscience
  David Sherrill - Executive Director, Institute for Data Science and Engineering
  May Dongmei Wang - Petit Faculty Fellow, Biomedical Engineering
   

Join the Office of Technology Licensing for our Coffee & Commercialization lecture series. 

Our guest speaker will discuss how faculty can take their idea and create a startup. 

 In this session, you’ll learn about:  

• Startup idea development
• Choosing your startup team
• Engaging with investors
• Equity splits/cap tables
• Why IP matters to investors  

RSVP to Amy Smith at asmith872@gatech.edu 

Virtual options via Teams below 

Microsoft Teams meeting

Join: https://teams.microsoft.com/meet/235299001242298?p=pviOBaqaww9HMt7gVY

Meeting ID: 235 299 001 242 298

Passcode: 2gp3BV3z

邀请你一起来认识上帝选民的故事，与神奇妙的作为！

聚会安排

6:30-9PM 吃饭与看电影（提供免费的Pizza）

 

Welcome to Chinese Christian Fellowship!

This week, we’ll be watching the movie “Prince of Egypt.”

You are invited to join us as we learn about the story of God’s chosen people and witness His amazing works!

Schedule

6:30–9:00 PM — Dinner & Movie (Free pizza provided)

Title: Applications of Optimization and Causal Inference

Date: Wednesday, May 6, 2026

Time: 3:00 pm – 5:00 pm ET

Location: Remote on Zoom — https://gatech.zoom.us/j/93388746171?pwd=IknDRQHq8fIL8QJi7YQTV9kveeYnxp.1 

Meeting ID: 933 8874 6171

Passcode: 734561

Committee:

Dr. Turgay Ayer (Advisor), Virginia C. and Joseph C. Mello Endowed Chair, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology

Dr. Shihao Yang, Harold E. Smalley Early Career Professor, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology

Dr. Santanu Dey, Anderson-Interface Chair and Professor, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology

Dr. David Goldsman, Coca-Cola Foundation Professor, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology

Dr. Jagpreet Chhatwal, Director, Center for Health Technology Assessment, Mass General Brigham; Associate Professor, Harvard Medical School

Abstract:

This thesis develops methods for causal learning under

data scarcity and distributional shift, combining transfer learning,

kernel learning, and doubly robust semi-parametric estimation in public health 

and personalized medicine.

The first part develops Transfer Learning Random Forests (TLRF) for

county-level epidemic growth-rate estimation and outbreak prioritization

under fixed public-health capacity. Its core technical step is a

proxy-identification argument that recasts latent

growth-rate estimation as conditional mean learning on observable

log-difference outcomes, followed by adaptive spatiotemporal transfer

via random-forest weighting. In a retrospective comparison against

historical decisions by the Colorado Department of Public Health and

Environment, TLRF yields substantially higher investigation positive

predictive value (including a 224% relative improvement as a point

estimate under the same decision budget).

The second part develops a causal-kernel subgroup discovery framework:

Neyman-orthogonalized CATE estimation and kernel learning under the

Robinson decomposition are followed by kernelized convex clustering to

identify subpopulations defined by treatment responsiveness rather than

covariate similarity alone. This component turns treatment-effect

heterogeneity into decision-ready subgroup structure to support

hypothesis generation, follow-up trial enrichment, and targeted

intervention design.

The third part develops a placebo-anchored transfer framework for

transporting individualized treatment effects across heterogeneous

trials under covariate shift, including settings where the target

population has limited or no treated outcomes. A sparsity-driven,

LASSO-based proxy-gold transfer correction is integrated with

cross-fitted doubly robust estimation to support target-population

effect learning under explicit transport assumptions. This provides a

practical evidence-synthesis pathway for target-population decision

support when standard IPD meta-analysis assumptions are not fully met.

Across these three settings, the unifying theme is principled

information borrowing: semiparametric identification, orthogonalized

causal kernel learning, and transfer-based doubly robust inference for

decision-making when target data alone are insufficient.

Doctor of Philosophy in Bioinformatics

in the Department of for Biomedical Engineering

Xinling Li

Will defend her thesis:

Quantitative Approaches for Identifying the Limitation of Single-cell Sequencing Technologies and Applying Sequencing Technologies in the Study of Infectious Diseases and Cancer

May 7, 2026

10:00 AM – 12:00 PM (EST)

EBB 4029

https://teams.microsoft.com/meet/23036548151103?p=UplOBRjEVYTj2NYCAm

Thesis Advisor:

Dr. Peng Qiu

Department of for Biomedical Engineering

Georgia Institute of Technology

Committee Members:

Dr. Gabe Kwong

Department of for Biomedical Engineering

Georgia Institute of Technology

Dr. Greg Gibson

School of Biological Sciences

Georgia Institute of Technology

Dr. May Wang

Department of for Biomedical Engineering

Georgia Institute of Technology

Dr. M.G. Finn

School of Chemistry and Biochemistry

Georgia Institute of Technology                        

Abstract:

This thesis focuses on identification of the limitation of single-cell sequencing technologies, and development of pipeline and models for analyzing sequencing data. The aim of this work is to contribute to a deeper understanding of sequencing technologies and a better utilization of the technologies in solving challenges in infectious diseases and cancer research. We first identified systematic under-detection of genes in scRNA-seq and Visium data through analyses of paired bulk RNA-seq and single-cell genomics datasets. These genes were enriched for poly(T) motifs near their 3′ ends, leading to the hypothesis that such motifs may form hairpin structures with poly(A) tails of mRNA transcripts, reducing capture efficiency during library preparation and explaining their lower detection compared to bulk RNA-seq. We also proposed a framework integrating graphical models and conventional machine learning to leverage scRNA-seq data for predicting COVID-19 severity and identifying biomarkers. Graphical models effectively distinguished healthy individuals from infected patients (F1-score > 0.9), while traditional machine learning accurately classified disease severity (F1-score > 0.99), revealing meaningful molecular signatures. In addition, we developed a bioinformatics pipeline to predict existence of proteases across bacterial species by leveraging known bacteria–protease relationships. Through large-scale protein–genome alignments, we identified previously unreported proteases across bacterial genomes that cause severe infectious diseases, with validation from bulk RNA-seq data. Finally, we constructed mathematical models based on mass action kinetics to evaluate multi-arm protease sensors, demonstrating their potential advantages in cancer detection. Overall, this work highlights limitations of current sequencing technologies and provides computational solutions to enhance their utility in early cancer detection and infectious disease treatment and diagnostics.

Thesis Title: "Secondary metabolite induction in marine commensals through coculture and OSMAC approaches"

Friday May 8th, 2026 at 2:00 PM

Marcus Nanotechnology Research Center 1117

Or join via Zoom: https://gatech.zoom.us/j/97435049677?pwd=ZQyGahgI0FyrOWLDSQ5UR19cKtSuCT.1

Meeting ID: 974 3504 9677
Passcode: 803995

Committee members:

Dr. Neha Garg (Advisor) - School of Chemistry and Biochemistry 

Dr. Facundo Fernández - School of Chemistry and Biochemistry 

Dr. Julia Kubanek - School of Chemistry and Biochemistry 

Dr. Vinayak Agarwal - School of Chemistry and Biochemistry 

Dr. Valerie Paul - Smithsonian Marine Station, Fort Pierce

Feeling the finals crunch? Visit the IBB Nerem Atrium to enjoy games, coffee, and refreshments to re-charge during finals!

Open to all in the bio-community, while supplies last.

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.

📍 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

 

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. 

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.

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.

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.

Committee:

Dr. Ayazi, Advisor

Dr. Taylor, Chair

Dr. Ansari

Committee:

Dr. Gaylord, Advisor

Dr. Cai, Chair

Dr. Adibi