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.

Committee Members:

Dr. Zhigang Jiang, School of Physics, Georgia Institute of Technology

Dr. Martin Mourigal, School of Physics, Georgia Institute of Technology

Dr. Xueda Wen, School of Physics, Georgia Institute of Technology

_____________________________

Gary M. Longstreet

Academic Program Manager

REU Program Coordinator

School of Physics

Georgia Institute of Technology

837 State Street, Room N109

Atlanta, GA 30332

404-894-7073

longstreet@gatech.edu

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
,

Doctor of Philosophy in Physics 

School of Physics Thesis Dissertation Defense

Mateo Reynoso

Dr. Roman Grigoriev, School of Physics, Georgia Institute of Technology (Advisor)

Dynamical Mechanisms of Nonuniversality in 2D Turbulence in the Presence of Coherent Structures

Date: Tuesday, April 21, 2026

Time: 1:00 p.m.

Location: Howey Physics Building, Room N110 

Virtual: https://gatech.zoom.us/j/98641369716

Committee members:

Dr. Predrag Cvitanović, School of Physics, Georgia Institute of Technology

Dr. Michael Schatz, School of Physics, Georgia Institute of Technology

Dr. Alexander Blumenthal, School of Mathematics, Georgia Institute of Technology

Dr. Rafael de La Llave, School of Mathematics, Georgia Institute of Technology

Abstract:

Turbulence is one of the most important problems in physics and applied mathematics, with applications ranging from astrophysics and engineering to plumbing and more. It is fundamentally a multiscale phenomenon, characterized by cascades that dynamically transfer energy and other invariants across a wide range of length scales. While these interactions are often described through a statistical framework, a more holistic understanding requires accounting for the spatial structure of the flow. Aspects such as the coherence of large-scale structures cannot be captured by statistical assumptions alone; therefore, a more general theory for scaling in turbulence must explicitly account for the dynamical role of coherent structures.

This dissertation examines how coherent structures shape scale generation and nonuniversality in two-dimensional turbulence, with an emphasis on both the direct enstrophy cascade in steady turbulence and the inverse energy transferr in freely decaying turbulence. Although classical Kraichnan–Leith–Batchelor theory predicts universal scaling under assumptions of homogeneity, isotropy, and locality, the results presented here show that the observed scaling is instead governed by the geometry and interactions of coherent structures, often organized as large-scale flows. By combining direct numerical simulations, reduced-order modeling, and statistical closure arguments, this work demonstrates that a predictive theory of turbulence scaling must move beyond statistical assumptions and account for the transport and lifecycle of coherent structures.

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.

will propose a doctoral thesis entitled,

Mechanical role of porous mesh for improved dewatering

On

Thursday, April 23 at 10:00 am
EBB Krone 4029

And

Virtually via

https://teams.microsoft.com/meet/24976321543276?p=ANkvYNTOTzqcYcTy16

Meeting ID: 249 763 215 432 76

Passcode: dG9Y6Q9w

Committee
            Dr. Blair Brettmann – School of Materials Science and Engineering (Advisor)
            Dr. Victor Breedveld – School of Chemical and Biological Engineering (Co-advisor)
            Dr. Christopher Luettgen – School of Chemical and Biological Engineering

            Dr. Donggang Yao– School of Materials Science and Engineering

            Dr. Mary Lynn Realff– School of Materials Science and Engineering

Abstract

The papermaking process is highly energy-intensive, with the drying stage accounting for up to 50% of a paper mill’s total energy consumption. To reduce this energy demand, one promising approach is to maximize water removal during the forming and pressing stages prior to evaporation. During pressing, the wet paper is compressed against a felt to drive water out of the paper and into the felt. However, during decompression, the paper web undergoes pore expansion, leading to undesired return of water (rewet). Introducing a stiff, porous mesh layer between the paper and felt improves dewatering by disrupting the liquid bridges that flow back into the fibrous paper. This thesis investigates how the design and intrinsic properties of the mesh porous layer affect dewatering. This includes evaluating the role of mesh geometry, understanding how stress distribution from these porous layers affects the wet fibrous paper, and analyzing transient behavior during compression-decompression cycles.

In the first part of this thesis, I will investigate how the void volume of the mesh provides pathways for fluid to travel between the paper and the felt. I will also study how stacking multiple porous mesh layers between the paper and felt affects dewatering performance. In the second part, I will focus on the role of pressure distribution, specifically how uniform stress and compaction of the paper sheet affect the efficiency of the dewatering process. In the last part, I will examine time as a key process variable and evaluate its influence on the overall pressing system during cyclic pressing. Overall, this research seeks to develop a framework to optimize the mesh–felt system and improve dewatering efficiency by understanding the interactions among layered porous materials during press cycles.

Ayush Dhande

Advisor: Prof. Antonio Facchetti

  will defend master’s thesis entitled,

Development of Intrinsically Stretchable Vertical Organic Electrochemical Transistors via Interfacial Top Electrode Modification

On

Monday, April 20 at 11 a.m.

MRDC 4211

and virtually via MS Teams

https://teams.microsoft.com/meet/265405960763671?p=3K2IxWxSKMV9gb9ZJc

Abstract

Organic electrochemical transistors (OECTs) have emerged a promising technology for bio-integrated electronics due to their low voltage operation and high transconductance, yet achieving both performance and mechanical stretchability remains challenge. In this work, an intrinsically stretchable vertical OECT (svOECTs) is fabricated to bridge the gap, utilizing a vertically stacked architecture that enables high device density and nanometer scale channel lengths. To address the failure of electrodes under mechanical strain, a top electrode engineering strategy is introduced using a trilayer top electrode mechanism. This significantly enhances interfacial adhesion and charge transport. The resulting svOECTs exhibit electromechanical stability, durability and maintain functionality under different mechanical conditions. This study demonstrates that combining vertical device architecture with interfacial molecular engineering provides a framework for next generation bioelectronics of high fidelity sensing on dynamic biological surfaces.

Committee

• Prof. Antonio Facchetti – School of Materials Science and Engineering (advisor)
• Prof. Jason Azoulay – School of Materials Science and Engineering
• Prof. Seung Soon Jang – School of Materials Science and Engineering

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

Come to the Georgia Tech Permaculture Interest Group Meeting to learn about the RCE (UN Regional Centre of Expertise for Education) SDG Fellowship Program! Irene Jacob will deliver a guest lecture about her 2025-26 fellowship project on Pollinator Gardens, in partnership with the non-profit My Green Earth. The talk will be followed by a Permaculture Interest Group informational meeting.

Instructional information and seeds may be available.

Michael received his B.S. in Materials Science in 2007 from The University of Michigan and M.S. and Ph.D. degrees from The Georgia Institute of Technology in Mechanical Engineering in 2010 and 2014 respectively. Michael was the 2021 recipient of the TMS Young Innovator in Additive Manufacturing award for the impacts of his research on the field. Michael is also an active member of the ASTM F42 additive manufacturing standards committee, serving as the co-chair of the materials committee. 

Abstract: Pending

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.

• Wednesday, April 22: 9:00 AM - 2:00 PM
• Thursday, April 23: 9:00 AM - 3:00 PM
• Friday, April 24: 9:00 AM - 2:00 PM
• Monday, April 27: 11:30 AM - 4:00 PM
• Tuesday, April 28: 9:00 AM - 4:00 PM
• Wednesday, April 29: 9:00 - 2:00 PM
• Thursday, April 30: 9:00 AM - 4:00 PM
• Friday, May 01: 9:00 AM - 4:00 PM

We have really beautiful items for you, so please come and pick them up during the week of April 22 - May 1.  Questions or concerns? Missed the pickup days?  Just email Dr. D'Unger for information on alternate pickup times.

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.

Join President Cabrera with Rodney Adkins, former Senior Vice President at IBM, to discuss leadership in technology. 

GameFest 2026 is FREE collegiate gaming experience where attendants can enter tournaments and challenges featuring your favorite games. The more games you play, the more points you earn and the attendant with the most points at the end of GameFest 2026 will win The Director's Cup Grand Prize: A New Custom-Built Gaming PC!!

With returning staples like Marvel Rivals, Valorant, League of Legends, and Rocket League plus new titles like Mario Kart World, 2XKO, Brawl Stars, Pokemon, and so much more, GameFest 2026 has something for everyone!

Join us APRIL 25TH and 26TH in the GEORGIA TECH CAMPUS RECREATION CENTER for GAMEFEST 2026!! Whether you’re a casual gamer or looking to dominate as many events as possible, our celebration of gaming is an event you won't want to miss!

Register for the event TODAY on The Official GameFest 2026 Website and signup for your favorite games at start.gg/gamefest-2026!

This year, our theme is Bloom 🌸💮🪻, and the dress code is business professional and Spring-inspired attire. Tickets are available now, and all sales go to each student chapter as we prepare for competition this upcoming fall.

Location: Haworth Atlanta Showroom

383 17th St NW, Suite 120

Atlanta, GA 30363

Tickets (purchase here):

NOMAS Members: Free Entry (request a ticket)

Students: $15.00

Faculty: $30.00

Professionals: $30.00

Don't have time to play games? Feel free to drop in anytime between 2-7 PM to fuel for your next final. We will have food and refreshments for attendees!

We will be meeting Monday, April 13th, from 6:30 to 7:30 PM in CoC 053, and hope to see you there! If you can't make it in person, a zoom link is also provided.

For any additional information, please don't hesitate to reach out via our discord: https://discord.gg/cyNvsmtjqY

Below is an abstract of the work:
Reasoning about the correctness of distributed systems is a significant challenge, with precise correctness
specifications serving as an essential prerequisite to verification. However, identifying and formulating
specifications remains a major hurdle for developers in practice. Specy addresses this challenge by automatically
learning specifications from observable event traces generated by message exchanges in distributed systems.
The system employs a specialized grammar tailored for event-based specifications, incorporating support
for quantifiers over events – a capability essential for capturing the complex behavioral patterns inherent in
distributed protocols. Specy utilizes a novel learning procedure that combines grammar-based enumerative
search with dynamic learning from event traces, providing effective control over the specification search. We
evaluated Specy on established distributed protocols and industrial case studies, demonstrating its ability to
successfully learn important protocol specifications. Specy can discover previously unidentified specifications
overlooked by developers, automatically derive inductive invariants that were previously constructed manually
for verification purposes, and, through run-time monitoring in production systems, reveal gaps in testing
coverage – highlighting opportunities to leverage specifications in practice.

Expect conversations about everything from cyber trends and career paths to favorite tools, classes, projects and our new club — no technical expertise required. Come build your network, find potential teammates for competitions, and get a feel for the community behind the keyboards.

Stop by, say hello, eat some delicious free pizza and start making connections that could last throughout your time at Georgia Tech and beyond. Everyone interested in cybersecurity is welcome! 🔐💻

Enjoy good food, productive energy, and meaningful conversations with fellow students. Let’s learn, grow, and succeed together!

All majors and years are welcome!

Whether you’re new to campus or back for another term, come take a break, grab a s’more, and meet other transfer students in a relaxed, welcoming space.

We know summer can feel a little quieter—and finding your people isn’t always easy. That’s why TSA is hosting this bonfire to help you connect, unwind, and start the semester with some friendly faces.

Stop by anytime between 7–9 PM on May 22nd—we’d love to meet you!

RSVP via Suitable here. Attending the event is worth 10 points in Scholarly & Creative Engagement. 

Learn more on Engage
 

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.

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.

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.

Enjoy refreshing treats like King of Pops and chips and queso from Bro-Ritos ATL, while participating in interactive activities. There will even be opportunities to win prizes!

Stick around after the carnival to enjoy a screening of Back to the Future with Campus Tickets!

Don’t miss this opportunity to unwind and meet new people!

Date: Wednesday, April 29, 2026, at 12:00 p.m.

Location: Coda, Room 114 (Google Maps link)

Lunch provided!

Title: Safety Alignment of Generative Foundation Models

Abstract: Modern LLMs are safety-aligned through supervised finetuning (SFT) and reinforcement learning from human feedback (RLHF) to mitigate harmful, undesirable, or disallowed outputs. Despite ongoing progress, LLMs still exhibit critical safety gaps: models can be jailbroken into revealing harmful content, often overrefuse benign queries, and fail to maintain safety under adversarial scenarios. My dissertation research advances the safety alignment of generative foundation models by developing principled tools, architectures, and training methods that strengthen their robustness and reliability at scale. Specifically, this thesis focuses on three complementary thrusts: a) Understanding and shaping the safety landscape of LLMs, b) Internalizing safety in agentic reasoning intelligence, and c) Grounding safety and robustness in multimodal perception.

Bio: Anthony is a CS Ph.D. candidate at Georgia Tech working with Polo Chau. His thesis research has elevated foundational AI efforts at Nvidia, Meta, IBM, Intel, and ADP via internships and collaborations, and has resulted in several first-author publications and awards at NeurIPS, ACL, ICCV, EMNLP, CVPR, and BMVC. His research has contributed to the AI foundation of multiple funded industry research grants totaling over $1.4M. Learn more about him at shengyun-peng.github.io.

About HotCSE

HotCSE is an academic seminar series to bring Ph.D. students in Computational Science and Engineering together to discuss interesting topics. The topics consist of high-performance computing, machine learning, data analysis, simulation, computational sustainability, medical informatics, etc.

The talks have always been enjoyable and have ranged from quite informal to formal conference style talks. Either chalks or slides can be used to help people understand your talk. It is also a great forum to practice conference talks and bounce around new ideas.

Currently the talks are sponsored by the School of Computational Science and Engineering. The goal of CSE is slightly broader than that of these talks - we want to bring more people outside CSE to discuss their related work here.

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.

Committee:

Dr. Chin-Hui Lee, Advisor

Dr. Moore, Chair

Dr. Anderson

Committee:

Dr. Sathe, Advisor

Dr. Romberg, Chair

Dr. Raychowdhury

Committee:

Dr. AlRegib, Advisor

Dr. Muthukumar, Chair

Dr. Heck

Committee:

Dr. Yu, Advisor

Dr. Hao, Chair

Dr. Mukhopadhyay

Committee:

Dr. AlRegib, Advisor

Dr. Vela, Chair

Dr. Calhoun

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 the 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.

Host: Prof. John Wise

Title: Cataclysmic variables as radio sources

Abstract

Cataclysmic variables (CVs) are a common type of binary star, consisting of an Earth-sized white dwarf accreting matter from a small, cold red dwarf companion. While CVs have been known of for over a century and well-studied across the electromagnetic spectrum by hobbyists and professionals alike, we still lack a comprehensive picture for how they produce radio emission. In this talk, I will introduce the major hypothesis for where and how radio emission is produced in these binary stars: astrophysical jets from the white dwarf and coherent flaring from the companion star. In particular, I will focus on the latter scenario. Some of my recent work has revealed a wide variety of behavior that reinforces CVs as dynamic and interesting laboratories for astrophysics. The study of these coherent mechanisms is broadly applicable to stellar magnetic activity, opening up new pathways for observing stellar coronal mass ejections and exospace weather, which has direct consequences for exoplanet habitability.

• 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)

Presenter: Seok Joon Kim 

Seminar Title

AHEAD of Time: Toward Robots That Behave Like Human Companions 

Affiliation 

George W. Woodruff School of Mechanical Engineering (Georgia Tech College of Engineering), Symbiotic and Augmented Intelligence Laboratory (SAIL)

Participation & Visibility 

We actively encourage cross-departmental participation and welcome engagement from both internal academic units and external industry partners. Our goal is to foster a robust community of practice around XR at Georgia Tech. 

Subscribe & Sign Up 

To join our mailing list and RSVP your attendance for these seminar series, please sign up here: RSVP HERE. Lunch will be served for those who RSVP.

Presenter: Alex Yang

Seminar Title

 LitForager: Exploring Multimodal Literature Foraging Strategies in Immersive Sensemaking 

Affiliation

IVI Lab (Immersive Visualization & Interaction Lab), School of Interactive Computing (Georgia Tech College of Computing)

Seminar Title

We actively encourage cross-departmental participation and welcome engagement from both internal academic units and external industry partners. Our goal is to foster a robust community of practice around XR at Georgia Tech. 

Participation & Visibility

We actively encourage cross-departmental participation and welcome engagement from both internal academic units and external industry partners. Our goal is to foster a robust community of practice around XR at Georgia Tech. 

Subscribe & Sign Up 

To join our mailing list and RSVP your attendance for these seminar series, please sign up here: RSVP HERE. Lunch will be served for those who RSVP.

The goal of the National Safety Council's DDC is to provide drivers with knowledge and safe driving techniques to prevent accidents and violations. This course focuses on accident prevention through hazard recognition and application of accident-avoidance techniques. In addition, the course addresses common driving violations that result in accidents and how to change driving habits to eliminate moving violations.

Throughout the 6-hour course, participants learn how to recognize both potential and immediate hazards, how to avoid accidents in a variety of driving conditions and how to choose safe and legal driving behaviors. The emphasis is on identifying and choosing safe and legal behind-the-wheel behaviors and actions.

In order to attend this course you must have a valid state driver license.

You are entitled to a discount on your auto insurance in Georgia after the completion of this course.

To Register:

1. Go to Genius LMS and use your GT Login
2. Click "View Course Catalog"
3. Search for "defensive"
4. Click "Register" for your preferred date
5. Click "Proceed to Checkout" and follow the instructions

IF YOU HAVE TROUBLE REGISTERING, or IF YOU DON'T GET AN EMAIL IMMEDIATELY AFTER REGISTERING, please contact ors@ors.gatech.edu.

Who Should Attend?:

All current Georgia Tech employees and students. Individuals who drive vans to transport 15 or more people must take the Van Safety course. To register for Van Safety, email generalsafety@ehs.gatech.edu.

Register Online to Attend

Date: Friday, April 24th, 2026  

Time: 2:00 PM – 4:00 PM ET  

Location: Remote (Zoom)  

Virtual Link: https://gatech.zoom.us/j/95008488072  

Bangyuan Liu  

Robotics Ph.D. Candidate  

School of Mechanical Engineering  

Georgia Institute of Technology  

Committee:  

Dr. Frank L. Hammond III (Advisor) – School of Mechanical Engineering, Georgia Institute of Technology  

Dr. Daniel I. Goldman – School of Physics, Georgia Institute of Technology  

Dr. Alper Erturk – School of Mechanical Engineering, Georgia Institute of Technology  

Dr. Layne R. Churchill – Georgia Tech Research Institute  

Dr. Howie Choset – Robotics Institute, Carnegie Mellon University  

Abstract:  

Maneuverability and adaptability are essential capabilities for underwater robots operating in cluttered and constrained environments, where conventional rigid-body vehicles often struggle. This dissertation investigates a family of soft, modular, non-biomorphic swimming robots that leverage mechanical compliance and underactuated design to achieve robust locomotion and interaction with complex environments without reliance on complex sensing or feedback control. We present a series of robotic systems and experiments that progressively explore the relationship between body morphology, actuation, and body–environment interaction. First, we demonstrate how a minimal actuation architecture enables diverse locomotion behaviors, including a novel roll rotation maneuver that provides full three-dimensional maneuverability. Next, we show that intrinsic morphological features, such as compliant, vertebra-like body structures and passive foldable fins, allow the robot to traverse narrow gaps and complex obstacles through passive mechanical adaptation. Building on these insights, we investigate how body scaling and structural parameters influence locomotion performance, revealing the existence of an effective body compliance regime that balances deformation and force transmission for efficient propulsion. Finally, we introduce an actively controllable fin system that extends morphological adaptability through fluidic actuation, enabling dynamic stiffness modulation and improved environmental interaction. Together, this work establishes a design framework for underwater robots that integrates maneuverability, compliance, and adaptability, offering a pathway toward robust operation in complex real-world aquatic environments.

School of Psychology – Masters Thesis Defense Meeting

Date: Monday, April 20, 2026

Time: 4:00 pm 

Location: virtual Microsoft Teams

Link:  https://teams.microsoft.com/meet/267751979208573?p=JWJyB7BPalMYz11ucv

Meeting ID: 267 751 979 208 573

Passcode: z7KY2jJ9

Thesis Chair/Advisor:

Scott Moffat, Ph.D. (Georgia Tech)

Thesis Committee Members:

Thackery Brown, Ph.D. (Georgia Tech)

Bruce Walker, Ph.D. (Georgia Tech)

Mark Wheeler, Ph.D. (Georgia Tech)

Title: Effects of Sensory Input Modality on Cognitive Mapping and Landmark Memory

Abstract:

Previous research suggests that vestibular and proprioceptive input may enhance learning and memory of an environment; however, most virtual navigation studies limit these inputs by utilizing stationary desktop virtual interfaces. The present study examined whether different sensory input modalities in virtual reality (VR) influence the acquisition of spatial knowledge. Participants were assigned to one of three VR conditions: Desktop, Head-Mounted Display (HMD), or HMD combined with an omnidirectional treadmill (HMD + Omni). Following a spatial learning task, participants completed a landmark recognition task, a pointing task, and a landmark placement task. 

Contrary to predictions, increased immersion and the addition of locomotion did not improve spatial performance. While the VR condition did significantly affect learning time, analyses revealed that factors such as perceived workload and individual differences predicted performance more strongly than the VR condition. Although aspects of presence increased with immersion, further analyses showed that presence did not mediate the relationship between condition and performance. Findings suggest that increased immersion introduces additional cognitive demands that may have offset the potential benefits of immersion for spatial learning. 

Advisor: Dr. Dimitri Mavris

Milestone: PhD Thesis Proposal

Degree Program: Aerospace Engineering

Title: A Model-Based System-of-Systems Architecting Framework for Lunar Base Master Planning, Evaluation, and Design Space Exploration

Abstract: This research addresses the problem of architecting the lunar base system-of-systems for long-term human presence and driven by stakeholder objectives. As global space agencies, militaries, and commercial companies pursue a sustained presence on the lunar surface, there is a need to establish a physical lunar base capable of supporting scientific, commercial, and national objectives while evolving over time. However, current lunar base site plans lack a comprehensive, systematic process for planning the physical layout and do not account for spatial placement, connectivity, environmental constraints, or how the architecture will grow and adapt under changing requirements and increasing operational complexity. This work approaches the lunar base as a complex system-of-systems and develops a model-based systems architecting framework for generating, evaluating, and evolving lunar base architectures. System architecture decisions are formalized and structured to produce architecture instances through a generative process informed by urban planning practices, capturing the mapping of function to form, as well as spatial/topological and connectivity relationships among the objects of form. The framework leverages model-based systems engineering (MBSE) to capture, query, and trace architectural decisions to the resulting configurations. To enable quantitative evaluation, the framework establishes a connection between MBSE architecture models and multidisciplinary analysis and optimization (MDAO) models through a graph-based transformation mechanism, enabling architecture-specific evaluation of system interactions, resource flows, and infrastructure networks. This allows consistent evaluation of architecture performance while preserving traceability between architectural decisions and analysis results. Given the combinatorial explosion of possible architecture configurations, the design space is characterized by mixed-discrete, hierarchical decision variables and multiple conflicting objectives. To address this system architecture optimization (SAO) problem, population-based multi-objective algorithms augmented with surrogate models are used to efficiently explore the design space and converge to pareto-optimal architectures.  In all, this research establishes a repeatable and systematic methodology for lunar base master planning that integrates architecture generation, transformation for evaluation, and optimization. The methodology enables the analysis and evolution of architecture alternatives for long-term, scalable, and adaptable lunar surface infrastructure.

Date and time: 2026-04-24, 9 AM

Location: Collaborative Visual Environment (CoVE) Room in Weber Space Science and Technology Building

Committee:
Dr. Dimitri Mavris (advisor), School of Aerospace Engineering
Dr. Daniel Schrage, School of Aerospace Engineering
Dr. Perry Yang, School of City and Regional Planning
Dr. Jud Ready, GT Space Research Institute
Dr. Michael Balchanos, School of Aerospace Engineering
, 

Advisor: Dr. Dimitri Mavris

Milestone: PhD Thesis Final Examination (Defense)

Degree Program: Aerospace Engineering

Title: A Design Methodology For Resilient Cislunar Space Domain Awareness Architectures

Abstract: Cislunar space is the next frontier of human space exploration as scientific and commercial missions by national agencies and private entities are driving a renewed race to the Moon. Long-term human presence in this domain depends on supporting infrastructure, of which space domain awareness is a critical component that tackles the detection, tracking, and identification of space objects. SDA has been crucial for near-Earth applications, and the same need will follow as activity in cislunar space grows. Systems resilience as developed for terrestrial infrastructure and for space mission success motivates this work on resilient SDA design. Traditional design processes consider resilience analysis as robustness or disruption analysis after the conceptual design phase, when high-level performance and cost trades are already conducted. Layering resilience onto fixed architectures yields a narrow solution space and weak traceability between resilience outcomes and design decisions. The core technical barrier that blocks resilience from entering the conceptual phase is the modeling complexity of resilience evaluation, where it must be concretely defined, simulated, and measured rapidly enough to participate within the optimization loop. This thesis develops a methodology that brings system-of-systems resilience into the conceptual design of cislunar SDA architectures through a phase-based performance framework, with three research questions derived from the technical gaps. First, a flow-weighted supra-adjacency matrix (FW-SAM) was developed as a graph-based replacement for discrete-event DTN simulation. Encoding sensing, communication, and relay flow in a single sparse matrix, the FW-SAM evaluates downlink efficiency directly from its structure. The second research question addresses graph-based robustness metrics, with sensor performance loss, downlink efficiency loss, and relay efficiency impact derived directly from the FW-SAM. The third research question formulates an analytic resilience integral combining preemption probability, robustness, recovery time, and an adaptation trajectory with a phase-decomposed cost model, where the integral couples resilience investment to the architecture being designed. The validated methods are composed into a multi-objective optimization spanning constellation architecture and resilience phase variables. All-in-One Integrated Resilience Optimization (AIRO) jointly optimizes all variables with, tested against two sequential baselines, with and without resilience optimization. AIRO produced wider Pareto spread on all three objectives and discovered more than twice the architecture classes of the sequential approach, including constellations with lower sensor capability paired with high adaptation investment that the sequential methods cannot find. The resilience objective also drove orbit diversity beyond what performance and cost optimization together could produce. The graph surrogate, the analytic resilience integral, and the architecture-coupled cost model make resilience tractable inside the computational budget of evolutionary optimization, enabling enhanced resilience tradespace exploration for future cislunar SDA systems. 

Date and time: 2026-04-24, 1:30 PM

Location: Code/Cove

Committee:
Dr. Dimitri Mavris (advisor), School of Aerospace Engineering
Dr. Brian Gunter, School of Aerospace Engineering
Dr. Kyriakos Vamvoudakis, School of Aerospace Engineering
Dr. Michael Balchanos, chool of Aerospace Engineering
Dr. Michael Steffens, Draper Laboratory
, 

Advisor: Dr. Dimitri Mavris

Milestone: PhD Thesis Proposal

Degree Program: Aerospace Engineering

Title: A Model-Based Methodology Supporting Commonality Integration in the Lunar Surface Mobility Vehicle Family

Abstract: The goal of the Artemis program is to establish a sustainable lunar settlement, which requires surface mobility vehicles capable of diverse, evolving missions. These use cases support exploration and infrastructure deployment activities. With challenges related to affordability and complexity, NASA has established commonality and interoperability, which relate to sharing common parts and ensuring diverse systems are compatible, as key design principles. Despite the widespread success of leveraging common vehicle platforms in the automotive and aerospace industries, current proposals for lunar surface vehicles remain largely treated as isolated systems rather than a cohesive family of systems where commonality can be leveraged. This fragmented approach overlooks the benefits that commonality can bring, such as reduced development costs and simplified lunar surface logistics and maintenance. However, transitioning to a family of systems perspective introduces complex tradeoffs between commonality and mission-specific performance, necessitating a methodology to identify where commonality should be integrated. To navigate this tradeoff, this research proposes a hybrid approach that leverages the analytical commonality and performance assessments of Product Family Design with the variability management frameworks from Product Line Engineering to support commonality integration decisions. The hybrid approach addresses three main phases of the decision-making process for commonality integration: Modeling of the variability of the lunar surface mobility family of vehicles across its operational, functional, and physical layers of abstraction in a feature model to elicit alternative designs through generative artificial intelligence. Linking simulation instances to the feature model to automatically generate physics-based constraints between these alternatives and ensure the feasibility of design alternatives. Evaluation of the commonality and performance tradeoff and the sensitivity of metric selection for the decision on integrating commonality across parts in different vehicles of the family. Overall, these contributions formulate a methodology that provides decision support on where it is appropriate to leverage commonality.  This research enables physics-driven decisions on a comprehensive description of the alternatives to decide commonality and interoperability. Ultimately, these results support the deployment of robust mobility capability across the lunar terrain through a family of vehicles that addresses the various needs for sustained human habitation on the lunar surface. 

Date and time: 2026-05-01, 9:30 am

Location: COVE (Weber Building)

Committee:
Dr. Dimitri Mavris (advisor), School of Aerospace Engineering
Prof. Daniel Schrage, School of Aerospace Engineering
Prof. Yashwanth Kumar Nakka, School of Aerospace Engineering
Dr. Michael Balchanos, School of Aerospace Engineering
Dr. Hugo Guillermo Chale Gongora, Airbus
, 

will propose a doctoral thesis entitled,

Interface-Engineered MBE of InSe: Early-Stage Growth Control and Hall Sensor Performance

On

Wednesday, April 29 at 11 a.m. (EDT)
Pettit Microelectronics Building Room 102B

and/or

Virtually via MS Teams or Zoom

https://teams.microsoft.com/meet/253152803479756?p=3APd10saNQuRogNWn9

Meeting ID: 253 152 803 479 756

Passcode: Nr2U7Wa9

Committee
            Prof. Eric Vogel – School of Materials Science and Engineering (advisor)
            Prof. Mark Losego– School of Materials Science and Engineering
            Prof. Faisal Alamgir – School of Materials Science and Engineering

            Prof. Alan Doolittle – School of Electrical and Computer Engineering
            Dr. Brent Wagner – Georgia Tech Research Institute

Abstract
               This work aims to enable high-sensitivity, BEOL-compatible InSe Hall sensors by clarifying how the very first layers of film growth set the ultimate limits on mobility and noise. Although indium monoselenide (InSe) offers high intrinsic mobility, a layered van der Waals structure, and a suitable bandgap, MBE-grown films typically underperform exfoliated crystals because defects and competing phases are “baked in” during the first one to three monolayers at the substrate.

               To address this challenge, interface-engineered MBE is used to deliberately shape nucleation and early-stage growth. Metal-precursor layers, chalcogen pretreatments, and time-structured flux schemes are tuned to control nucleation density, phase stability, and grain coalescence, with the goal of producing phase-pure, continuous, low-roughness InSe films within BEOL thermal budgets. Structural and morphological characterization (Raman phase fraction analysis, AFM, TEM) is used to map how these interface-engineering strategies change phase composition, grain size, and interface disorder.

               The influence of substrate bonding and symmetry is further examined by applying these interface-engineered approaches across van der Waals, ionic, and covalent or amorphous platforms. Hall bar devices then relate interface and microstructural metrics directly to Hall mobility, carrier density, sheet resistance, and Hall sensitivity. By combining targeted interface control with quantitative electrical measurements, this thesis seeks to narrow the mobility gap between deposited and exfoliated InSe and to provide actionable guidelines for integrating InSe Hall sensors in future monolithic 3D electronics.

will propose a doctoral thesis entitled,

Multi-scale Computational Investigation of Interfacial Dynamics in Zinc-ion Batteries

On

Tuesday, April 28 at 12:30 p.m.
MRDC 4211

And

Virtually via

https://teams.microsoft.com/meet/24173259120565?p=3RRFE13az92HpDpwO2 

Committee
            Prof. Seung Soon Jang – School of Materials Science and Engineering (advisor)
            Prof. Matthew McDowell – School of Materials Science and Engineering
            Prof. Emma Hu – School of Materials Science and Engineering

      Prof. Marta Hatzell - School of Mechanical Engineering

      Prof. Julia Yang - School of Chemical and Biological Engineering

Abstract

Aqueous zinc-ion batteries (AZIBs) have emerged as a compelling alternative for next-generation energy storage systems due to their inherent safety, cost-effectiveness, and environmental sustainability. However, the practical deployment of AZIBs is severely hindered by fundamental challenges at the zinc anode interface, including uncontrolled dendritic growth, hydrogen evolution reactions (HER), and surface corrosion. This research proposes a multi-scale computational framework—integrating density functional theory (DFT), molecular dynamics (MD), and machine-learned interatomic potentials (MLIP)—to design high-performance zinc anode protective layers and optimize electrolyte systems.

The first part of this work investigates the atomic-scale mechanisms of a zinc-doped polypyrrole (Zn-doped PPy) conductive polymer as a functional protective layer. Using DFT calculations, we analyze the structural and electronic properties of PPy, demonstrating that Zn doping at nitrogen sites significantly enhances electrical conductivity by reducing the bandgap and creates potent nucleation sites for uniform Zn deposition. Furthermore, the synergistic effect of incorporating carbon nanotubes (CNTs) into the Zn-doped PPy matrix is explored, focusing on the enhanced mechanical adhesion and the formation of a stable, conductive network that effectively regulates the electric double layer.

The second phase of the research focuses on electrolyte engineering through MD simulations. We examine the solvation and desolvation dynamics of zinc ions in aqueous environments, specifically investigating the impact of organic additives such as ethylene glycol (EG). By analyzing the competitive coordination between water molecules and additives within the Zn2+ solvation shell, we evaluate the advantages and trade-offs of additive engineering in suppressing water-induced side reactions while maintaining high ionic conductivity.

Finally, to bridge the gap between atomic-level interactions and mesoscopic electrochemical behavior, MLIPs are developed and deployed to simulate large-scale zinc deposition processes. This integrated approach allows for a comprehensive analysis of dendrite inhibition and the evolution of the electrode-electrolyte interface. The findings of this research will provide fundamental theoretical insights and design principles for developing dendrite-free, long-life aqueous zinc-ion batteries for large-scale energy storage applications.

Master of Science in Bioinformatics

in the

School of Biological Sciences

Natalie Miller

Will defend her thesis

“Metagenome-based Quantification of the Urban Effects on the Chattahoochee River”

April 21st, 2026

2:00 PM (EST) in EBB 4029

Zoom Link: https://gatech.zoom.us/j/91878010913?pwd=Mhbbrp4zUQ0bBkjjFCMqB03afW4wYa.1

Thesis Advisor:

Dr. Kostas Konstantinidis

School of Biological Sciences, School of Civil and Environmental Engineering

Georgia Institute of Technology

Committee Members:

Dr. Joel Kostka

School of Biological Sciences

Georgia Institute of Technology

Dr. Katherine Graham

School of Civil and Environmental Engineering

Georgia Institute of Technology

Abstract:

The Chattahoochee River serves as a major freshwater resource in the southeastern United States. Atlanta is by far the largest metropolitan area on the river, with up to 1/3 of the river flow being used by its ~6 million inhabitants. The sources of fecal pollution in the river, and more importantly their relative contributions, remain essentially speculative since previous studies targeted only specific microbial indicators. We addressed this gap using short-read shotgun metagenomic sequencing of nine riverine sampling sites transecting the Atlanta metropolitan area, including three sites located downstream of the city every ~20km and three wastewater treatment plants (influent and effluent samples) within the city, across three sampling times (periods) within a year (2018), in the summertime. We recovered 190 good-to-high quality representative metagenome-assembled genomes (rMAGs) and used their relative abundance to infer the impact of the city on the indigenous freshwater microbial communities. We noted substantial fecal pollution within the Atlanta area, including elevated abundance of beta-lactamase antibiotic resistance genes (up to a 4.56-fold increase) and human-associated and wastewater-derived MAGs in the high vs. the low precipitation sampling period. Consistently, SourceApp (Lindner et al., ES&T, 2025) and further analysis revealed that untreated sewage MAGs comprised up to 2.5% of the total river metagenomes, indicating sewage escape events. However, all these taxa and genes that peaked in abundance within the city limits were largely undetectable 20 or 40 km downstream, indicating substantial resilience of the ecosystem against pollution and/or dilution of fecal pollution.

Location: 
Kendeda Building, 422 Ferst Dr, porch (or if raining, inside lobby)

Learn more at https://crc.gatech.edu/member-appreciation-week-2/ 

Developing a community-based perspective on data center development

The Sound of Motion: Transforming Artistic Body Movement into Music for Motor Therapy

Designing Ethical Robot Behaviors for Unforeseen Human-Robot Interactions Using LLMs

---

IPaT: GVU Lunch Lecture Series

PLEASE NOTE SEMINAR IS TUESDAY THE 21st!

Abstract: How should we act under incomplete information? We must integrate over missing values to assess the risk of a downstream decision, or the expected risk reduction from gathering further information.  Applications include NLP annotation, LLM workflows, clinical medicine, experimental design, and many others.  The classical approach to predicting missing values involves fitting some generative model to the incomplete data.  Alas, classical methods are slow and often inaccurate due to local optima, poor mixing, or model misspecification.  We propose a low-fuss, efficient alternative with fewer assumptions: given incomplete data, train a neural network to directly predict masked values. Our generic network architecture resembles BERT: it constructs a representation for each random variable's posterior marginal distribution and iteratively refines it through attention on other random variables and entities.  We find that this posterior marginal Transformer ("Marformer") works much better than EM and almost as well as MCMC even when those methods know the true data generating model, and it outperforms them on naturally occurring data. We outline future methods for adaptive masking and a future design for managing data collection efforts.

Bio: Jason Eisner is Professor of Computer Science at Johns Hopkins University and a Fellow of the Association for Computational Linguistics. At Johns Hopkins, he is also affiliated with the Center for Language and Speech Processing, the Mathematical Institute for Data Science, the Cognitive Science Department, and the Data Science and AI Institute. His goal is to develop the probabilistic modeling, inference, and learning techniques needed for a unified model of all kinds of linguistic structure, and to connect existing models (such as LLMs) to commonsense reasoning, formal reasoning, and downstream applications. His 180+ papers have presented various algorithms for parsing, machine translation, and weighted finite-state machines; formalizations, algorithms, theorems, and empirical results in computational phonology; unsupervised or semi-supervised learning methods for syntax, morphology, and word-sense disambiguation; and principled methods for conversational AI, including neural language modeling and semantic parsing. From 2019-2024 he was Director of Research at Microsoft Semantic Machines, which developed new approaches to conversational AI. He is also the lead designer of Dyna, a declarative programming language that provides an infrastructure for AI algorithms. He has received 3 school-wide awards for excellence in teaching, most recently in 2025, as well as recent 

 Zoom Link

Meeting ID: 977 5500 1285  | Passcode: 247306 

For more information, or for CODA guest access, please contact shatcher8@gatech.edu at least 2 business days prior to the event.

Participants will design and create items such as keychains, stickers, charms, and bracelets, all in a relaxed social setting. No orchestra experience is required, making the event open to anyone interested in music, crafting, or meeting new people.

Food will be provided, adding to the casual and community-focused atmosphere of the evening.

Hosted in the Crosland Tower Data Visualization/retroTECH Lab, this drop-in event invites students to revisit classic versions of Mario Kart across multiple Nintendo systems, including Super Mario Kart, Mario Kart 64, and Mario Kart: Double Dash.

Participants can jump into friendly competition, explore retro gaming consoles, and enjoy a relaxed social atmosphere centered around nostalgia and gameplay. No registration is required—just show up and play.