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. 

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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.

Sponsored by The Institute for Data Engineering and Science (IDEaS).
Supported by the School of Computer Science.

Abstract: Generative AI systems are no longer just tools — they are becoming active participants in how people make sense of themselves, seek support, and navigate moments of vulnerability. Nowhere is this shift more consequential than in mental health, where large language models (LLMs) are increasingly mediating help-seeking, shaping perceptions of care, and producing guidance at scale. This talk positions digital mental health as a critical testbed for interrogating the foundations of generative AI, where questions of reliability, alignment, and human impact are especially salient.

Drawing on empirical research, I describe how LLMs participate in mental health discourse: as conversational agents, sources of guidance, and mediators of support. While these systems demonstrate remarkable fluency and scalability, they also exhibit important limitations: variability in correctness and consistency, cultural and therapeutic misalignment, and challenges in capturing the nuance of lived experience. I further discuss emerging risks associated with generative systems in this context, including over-reliance, sycophantic responses, and the potential erosion of human agency and social connection. These findings underscore a broader tension in generative AI: systems optimized for engagement and responsiveness may not align with the goals of care, safety, and long-term wellbeing. I conclude by outlining a human-centered foundation for generative AI — one that foregrounds identity, agency, and institutional context, and advances toward systems that are not only capable of generating language, but are accountable to the human conditions they shape.

Bio: Munmun De Choudhury is J. Z. Liang Professor at the School of Interactive Computing and Co-Lead of Patient-Centered Care Delivery at the Children's Healthcare of Atlanta-Pediatric Technology Center in Georgia Institute of Technology. Dr. De Choudhury is known for her contributions to the fields of computational social science, human-computer interaction, and digital mental health. Through fostering interdisciplinary collaborations, Dr. De Choudhury and her collaborators have contributed significantly to advancing the development of computational techniques for early detection and intervention in mental health, as well as in unpacking how social media use benefits or harms mental well-being.

De Choudhury's contributions have been recognized through awards like the 2023 SIGCHI Societal Impact Award, the 2023 ICWSM and the 2022 Web Science Trust Test-of-Time Awards, the 2021 ACM-W Rising Star Award, as well as nearly two dozen paper awards. In 2024, she was inducted into the SIGCHI Academy and in 2025 was named an ACM Distinguished Member. Beyond her academic contributions, Dr. De Choudhury is a persistent contributor to policy-framing and advocacy initiatives, and is frequently sought for expert advice to governments and media. Notably, Dr. De Choudhury was an invited contributor to the Office of U.S. Surgeon General’s 2023 Advisory on The Healing Effects of Social Connection. Currently, she serves as a member of the Technical Advisory Group of the Commission for Social Connection at the World Health Organization and also advises the World Bank.

Committee:

Dr. Rincon-Mora, Advisor

Dr. Gu, Chair

Dr. Mukhopadhyay

Thesis Title: "Assessing Myocilin Variants for Disease Characteristics with Cellular Misfolding to Infer Glaucoma Risk"

Friday, April 17th, 2026 at 12:00 PM

Location: Whitaker 1103, or Join via Zoom: https://gatech.zoom.us/j/99597510839?pwd=bwwAsdBcdnobsgqXcb7o8QPU4N5Ukf.1

Meeting ID: 995 9751 0839

Passcode: 335053

Committee Members:

Prof. Raquel Lieberman (Advisor) - School of Chemistry and Biochemistry 

Prof. Amit Reddi - School of Chemistry and Biochemistry 

Prof. Loren William - School of Chemistry and Biochemistry 

Prof. Robert Dickson - School of Chemistry and Biochemistry 

Prof. Matthew Torres - School of Biological Sciences

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

Registration: Coffee and Compost: In-Vessel Tour Sign Up, April 17, 2026 9 a.m. or 10 a.m. start options – Fill out form

Showcasing student projects that highlight ECE threads, with live demos, awards, and an informal reception.

Title: Breaking the Scaling Wall in Distributed Deep Learning

Abstract: Significant advances in computer architecture (development of extremely powerful server-class GPUs) and parallel computing (scalable libraries for dense and sparse linear algebra) have contributed to the on-going AI revolution. In particular, distributed training of deep neural networks (DNNs) relies on scalable matrix multiplication algorithms and efficient communication on high-speed interconnects. Pre-training and fine-tuning large language models (LLMs) with hundreds of billions to trillions of parameters and graph neural networks (GNNs) on extremely large graphs requires hundreds to tens of thousands of GPUs. However, such training often suffers from significant scaling bottlenecks such as high communication overheads and load imbalance.

In this talk, I will present several systems research directions that directly impact AI model training. First, I will describe my group's work in using a three-dimensional parallel algorithm for matrix multiplication in large-scale LLM training.  Second, I will demonstrate the application of the same algorithm to full-graph and mini-batch GNN training when working with extremely large graphs. Finally, I will also discuss the need for scalable collective communication routines for large-scale DNN training.

Bio: Abhinav Bhatele is an associate professor in the department of computer science, and director of the Parallel Software and Systems Group at the University of Maryland, College Park. His research interests are broadly in systems and AI, with a focus on parallel computing and distributed AI. He has published research in parallel programming models and runtimes, network design and simulation, applications of machine learning to parallel systems, parallel deep learning, and on analyzing/visualizing, modeling and optimizing the performance of parallel software and systems. Abhinav has received best paper awards at Euro-Par 2009, IPDPS 2013, IPDPS 2016, and PDP 2024, and a best poster award at SC 2023. He was selected as a recipient of the IEEE TCSC Award for Excellence in Scalable Computing (Early Career) in 2014, the LLNL Early and Mid-Career Recognition award in 2018, the NSF CAREER award in 2021, the IEEE TCSC Award for Excellence in Scalable Computing (Middle Career) in 2023, and the UIUC CS Early Career Academic Achievement Alumni Award in 2024.

Abhinav received a B.Tech. degree in Computer Science and Engineering from I.I.T. Kanpur, India in May 2005, and M.S. and Ph.D. degrees in Computer Science from the University of Illinois at Urbana-Champaign in 2007 and 2010 respectively. He was a post-doc and later computer scientist in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory from 2011-2019. Abhinav was an associate editor of the IEEE Transactions on Parallel and Distributed Systems (TPDS) from 2022-2024. He was one of the General Chairs of IEEE Cluster 2022, and Research Papers Chair of ISC 2023.

Date and Time: Friday, April 17, 2026, 9:00 AM – 11:00 AM
Location: Groseclose 404
Microsoft Teams: https://teams.microsoft.com/l/meetup-join/19%3ameeting_N2E3MjIxZGYtOTk4MS00MzhjLWI4YjctODA5OGM5YjdiZjFk%40thread.v2/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%2205b99bdc-ec60-4153-8a6d-ce83fbf47cd7%22%7d

Thesis Committee
Dr. Enlu Zhou (advisor), H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Seong-Hee Kim, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Eunhye Song, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Guanghui Lan, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
Dr. David Eckman, Industrial and Systems Engineering, Texas A&M University

Abstract

This dissertation studies simulation-based decision making in the presence of streaming data and model uncertainty. The central objective is to develop methodologies that integrate simulation, optimization, and learning in dynamic environments where data arrive sequentially over time.

The first part of the dissertation studies the ranking and selection problem under input uncertainty with streaming data. A data-driven framework is developed to simultaneously allocate resources to collect input data and conduct simulation experiments, while providing statistical guarantees on selection performance.

The second part focuses on simulation-based bilevel optimization problems, where each alternative system involves an internal continuous optimization problem. Both multi-stage and fully sequential procedures are proposed to integrate pruning of suboptimal systems with optimization of decision variables, significantly improving computational efficiency.

The third part investigates reinforcement learning under model uncertainty through a Bayesian risk framework. A Bayesian risk-averse formulation of Markov decision processes is developed, together with a Q-learning algorithm that updates posterior beliefs using streaming observations.

The final part extends this framework to online reinforcement learning, where policies are adaptively updated as new data arrive. The proposed methods achieve sublinear regret and demonstrate how Bayesian risk formulations naturally adjust the degree of risk aversion over time.

Overall, this dissertation contributes flexible and adaptive methodologies for simulation-based decision making in modern data-rich stochastic systems.

Committee:

Dr. Azad Naeemi, ECE, Chair, Advisor

Dr. Asif Khan, ECE, Co-Advisor

Dr. Shimeng Yu, ECE

Dr. Lauren Garten, MSE

Dr. Jeffrey Davis, ECE

Dr. Muhannad Bakir, ECE

Abhishek Vasisht Bhaskar

PhD Candidate, Computer Science
School of Cybersecurity and Privacy
Georgia Institute of Technology

Committee:

Dr. Paul Pearce (advisor) - School of Cybersecurity and Privacy, Georgia Institute of Technology
Dr. Michael Bailey - School of Cybersecurity and Privacy, Georgia Institute of Technology
Dr. Frank Li - School of Cybersecurity and Privacy, Georgia Institute of Technology
Dr. Alberto Dainotti - School of Computer Science, Georgia Institute of Technology
Dr. Michael Specter - School of Cybersecurity and Privacy, Georgia Institute of Technology

Abstract:

Internet measurement enables researchers to study critical problems, such as botnets, vulnerabilities, and Internet censorship, at global scale. Prior work has largely focused on improving measurement scale and speed, with elements of reliability and correctness being largely neglected. In particular, censorship measurement has frequently observed non-determinism and inconsistency that has not been systematically explained. In this thesis, we develop empirical methods and systems to evaluate factors that influence censorship measurement results, showing that routing dynamics, packet parameters, measurement direction, and IP version all significantly influence censorship results.

We begin by showing that external DNS censorship measurement in China is directly impacted by Equal-Cost Multi-Path (ECMP) routing induced by packet header fields like source IP and port. We find that 37% of IPs across 56% of ASes exhibit changes in observed censorship, and magnitude of censorship differences can more than double based on part of the source IP, consistent with known router load balancing techniques. We then generalize this result globally, finding routing-induced variation across DNS, HTTP, and HTTPS in 17 of 21 countries studied. These differences arise from multiple sources, including intra-AS and inter-AS routing differences, path diversity to the same destination, and geographically non-uniform censorship deployment, demonstrating that the problem is not unique to China or a single protocol. Finally, we develop a unified measurement framework spanning inside-out and outside-in measurements over both IPv4 and IPv6, showing that remote measurements can observe disruption in 21 countries without requiring in-country resources, that IPv6 disruption is less prevalent than IPv4 though still present, and that routing- and parameter-induced variation persists across measurement direction and IP version.

All told, this thesis shows that Internet censorship measurement results are not only influenced by methodological choices, but also by seemingly benign network behaviors unrelated to censorship itself, such as routing dynamics and on-path behavior. Measurement direction, IP version, routing path, and packet construction can all influence observed results, leading to variation across measurements of the same destinations. We further show that censorship observed along a path toward a destination is not necessarily the same as censorship at the destination itself, as externally observed disruption can be introduced by on-path behavior. Taken together, these results establish the need for more rigorous, route-aware, and methodologically controlled approaches to censorship measurement, and provide concrete guidance for designing studies that are more reliable, consistent, and interpretable.

Date: Friday, 17th  April 2026

Time: 10:00 AM - 12:00 PM (Eastern Time)

Location: CODA C0908 Home Park

Teams Link: https://teams.microsoft.com/l/meetup-join/19%3ameeting_NzQyYTVjMzQtZjY1NS00ZDc0LTljOGQtYjY1YzhkZGI0MjQ2%40thread.v2/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%22be1e8809-9fed-468d-b479-4e061696ba91%22%7d

Mengdi Wang

Ph.D. Student

School of Interactive Computing

Georgia Institute of Technology

Committee members

Dr. Bo Zhu (advisor): School of Interactive Computing, Georgia Institute of Technology

Dr. Gregory Turk: School of Interactive Computing, Georgia Institute of Technology

Dr. Sehoon Ha: School of Interactive Computing, Georgia Institute of Technology

Dr. Eftychios Sifakis: School of Computer, Data & Information Sciences, University of Wisconsin-Madison

Dr. Matthew Cong: Senior Research Scientist, NVIDIA Corporation

Abstract

In this thesis, we develop representations, solvers, and transport algorithms for high-performance multiscale fluid simulation, addressing the challenge that important fluid phenomena such as thin films, sub-grid interfaces, and fine vortical structures span spatial scales far smaller than the simulation domain. First, we introduce specialized representations for regimes where standard volumetric methods are insufficient, including a codimensional SPH method for thin fluid films and a sub-grid interface tracking approach on triangle meshes, enabling accurate and efficient simulation with strong geometric fidelity and mass conservation. Next, we present a fully GPU-resident adaptive simulation framework for large-scale three-dimensional flows, combining an efficient octree data structure, a matrix-free algebraic multigrid solver, and a hybrid flow-map transport scheme to capture rich vortical structures on adaptive grids. Finally, we demonstrate that the proposed system achieves effective resolutions of hundreds of millions of cells on a single GPU and produces high-fidelity simulations with significantly enhanced vortical detail. These results show that combining specialized representations with adaptive GPU algorithms provides an effective approach to simulating multiscale fluid phenomena.

Allison Traylor
Assistant Professor
College of Behavioral, Social, and Health Sciences
Clemson University

Whether your team is starting a new project, working to establish new norms, or setting expectations for new members, a team charter can help your team lay the foundation for success. A team charter is a codified plan for how a team will manage teamwork activities. Team charters are evidence-based and are associated with improved team performance. This hands-on workshop will describe the benefits of team charters and provide the resources necessary to implement an effective charter in your team.

Team consult opportunity:

Our Team Science Skills presenters have graciously opened their schedules to provide a limited amount of attendees with direct feedback and guidance on their personal team issues. During a one-hour consulting session, attendees will be able to directly ask our experts questions and get personalized advice on how to effectively move their team forward.  Requesters must attend the full session and complete the post-evaluation survey to be eligible. 

What Participants Will Learn:                                                                                   

•     Deploy secure, scalable AI agents using AgentCore Runtime
•   Implement robust authentication and authorization with AgentCore Identity
•   Integrate external services through AgentCore Gateway
•   Extend agent capabilities using AgentCore Tools
•   Manage agent memory and context with AgentCore Memory
•   Monitor agent performance with AgentCore Observability

Schedule:

•     1 hour of presentation
•   3 hours of hands-on activities
•   1 hour lunch + networking + QA
•   The session will remain active for 72 hours afterward so students can continue practicing

REGISTER HERE

This high-energy event brings together creativity, music, and community with something for everyone to enjoy. Attendees can explore a vibrant vendor market, grab food from local food trucks, and enjoy live DJ music throughout the afternoon. The event also features interactive activities, including lawn games, chalk art, and a game truck available from 1–3 PM.

Pop-up performances will take place every half hour, showcasing talent from student groups across campus. Featured groups include DramaTech, Ballroom Dance Club, Tekstyles, and Dance Company, among others.

Whether you’re looking to relax, explore creative work, or enjoy live entertainment, the Block Party offers an exciting and engaging way to wrap up Tech Arts Fest 2026.

The Community Innovation Program offers ALL undergraduate students, no matter their class year or major, a chance to collaborate with local Atlanta organizations on impactful, real-world projects. Through experiential learning, students build professional skills while supporting community partners and contributing to civic change. Information sessions will be held at Clough Lounge on April 13th (4-5 PM) and April 17th (2-3 PM), which will prepare you for your application, due April 22nd.

The Community Innovation Program is hosted by Community-Based Learning in the Office of Experiential and Engaged Learning.

🗓 Friday, April 17th
⏰ 10 AM–2 PM
🚐 Transportation Provided

Help us make tails wag and hearts happy!

Registration: here

PACE has recently expanded containerized software workflows on Phoenix and Hive. Users are now able download, build, and run their own containers with Apptainer, a container runtime that is widely used in HPC centers. In this workshop, you will learn how to use Apptainer to:

• Easily download and run Docker images from DockerHub and other registries.
• Build new images using an Apptainer definition file, similarly to a Dockerfile.
• Learn tips and tricks for portable software workflows

This workshop will be taught virtually over Microsoft Teams. Please contact PACE support (pace-support@oit.gatech.edu) if you have any questions or concerns. We hope to see you there!

1. Go to Genius LMS and use your GT Login
2. Click "View Course Catalog"
3. Search for Laser
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.