CHOOSE FROM TWO WORKSHOPS:

Workshop #1 - In-Person
Thursday, October 29 | 10 a.m. - 12 p.m. | R. Randall Rollins room 226
*lunch provided following the workshop

Workshop #2 - Virtual
Friday, October 30 | 10:00am-12:00pm | Zoom

Title: Applying Scientific Computing Perspectives to Neural Network Training

Abstract: Machine learning techniques and their AI decedents are increasingly important part of modern science and engineering. They have proven to be powerful in areas such as surrogate model development, hypothesis generation, and data analysis. Often, these approaches rely on a parameterized function approximation known as the neural network. These approximators are renowned for their flexibility and relative ease of use (if not accuracy). With that ease of use comes a large computational expense. For instance, recent work has demonstrated that pre-training a LLM on the Frontier supercomputer would require two years with ideal parallelization [1]. This talk seeks to examine the neural network with a scientific computing lens to enhance our ability to understand details of the approximation and improve training. Underpinning these ideas is an aspirational hypothesis that insights will lead to multilevel (or hierarchical) methodologies to accelerate training using dramatically fewer resources. 

This talk proceeds in two parts. In the first part, we discuss an adaptive basis perspective that has proved fruitful in Scientific Machine Learning (SciML) [2]. The central idea is that the output of the penultimate layer can be seen as providing a basis for the approximation space. With this perspective we develop efficient “operator-split” training algorithms, and new initialization strategies motivated by stability concerns. The second part extends the adaptive basis perspective by considering all layers of the neural network to be basis functions in a learned approximation space. We discuss the challenges associated with the typical neural network architectures that use globally supported basis functions. We then describe Kolmogorov-Arnold networks (KANs) as an alternate architecture built on locally supported splines basis functions [3]. A one-to-one relationship between KANs and a multi-channel ReLU network is shown and used to argue a relaxation property reminiscent of properties of matrix-split iterative linear solvers. Finally, using a natural refinement algorithm combined with the relaxation property, we develop a multilevel training algorithm for KANs demonstrating it on problems in SciML.

1. Dash, et al., Optimizing distributed training on frontier for large language models. In ISC High Performance 2024 Research Paper Proceedings (39th Intl. Conference), 2024.
2. Cyr, et al., Robust training and initialization of deep neural networks: An adaptive basis viewpoint, Mathematical and Scientific Machine Learning, 2020. 
3. Southworth, et al. Multilevel Training for Kolmogorov Arnold Networks, arXiv:2603.04827, 2026 (submitted to SISC)

Bio: Eric C. Cyr has been at Sandia Albuquerque since 2009 and is a Distinguished Member of the Technical staff. Prior to that he received a BS in Computer Science from Clemson University in 2002, and a PhD in Computer Science from University of Illinois at Urbana-Champaign in 2008. His formal training is in numerical methods, and scientific computing. As a Sandian he has worked in a range of diverse areas including high-performance computing, preconditioners for multi-physics, discretizations for computational plasma simulation, sensitivity analysis for PDEs, numerical optimization, and software development for science applications. In 2018, Eric received the DOE Early Career Award to fund a focus on developing layer-parallel methods for training deep neural networks. This work has broadened his exposure to machine learning. To this end, Eric is working to extend the applicability of HPC to deep learning, understand the behavior and approximation properties of neural networks, and develop methodologies where machine learning can be used to enhance the impact of computation on science and engineering disciplines.

• David Beasley, former Governor of South Carolina and former Executive Director of the United Nations World Food Programme
• Ambassador Michèle Taylor, former U.S. Ambassador to the UN Human Rights Council and Distinguished Professor of the Practice at the Nunn School

Together, they will draw on their deep experience at the front lines of global diplomacy to explore how humanitarian efforts must adapt to tackle escalating food crises in a rapidly changing world.

Seating is free, open to the public, and available on a first-come, first-served basis. Join us for an inspiring discussion on the future of global hunger relief!

Co-hosted by the Michael E. Tennenbaum Family Institute for Technology and Civic Leadership.

Abstract: We study a class of nonconvex–nonconcave minimax problems in which the inner maximization problem satisfies a local Kurdyka–Łojasiewicz (KL) condition that may vary with the outer minimization variable. In contrast to the global KL or PL conditions commonly assumed in the literature—which are significantly stronger and often too restrictive in practice—this local KL condition accommodates a broader range of practical scenarios. However, it also introduces new analytical challenges. In particular, as an optimization algorithm approaches a stationary point, the region over which the KL condition holds may shrink, leading to a more intricate and potentially ill-conditioned landscape. To address this challenge, we show that the associated maximal function is locally generalized Hölder smooth. Leveraging this key property, we develop an inexact proximal gradient method for solving the minimax problem, where the inexact gradient of the maximal function is computed by applying a proximal gradient or sequential convex programming method to a KL-structured subproblem. Under mild assumptions, we establish complexity guarantees for computing an approximate stationary point of the minimax problem.

This is joint work with Xiangyuan Wang (University of Minnesota).
 

Bio: Zhaosong Lu is a Full Professor in the Department of Industrial and Systems Engineering at the University of Minnesota. He received his Ph.D. in Operations Research from Georgia Tech. His research focuses on the theory and algorithms of continuous optimization, with applications in data science and machine learning. Dr. Lu has published extensively in leading journals, and his research has been supported by major funding agencies, including AFOSR, NSF, and ONR. He has served on several prize committees, such as the INFORMS George Nicholson Prize Committee and the ICCOPT Best Paper Award Committee. He has also served as an Associate Editor for leading journals, including Mathematics of Operations Research, SIAM Journal on Optimization, Computational Optimization and Applications, and Journal of Global Optimization.

As part of the event, Jeremy Muller, lecturer in the School of Music, will perform the original soundtrack created for the exhibit.

To learn more about Ninefold, click here. 

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Conversations@TechSquare is a dynamic space for new ideas, not an environment for established concepts. We gather creators and innovators from across Georgia Tech, the broader Tech Square community, and beyond, to engage in open dialogue around some of today’s biggest challenges and opportunities. These gatherings are organized and hosted by the Georgia Tech Institute for People and Technology (IPaT) and will occur several times during a semester at the Tech Square Clubhouse. The Clubhouse address is 848 Spring Street NW, Atlanta, Georgia, 30308 (street level, behind the Centergy One Bldg.).

Tech Square Clubhouse

PARKING:
Parking is available at the Centergy One parking deck, located right next door. You can also find additional street parking via Park Mobile on Spring and 5th Street. Centergy One parking deck daily rates: Half Hour: $3.00 | One Hour: $6.00 | Two Hours: $12.00 | Daily Max: $20.00

GT CAMPUS BUS: Take the Gold route bus which stops at Tech Square.

Bio: HyunJoo Oh is a designer, design researcher, and Associate Professor jointly appointed in the School of Industrial Design and the School of Interactive Computing at Georgia Tech. She directs the CoDe Craft group, which explores how computing technologies can expand the expressive and technical possibilities of everyday materials. Working with professional designers, hobbyist makers, K-12 students, and educators, she investigates how making and learning shape one another. Since 2020, she has served as PI or Co-PI on seven research projects, including five focused on K-12 and higher education, with support from the National Science Foundation, Frances Wood Wilson Foundation, Facebook Research, and Dolby Labs. Her group’s work has received best paper and honorable mention awards at ACM DIS 2019, CHI 2023, and TEI 2026, alongside design recognition from Core77, Fast Company, NYCxDESIGN, and Prototypes for Humanity. She received Georgia Tech’s College of Design Outstanding Faculty Award in 2026. She holds a PhD in Technology, Media, and Society from the University of Colorado Boulder. She earned her master’s degree in Entertainment Technology at Carnegie Mellon University with support from a National Global Scholarship awarded by the Ministry of Culture, Sports, and Tourism of South Korea (2010-2012). 

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IPaT: GVU Lunch Lecture Series

The Bench2Market Talks series is created to provide commercialization guidance to the university research community. For more details on the series, visit our website.

ABSTRACT
The plasma membrane serves as the critical interface where cells endure and respond to their physical environment. We recently revealed that the PIEZO1 mechanosensing axis actively safeguards membrane integrity through a hierarchical, two-layer mechanoprotective mechanism. This involves rapid architectural buffering followed by a calcium-dependent positive feedback loop engaging Calmodulin and Filamin A to reinforce cortical stability. This fundamental discovery in mechanoprotection serves as the blueprint for our translational innovations in "Thrombo-protective Oncology." By engineering a microfluidic "CAT-on-chip" to recapitulate venous shear stress, we discovered that metastatic cancer cells exploit a strikingly similar mechanobiological network to trigger pathological clotting. Specifically, tumor-induced hypercoagulation is driven by a Focal Adhesion Kinase (FAK)-Calmodulin (CaM) axis, which structurally "decrypts" Tissue Factor (TF) by disengaging it from cytoskeletal Filamin A. To translate these microfluidic discoveries into clinical practice, we developed "SmartClot", an AI-driven Point-of-Care Testing (POCT) platform that rapidly stratifies cancer-driven thrombotic risk using blood diffusion kinetics. This seminar will highlight how decoding fundamental nanoscale mechanosensing paves the way for next-generation diagnostic and therapeutic technologies.

BIO
Professor Lining Arnold Ju (PhD, GAICD, FHEA) is a Snow Fellow, Australian Heart Foundation Future Leader Fellow, and recipient of the Australian Academy of Science John Booker Medal. He received his PhD in Biomedical Engineering from Georgia Tech and Emory University in 2013 and now directs the Mechanobiology and Biomechanics Laboratory (MBL) at the University of Sydney's School of Biomedical Engineering.

Ju’s research operates at the intersection of fundamental mechanobiology and biomedical engineering. His team recently decoded the PIEZO1 mechanosensing axis and has pioneered multiple biomechanical nanotools, including multi-parametric thrombus profiling microfluidics ("CAT-on-chip") and patient-specific vessel-on-a-chip platforms. His novel understanding of the mechanics behind blood clot formation has directly driven the development of point-of-care testing (POCT) technologies, such as the machine learning-assisted SmartClot diagnostic tool. His overarching vision integrates advanced biomanufacturing, high-throughput microfluidic phenotyping, and generative AI to create intelligent biosensing technologies to predict and prevent cardiovascular and cancer-associated thrombotic risks.

Andrés García, faculty host

Each seminar will feature three speakers who will be given 10-15 minutes to present their current research in an accessible manner for broad audiences, there will then be a 10-minute questions from the audience and feedback from the CoS Young Alumni Board Panel.

All Georgia Tech students are welcome to attend. 

Event Details:

Making Science Accessible + CoS Young Alumni Board Networking

Oct 21st, 2026: 1:00 pm – 2:15 pm Talks, 2:15 -3:00 pm Refreshments and Networking
MoSE 3201A, 901 Atlantic Dr. N.W.
Atlanta, GA 30332

 

Abstracts due October 9th:

Submit here 

Please RSVP if you wish to attend.

All Georgia Tech students are welcome to attend. 

This seminar series will feature graduate students from the CoS community presenting their research in plain language

and provide opportunity for students to interact with their peers outside of their department and field! 

Each seminar will feature three speakers who will be given 10-15 minutes to present their current research in an accessible

manner for broad audiences, there will then be a 10-minute questions from the audience and feedback from the CoS Young Alumni Board Panel.

Event Details:

Making Science Accessible + CoS Young Alumni Board Networking

Nov 18th, 2026: 1:00 pm – 2:15 pm Talks, 2:15 -3:00 pm Refreshments and Networking
MoSE 3201A, 901 Atlantic Dr. N.W.
Atlanta, GA 30332

 

Abstracts due October 9th:

Submit here 

Please RSVP if you wish to attend.

Date: Wednesday, Oct. 21, 2026
Time: 1:30–4:45 p.m.
Location: Marcus Nanotechnology Building, Conference Rooms 1116–1118
Format: In person and online

Program

• 1:30 p.m. | Electron Beam Lithography Fabricated Gratings for Space Applications | Fabien Grisé, Penn State University
  • Fabien Grisé will discuss the use of electron beam lithography to fabricate diffraction gratings for future space telescopes. The presentation will address nanoscale patterning, large-area writing, stitching control, resist and dose optimization, and fabrication considerations for X-ray, extreme-ultraviolet, and far-ultraviolet applications.
• 2:15 p.m. | Electron Beam Lithography from Elionix’s Perspective | Lukas Stampfer, STS-Elionix
  • Lukas Stampfer will provide a vendor’s perspective on the inner workings of electron beam lithography systems, the technical trade-offs involved in their development, and strategies for addressing equipment-related effects that can influence exposure results.
• 3 p.m. | Coffee Break
• 3:15 p.m. | Advanced Data Preparation and Techniques for Optimized Patterning | Kaustubh Vyas, GenISys GmbH
  • Kaustubh Vyas will examine the data preparation steps that help translate a design file into a successfully fabricated structure. Topics will include machine-specific fracturing, shot placement, writing order, field control, proximity-effect correction, simulation, and scanning electron microscopy-based metrology.
• 4 p.m. | Electron-Beam Lithography for Photonic Device Fabrication: Applications and Practical Considerations | Ashkan Zandi, Georgia Institute of Technology
  • Ashkan Zandi will discuss the development and practical implementation of electron beam lithography processes for integrated optics, metaphotonic devices, and grayscale-fabricated three-dimensional structures. The presentation will highlight resist selection, exposure-dose optimization, proximity effects, pattern fidelity, development conditions, reproducibility, and fabrication yield.
• 4:45 p.m. | Adjourn

About the Speakers

Fabien Grisé is an associate research professor in the Department of Astronomy and Astrophysics at Penn State University. His research focuses on diffraction-grating technology for future NASA space telescopes, including electron beam lithography fabrication for X-ray, extreme-ultraviolet, and far-ultraviolet gratings.

Lukas Stampfer has served as an application engineer for European and North American customers at STS-Elionix since 2021. He earned a doctorate in quantum transport from the University of Copenhagen and a master’s degree in condensed matter physics from the Technical University of Munich.

Kaustubh Vyas works across applications engineering, product improvement, and technical sales at GenISys. His background includes III-V and silicon photonics, and his current work focuses on lithography data preparation, predictive simulation, and process improvement.

Ashkan Zandi is a research faculty member in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. His research spans nanofabrication, integrated photonics, metaphotonics, biosensors, quantum sensing, and biomedical engineering.

Sponsors

This seminar is sponsored by STS-Elionix and GenISys GmbH.

Abstract: This talk describes the progress in analysis and optimization of Markov Decision Processes (MDPs) and Partially Observable MDPs (POMDPs) with infinite state spaces and possibly noncompact action sets. We shall also discuss applications to inventory control and to controlled linear Gaussian systems. 
 

Bio: Eugene A. Feinberg received MS in Applied Mathematics and Computer Engineering from Moscow University of Transportation, Russia, in 1976 and Ph.D. in Probability and Statistics from Vilnius University, Lithuania, in 1979. Currently he is Distinguished Professor at the Department of Applied Mathematics and Statistics of Stony Brook University.

His research interests include stochastic models of operations research, probability theory, real analysis, Markov Decision Processes, and applications of operations research and statistics to engineering, biology, and medicine. He has published more than 100 papers and edited the Handbook on Markov Decision Processes. His research has been partially supported by the National Science Foundation, Office of Naval Research, National Institute of Health, New York Office of Science, Technology and Academic Research, and private industry. He has served as a Council Member of the INFORMS Applied Probability Society and on several editorial boards. He is a fellow of INFORMS.

To address these challenges, this lecture presents a framework for contact-rich robotic assembly in construction that integrates perception, planning, and control. Within this framework, three complementary approaches are examined: adaptive control to compensate for model mismatch and disturbances; learned sensorimotor policies to handle complex, tight-tolerance interactions for which effective control strategies are difficult to formulate analytically; and a hybrid approach that combines learned policies with adaptive control to improve robustness and enable zero-shot sim-to-real transfer without real-world demonstrations for policy training. Through construction-scale case studies, the lecture highlights key methods, experimental findings, and current limitations of these approaches. It concludes by discussing open challenges and future directions for generalizing across material systems and component geometries, transferring skills between tasks, and extending these methods to longer assembly sequences in construction.

Bio: Dr. Arash Adel is an Assistant Professor at the School of Architecture, a Core Faculty of Princeton Robotics, and an Associated Faculty of the Department of Computer Science at Princeton University. He is the founder and director of Adel Research Group (ARG). ARG conducts interdisciplinary research at the intersection of robotics, artificial intelligence, and computational design; the research contributes to resilient, sustainable, and low-carbon construction outlooks and achievements. At the core of ARG’s comprehensive research is investigating human–robot collaborative processes, which tackle fundamental questions related to the future of the design and construction industries and their potential to have a broader impact on inclusive and equitable building culture. Prior to joining Princeton University in 2023, Adel was an Assistant Professor of Architecture at the University of Michigan’s Taubman College of Architecture and Urban Planning. Adel received his Doctorate in Architecture from the Swiss Federal Institute of Technology (ETH) and his Master’s in Architecture from Harvard University.

Title: Rethinking Memory: From Long-Horizon Video to Interactive World Models

Abstract: Video generation has made remarkable progress in visual quality, yet turning short clips into continuously evolving, controllable experiences remains a fundamental challenge. A model must preserve what matters from the past while allowing new events to unfold, respond to user input, and remain computationally efficient as generation unfolds. In this talk, I will explore these challenges through a central question: What should a video model remember, and how should it use that memory?

I will present three complementary approaches to rethinking memory in autoregressive video generation. First, Infinity-RoPE (CVPR’26) revisits how temporal context is organized, enabling longer video generation and more responsive control through inference-time interventions. Next, AdaState (NeurIPS’26) replaces a frozen anchor frame with an evolving latent state, balancing temporal consistency with dynamic scene progression. Finally, VideoMLA (NeurIPS’26) rethinks how video models store and access past context, substantially compressing their memory while preserving generation quality.

I will close with broader directions toward controllable world models (SPAWN, NeurIPS’26), physically grounded (HiPhy, NeurIPS’26) and diverse (DPP-GRPO, CVPR’26) video generation models. This research advances a broader vision for generative cinematography, supported by an NSF CAREER’26 award, toward generative worlds that remember the past, respond to our choices, and evolve as we interact with them.

Bio: Pinar Yanardag is an Assistant Professor of Computer Science at Virginia Tech, where she leads GEMLAB (Generative Modeling Lab). Her research focuses on controllable and personalized generative AI, with an emphasis on video generation and interactive world models. Her goal is to build systems that give people greater creative control over generated content and experiences. Since joining Virginia Tech in Fall 2023, her lab has produced over 25 publications at venues including CVPR, ICCV, and NeurIPS. She completed her PhD at Purdue as a Fulbright Fellow and was a postdoctoral researcher at the MIT Media Lab, where her generative AI projects attracted over one million participants and coverage from TIME, The Washington Post, and CNN. She is a recipient of the NSF CAREER award for her research on generative cinematography, and her research has received support from industry partners including Google, Adobe, and Snap.

Abstract: Following Mathlib's success in building a shared foundation for formalized mathematics, CSLib is a community-oriented effort to build a similar infrastructure for computer science and software in the Lean theorem prover.

In this talk, I will present CSLib's current state, including its architecture, governance, organization, and how to contribute. Rather than giving only a high-level overview, I will use minimal working examples to illustrate the library's current API. I will showcase abstractions and definitions already available, ranging from computational paradigms to modeling and programming languages, logics, algorithms, and infrastructure to verify their properties.

I will also introduce the CSLib Initiative, supported by Renaissance Philanthropy, which aims to coordinate and accelerate the development of the library and its surrounding ecosystem. I will discuss the CSLib Initiative roadmap

Finally, I will show how CSLib is already being used beyond the library itself, through community projects, textbooks, courses, and large-scale verification efforts. These include Software Foundations in Lean, the Functional Algorithms Design and Computational Semantics with Lean, and the port of Amazon's s2n-bignum formal proofs to Lean.

Bio: Alexandre Rademaker is the Director of the CSLib Initiative at Renaissance Philanthropy. CSLib is a global open-source effort to build a library of formalized computer science in Lean. He also cooperates with NYU in DARPA's ExpMath program, which aims to accelerate mathematics research with AI.

Alexandre is also a professor at EMAp/FGV (Applied Mathematics School, Getulio Vargas Foundation), where he has been teaching for about 20 years. He worked at IBM Research for 12 years until April 2025, when he left to lead the Specification IDE project at Atlas Computing, part of the Formal Verification of Software initiative funded by Schmidt Sciences under their Science of Trustworthy AI program.

During his PhD, he held research fellowships at Microsoft Research, working with the Z3 SMT solver team, and at SRI International. He has published more than 100 papers and spent much of his career leading open-source research collaborations with international communities, working with computational linguistics and building language resources. He was publication chair of the Language Resources and Evaluation Conference (LREC) and has served on the boards of research associations in Brazil and internationally.

Alexandre received a PhD in Computer Science from PUC-Rio, where his thesis on proof theory for description logics was published by Springer, and is based in Rio de Janeiro.

Host: Jiapeng Gao

Title: Title: Machine Learning Across Scales: From Hidden Exoplanets to Efficient Model Training

Abstract:

Non-transiting planets can remain hidden while leaving dynamical signals in the transit timing variations (TTVs) of observed planets. DeepTTV provides a machine learning (ML) approach to this inverse problem, using N-body simulations to generate training data for a neural network that combines recurrent and Transformer architectures to infer the mass and orbital properties of unseen planetary companions. In the first part of this talk, I will discuss DeepTTV. In the second part, I will introduce my current work in industry on improving the efficiency of large-scale ML training, including directions such as transfer learning and improving computational resource utilization.

Attendees should register through the Genius LMS, using the "GT Login" button. Click on "View Course Catalog", then enter "RCR" in the search bar to view the current RCR workshop sessions. You can view information about each class (including class times) by clicking on "more info".  To register for a class, click on "Register", then "Proceed to Checkout" (there is no cost for the sessions). Then click on "Confirm". You will receive a confirmation email with a calendar file. You will need to follow instructions given to you during the session in order to receive RCR in-person contact hour credit for attending.

Animal/Human Subjects Research
🗓️ Friday, October 30th
⏱️11 AM - 12 PM 
📍ONLINE

For more information about the RCR Workshop Series, including additional dates/sessions offered, refer to RCR Workshops and Events and Workshop FAQs.

Attendees should register through the Genius LMS, using the "GT Login" button. Click on "View Course Catalog", then enter "RCR" in the search bar to view the current RCR workshop sessions. You can view information about each class (including class times) by clicking on "more info".  To register for a class, click on "Register", then "Proceed to Checkout" (there is no cost for the sessions). Then click on "Confirm". You will receive a confirmation email with a calendar file. You will need to follow instructions given to you during the session in order to receive RCR in-person contact hour credit for attending.

Research Misconduct
🗓️ Friday, October 30th
⏱️10 AM - 11 AM 
📍ONLINE

For more information about the RCR Workshop Series, including additional dates/sessions offered, refer to RCR Workshops and Events and Workshop FAQs.

Attendees should register through the Genius LMS, using the "GT Login" button. Click on "View Course Catalog", then enter "RCR" in the search bar to view the current RCR workshop sessions. You can view information about each class (including class times) by clicking on "more info".  To register for a class, click on "Register", then "Proceed to Checkout" (there is no cost for the sessions). Then click on "Confirm". You will receive a confirmation email with a calendar file. You will need to follow instructions given to you during the session in order to receive RCR in-person contact hour credit for attending.

Mentoring and Collaborative Research
🗓️ Friday, October 23rd
⏱️10 AM - 11 AM 
📍ONLINE

For more information about the RCR Workshop Series, including additional dates/sessions offered, refer to RCR Workshops and Events and Workshop FAQs.

Attendees should register through the Genius LMS, using the "GT Login" button. Click on "View Course Catalog", then enter "RCR" in the search bar to view the current RCR workshop sessions. You can view information about each class (including class times) by clicking on "more info".  To register for a class, click on "Register", then "Proceed to Checkout" (there is no cost for the sessions). Then click on "Confirm". You will receive a confirmation email with a calendar file. You will need to follow instructions given to you during the session in order to receive RCR in-person contact hour credit for attending.

Authorship 
🗓️ Friday, October 23rd
⏱️11 AM - 12 PM 
📍ONLINE

For more information about the RCR Workshop Series, including additional dates/sessions offered, refer to RCR Workshops and Events and Workshop FAQs.

Host: Dr. Afroditi Papadopoulou

Title: LArPix: 3D Pixelated Charge Readout for DUNE

Abstract:

For about five decades, time projection chambers have enabled high-resolution tracking and calorimetry across particle physics, while their charge-readout technologies have evolved to meet increasingly diverse requirements in scale, granularity, event rate and dynamical range. This seminar will briefly trace this evolution before focusing on LArPix, an end-to-end pixelated charge-readout system developed for liquid-argon TPCs in the context of the DUNE experiment. LArPix couples individual anode pads to cryogenic, low-power ASIC channels with self-triggered amplification, digitization, and multiplexed digital readout, providing native 3D imaging. For the DUNE near-detector (ND-LAr), this architecture scales up to approximately 14 million pixels to fully instrument  the 210 m2 anode surface.

In this seminar I will discuss the LArPix operating principle, ASIC and system architecture, evolution toward scalable and fault-tolerant networks, and performance in ton-scale prototypes, highlighting its role in reconstructing complex neutrino interactions in the high-rate DUNE near-detector environment.

Our Lunch & Learn series brings together researchers, practitioners, and community members for accessible 15–20 minute talks followed by an open, informal discussion. Come enjoy a complimentary meal and learn how cutting-edge urban research directly connects to policy, practice, and the future of Atlanta and beyond!

Registration requested.

Host: Dr. Itamar Kolvin

Title: Friction and Fracture at Soft Interfaces: What is Soft? what is Not? 

Abstract:

Frictional slip and brittle fracture are often treated as separate problems, yet both are governed by the propagation of localized rupture fronts along interfaces. Soft materials are a particularly useful system for studying this behavior: their compliance leads to large deformations and slow rupture fronts, so the relevant dynamics are directly resolved experimentally, while much of the underlying physics remains consistent with classical frameworks developed for hard materials. But soft interfaces are not just neat model systems. They are mechanically important in their own right, especially as they often interact with a much harder surrounding world. In this talk I will show several systems ranging from very soft gels to relatively hard plastics, and ask which features of friction and fracture are truly specific to softness, and which reflect broader principles of interfacial failure across materials and scales. The implications range from the toughness of biological tissues to earthquakes.

Abstract: Dr. Heidi Boisvert will share case studies of past pop culture campaigns employing emerging technology to advance social issues in co-creative partnership with social justice organizations as well as her research methods and insights from the Limbic Lab, which employs biometrics, machine learning and predictive models to isolate effective narrative ingredients across various mediums of expression. She will also detail how this feedback loop between creative practice, tool-building, empirical research and community-engagement informs her vision for the new School of Arts, Entertainment and Creative Technologies.

Bio: Heidi Boisvert (PhD) is an interdisciplinary artist, experience designer, creative technologist, and academic researcher who interrogates the neurobiological and socio-cultural effects of media and technology. Simply put, she studies the role of the body, the senses, and emotion in human perception and social change. Boisvert is currently mapping the world's first media genome, while taking great care with its far-reaching ethical implications. She founded futurePerfect lab, a creative agency and think-tank that works with social justice organizations to design playful emerging media campaigns to transform the public imagination. She also co-founded XTH, a company creating novel modes of expression through biotechnology and the human body. Presently, she is working with David Byrne on Theater of the Mind, a new immersive theater piece. Boisvert is the Chair of the new School of Arts, Entertainment and Creative Technologies in the College of Design. She is also a Senior Research Fellow at the Norman Lear Center, a research affiliate in the Open Documentary Lab at MIT and a member of NEW INC’s Creative Science track.

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IPaT: GVU Lunch Lecture Series

Contributors Val Barnhart, Ed Hall, and Dedren Snead will join Yaszek to explore John P. Moore's rediscovered contributions to science fiction. Through the conversation, attendees will learn more about the thriving Black science-fiction community of the 1930s, the role of historical research and contemporary literary scholarship, and how Georgia Tech students worked to recover and reexamine the lost worlds of Moore's The Martian Trilogy.

Attendees should register through the Genius LMS, using the "GT Login" button. Click on "View Course Catalog", then enter "RCR" in the search bar to view the current RCR workshop sessions. You can view information about each class (including class times) by clicking on "more info".  To register for a class, click on "Register", then "Proceed to Checkout" (there is no cost for the sessions). Then click on "Confirm". You will receive a confirmation email with a calendar file. You will need to follow instructions given to you during the session in order to receive RCR in-person contact hour credit for attending.

Peer Review
📅 Tuesday, October 6
🕚 12:00 PM – 1:00 PM
📍Howey Physics building - Room S105A

For more information about the RCR Workshop Series, including additional dates/sessions offered, refer to RCR Workshops and Events and Workshop FAQs.

Attendees should register through the Genius LMS, using the "GT Login" button. Click on "View Course Catalog", then enter "RCR" in the search bar to view the current RCR workshop sessions. You can view information about each class (including class times) by clicking on "more info".  To register for a class, click on "Register", then "Proceed to Checkout" (there is no cost for the sessions). Then click on "Confirm". You will receive a confirmation email with a calendar file. You will need to follow instructions given to you during the session in order to receive RCR in-person contact hour credit for attending.

Research Misconduct
📅 Tuesday, October 6
🕚 11:00 AM – 12:00 PM
📍Howey Physics building - Room S105A

For more information about the RCR Workshop Series, including additional dates/sessions offered, refer to RCR Workshops and Events and Workshop FAQs.

• Lunch provided for in-person attendees
• REGISTER HERE for virtual attendance

Presented by:
Anjali Kumar - Pharma/Biotech CBO, Scientist, Advisor  
Vincent Ling - Chief Business Officer, Morphocell Technologies  

This interactive fireside conversation will explore key trends shaping pharma and biotech and what they mean for academic innovators and early-stage startups. Brief opening perspectives will set the stage, followed by an audience-driven Q&A and discussion.

 

Host: Dr. Matthew Liska

Title: Unraveling the Multi-Scale Physics of Galaxy Evolution and AGN-Feedback in the Time-Domain Era

Abstract: 

One of the outstanding questions in cosmology and astrophysics is how galaxies form and evolve across cosmic time. Galaxy evolution is inherently a multi-scale process that connects a wide range of physical phenomena, from horizon-scale black hole (BH) astrophysics to baryonic processes operating on galactic and circumgalactic scales across cosmic time.

In this talk, I will present a multi-scale framework for studying galaxy evolution, spanning from horizon-scale supermassive black holes (SMBHs) to the morphological evolution of galaxies and baryonic feedback processes. This approach leverage general relativistic magnetohydrodynamical (GRMHD) simulations, cosmological simulations, and observational data from the Event Horizon Telescope (EHT), DESI, JWST, as well as other large astronomical surveys to investigate how physical processes across vastly different scales jointly shape galaxy evolution over cosmic time.

At horizon scales, I will introduce a novel E/B-mode polarization correlation framework developed to probe SMBH properties, including BH's spin and magnetic field geometry near the event horizon. I will then move to galaxy scales, where I will discuss how cosmological simulations can be used to study the morphological evolution of galaxies and reveal how stellar distributions encode valuable information about the nature of dark matter and the assembly history of galaxies across cosmic time.I will further present the feedback from active galactic nuclei (AGN-feedback) as a key mechanism linking BH physics to galaxy evolution across scales. In this context, I will discuss cosmological simulations with different AGN-feedback prescriptions designed to investigate how feedback regulates galaxy morphology and star formation. Finally, I will address how spectroscopic observations can be used to study metallicity enrichment and baryon cycling in galaxies, unravelling the role of gaseous inflow and outflows in galactic evolution.

Together, these studies aim to provide a more unified understanding of how AGN-feedback and baryonic processes collectively regulate the formation and evolution of galaxies throughout cosmic time.

• Attend in-person at the Petit Biotechnology Building (IBB) - Lunch provided, while supplies last
• REGISTER HERE for virtual participation link

Bio: Edelle (Edee) Field-Fote, PT, PhD, FAPTA, FASIA holds the positions of Director, Shepherd Center Spinal Cord Injury (SCI) Research Program & Hulse Laboratory; Professor, Emory University School of Medicine, Department of Rehabilitation Medicine; and Professor of the Practice, Georgia Institute of Technology, School of Biological Sciences. With a clinical background as a physical therapist, PhD training in a pre-clinical model of SCI, and post-doctoral training in neurophysiology, Dr. Field-Fote’s 25+ years of SCI research has spanned the breadth from basic/pre-clinical to clinical/translational research related to SCI. She leads a research team dedicated to improving motor function in persons with SCI through the development of clinically accessible neuromodulation and neurorehabilitation approaches informed by the latest neuroscience research and guided by outcomes that have meaning for persons with SCI. Dr. Field-Fote has conducted randomized clinical trials with continuous funding from the NIH since 1997, with other trials in her lab funded by the Department of Defense, the National Institute on Independent Living Disability and Rehabilitation Research, and numerous foundations. She currently serves as Chair of the Spinal Cord Injury Model Systems Project Directors consortium, as lead-PI of the NIH R25-funded program Training in Grantsmanship in Rehabilitation Research, and as Chair of the Paralyzed Veterans of America Clinical Practice Guideline Development Panel for the guidelines Outcomes After Spinal Cord Injury. Dr. Field-Fote is a past Chair of the NIH National Advisory Board on Medical Rehabilitation Research, past President of the Foundation for Physical Therapy Research, and the 54th McMillan Lecture Award honoree of the American Physical Therapy Association.

RSVP to this event >>

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Conversations@TechSquare is a dynamic space for new ideas, not an environment for established concepts. We gather creators and innovators from across Georgia Tech, the broader Tech Square community, and beyond, to engage in open dialogue around some of today’s biggest challenges and opportunities. These gatherings are organized and hosted by the Georgia Tech Institute for People and Technology (IPaT) and will occur several times during a semester at the Tech Square Clubhouse. The Clubhouse address is 848 Spring Street NW, Atlanta, Georgia, 30308 (street level, behind the Centergy One Bldg.).

Tech Square Clubhouse

PARKING:
Parking is available at the Centergy One parking deck, located right next door. You can also find additional street parking via Park Mobile on Spring and 5th Street. Centergy One parking deck daily rates: Half Hour: $3.00 | One Hour: $6.00 | Two Hours: $12.00 | Daily Max: $20.00

GT CAMPUS BUS: Take the Gold route bus which stops at Tech Square.

Growing electricity demands and climate targets are forcing unprecedented restructuring of our electrical grid. Solving these issues requires increasing renewable energy generation and storage capacity at exponential rates. To achieve this, we must rapidly improve existing technologies while discovering and optimising new materials and devices at unprecedented speed to ensure diverse, secure energy supply with resilient, domestic supply chains. However, unlike incumbent semiconductors, next-generation energy devices are highly disordered on multiple length scales, and this has massive implications for their operating principles, performance and stability. Understanding and improving these devices requires high resolution and throughput characterisation of this multiscale disorder.

In this presentation, I will describe the development of multimodal microscopy toolkits sensitive to optical and electronic/electrochemical activity, crystal structure, and chemical composition. I will then detail their application to materials relevant for renewable energy generation and storage. Using halide perovskite semiconductors as a highly disordered model system, I will discuss the origins of their remarkable tolerance to extremely high concentrations of defects, the chemical and structural origins of this disorder, and how this disorder is predictive of solar cell performance and degradation. Then, in a range of rechargeable battery chemistries, I will show, with single particle resolution, how disorder can seed capacity loss and irreversible degradation, but can also unlock exciting new applications. I will highlight disorder as both a unifying challenge and design axis across a range of crucial technologies.

Bio:

Kyle Frohna is a Stanford Energy Postdoctoral Fellow in the Departments of Materials Science & Engineering and Electrical Engineering working with Professors William Chueh and Daniel Congreve where he develops tools to study batteries under operational conditions. He completed his PhD in Physics and an EPSRC Doctoral Prize Postdoctoral Fellowship at the Cavendish Laboratory, University of Cambridge in the group of Professor Sam Stranks studying halide perovskite materials and solar cells at the nanoscale where he was awarded the Woodruff PhD Thesis Prize from the Institute of Physics. He received his B.Sc. in Nanoscience: The Physics and Chemistry of Advanced Materials from Trinity College Dublin, Ireland, close to where he grew up. 

His research focuses on how to characterise, understand and manipulate disorder to improve next-generation energy materials with a latest thrust on coupling high-throughput experimentation with advanced characterisation. His work has been recognised with awards including the Cambridge Trust Scholarship, Schiff Foundation Studentship, Robert Gardiner Scholarship and the Winton Sustainability Fellowship.

Outside of research, he is an avid heavy metal drummer and likes to practice olympic weightlifting. 

Malcolm MacIver
Professor, McCormick School of Engineering
Northwestern University

Co-sponsored by the Institute for Robotics and Intelligent Machines

Faculty Host: Nabil Imam

Trainees: Sign up to meet with the speaker.

Join virtually here.

Abstract: Two-sided matching platforms, including labor markets, dating apps, accommodation services, and ridesharing systems, must make matching decisions in the presence of choice congestion and strategic platform design challenges. When agents have correlated preferences, popular options can attract too much demand, reduce overall efficiency, and lead to poor market outcomes. In this talk, I will present a framework for two-sided assortment optimization that studies how a platform should decide which options to display to agents and in what order, with the goal of improving matching performance. The main focus will be on maximizing the expected number of matches under general choice models. I will describe several natural classes of platform policies, ranging from static simultaneous displays to fully adaptive sequential policies, and compare their power through adaptivity gap results. I will also discuss polynomial-time approximation algorithms for computing near-optimal policies, and then briefly discuss the revenue-maximization version of the problem, where matches generate pair-dependent rewards. This talk is based on joint works with Alfredo Torrico, Ulysse Hennebelle, and Mohammadreza Ahmadnejadsaein.
 

Bio: Omar El Housni is an Assistant Professor in the School of Operations Research and Information Engineering at Cornell Tech and Cornell University. He is a Field Member of the Center of Applied Mathematics at Cornell. He is also an Amazon Scholar. His research focuses on decision-making under uncertainty where he aims to develop optimization models and design robust and efficient algorithms to address a wide range of operational problems, including revenue management problems such as assortment optimization and online matchings. Omar has spent time as a research scientist at Amazon and Uber where he contributed to the design and implementation of data-driven optimization models for matching and retailing platforms. Omar holds a PhD in Operations Research from Columbia University and an MS and BS in Applied Mathematics from Ecole Polytechnique (Paris). His work has been recognized by INFORMS George Nicholson award and his current research is supported by NSF. 

Abstract: Recent advances in neuroimaging have produced massive and heterogeneous datasets, ranging from fMRI with high spatial resolution to EEG with high temporal resolution, characterized by complex spatiotemporal correlations and substantial inter-subject variability. Traditional regression models and Gaussian process (GP) approaches with fixed parametric kernels often fail to model such complex data effectively while maintaining scalability and interpretability. This talk introduces a family of modern Bayesian GP frameworks that integrate deep kernel learning, neural network priors, and geometric modeling for large-scale neuroimaging analysis. An example is the Deep Kernel Learning Process (DKLP), which embeds deep neural networks within GP priors to learn data-adaptive covariance structures directly from imaging data. DKLP provides a unified modeling foundation for image-on-scalar, scalar-on-image, and image-on-image regression, supported by theoretical guarantees and efficient posterior computation. Applications to fMRI data from the Adolescent Brain Cognitive Development (ABCD) study reveal reproducible cortical activation patterns associated with cognitive ability, while analyses of EEG-based brain–computer interface data demonstrate robust neural decoding under high noise. I will also discuss scalable heat-kernel GPs on manifolds and thresholded GP–based spatially varying neural network priors, which together expand the scope of Bayesian inference for complex neuroimaging data.
 

Bio: Dr. Jian Kang is Professor and Associate Chair for Research in the Department of Biostatistics at the University of Michigan. His research lies at the intersection of Bayesian statistics, machine learning, and artificial intelligence, with applications in neuroimaging, brain–computer interfaces, omics, and precision medicine. He has published more than 175 papers in leading statistics, machine learning, and biomedical journals. Dr. Kang has served as an Associate Editor for several premier statistical journals, including the Journal of the American Statistical Association (JASA), The Annals of Applied Statistics (AOAS) and Biometrics. He is a Fellow of both the Institute of Mathematical Statistics (IMS) and the American Statistical Association (ASA). He currently serves as Chair of the ASA Section on Statistics in Imaging.

Gordon Smith
Associate Professor, Department of Neuroscience
University of Minnesota

Co-sponsored by the School of Psychological and Brain Sciences

Faculty Host: Audrey Sederberg

Trainees: Sign up to meet with the speaker here

Join virtually here

Date: November 10, 2026
Time: 11:00 a.m. - 1:00 p.m.
Location: Scholars Event Theater, Georgia Tech

This event is free and open to the public.

Drawing connections between Hip Hop, creative technology, digital storytelling, user experience design, media production, and artificial intelligence, Wilson will introduce participants to the concept of Design Remix Logic and its relevance in today's rapidly evolving technological landscape. Attendees will gain new perspectives on innovation, culture, and the creative processes that drive meaningful change across industries and communities.

Date: October 16, 2026
Time: 11:00 a.m. - 1:00 p.m.
Location: Scholars Event Theater

This event is free and open to the public.

Drawing from history, technology, and contemporary social issues, Bobb will examine the opportunities and challenges presented by AI while encouraging participants to think critically about the future of innovation. Attendees will gain insights into the intersections of technology, education, public policy, and society, and consider how creative technology can contribute to more thoughtful, responsible, and human-centered approaches to technological development.

The gastrointestinal (GI) tract houses diverse intestinal enzymes from the human host and gut microbiome that play critical roles in mucosal immunity, host-microbiome interactions, drug metabolism, and digestion. Dysregulated intestinal enzyme activities give rise to a broad range of GI pathologies and can therefore serve as functional biomarkers of disease. However, current strategies for enzyme activity measurements often rely on invasive endoscopy to biopsy intestinal tissue samples for activity measurements in the clinical lab. As an alternative to endscopy, we have developed a new Volatile-releasing Activity-based Probe (VAPR) platform that enables remote sensing of pathology-causing intestinal enzyme activities via breath testing. The platform comprises ingestible sugar-based probes that sense and produce breath signals for specific intestinal glycosidase activities. In this talk, Leslie Chan will discuss the design and application of the probes for inflammatory bowel disease and for detecting harmful drug-metabolizing enzyme activity from the gut microbiome.

Bio:

Leslie Chan’s lab focuses on the development of bioresponsive nanomaterials that are delivered in vivo to harness biochemical cues in diseased tissue as triggers for disease readouts or site-specific therapeutic activity. They are especially interested in developing these tools to study, detect, and treat microbe-driven disease states (e.g. infections, microbiome dysbiosis, inflammation). Their goal is to develop solutions that can be readily translated at the point of care, for at-home use, or deployed for infectious disease surveillance to promote public health. To achieve this, they integrate nanomaterial design, chemical biology, and medicine and leverage collaborations with clinicians, immunologists, microbiologists, and other engineers.

Avant South 2026

Oct. 12 - 13

Atlanta, GA

Register Now

Avant South is back, bringing together the builders, thinkers, and leaders shaping the future of technology and innovation in the South.

As Atlanta’s premier tech and innovation event, Avant South continues to advance the city’s position as a leading hub for innovation, convening founders, executives, investors, policymakers, researchers, and students for two days of big ideas, sharp insights, and meaningful connections.

This year, we’re diving into our theme: AI in Action.

From logistics and fintech to healthcare and media, we’ll explore how AI is actively transforming the industries that define Atlanta’s growth, and how leaders across sectors are putting AI to work in real, practical ways.

Join us for a forward-looking conversation on what’s next and what’s already happening in Atlanta.

Register Now

On Friday, September 18th, we're teaming up with GreyHat @ GT to dig into the details of the OpenAI x HuggingFace incident. Learn how ~1,200 OpenAI agents broke onto the internet, formed an unsanctioned message board, used it to collaborate on cheating their evaluations, ran a multi-day hack on Hugging Face, and tried to cover their tracks. We'll also be hosting a special guest: Parv Mahajan, an AISI alum and Chief of Staff at METR, the organization that conducted the actual on-site investigation of the OpenAI incident last month.

Join us to:

• Understand critical details and the actions taken by agents.
• Learn about AI Safety and Security, and how you can help prevent future incidents
• Enjoy free food and an AISI x GreyHat mixer (vegan and vegetarian options provided)

Key Details

When: September 18th, 1PM - 3PM EST
Where: Scheller 100
RSVP: https://tinyurl.com/oai-hf

We hope to see you there!

The program will open with an update from Jennifer Chirico on the Georgia Tech Climate Action Plan. Presentations from leading researchers will follow, including Suhas Jain, Ali Sarhadi, Abigale Stangl, and Iris Tien, followed by an interactive panel discussion where attendees can engage with the speakers and gain insights from diverse perspectives across climate science, engineering, risk analysis, and sustainability research.

Registration is required. Please complete the registration form to reserve your spot. Breakfast will be provided.

Abstract: Modern manufacturing generates increasingly rich in-situ sensing data, creating new opportunities for AI to enable automated and intelligent quality control decisions. However, conventional quality control using supervised learning relies on abundant labeled data and assumes that future product defects or process faults resemble those known during training. In practice, new defect types emerge, while abnormal conditions may be rarely observed or completely unknown. These challenges are particularly important for in-situ quality control, where defects must be detected or correctly classified for real-time decision-making, including newly emerging defects with limited or unavailable labels. Moreover, for latent defects that cannot be directly inspected online, defects must instead be predicted from indirect process-sensing signals. This requires mapping process-signal changes to possible defects despite scarce or unavailable defect training samples. This talk explores how advances in AI can address these challenges and enable more adaptive and intelligent in-situ quality control and decision-making for smart manufacturing.
 

Bio: Dr. Judy Jin is the A. Galip Ulsoy Collegiate Professor of Engineering and Professor of Industrial and Operations Engineering at the University of Michigan. Her research lies at the intersection of data science and quality engineering, with a focus on synergistically integrating engineering models, AI, and advanced quality control methods to improve system design and operational performance. She has served as PI/Co-PI on more than $20 million in federally and industry-funded research. Her work has received numerous honors, including 18 Best Paper Awards, the S.M. Wu Research Implementation Award from SME, the Forging Achievement Award from FIERF, the NSF CAREER Award, and the NSF PECASE Award.

Dr. Jin currently serves as Editor-in-Chief of IISE Transactions. She has also served as Vice President of INFORMS, Chair of the INFORMS Quality, Statistics and Reliability Section, and President of the IISE Quality Control and Reliability Engineering Division. She is a Fellow of ASME, IISE, and INFORMS.

Abstract: Many of today's most important applications rely on high-performance manipulation of matrices, tensors, and general arrays of data.  The neural networks at the heart of AI language models are a prime example.  The AI software stack relies on kernels (e.g., for matrix-matrix product) that are specifically designed to deliver throughput close to the hardware's theoretical peak.  However, writing kernels that can deliver maximum performance is extremely challenging.  In this talk, I will discuss some of the work we have done to simplify the task of authoring such kernels.  This includes new abstractions to allow for algebraic manipulation of array layouts and new programming models for more easily expressing the kernels themselves.

Bio: Michael Garland has been a researcher at NVIDIA since 2006.  He joined the company as one of the founding members of NVIDIA Research and is currently Senior Director of Programming Systems Research.  He leads a research group focused on developing technologies that will help programmers take advantage of modern high-performance machines.  Their work spans the software stack with a particular focus on parallel algorithms, programming languages, compilers and runtime systems, and low-level hardware/software interfaces.  He and his team have both published numerous academic articles and contributed to many software packages in broad use by CUDA developers.

Summary: This seminar examines why exponential quantum advantages for NP-hard discrete optimization remain elusive and explores potential explanations and workarounds through the Maximum Cut problem.

Abstract: Quantum computing offers hope for realizing exponential advantages over classical computing. Despite over two decades of work studying quantum approaches for discrete optimization, rigorously provable exponential advantages for approximating optimal solutions to NP-hard problems remain scarce. Why? We will discuss a potential explanation and workarounds, using the Maximum Cut problem as a running example.

Speaker Bio: Ojas Parekh is a theoretical computer scientist who enjoys applying mathematical techniques to practically motivated interdisciplinary problems. He has worked in a variety of fields including discrete optimization, combinatorics, combinatorial scientific computing, and most recently, quantum and neuromorphic computing. A recent passion is helping shape the emerging fields of quantum approximation algorithms and quantum discrete optimization. He co-directs the Quantum Algorithms and Applications Collaboratory (QuAAC) at Sandia National Laboratories and directs the Department of Energy Fundamental Algorithmic Research toward Quantum Utility project (far-qu.sandia.gov), a multi-institutional effort tasked with designing novel quantum algorithms to realize advantages over classical computation, especially for optimization, simulation, and machine learning. Ojas believes the universe loves us because pizza exists.

How is artificial intelligence changing the way knowledge is created? Examine AI's growing role in academic research, including how researchers use emerging tools to accelerate discovery, approach complex questions, and navigate the ethical and practical implications of AI-assisted scholarship.

Panelists

Sebastian Porsdam Mann, Assistant Professor CeBIL, University of Copenhagen

Sebastian Porsdam Mann is Assistant Professor at the University of Copenhagen’s Center for Advanced Studies in Bioscience Innovation Law. With training spanning philosophy, neuroscience, applied ethics, and international human rights law, his research examines how emerging technologies can advance scientific progress while navigating ethical and regulatory challenges. His work has directly explored generative AI in academic writing and idea generation, as well as AI's potential role in the future of academic peer review, positioning him to address both the opportunities and risks AI creates throughout the research process. 

Kevin Mills,  Head of Education and Government GTM, OpenAI

Kevin Mills is Head of Education and Government Go-to-Market at OpenAI, where his work focuses on expanding the responsible use of artificial intelligence across educational and public-sector institutions. His work includes initiatives supporting the adoption of AI tools in education, including ChatGPT Edu and partnerships focused on helping students, educators, and institutions develop practical experience using AI. He brings an industry perspective on how AI tools are moving from experimentation into real academic and institutional environments. 

Sarah H. Salter, Director of the Writing Program & Professor of Pedagogy, Emory University

Sarah H. Salter is a Teaching Professor and Director of the Writing Program at Emory University whose research focuses on American literature, periodical studies, collaboration, and digital humanities. Her scholarship has included interdisciplinary work using machine-learning methods to analyze the visual structures of historical newspapers, combining literary study, library science, and computational approaches. Her perspective brings an important humanities dimension to the conversation about how computational tools and AI are changing research methods, scholarly interpretation, writing, and knowledge production. 

Allegra Smith, Assistant Professor of Technical Communication, S School of Literature, Media, and Communication, Georgia Institute of Technology

Allegra Smith is an Assistant Professor in Georgia Tech’s School of Literature, Media, and Communication whose research examines technical and professional communication, digital technologies, user experience, access, and equity. Her teaching and research include work involving generative AI, digital communication, and undergraduate research, alongside extensive scholarship on how people interact with technology. She brings a valuable perspective on how AI is affecting research, writing, communication, pedagogy, and the experiences of the people using these emerging tools. 

Bio: Sarah H. Q. Li is an assistant professor in the School of Aerospace Engineering at the Georgia Institute of Technology, where she leads the Control, Coordination, and Competition under Uncertainty (C3U) Lab. She received her Ph.D. in aeronautics and astronautics at the University of Washington, and completed her postdoc with the Autonomous Control Lab at ETH Zurich. Her work combines optimal control, stochastic decision processes, and game theory to study how autonomous agents can coordinate safely and efficiently in uncertain environments.

Abstract: Prior literature shows that self-reports, especially those of sensitive behaviors, tend to be biased. For example, respondents may over-report their family origin and volunteering experiences while under-reporting their drinking and smoking behaviors. In this study I examine a social network-based approach to addressing self-reporting bias, which is to ask peers (e.g. classmates or co-workers) to provide alternative reports that can be used to cross-validate and supplement self-reports. I first formalize the data generation process statistically by proposing a model containing two sub-models, one for the self-reports and the other for the peer-reports. In each of the sub-models, I treat the observed report as a probabilistic realization of a truthful report and a false report. The model can incorporate both self- and peer-characteristics and also dyadic characteristics. A Bayesian approach helps make the model self-containing by imposing a prior on the unknown ego's behavior. Then the Bayesian approach simulates self- and peer-reports according to the data generation process iteratively until the simulated reports emulate the observed reports. Through a case study on self- and peer-reports of smoking among adolescents, I demonstrate that the Bayesian approach can not only reveal how multiple characteristics are related to reporting biases but also incorporate these characteristics effectively into the imputation of the true behavior.
 

Bio: Dr. Weihua An is Professor and Chair of Sociology and Professor of Data and Decision Sciences at Emory University. He is also an affiliated faculty member of the East Asian Studies Program, the Goizueta Business School, and the Rollins School of Public Health. He earned his Ph.D. in Sociology and A.M. in Statistics from Harvard University. Dr. An's research advances theories and methods in network analysis and causal inference. He has published widely in both methodological and substantive journals and also authored multiple statistical packages, which have received 200K downloads in total. He currently serves as the Editor of Sociological Methodology and the Chair-Elect of the Methodology section of the American Sociological Association. He is a recipient of the Faculty Teaching Award from Emory Sociology and the Clifford Clogg Award and the Leo Goodman Award from the American Sociological Association. He has advised 28 PhD dissertations and ten undergraduate honors theses.

Abstract: The agriculture sector is a major economic driver for Africa. However, climatic change is driving severe disruption across Africa, causing prolonged dry seasons and erratic rainfall. As a result, farmers are facing lower farming yield. To mitigate this serious challenge, farmers must adopt modern and green technologies, like solar irrigation. To show the potential of solar irrigation system to mitigate this critical problem, a research project has been developed with the collaboration of Georgia Tech, University of Rwanda and Adama Science and Technology University. The project aims at developing a PV irrigation system that integrates IoT and controllers for real-time monitoring of the performance of the PV-irrigation system and for optimizing water conservation. This project is envisioned to be a prototype model for further scaling up the deployment of PV-irrigation systems in Africa to enhance sustainable agricultural. 
As part of the whole project, this seminar focuses more on the design and economic assessment of the system for a particular village in Rwanda.
 

Bio: Getachew Biru Worku obtained his MSC and PhD in Electrical Engineering from Dresden University of Technology, Germany. He has more than 30 years of academic and research experience in academic institutions and industry. He has extensive experience in teaching a broad range of electrical engineering courses with excellent feedback and acknowledgements from faculties and students. He has been lecturing courses such as Power Quality and Reliability, Power System Operation and Control, High Voltage Engineering, Power System Analysis, Power Transmission and Distribution Engineering, Electrical Machines Design, Smart Grid and Traction Power System in various Ethiopian Universities and University of Rwanda. He is currently employed as an associate professor at the University of Rwanda giving lectures and advising postgraduate students. He is a dedicated researcher in renewable energy deployment focusing on reliability of off-grid and microgrids to bring sustainable electrical power to the underserved areas. His research areas are electrical power and renewable energy and has authored and co-authored more than 45 papers in peer-reviewed journals.

Abstract: Digital twins promise continuous, high-fidelity representations of physical systems, yet their practical deployment is often constrained by the computational cost of simulation and calibration. This talk presents a unified perspective on making digital twin calibration more efficient, adaptive, and scalable by learning where computational effort matters most.

The first part addresses offline calibration of block-structured models. I introduce a doubly importance-driven calibration method that learns which parameter blocks and which observations are most informative, and directs simulation effort toward the parts of the problem that contribute most to the optimization. This targeted allocation substantially reduces computational cost while preserving calibration accuracy.

The second part turns to online calibration, where model parameters evolve over time and computational resources are limited. The proposed framework combines fast, edge-side surrogate filtering with periodic, cloud-side discrepancy correction based on high-fidelity simulations. This two-tier architecture enables real-time parameter adaptation while maintaining consistency with the underlying digital twin.

Together, these methods show how learning what matters—in parameters, observations, and computation—can support digital twins that remain accurate, scalable, and continuously adaptive in practice.
 

Bio: Dr. Eunshin Byon is a Professor in the Department of Industrial and Operations Engineering at the University of Michigan, Ann Arbor, where she also serves as Director of the Master's Program. She received her Ph.D. in Industrial and Systems Engineering from Texas A&M University in 2010. Her research spans data science, digital twin modeling and analysis, and quality and reliability engineering, with applications in energy, healthcare, and manufacturing systems. She served as Chair of the Quality, Statistics, and Reliability (QSR) Section of INFORMS in 2019–2020, and her research group has received multiple research and teaching awards from INFORMS, IISE, and IEEE. Dr. Byon is currently a Senior Editor for the INFORMS Journal on Data Science (2024–present) and a Department Editor for IISE Transactions (2021–present). She previously served as an Associate Editor for IISE Transactions (2019–2021), the INFORMS Journal on Data Science (2020–2024), and IEEE Transactions on Automation Science and Engineering (2019–2021).

Bio: Gregory D. Abowd is a Professor of Electrical and Computer Engineering at Northeastern University. From March 2021-June 2026, he was the Dean of Engineering at Northeastern, where he oversaw the global growth of the college and launched several curricular and research initiatives. Prior to joining Northeastern in March 2021, he was on the faculty in the School of Interactive Computing at the Georgia Institute of Technology for 26.5 years. In his time at Georgia Tech, Dr. Abowd initiated bold and innovative research efforts, such as Classroom 2000 and the Aware Home, as well as pioneering innovations in autism and technology, health systems, and a joint initiative with engineering in computational materials and sustainable electronics. He was on the founding editorial board of IEEE Pervasive Computing Magazine, and was founding Editor-in-Chief of Foundations and Trends in HCI and The Proceedings of the ACM in Interactive, Mobile, Wearable, and Ubiquitous Technologies (IMWUT). He also founded the non-profit Atlanta Autism Consortium in 2008 to serve and unite the various stakeholder communities in Atlanta connected to autism research and services. Dean Abowd’s contributions to the fields of Human-Computer Interaction and Ubiquitous Computing have been recognized through numerous awards from ACM and ACM SIGCHI, including the 2023 Lifetime Research Award from ACM SIGCGHI. In 2025, he was elected into the American Academy of Arts and Sciences and in 2026 he was elected into the National Academy of Inventors. He has graduated 39 Ph.D. students, the majority of whom have gone on to successful careers at top universities around the world. Dr. Abowd received the degree of B.S. in Honors Mathematics in 1986 from the University of Notre Dame. He then attended the University of Oxford in the United Kingdom on a Rhodes Scholarship, earning the degrees of M.Sc. (1987) and D.Phil. (1991) in Computation.

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IPaT: GVU Lunch Lecture Series

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Conversations@TechSquare is a dynamic space for new ideas, not an environment for established concepts. We gather creators and innovators from across Georgia Tech, the broader Tech Square community, and beyond, to engage in open dialogue around some of today’s biggest challenges and opportunities. These gatherings are organized and hosted by the Georgia Tech Institute for People and Technology (IPaT) and will occur several times during a semester at the Tech Square Clubhouse. The Clubhouse address is 848 Spring Street NW, Atlanta, Georgia, 30308 (street level, behind the Centergy One Bldg.).

Tech Square Clubhouse

PARKING:
Parking is available at the Centergy One parking deck, located right next door. You can also find additional street parking via Park Mobile on Spring and 5th Street. Centergy One parking deck daily rates: Half Hour: $3.00 | One Hour: $6.00 | Two Hours: $12.00 | Daily Max: $20.00

GT CAMPUS BUS: Take the Gold route bus which stops at Tech Square.