Small nonprofits are often told that sustainability comes from better grant writing, stronger boards, and more diversified revenue. But many of their barriers are structural: limited public investment, funding requirements built for larger institutions, and little access to policymakers. In Metro Atlanta, half of the region’s arts nonprofits operate on budgets under $150,000, and public arts funding averages about $1.96 per person against a national average of $5.44.

Arts Capital | Atlanta, a coalition of arts nonprofits, funders, and public partners across 11 counties, has been testing what happens when small organizations stop working alone. Its experiments include public funding advocacy, a regional pooled fund, and a non-competitive pilot that sends unrestricted dollars to every eligible organization. This conversation will ask whether coalitions can actually shift the structures that govern funding, who holds power inside them, and what research and technology might contribute.

Speaker: Laura Hennighausen 
Executive Director Arts Capital | Atlanta and
Director of Strategic Philanthropy, Purpose Possible

Laura Hennighausen is a strategist dedicated to reshaping the nonprofit landscape by empowering organizations to operate with greater equity, transparency, and impact. As Director of Strategic Philanthropy, Laura sits at the intersection of donor intent and mission-driven reality, partnering with corporate, private, and individual funders to establish, evaluate, and modernize charitable practices.

Laura’s work is characterized by a sophisticated understanding of the third sector, which she tracks closely through her insightful monthly publication, Dispatches from the Philanthrosphere. Whether she is facilitating complex board retreats, navigating capital campaigns, or guiding organizations through the nuances of interim leadership, Laura brings a steady, research-backed approach that helps clients align their internal operations with their broader philanthropic goals.

Her client portfolio spans a wide spectrum of the philanthropic world, including Arts Capital | Atlanta, Mailchimp, the Intuit Financial Freedom Foundation, the T. Rowe Price Foundation, Borealis Philanthropy, and the Kataly Foundation. Her deep involvement in the field is bolstered by her leadership; she currently serves as the Board Chair for both MODA and Soul Food Cypher.

Laura’s commitment to the field is both practical and academic. Having built a strong career in public space, historic preservation, and visual arts, she is now deepening her expertise as a PhilD candidate at the Lilly Family School of Philanthropy—the premier institution for the study of the philanthropic sector. She holds a Master of Arts in Arts Administration and dual Bachelor of Arts degrees in Japanese Language and Literature and Art History.

---------------
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.

The lecture introduces Shared Materiality, Coersmeier’s framework for understanding design as a distributed process among human, artificial, material, social, and ecological actors. It proposes that intelligence cannot be separated from its substrate, whether biological, computational, or material. Through architectural research, design pedagogy, and experimental work with AI, geometry, material behavior, and fabrication, the lecture traces a movement beyond image generation toward spatial and embodied intelligence. It also insists that AI is never immaterial: computation is entangled with energy, water, minerals, labor, buildings, and planetary systems.

This argument is not anti-human, nor does it relieve humans of responsibility. It distinguishes human ethical responsibility from the generative agency of machines and matter. Rather than asking only how humans can use AI more effectively, the lecture asks how humans, technologies, and materials might participate more responsibly in a shared material world from which none of them stands apart.

Bio: Jonas Coersmeier is a German architect and American professor based in Brooklyn, New York. He explores the intersection of technology and material philosophy in design. His research and teaching at Pratt Institute and the University of Pennsylvania focus on social and ecological equity in urban housing and urban design. Educated at Columbia University, MIT, and the Technical University of Darmstadt, he brings an international perspective to architectural thinking—combining engineering discipline with speculative design experimentation. An award-winning designer, his projects have been exhibited and published internationally, contributing to a broader discourse on architecture and design.

---

IPaT: GVU Lunch Lecture Series

Date: Wednesday, Nov. 11, 2026, at 12:00 p.m.

Location: Coda, Room 230 (Google Maps link)

Lunch provided!

Title: Algorithms for Random Phase Approximation Correlation Energy Computations on Exascale Architectures

Abstract: This presentation presents massively parallel algorithms for computing the random phase approximation (RPA) correlation energy within Kohn-Sham density functional theory. Standard RPA formulations are extremely expensive for large chemical systems due to their quartic algorithmic scaling, which is typically associated with the explicit construction and eigen-decomposition of the large response function matrix. We discuss recent advancements in the SPARC electronic structure software package that permit cubic algorithmic scaling and nearly ideal speedups on the Frontier supercomputer. In particular, we use Lanczos quadrature to evaluate the trace of a matrix function on a real-space grid. We show it is possible to exploit the spatial smoothness of the RPA correlation energy density in real-space to reduce computational cost. We extend this method to multi-GPU systems by employing block Lanczos quadrature. Our matrix-free matrix-vector multiplication operator involves solving the complex symmetric Sternheimer equations in mixed precision across many complex diagonal shifts and right-hand side (RHS) vectors with coarse convergence tolerances. This is the first RPA formulation that solves the Sternheimer equations on distributed GPUs in mixed precision.We compute the RPA correlation energy for a lithium-hydride system with 512 valence electrons in less than 20 minutes on 512 MI250X GPUs. The proposed multi-GPU formulation achieves speedups of up to 74.4x in terms of node-hours across 128 nodes on the Frontier supercomputer versus a CPU-only baseline.  

Bio: Abir Haque is a PhD student in the College of Computing at Georgia Tech advised by Prof. Edmond Chow. His research focuses on developing novel high-performance computing and numerical linear algebra techniques that support scientific applications. He is currently focusing on accelerating electronic structure calculations by developing theory, algorithms, and software for the Simulation Package for Ab-initio Real-space Calculations (SPARC).

About HotCSE

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

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

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

Ambassador Kulminski has over two decades of experience in conflict resolution, foreign policy, strategic planning, and international development. He has served as Deputy Prime Minister for Reintegration, foreign policy and strategic planning Advisor to the Prime Minister, Secretary of State at the Ministry of Foreign Affairs, and worked with the UN, UNDP, World Bank, GIZ, and Humanitarian Dialogue on negotiation, mediation, and peacebuilding across Eurasia region. His work has included the Black Sea Grain Initiative and programs in conflict resolution, social cohesion and post-conflict recovery.   

He holds Master's degrees in Public Administration from the Maxwell School of Citizenship and Public Affairs (USA) and in International Relations and European Studies from the Central European University in Budapest. He is a Fulbright, John Smith, and Munich Security Conference Fellow.   

This event is part of the Nunn School's Diplomats-in-Residence Program and is generously supported by the Arthur M. Blank Family Foundation.

Bio: Abigale Stang is a researcher at Georgia Tech’s MultiSense.Studio in the School of Industrial Design. Her work focuses on accessibility, inclusive design, and the development of accessible visual and tactile media. Her research explores AI-generated image descriptions, tactile media design and production, and the collaborative processes involved in making information accessible. She also directs the Tactile Media Alliance, an initiative connecting researchers, practitioners, and organizations to advance tactile media accessibility.

---

IPaT: GVU Lunch Lecture Series

Bio: Lyle Ungar is a Professor of Computer and Information Science at the University of Pennsylvania, where he holds secondary appointments in Psychology, Bioengineering, Genomics and Computational Biology, and Operations, Information and Decisions. His group builds chatbots and language analysis tools for psychological and medical research.  They study the drivers of physical and mental well-being and their cultural variation, and develop socially sensitive AI-based tutors and coaches.

For CODA guest access, please contact shatcher8@gatech.eduat least 2 business days prior to the event.

Zoom link: https://gatech.zoom.us/j/91607375625?pwd=ybCsFPzknA8VqYZaSqvenqlWbrvyzx.1

Meeting ID: 916 0737 5625
Passcode: 038828

• Jeff Leaders, President & Co-Founder, Quantasonix Technologies, a deep‑tech startup developing a “Fluid Intelligence Platform” for real‑time, non‑destructive sensing and analytics of industrial fluids  
• Jonathan Rogers, Lockheed Martin Professor of Avionics Integration, School of Aerospace Engineering, Georgia Tech  
• Matthew McDowell,  Associate Chair for Research, Carter N. Paden, Jr. Distinguished Chair, Professor, and co-director of the Georgia Tech Advanced Battery Center

We eagerly look forward to your positive response and your valuable presence at this event. For more information including registration, please check out the ATL CleanTech Connect webpage.

Follow us on LinkedIn, X and subscribe to our mailing list to get the latest updates.

Join Georgia Tech's Marilyn Brown, Valerie Thomas, and Susan Graff as they celebrate two new Springer Nature books — Energy Technology and Policy Innovation and Sustainability Leaders: A Guide to Transforming Organizations — and explore competing visions for change. Drawing on research and real-world experience, this lively conversation will challenge assumptions, examine trade-offs, and ask who should lead, who should pay, and how to turn promising ideas into lasting progress.

About the Books

Hear directly from Howard Hughes Medical Institute (HHMI) program leads about research support opportunities for biological and biomedical scientists at every career stage, from undergraduates to faculty. Program leads will answer your questions live. Open to faculty, postdocs, Ph.D. students, and undergrads. 

Panelists:

• Joshua Hall - Lead Senior Director, Scientific Program Officer
• José Faraldo-Gómez - Senior Director, Scientific Program Officer
• Michelle Reniere - Senior Director, Scientific Officer

Following the presentation, three current HHMI scientists representing the Freeman Hrabowski Scholars and Hanna H. Gray Fellows programs will join the discussion to share their experiences and answer audience questions:

• HaoSheng Sun - Freeman Hrabowski Scholar at The University of Alabama at Birmingham
• Christopher Medina - Hanna Gray Faculty Fellow at the Moffitt Cancer Center
• Shan Meltzer - Hanna Gray Faculty Fellow at Vanderbilt University School of Medicine

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.

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

Panelists

Sebastian Porsdam Mann, Assistant Professor, CeBIL, University of Copenhagen

Sebastian Porsdam Mann is an 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. 

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.

Speaker: Dr. Carlos A. Meriles - City College of New York

Host: Chunhui Du

Abstract: Color centers in diamond offer a versatile platform for quantum information processing, combining long-lived spin states with optical interfaces. While most efforts have focused on coherent spin manipulation and photon-mediated interactions, the ability to control and probe charge at the individual-defect level opens complementary opportunities for building scalable quantum architectures.

In this talk, I will discuss recent advances in the optical interrogation and charge-state manipulation of nitrogen-vacancy centers in diamond. I will first describe how resonant optical excitation enables reversible charge control and nondestructive readout with spatial resolution well below the diffraction limit. I will then show how correlated spectral fluctuations reveal the local electrostatic environment, allowing us to reconstruct the three-dimensional arrangement of neighboring defects. Finally, I will discuss experiments resolving individual carrier-capture events and their dependence on the surrounding charge environment.

Our results illustrate how optical spectroscopy can provide access to charge dynamics at the single-defect level, opening new possibilities for controlling interactions between solid-state quantum systems.

Short Bio: Carlos Meriles was born and raised in Córdoba , Argentina . He obtained his Ph.D in physics at FaMAF – the mathematics, physics and astronomy division of the Universidad Nacional de Córdoba – under the supervision of Prof. A.H. Brunetti. His doctoral studies focused on the use of Nuclear Quadrupole Resonance (NQR) to probe structural and dynamical disorder in organic crystals. After graduating in 2000, he joined the group of Prof. Alexander Pines at the University of California , Berkeley to work on Nuclear Magnetic Resonance (NMR), his present field of research. In 2004, he joined the Department of Physics at City College where he now teaches as a Professor.

Host: Prof. John Wise

Title: Spectral survey instrument reveals notes of discord with cosmic sound waves: DESI overview, BAO measurements and their cosmological implications

Abstract:

We have known that the Universe is vast and expands with acceleration for many years, yet the physical origin of this acceleration remains an open question. While the concordance cosmological model, ΛCDM, has been remarkably successful, recent cutting-edge observational results suggest that dark energy may evolve with time or that the standard model might not keep up with all the highest-quality data.

In this talk, I will introduce baryon acoustic oscillations (BAO), relic sound waves from the early Universe whose imprint in the large-scale distribution of galaxies provides a robust standard ruler for measuring the cosmic expansion history. I will then present an overview of the Dark Energy Spectroscopic Instrument (DESI), a 5000-object fiber spectrograph with robotic positioning highly optimized for conducting a spectroscopic galaxy survey at a record-breaking speed and scale.

One of DESI's main objectives is to measure BAO across cosmic time. I will discuss the methodology and results of the recent BAO analysis from DESI Data Release 2, based on the first three years of observations. Combining these measurements with cosmic microwave background and Type Ia supernova data yields intriguing indications of departures from the standard ΛCDM model, including possible evidence for evolving dark energy.

I will conclude with an outlook on upcoming DESI cosmological analyses, including full-shape clustering measurements from Data Release 2 and BAO results from the 5-year Data Release 3.

Learn more and register at https://oit.gatech.edu/ai/forge. 

Presenter:
Samantha Yeligar
Associate Professor of Medicine
Emory University School of Medicine / Atlanta VA Medical Center

As artificial intelligence becomes increasingly capable of conversation, companionship, and emotional responsiveness, what happens to our understanding of human connection? Unpack AI, empathy, intimacy, and relationships while considering what it means to form meaningful connections in an increasingly AI-mediated world.

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

What does creativity mean when machines can generate images, music, writing, and other forms of creative work? Explore how artificial intelligence is reshaping creative industries, changing the relationship between technology and human expression, and raising new questions around authorship, originality, labor, and the future of creative work.

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

Join Justin Burns, PhD, Chief Innovation Officer and Vice President of Innovation and Entrepreneurship, Georgia Research Alliance, from noon to 1:00 PM via Zoom. Drawing on tech transfer, venture investing, and running GRA’s commercialization funding, Burns will walk through how funders size technical, market, and regulatory risk, which milestones change a valuation, and what to stop spending on before your next raise or license.

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

• Dr. Pengfei Liu - Associate Professor, School of Earth & Atmospheric Sciences at Georgia Tech, studying air pollution, wildfire smoke, and their effects on climate and human health.
• Dr. Katherine Graham - Assistant Professor, School of Civil & Environmental Engineering at Georgia Tech, researching water, climate, and health, including how pathogens spread through water systems.
• Dr. Jenny McGuire - Associate Professor, School of Earth & Atmospheric Sciences at Georgia Tech, studying how climate change reshapes biodiversity and how species and ecosystems adapt.

Register Online to attend via Microsoft Teams

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

BIO 
Melissa Lambeth Kemp received her B.S. in Nuclear Engineering from MIT and her Ph.D. in Bioengineering from University of Washington. Kemp joined the faculty at Georgia Tech in 2006 after completing postdoctoral training at MIT. Her expertise is in computational modeling of metabolism and signal transduction, as well as developing statistical modeling tools to examine network relationships in high-dimension datasets. One major aspect of her research program linking ROS – the byproducts of aerobic metabolism – to the fundamental way that cells interpret instructions from their environment, their neighbors, and their own genetic blueprint. Specific applications of her diverse work include systems modeling of transient phosphatase oxidation of kinase cascades, patient-specific differences in cytotoxicity to redox-cycled chemotherapeutics and radiation, and the coordination of oxidative metabolism with epithelial-to-mesenchymal transition. Her research program also includes a component of developing high-throughput screening methods for assaying cue-signal-response relationships in cells and analytical tools for single cell gene expression. 

Kemp currently serves as the Research Director of the multi-site NSF Engineering Research Center “Cell Manufacturing Technologies”. In her former role as Associate Director of the NSF Science and Technology Center “Emergent Behavior of Integrated Cellular Systems,” she spearheaded the multi-site center’s computational activities by developing agent-based models of context-dependent cellular decisions to generate new hypotheses of intercellular communication in pluripotent stem cell differentiation and emergent patterning; this work continues currently in quantifying organizational principles and spatial relationships in iPSC-derived tissues from multi-omics data. Kemp’s career honors include a Whitaker Graduate Fellowship, Merck/CSBi postdoctoral fellowship, Georgia Cancer Coalition Distinguished Scholar, NIH New Innovator Award, and the CSB2 Prize for Innovative Measurement Methods from the Council for Systems Biology in Boston.

The Spatial Omics and Data Analytics (SODA) Center meets monthly for networking, information transfer, and active partnership formation. These seminars will feature internal faculty spotlights, external leaders, and dedicated time slots for “Synergy Sessions” where faculty pitch data challenges or analytical solutions, directly seeding joint grant applications and cross-departmental Ph.D. mentorship.

Register HERE to participate virtually

ABSTRACT
Focused ultrasound (FUS) and nanomedicine offer promising opportunities to improve cancer treatment through targeted drug delivery, localized tumor ablation, and modulation of antitumor immunity. However, translating these technologies into clinical practice remains challenging, partly because conventional preclinical models do not fully capture the biological complexity of naturally occurring cancers. Our research integrates focused ultrasound, nanomedicine, and comparative oncology within a One-Medicine framework to accelerate therapeutic development for both human and veterinary patients. We are investigating multiple ultrasound modalities, including hyperthermia, thermal ablation, and histotripsy, and their potential to enhance the therapeutic efficacy of chemotherapy, radiation, and immunomodulatory nanomedicines. These approaches are designed not only to improve local tumor control but also to stimulate systemic antitumor immune responses and address metastatic disease. Building on mechanistic studies in preclinical models, we are translating these technologies into clinical trials involving companion animals with naturally occurring cancers through the Veterinary Research and Oncology Clinic (VROC) at UT Southwestern. This presentation will highlight our progress in developing ultrasound-based combination therapies, the challenges of clinical translation, and the opportunities offered by comparative oncology to accelerate therapeutic innovations across species.

BIO
Ashish Ranjan is an Endowed Professor in the Department of Radiation Oncology. He also serves as Vice Chair for Comparative Oncology and Research Innovation and Director of the Veterinary Research and Oncology Clinic. He earned his B.V.Sc. degree (DVM equivalent) from Madras Veterinary College, in Chennai, India, followed by a Ph.D. in biomedical and veterinary sciences from Virginia Tech. Ranjan was then a visiting fellow at the Center for Interventional Oncology, Department of Radiology and Imaging Sciences at the National Institutes of Health (NIH).

Ranjan's research interests are in the application of device-directed nanoparticles for solid tumor chemo-immunotherapy. His lab is actively engaged in preclinical and bench-to-bedside veterinary clinical trials and has received funding from several federal, state, and private sources. Ranjan has received several distinguished awards, including the NIH Merit Award. He also serves on the Scientific Advisory Board of Focused Ultrasound Foundation, UK's Humanimal Trust, and NIH panels.

Faculty host: Yue Chen

 

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.

Unable to make it in person? This hybrid event will be streamed online. Please register for meeting link.

Bio: Jeremy D. Brown is the John C. Malone Associate Professor in the Department of Mechanical Engineering at Johns Hopkins University and the Solomon H. Snyder Department of Neuroscience at the Johns Hopkins School of Medicine. He is also a member of the Laboratory for Computational Sensing and Robotics (LCSR) and the Malone Center for Engineering in Healthcare. Prior to joining Hopkins, Jeremy was a Postdoctoral Research Fellow at the University of Pennsylvania in the Haptics Research Group, which is part of Penn’s General Robotics, Automation, Sensing, and Perception (GRASP) Laboratory. He received his M.S. and Ph.D. degrees in Mechanical Engineering at the University of Michigan. He also holds B.S. degrees in Applied Physics and Mechanical Engineering from Morehouse College and the University of Michigan, respectively, as a graduate of the Atlanta University Center’s Dual Degree Engineering Program. Jeremy directs the Haptics and Medical Robotics (HAMR) Laboratory, whose research focuses on human-robot interaction and haptic feedback, with applications in upper-limb prosthetics, robot-assisted surgery, and rehabilitation. By integrating human perception, motor control, and biomechanics, his work advances the design of intuitive and effective human-machine interfaces. He has been honored to receive several awards, including the NIH R21 Trailblazer Award, the IEEE RAS Technical Committee on Haptics Early CAREER Award, the NSF CRII Award, the NSF CAREER Award, and the Sloan Foundation Fellowship. He was also named a scholar to the NIH funded Interdisciplinary Rehabilitation Engineering Career Development Program (IREK-12). He is a member of the Institute of Electrical and Electronics Engineers’ (IEEE) Robotics and Automation Society, the American Society of Mechanical Engineers (ASME), and the National Society of Black Engineers (NSBE). Brown’s work has appeared in several peer-reviewed journals and news outlets, including Nature Scientific Reports, the Journal of Neuroengineering and Rehabilitation, IEEE Transactions on Haptics, IEEE Transactions on Biomedical Engineering, IEEE Transactions on Neural Systems and Rehabilitation Engineering, the IEEE Transactions on Medical Robotics and Bionics, Forbes, and The New York Times.

Bio:  Tapomayukh "Tapo" Bhattacharjee is an Assistant Professor in the Department of Computer Science at Cornell University where he directs the EmPRISE Lab (https://emprise.cs.cornell.edu/). He completed his Ph.D. in Robotics from Georgia Institute of Technology and was an NIH Ruth L. Kirschstein NRSA postdoctoral research associate in Computer Science & Engineering at the University of Washington. His primary research interests are in the area of physical robot caregiving and physical human-robot interaction. He is the recipient of TRI Young Faculty Researcher Award'24, NSF CAREER Award'23, AFCEA 40 under 40 Award'22, and his work has won Best Systems Paper Award at HRI’26, Best Paper Award at RSS’25, Best Paper and Student Paper Award Finalist and Best HRI Paper Award Finalist at ICRA’25, Best Systems Paper Award Finalist at HRI'24, Best Demo Award at HRI'24, Best RoboCup Paper Award at IROS’22, Best Paper Award Finalist and ABB Best Student Paper Award Finalist at IROS’22, Best Technical Advances Paper Award at HRI'19, and Best Demonstration Award at NeurIPS’18. His work has also been featured in many media outlets including the BBC, Reuters, New York Times, IEEE Spectrum, and GeekWire and his robot-assisted feeding work was selected to be one of the best interactive designs of 2019 by Fast Company.

Seminar Title

From Virtual to Operational: XR Applications in Aviation

Affiliation

Delta Innovation

XR Bytes is a graduate-student-led initiative dedicated to showcasing the trajectory of Extended Reality (XR) research across the Institute. Our goal is to highlight the versatile ways XR technologies are applied across various disciplines and majors, fostering an interdisciplinary community of innovation 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.

Lunch will be served for those who RSVP.

Subscribe & Sign Up

To join our mailing list and RSVP for these seminar series, please sign up here: RSVP HERE.

Morning Session - Students, leaders, faculty, and staff are all welcome! 
9:30am Welcome with Coffee & Bagels
10:00am Makerspace Lightning Talks
10:30am Future of Makerspaces Panel
11:30am Lunch & Breakout Sessions

Afternoon Session for makerspace student leaders at the OUESS Experiential Learning Summit
1:00pm Makerspace & Project Showcase

Future of Makerspaces Industry Panelists

• Dariel Tellez, Additive Manfacturing Research Engineer, from Siemens
• John Boezi, Fractional Director of Engineering, from Boezi
• Lorraine LaGasse, Program Director, from Decatur Makers
• Addison Shelton, CEO, from Kicker Design

GT Makerspaces Lightning Talk

• ADC XR Makerspace
• Aero Maker Space (AMS)
• AI Makerspace
• BME Design Shop
• IPaT Labs
• Flowers Invention Studio
• Interactive Product Design Lab (IPDL)
• Interdisciplinary Design Commons (The HIVE)
• Materials Innovation and Learning Laboratory (The MILL)

Abstract: Curious about IPaT's current research? Join us for a fast-paced Lunch Lecture featuring lightning talks from a diverse group of IPaT faculty. In just a few minutes each, speakers will share exciting snapshots of current and recent projects, offering a behind-the-scenes look at the ideas, technologies, and collaborations shaping the future. From Animal-Computer Interaction and Coastal Ecology to Health Technologies, Design Innovation, Mixed and Augmented Reality, AI, and more, this session will highlight some of the remarkable breadth of research happening across IPaT. Whether you're looking to discover new research areas, find potential collaborators, explore emerging technologies, or just learn a bit about what we do in IPaT, you'll leave with a deeper appreciation for the creative and interdisciplinary work taking place throughout our community.

The Institute for People and Technology (IPaT) is an Interdisciplinary Research Institute at Georgia Tech offering a unique people-first approach to research and innovation. IPaT assembles academics, industry, government, civil society, and other partners to create people-centered solutions to some of society’s thorniest challenges. Created in 2011, IPaT’s community of researchers has spearheaded breakthroughs in health, aging, usability, sustainability, smart cities, and more. IPaT focuses on real-world impact, translating research out of the lab and into people’s lives in the form of real products, applications, and community education and programming. It accomplishes this through the efforts of over twenty research faculty, more than 100 affiliated faculty, significant research infrastructure and systems, well regarded events and programming, and an incredible staff. 
---

IPaT: GVU Lunch Lecture Series

This event has been postponed.

We are working to reschedule the session and will share the new date as soon as it is confirmed.

Join the Georgia Tech Library in the Scholars Event Theater, located on the first floor of Price Gilbert (Room 1280), on Nov. 10 from 4 to 6 p.m. for TechScape: Exploring Extended Reality Showcase Day.

Host: Prof. Ignacio Taboada

Title: Looking Down the Jet: Blazars, Beaming, and Neutrinos

Abstract:

IceCube neutrino detections select some of the most extreme blazars known. I'll touch on population studies that demonstrate this pattern statistically, and will focus on two individual blazars singled out by IceCube: TXS 0506+056 and PKS 1424+240. High-resolution radio monitoring of these two blazars reveals the physics behind the population-level result. What look like slow jets are actually aimed almost straight at us, with their high flow speeds masked by viewing geometry and slower-moving bright features. This orientation has long been known to explain their electromagnetic brightness. Their association with IceCube neutrinos points to a deeper implication: the neutrino output of an active galaxy is also concentrated along its jet. The neutrino-producing protons must therefore move preferentially in the jet direction. I will discuss ongoing observational and modeling efforts to test this picture, uncover other jets with similarly extreme properties, and connect observable jet properties to proton acceleration and neutrino production.

The event celebrates the artists participating in Chalk the Walk, taking place Oct. 21 – 23. Artists will transform 9-by-9-foot sections of sidewalk leading to the Library into a vibrant outdoor gallery celebrating literature and art.

Participants will select a favorite book and create either an artistic rendition of its cover or a reimagined interpretation using sidewalk chalk.

Registration is open through Oct. 8. To submit a book cover concept and sketch, click here. Approved participants will receive space assignments Oct. 17.

Artists will create their work Oct. 21 – 23, transforming a highly visible campus pathway into a temporary public art installation that celebrates creativity, reading, visual storytelling, and Georgia Tech spirit.

Finished works will remain on display throughout Homecoming Week, weather permitting.

Chalk the Walk is a collaboration between the Georgia Tech Library and Georgia Tech Arts.

On the experimental side, testing theories at the world's largest experiments, such as the Large Hadron Collider, is yet another computational challenge. High-dimensional data arrive at 40 MHz (~40 TB/s), and decisions must be made rapidly. I will discuss recent advancements in robust neural inference techniques with reliable uncertainty quantification that let us exploit the full high-dimensional data in hypothesis tests. Additional machine learning methods make these genAI predictions trustworthy, enabling tests that outperform likelihood-ratio-based statistical tests.

Bio: Aishik Ghosh is an assistant professor in the School of Physics at Georgia Tech with a focus on developing AI methods to accelerate fundamental physics and astrophysics. His group works on theoretical physics, statistical methods, and experiment design. For robust scientific applications, Dr. Ghosh focuses on uncertainty quantification, interpretability, and verifiability of AI algorithms, targeting publications in physics journals and ML conferences. He has also published policy documents with the Organization for Economic Co-operation and Development and took a leading role in a book on AI and the Future of Science. He received his PhD in particle physics from Paris-Saclay University for introducing digital twins and neural inference techniques to a Large Hadron Collider experiment and was a postdoc at UC Irvine and Lawrence Berkeley National Laboratory. Website: https://profaishikghosh.github.io/

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

Join Via Zoom: https://gatech.zoom.us/j/97583133407?pwd=arlGKeB48JGRzqOQt3mPELCH1sfL5J.1 

Meeting ID: 975 8313 3407 

Passcode: 799040

Abstract: Despite the ubiquity of continuous physiological monitoring in critically ill patients, clinicians lack tools to use high-resolution electrocardiogram (ECG) and photoplethysmography (PPG) signals in clinical care, especially combined with the context of patient characteristics, treatments, and evolving clinical state. As a result, current patient ECG and PPG data remain largely under-utilized in clinical practice. Artificial intelligence (AI) methods for ECG and PPG signals could address this gap, but current approaches are often task-specific, of a short-window nature, and insufficiently contextualized for critical care. As a result, they typically do not model long-timescale physiologic dynamics or account for how comorbidities, treatments, and evolving illness shape observed signals and outcomes. To address these methodological gaps, we are developing the UNIPHY framework, which combines a pretrained physiologic foundation model (physioFM) for local signal representation with an efficient state-space model (SSM) for long-range temporal integration and contextualized patient modeling. In this talk, I will share our most recent progress and lessons learned in training and testing UNIPHY on multiple use cases in various inpatient care settings, and example ambulatory monitoring applications.
 

Bio: Dr. Xiao Hu is a Professor at the Nell Hodgson Woodruff School of Nursing, an associated professor at department of Computer Sciences at College of Arts and Sciences and a program faculty member of the joint GA Tech and Emory Biomedical Engineering department. Dr. Hu is also the Associate Director of the Center for Data Science. Dr. Hu earned bachelor’s and master’s of science degrees in biomedical engineering at the University of Electronic Science and Technology in Chengdu, China. He then studied at UCLA, receiving a PhD in biomedical engineering in 2004. Before joining Emory, he held faculty positions at UCLA, UCSF, and Duke University.

Hu conducts research at the intersection of computational and health sciences where he develops signal processing, mathematical modeling, machine learning, and deep learning algorithms to transform physiological and clinical data into actionable information for improving patient care. Through his research, Hu has created knowledge advancing the relationship among clinicians, scientists, engineers, innovators, policy makers, and healthcare administrators.

Abstract: Missing data are pervasive in modern data systems and are frequently not missing at random. For example, in service settings, a customer's decision to submit a rating may depend on the rating they would have provided. Similarly, clinical notes, reviews, and other unstructured data are often selectively available, influenced by latent patient conditions or user preferences. 

This talk considers how AI-generated measurements can improve identification and inference under nonrandomly missing outcomes. Large language models can convert auxiliary unstructured data, such as customer interaction histories, into measurements that inform about missing outcomes. We treat these AI-generated measurements as weak shadow variables that are conditionally independent of missingness given the true outcome and observed covariates. We then introduce two linear programs that provide sharp bounds on key population quantities, such as mean ratings, with the AI-generated measurements tightening these bounds by serving as additional constraints on the observed-data distribution. I will also discuss extensions to nonrandomly missing unstructured covariates, particularly in healthcare and digital platforms, and present methods that integrate observed content and availability patterns to learn latent state representations for improving prediction and sequential decision-making.
 

Bio: Ruoxuan Xiong is an Assistant Professor in the Department of Data and Decision Sciences (DDS) at Emory University since Fall 2021, with courtesy appointments in Information Systems & Operations Management and Economics. Before joining Emory, she was a postdoctoral researcher at the Stanford Graduate School of Business from 2020 to 2021. She received her Ph.D. in Management Science and Engineering from Stanford University in 2020 and her B.S. degrees from Peking University in 2014. Her research lies at the intersection of causal machine learning, experimental design, and econometrics, with applications in healthcare, digital platforms, and finance. Her work has appeared in Management Science, the Journal of Econometrics, ICML, NeurIPS, ICLR, KDD, AAAI, ACL, and EMNLP.

I explore what this shift means in practice through my work co-designing misinformation escape rooms. These projects offer a concrete setting for examining how interventions can move beyond teaching people how to identify false information and instead support critical reflection, collective sensemaking, and more agentic participation in increasingly complex information environments.

Bio: Chris Coward is a Senior Principal Research Scientist and Affiliate Associate Professor at the University of Washington Information School. His research examines media and information literacy (MIL) in complex information environments, with a particular focus on how individuals and communities develop the capacities and agency to navigate these environments. His recent work explores sociocultural approaches to MIL through the design and study of escape rooms and other play-based experiences. Chris works closely with communities around the world to co-design, implement, and study these interventions in culturally diverse contexts.

---

IPaT: GVU Lunch Lecture Series

Abstract

Abstract

Fred Wherry is the Townsend Marin, Class of 1917 Professor of Sociology at Princeton University, where he is also the Vice Dean for Faculty Development and Inclusion, and founder of the Debt Collection Lab.

Marie-Lou Laprise is a postgraduate research associate in the Department of Sociology at Princeton University. A quantitative and computational sociologist, she studies collective intelligence and the social impact of AI. 

Over a quarter of Americans report that their household has been contacted by a debt collector in the past year, and for many, this is their primary encounter with the institutions that enforce private claims. Building on the concepts of predatory inclusion and legal estrangement, we argue that civil debt enforcement constitutes a distinct, stratified pathway to institutional distrust—one that parallels and compounds the criminal justice pathway. We introduce the concept of financial estrangement to theorize how the terms of households' inclusion in credit markets shape their relations with the institutions that enforce private claims. Our concept of financial estrangement brings together concerns from economic sociology, law and society, criminal justice, and race and inequality, and applies them to the context of the marketplace for consumer debt. Drawing on an original probabilistic survey of 2,115 U.S. adults, we show that debt-collection contact is pervasive, racially unequal, and not reducible to differences in household income or financial hardship. Americans overwhelmingly find widespread debt-collection practices unfair, particularly those who have experienced them. Careful analysis reveals that debt-collection contact is associated with perceiving courts as rarely or never fair, even after accounting for hardship levels, income, race/ethnicity, and arrest history. These findings suggest that civil legal processes tied to mass debt contribute to race- and class-stratified patterns of institutional trust.

Dalney Building – Room 180
926 Dalney Street NW
Atlanta, GA 30318
West Midtown

Abstract: For decades, we have envisioned robots that can move, adapt, and collaborate with humans as fluidly as we do with each other. Yet even the most advanced systems struggle to achieve what I call fluid physical interaction: a regime where motion, force, and intent predictions are continuously co-regulated through constraint-aware estimation, learning, and control; enabling safe, adaptive, and seamless exchanges of energy and information. Achieving this requires robots that are stable yet flexible, adaptive yet predictable, and compliant yet purposeful. In this talk, I will show how a dynamical-systems perspective provides a unifying foundation for realizing fluid physical interaction. By shaping robot behavior through stable vector fields, viability manifolds, and adaptive dynamics models, we can generate motion policies that remain reactive, safe, and consistent even under uncertainty. I will present recent results that demonstrate how this framework enables robots to generalize complex behaviors from minimal demonstrations, adapt online to changes in their dynamics and environment, avoid obstacles in real time, and maintain long-horizon safety during physical collaboration. I will also discuss new efforts that combine dynamical-systems principles with visuomotor learning to achieve stable, compliant, and force-aware behavior from simulation-guided data. Together, these developments move us closer to embodied, adaptive, and compliant robot intelligence that can truly flow with humans and the environments they inhabit.
 

Bio: Nadia Figueroa is the Shalini and Rajeev Misra Presidential Assistant Professor of Mechanical Engineering and Applied Mechanics at the University of Pennsylvania, with secondary appointments in Computer and Information Science and Electrical and Systems Engineering. She is a core faculty member of the GRASP Lab. Her research integrates learning, control, and estimation to enable safe, adaptive, and fluid human–robot interaction and collaboration. Her work bridges control theory, machine learning, perception, and biomechanics, with an emphasis on formal guarantees for robot behavior in dynamic, human-centered environments. Before joining Penn, she was a Postdoctoral Associate in MIT’s CSAIL. She earned her Ph.D. in Robotics, Control, and Intelligent Systems from EPFL and conducted research at the German Aerospace Center (DLR) and NYU Abu Dhabi. She holds a B.Sc. in Mechatronics Engineering from Tecnológico de Monterrey and an M.Sc. in Automation and Robotics from TU Dortmund. She received a 2025 NSF CAREER Award and was a finalist for the 2020 Georges Giralt Ph.D. Award and the 2017 KUKA Innovation Award. Her publications have received multiple best paper honors at ICRA, CASE, RSS, and IEEE Transactions on Robotics. She chairs the IEEE RAS Women in Engineering Committee, leading global efforts to recognize and celebrate women in robotics. 

Title: Optimization in Scientific ML - is Accuracy Certification a Viable Goal?

Zoom Link: https://gatech.zoom.us/j/9923742334?pwd=aVdnMjZHUDFwWE44UkdNVmNhVVhwdz09

Abstract: Generating nonlinear reduced models for parametric families of PDEs or learning associated parameter-to-solution maps often leads to minimizing a convex energy in a Hilbert space over a nonlinear hypothesis class. Hence, optimization over the budget of trainable weights, defining the hypothesis class, becomes (highly) non-convex. In this talk we discuss adaptive strategies for enhancing robustness and accuracy of optimization success, revolving around "conditional convergence guarantees". The main conceptual constituents are natural gradient flow in combination with an adaptive network expansion. Adaptation aims at increasing alignment with an ideal descent path determined by the gradient in the ambient (infinite dimensional) Hilbert space. This can be further combined with perturbed proximal point methods that can be understood as approximate implicit Euler schemes for the ideal Hilbert space gradient flow. The rationale is that each proximal point step is (much) easier to realize than the global optimization task because in each step the initial guess gets the closer to the optimum, the smaller the step-size. This adaptation to an implicit ideal Hilbert space gradient directions is of interest because the dynamical systems arising in the context of natural gradient flows are generically extremely stiff or even DAEs. Exploiting stable variational formulations of the PDE system, allows one to derive in certain relevant cases a posteriori criteria to check whether an approximate proximal step is sufficiently accurate to guarantee convergence. Roughly, network expansions are triggered when these criteria fail to be met even when the step-size reaches some lower bound. In this sense the results can be understood as "conditional convergence" with accuracy certificates. Part of the material draws on the recent paper [1].

[1]  Wolfgang Dahmen, Wuchen Li, Yuankai Tengang, Zhu Wang, Expansive Natural Neural Gradient Flows for Energy Minimization, Journal of Computational Physics, 563 (2026), 115117

Bio: Wolfgang Dahmen is a Research Professor in the Department of Mathematics at the University of South Carolina, where he previously held the SmartState Chair and Williams-Hedberg-Hedberg Chair. He earned his Ph.D. from RWTH Aachen and his Habilitation from the University of Bonn and has held faculty appointments at the University of Bielefeld, the Free University of Berlin, and RWTH Aachen. His research spans applied and numerical analysis, approximation theory, model order reduction, high-dimensional problems, and scientific machine learning. Dr. Dahmen is a recipient of the Gottfried Wilhelm Leibniz Prize, the Robert Piloty Prize, and the Keck Futures Initiative Award of the US National Academies. He is an elected Fellow of both SIAM and the AMS, a member of the German National Academy of Sciences Leopoldina and the North Rhine-Westphalian Academy of Sciences, and former Chair of the Foundations of Computational Mathematics (FoCM).

Abstract: Time series learning has become a central problem in modern machine learning, with applications spanning science, engineering, healthcare, and finance. While much of the existing research has focused on improving forecasting accuracy, accurate prediction is only the starting point. This talk explores a broader view of time series learning, moving from forecasting toward reasoning and scientific discovery. I will first discuss our work on efficient time series forecasting, introducing cross-architecture knowledge distillation to develop lightweight models that achieve strong predictive performance with reduced computational costs. I will then explore spatio-temporal reasoning, presenting methods that integrate time series, spatial structures, and large language models to understand how events evolve and propagate across connected systems. Finally, I will discuss whether AI can move beyond prediction to discover the underlying mathematical and causal relationships governing temporal dynamics. These directions highlight the potential of time series learning to advance from accurate prediction toward deeper understanding and scientific discovery.

Bio: Wei Jin is an Assistant Professor in the Department of Computer Science and the Department of Biostatistics and Bioinformatics at Emory University. His research focuses on time series analysis and AI for health. His honors and achievements include two NIH R01 awards as principal investigator, IJCAI-ECAI Early Career Spotlights, AAAI New Faculty Highlights, KAUST Rising Stars, the INNS Doctoral Dissertation Runner-up Award, recognition for Most Influential Papers in KDD and WWW by Paper Digest, and top finishes in three NeurIPS competitions. His research has been published in Nature Portfolio journals, PNAS, and leading conferences including ICLR, KDD, ICML, and NeurIPS. He has also organized multiple tutorials and workshops at major AI conferences.

Host: David A. Raudales O. 

Title: Neutrino-Shining Coronae in Active Galactic Nuclei: Radiative Signatures and Insights into Particle Acceleration” 

Abstract:
“The emergence of the Seyfert galaxy NGC 1068 as the most significant source of high-energy astrophysical neutrinos observed by IceCube marked a turning point in our understanding of the neutrino sky. This neutrino signal implies the presence of hadronic acceleration to up to 100 TeV-PeV energies. Furthermore, as neutrino production is expected to be accompanied by electromagnetic radiation of similar energies, the lack of observation of TeV gamma-rays strongly hints at the existence of a radiatively compact emission region of neutrinos, where the accompanying photons are absorbed and reprocessed at lower energies starting an electromagnetic cascade. For active galactic nuclei (AGN), the hot X-ray corona in the vicinity of the central supermassive black hole is a natural candidate for this opaque, highly energetic environment.
In this talk, I will review the current understanding of neutrino emission from AGN coronae, focusing on two scenarios of non-thermal particle acceleration that successfully explained the neutrino signal of NGC 1068: acceleration in magnetic reconnection and turbulence. I will show how the multimessenger signals observed from sources like NGC 1068, within the framework of turbulent acceleration, favor a compact, strongly magnetized, and highly turbulent coronal environment. Furthermore, I will demonstrate how high radiative compactness and intense magnetic fields constrain the spectral shape of the reprocessed electromagnetic radiation, forcing the internal cascade into a universal spectral shape. Finally, I will discuss how applying these coronal models to jetted sources, such as the blazar TXS 0506+056, fails to explain their observed neutrino flux, highlighting how the production of neutrinos could happen simultaneously in different regions of the AGN, but suggesting that in blazars the most luminous emission region is located in the jet.”

Bio:
"Federico is a third-year PhD student at DESY Zeuthen. His research focuses on the production of neutrinos in astrophysical sources, with an emphasis on active galactic nuclei (AGN). His recent work centers on multi-messenger modeling of IceCube candidate neutrino sources, where he contributed to developing coronal models for Seyfert galaxies. Additionally, Federico works on applying numerical methods based on neural networks to significantly accelerate the theoretical modeling of blazar jets. He is currently a visiting researcher at the Center for Computational Astrophysics (CCA), working on plasma-simulation-informed models of coronal emission." 

Seminar Title

Evaluating Lightweight Contact Feedback in Dual-Operator Collaborative Humanoid Teleoperation

Affiliation

Ph.D. student in Robotics, George W. Woodruff School of Mechanical Engineering, College of Engineering

XR Bytes is a graduate-student-led initiative dedicated to showcasing the trajectory of Extended Reality (XR) research across the Institute. Our goal is to highlight the versatile ways XR technologies are applied across various disciplines and majors, fostering an interdisciplinary community of innovation 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.

Lunch will be served for those who RSVP.

Subscribe & Sign Up

To join our mailing list and RSVP for these seminar series, please sign up here: RSVP HERE.

About the Speaker

Robyn Gatens is globally recognized for her 40-year career of leadership and contributions to NASA’s human spaceflight missions. 

Robyn recently retired from NASA as director of the ISS and Commercial Spaceflight divisions where she led strategy, policy, integration, and stakeholder engagement for low-Earth orbit (LEO), including the ISS, commercial LEO development, and commercial crew programs. 

A 1985 chemical engineering graduate from Georgia Tech, Robyn spent the first decades of her career in the development and management of life support and habitation systems for the International Space Station (ISS) and other human spaceflight missions. She was inducted into Georgia Tech’s College of Engineering Hall of Fame in 2025. 

Since retirement, Robyn is staying active through advisory and consulting roles, including serving on the Georgia Tech Space Research Institute’s External Advisory Board.  Robyn also has a passion for global student engagement including speaking to and mentoring students of all ages interested in pursuing careers in STEM and the space industry. 

The Meg & Sam Flax Lecture Series 

The Meg & Sam Flax Lecture Series invites top scholars, policymakers, and practitioners to speak to a wide audience of Georgia Tech students and faculty on issues of urgent importance in public policy. Our goal is not just a better understanding of current issues, but to get diverse perspectives on the proper role(s) of government in solving crucial problems. Because they practice at the cutting edge of their fields or on the front lines of government policy, the Flax Lecturers speak not from philosophy or theory but from facts, data, and experience. Topics range from science & technology policy to energy and the environment, from pandemic threats to sustainable development, from artificial intelligence and cyber-security to monetary policy, and even outer space. All Flax lectures are free of charge. Members of the broader Georgia Tech and Atlanta community are welcome to attend. Started in the Fall of 2019, the series is still ongoing.

Agenda

09:00-09:25 Coffee and Snacks Marcus Nano Atrium  

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

INNS graduate students, postdocs, and research scientists are invited to apply by submitting:

• An abstract (maximum 1500 characters) describing their research
• A brief statement (maximum 1500 characters) outlining their contribution to the INNS community

Applications must by submitted here by EOD November 11. A selection committee will review all submissions and choose one graduate student and one postdoctoral researcher or research scientist who will each receive the STAR Award and present their research in a dedicated lecture on December 7 as part of the INNSight Forum 2026-2027. Awardees will be selected based on the scientific merit, originality, and impact of their research, as well as their contributions to the INNS community.

BIO
H. Tom Soh is the W.M. Keck Foundation Professor of Electrical Engineering, Bioengineering and Radiology at Stanford University. He earned his B.S. with Distinction with a double major in Mechanical Engineering and Materials Science from Cornell University and Ph.D. in Electrical Engineering from Stanford University. Prior to academia, he served as a technical manager of MEMS device research group at Bell Laboratories and Agere Systems. Between 2003 and 2015, he was the Ruth Garland Professor at UC-Santa Barbara (UCSB) in the departments of Materials and Mechanical Engineering. His lab moved to Stanford in 2015. He is a recipient of numerous awards including MIT Technology Review’s “TR 100” Award, ONR Young Investigator Award, Beckman Young Investigator Award, ALA Innovator Award, NIH TR01 Award, Guggenheim Fellowship, Humboldt Fellowship, and Chan-Zuckerberg Biohub Investigatorship. He is a fellow of the American Institute for Medical and Biological Engineering (AIMBE) and member of the National Academy of Inventors (NAI).

Faculty host: Todd Sulchek

 

Bio: Dominic Kao is an Assistant Professor in the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign, where he directs the Virtual Futures Lab. His research asks how virtual worlds shape the people who use them: how avatars and identity, feedback design, and virtual reality change motivation, performance, and learning. His lab builds its own games and VR systems and runs controlled experiments inside them, with more than 30,000 study participants to date. Before academia, he worked as a game developer at Electronic Arts. His work has been recognized with an NSF CAREER Award, a Best Paper Award from ACM Transactions on Interactive Intelligent Systems, and Honorable Mentions at CHI and CHI PLAY. He received his PhD in Computer Science from MIT.

---

IPaT: GVU Lunch Lecture Series

Featured Speakers

Adharsh Rajagopal works in DRAM Device and Cell Technology at Micron Technology, where he develops advanced memory technologies for next-generation semiconductor devices. His expertise includes semiconductor device physics, materials characterization, process integration, and thin-film technologies. Before joining Micron, he conducted doctoral research at the University of Washington focused on high-performance tandem perovskite solar cells and emerging photovoltaic technologies. He holds a doctorate in materials science and engineering.

Michael Hamilton is the James B. Davis Professor in electrical and computer engineering at Auburn University and leads wiring, interconnects, and packaging efforts for quantum computing at Google Quantum AI. His research spans superconducting and cryogenic devices, advanced packaging and integration technologies, nanotechnology-enabled electronic and photonic systems, and semiconductor device performance in extreme environments. He previously served as director of the Alabama Micro/Nano Science and Technology Center and worked at MIT Lincoln Laboratory.

Event Details

Date: Tuesday, November 3, 2026
Time: 9:15–10:45 a.m.
Location: Marcus Nanotechnology Building, Georgia Tech

Adharsh Rajagopal will speak from 9:15 to 10 a.m., followed by Michael Hamilton from 10 to 10:45 a.m. 

Registration

The Brand Lecture is presented as part of Microelectronics Day, a full-day event hosted by the Institute for Matter and Systems. Registration is free, and all Brand Lecture attendees must register for Microelectronics Day to attend.

Register for Microelectronics Day

Abstract: Abstract: Decision-making under uncertainties has been a subject of study for many years. In particular, the decision making (optimization) for complex systems such as engineering and economic systems involves human-decision phase. This triggers the occurrences of uncertainties in the decision making phase in addition to the widely-existed inherent uncertainties in the actual systems. In this talk, such challenges will be addressed by summarizing the year’s efforts of the presenter when he was with the University of Manchester before 2016. At first, a generic framework on the decision making for uncertainty systems has been formulated into a “closed loop control design problem”, where the uncertainties in both decision making phase and the actual systems have been represented by the uncertainty injections to the “controller” and to the “plant to be controlled”. The uncertainties in human decision making phase is characterized by their probability density functions (PDF) of the brain-cell’s signal collected via brain-computer-interface (BCI). Using such a generic framework, decision making under uncertainties are formulated as a PDF control problem of objective functions and constraints- leading to a total solution for the stochastic optimization for complex systems. Two case studies will be presented together with the rationale and claims that most of the existing stochastic optimizations are special cases of the presented theory. Note that the presentation is based upon the published materials by the author.
 

Bio: Professor Hong Wang (Member of European Academy of Sciences and Arts, FIEEE, FIET, FInstMC, SMIEEE) received the BSc form Anhui University of Science and Technology in 1982, the master’s and Ph.D. degrees from the Huazhong University of Science and Technology, Wuhan, China, in 1984 and 1987, respectively. He was a Research Fellow with Salford University, Salford, U.K., Brunel University, Uxbridge, U.K., and Southampton University, Southampton, U.K., before joining the University of Manchester Institute of Science and Technology (UMIST), Manchester, U.K., in 1992. He was a Chair Professor in process control of complex industrial systems with the University of Manchester (UoM), U.K., from 2002 to 2016, where he originated probability density function control theory in 1996 and was the Deputy Head of the Paper Science Department, the Director of the UMIST Control Systems Centre from 2004 to 2007, which is the birthplace of Modern Control Theory established in 1966. He was a University Senate member and a member of general assembly during his time in Manchester. Whilst he has been an Emeritus Professor of UoM, from 2016 to 2018, he was with the Pacific Northwest National Laboratory (PNNL), Richland, WA, USA, as a Laboratory Fellow and Chief Scientist,
and was the Co-Leader and the Chief Scientist for the Control of Complex Systems Initiative. He joined the Oak Ridge National Laboratory in January 2019.

His research focuses on stochastic distribution control, fault diagnosis and tolerant control, and intelligent controls with applications to complex system area. He has published 6 books and 200+ journal papers in these areas together with 10+ awards/prizes at international conferences.

He is an associate editor for IEEE Transactions on Neural Networks and Learning Systems and was an Associate Editor of IEEE Transactions on Automatic Control (2002 – 2004), IEEE Transactions on Control Systems Technology (2013 – 2018), and the IEEE Transactions on Automation Science and Engineering. He is also a member for three IFAC Committees for the three areas of his research.

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.

Bio: Saiprasad Ravishankar is currently an Associate Professor in the Departments of Computational Mathematics, Science and Engineering and Biomedical Engineering at Michigan State University (MSU). He directs the signals, learning, and imaging (SLIM) research group and the data-driven neuroscience of meditation (DANSOM) lab at MSU. He received the B.Tech. degree in Electrical Engineering from the Indian Institute of Technology Madras, India, in 2008, and the M.S. and Ph.D. degrees in Electrical and Computer Engineering in 2010 and 2014, respectively, from the University of Illinois at Urbana-Champaign, where he was then an Adjunct Lecturer and a Postdoctoral Research Associate. From August 2015 to 2018, he was a postdoc in the Department of Electrical Engineering and Computer Science at the University of Michigan, and then a Postdoc Research Associate in the Theoretical Division at Los Alamos National Laboratory from August 2018 to February 2019, before joining MSU. His research interests include machine learning, computational and biomedical imaging, signal processing, image processing, inverse problems, data science, neuroscience, and physics and astrophysics applications. His research has been recognized with numerous awards including an NSF CAREER Award, IEEE Signal Processing Society Young Author Best Paper Award, and best student paper awards or finalist at numerous conferences such as the IEEE International Symposium on Biomedical Imaging (ISBI) 2018, IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2017, ISBI 2020, and Optical Imaging Congress 2023.

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

While we prefer you join in person, if you are unable to attend, join online at:
https://gatech.zoom.us/j/98109284552?pwd=CrXDIye86vmEtCywAV7wmjjz1LqEY8.1
Meeting ID: 981 0928 4552
Passcode: 947902

Bio:  Saiprasad Ravishankar is currently an Associate Professor in the Departments of Computational Mathematics, Science and Engineering and Biomedical Engineering at Michigan State University (MSU). He directs the signals, learning, and imaging (SLIM) research group and the data-driven neuroscience of meditation (DANSOM) lab at MSU. He received the B.Tech. degree in Electrical Engineering from the Indian Institute of Technology Madras, India, in 2008, and the M.S. and Ph.D. degrees in Electrical and Computer Engineering in 2010 and 2014, respectively, from the University of Illinois at Urbana-Champaign, where he was then an Adjunct Lecturer and a Postdoctoral Research Associate. From August 2015 to 2018, he was a postdoc in the Department of Electrical Engineering and Computer Science at the University of Michigan, and then a Postdoc Research Associate in the Theoretical Division at Los Alamos National Laboratory from August 2018 to February 2019, before joining MSU. His research interests include machine learning, computational and biomedical imaging, signal processing, image processing, inverse problems, data science, neuroscience, and physics and astrophysics applications. His research has been recognized with numerous awards including an NSF CAREER Award, IEEE Signal Processing Society Young Author Best Paper Award, and best student paper awards or finalist at numerous conferences such as the IEEE International Symposium on Biomedical Imaging (ISBI) 2018, IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2017, ISBI 2020, and Optical Imaging Congress 2023.

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

While we prefer you join in person, if you are unable to attend, join online at
https://gatech.zoom.us/j/93328955144?pwd=qepbKpZ2pV9Jl0ocrT6JGbHCX169qw.1
Meeting ID: 933 2895 5144
Passcode: 331085

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.

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

• 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.

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. 

------------------
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). 

---

IPaT: GVU Lunch Lecture Series