Abstract: LLM watermarking has emerged as a promising tool for attributing AI-generated text, but its practical deployment
remains limited. In this talk, I will first present a unified theoretical framework that jointly optimizes watermark
design and detection, characterizing the fundamental trade-off between detectability, false positive control, and text
distortion. While this formulation yields sharp optimality insights, it typically requires the LLM provider to implement
the watermark during generation, which has limited its deployment in practice. I will then discuss the reasons
behind this gap, emphasizing stakeholder incentives and real-world deployment constraints. Motivated by these
challenges, I will present in-context watermarking, a model-agnostic approach that embeds detectable signals
through prompts rather than decoding-time access. This perspective suggests that effective watermarking requires
not only strong algorithms, but also incentive-aligned designs tailored to practical domains such as peer review
and education.

Bio: Yuheng Bu is an Assistant Professor in the Department of Computer Science at the University of California, Santa
Barbara. His research lies at the intersection of information theory, machine learning, and trustworthy AI. He
received the NSF CAREER Award in 2026. Before joining UC Santa Barbara, he was an Assistant Professor in the
Department of Electrical and Computer Engineering at the University of Florida. Prior to that, he was a Postdoctoral Research Associate at the Research Laboratory of Electronics and the Institute for Data, Systems, and Society at the Massachusetts Institute of Technology. He received his Ph.D. in 2019 from the Coordinated Science Laboratory and the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign. He earned his B.S. degree with honors in Electronic Engineering from Tsinghua University in 2014.

Abstract: Discrete diffusion models achieve strong performance in text and image generation, but inference remains slow
and requires balancing sampling efficiency with sample quality. We systematically study how the degree of
stochasticity in Markov transitions governs this tradeoff, finding that deterministic transitions converge quickly but
accumulate errors, while stochastic transitions converge more slowly but can achieve better final sample quality.
Through an information-theoretic analysis, we show that redundant transitions that symmetrically exchange
probability mass between states can contract sampling errors, providing an error-correcting effect. Motivated by
this insight, we introduce Discrete Churn and Restart Sampling (DCRS), which injects controlled stochasticity by
alternating between forward and reverse diffusion. Across synthetic, image, and language benchmarks, DCRS
improves the speed-quality tradeoff at low numbers of function evaluations, achieving up to a 10× reduction in
image sampling steps while revealing more nuanced behavior for language generation.

Bio: William Yuan is a Ph.D. student in Electrical and Computer Engineering at the Georgia Institute of Technology,
advised by Prof. Amirali Aghazadeh. His research focuses on developing methods to accelerate the inference in
deep generative models. He received his B.S. from Cornell University.

Abstract: We present a new computationally efficient method for multi-beamforming in the broadband setting. Our “fast
beamspace transformation” forms B beams from M sensor outputs using a number of operations per sample that
scales linearly (to within logarithmic factors) with M when B∼M . While the narrowband version of this
transformation can be performed efficiently with a spatial fast Fourier transform, the broadband setting requires
coherent processing of multiple array snapshots simultaneously. Our algorithm works by taking N samples off of
each of M sensors and encoding the sensor outputs into a set of coefficients using a special non-uniformly spaced
Fourier transform. From these coefficients, each beam is formed by solving a small system of equations that has
Toeplitz structure. The total runtime complexity is O(M log N + B log N ) operations per sample, exhibiting
essentially the same scaling as in the narrowband case. Our method offers significantly higher accuracy and lower
computational complexity than delay-and-sum filtering techniques and significantly higher accuracy and lower
latency than standard frequency- domain techniques based on sub-band processing. An additional benefit of the
proposed approach is that it allows for a careful error analysis; we give concrete formulas for the bias and variance
of the beamformer given the array parameters. We also provide a host of numerical experiments demonstrating the
algorithm’s favorable computational scaling and high accuracy. Finally, we demonstrate how tasks such as
interpolating to “off-grid” angles and nulling an interferer are more computationally efficient when performed directly
in beamspace.

Bio: Nakul Singh received a bachelor’s degree in electrical engineering from the Indian Institute of Technology, Kanpur,
India, in 2022. He is currently working toward the Ph.D. degree at the Georgia Institute of Technology, advised by
Prof. Justin Romberg and Prof. Mark Davenport. His research interests include machine learning, signal
processing, and massive MIMO.

Participants will learn the foundational concepts of mindfulness, what it is, why it matters, and how it can positively impact mental and physical well-being. This session includes guided mindfulness exercises focused on cultivating gratitude, helping participants intentionally notice and appreciate positive experiences, personal strengths, and everyday moments that are often overlooked.

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Virtual Event | Register

Build Your Plate: Healthy Choices Made Simple  | 10:30 – 11:30 am.

Presented by Mitzie Rojas, Registered Dietitian

In recognition of National Diabetes Awareness Month, this interactive session will explore practical ways to build balanced meals that support steady energy, support your well-being, and sustainable long-term health. Join us to learn about Diabetes, nutrition basics, and building a balanced plate and sustainable nutrition habits into busy schedules.

Pause & Recharge  | 11:30 a.m. – 1:30 p.m.

Employees can stop by at their convenience for stretching exercises (massage), relaxing activities, and other simple ways to pause and recharge.

Register

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Talk Title: From GenAI to Agentic AI: The Transformative Impact on Academics

Date/Location: Friday, Nov. 20, 2026; 11 a.m., KACB 1447

Abstract: Agentic AI represents the shift from stateless inference and reactive generation to autonomous and goal-driven orchestration. By chaining generative models together with goal decomposition, multi-round planning, memory retention, and tool-routing, Agentic AI systems solve complex and multi-step reasoning problems end-to-end. Realizing this vision demands a tight equilibrium: efficiency through adaptive learning and scaling, and robustness through continuous governance. Drawing on empirical research and classroom experience, this talk explores how agentic AI is transforming learning and research.

Bio: Ling Liu is a Professor in the School of Computer Science at Georgia Institute of Technology. Her recent research is mainly centered on efficient AI systems, algorithms and optimizations, as well as responsible AI through developing failure-resilient and trustworthy guardrails. She has graduated 40 PhD students since joining Georgia Tech in 1999, all well placed in either academic institutions or industry. Her current research is primarily supported by USA National Science Foundation, CISCO and IBM. Prof. Liu is a recipient of the best paper award from numerous top venues, including IEEE ICDCS, WWW, ACM/IEEE CCGrid, IEEE Cloud, ACM ICMR. She served as the editor in chief of multiple IEEE/ACM Transactions, incl. IEEE Transactions on Services Computing (2013-2016), ACM Transactions on Internet Computing (2019-2024), and currently the editor in chief of IEEE Transactions on Big Data (since 2025). Prof. Liu is a frequent keynote speaker in top-tier venues in Big Data, AI and ML systems, Cloud Computing and Distributed Computing Systems.

Join us for the final virtual artist talk of the Craft of Paper: Contemporary Takes on Tradition exhibition. We'll be closing the series with presentations from Sara Rayo, lucha rodríguez, and Claude-Gerard Jean.

About the Exhibit: 

Curated by paper artist Michael Velliquette, The Craft of Paper: Contemporary Takes on Tradition explores modern interpretations of paper crafts, including cutting, folding, rolling, weaving, and layering. This exhibition aims to establish a unified language of paper craft techniques, while highlighting how contemporary artists are adapting these methods in innovative ways. It underscores the tools and techniques that have contributed to the recent resurgence of paper crafts, such as the development of high-quality, commercially available paper stocks. Central themes include the evolution of paper as an artistic medium, the fusion of traditional and modern techniques, and the diverse applications of paper in both art and design.

How to Register for this event:

Register here for workshop. Please email anna.doll@rbi.gatech.edu for questions or more information.

Weaving a Web is the fourth in a series of five talks featuring artists on view in The Craft of Paper: Contemporary Takes on Tradition exhibit at the Robert C. Williams Museum of Papermaking.

In this virtual talk, we will be hearing from exhibiting artists, rhiannon skye tafoya and Julie VonDerVellen, both working the tradition of weaving into their paper art.

About the Exhibit: 

Curated by paper artist Michael Velliquette, The Craft of Paper: Contemporary Takes on Tradition explores modern interpretations of paper crafts, including cutting, folding, rolling, weaving, and layering. This exhibition aims to establish a unified language of paper craft techniques, while highlighting how contemporary artists are adapting these methods in innovative ways. It underscores the tools and techniques that have contributed to the recent resurgence of paper crafts, such as the development of high-quality, commercially available paper stocks. Central themes include the evolution of paper as an artistic medium, the fusion of traditional and modern techniques, and the diverse applications of paper in both art and design.

How to Register for this event:

Register here for workshop. Please email anna.doll@rbi.gatech.edu for questions or more information.

Admission is free and open to the public.

Seminar Title

How Does Active Exploration in Virtual Reality Shape Spatial Memory?

Affiliation

Ph.D. student in Adult Development and Aging, College of Sciences

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 ACADEMY:
Eight standalone sessions will orient researchers to NIH and its most frequently used award mechanisms – the R01. This program will review the core components of R01 applications, provide suggestions for structuring your proposal, and demystify the review process.

WHO SHOULD PARTICIPATE? 
This academy is best suited for early-career faculty members and others who have not yet secured an R01 or equivalent award. Applicants at any stage of readiness are welcome, and those committed to applying within the next six to 12 months are particularly encouraged to join.

WHEN & WHERE?
Courses will take place via Zoom on Mondays from 12 - 1 p.m., Sept. 28 - Nov. 16.

ABOUT THE ACADEMY:
Eight standalone sessions will orient researchers to NIH and its most frequently used award mechanisms – the R01. This program will review the core components of R01 applications, provide suggestions for structuring your proposal, and demystify the review process.

WHO SHOULD PARTICIPATE? 
This academy is best suited for early-career faculty members and others who have not yet secured an R01 or equivalent award. Applicants at any stage of readiness are welcome, and those committed to applying within the next six to 12 months are particularly encouraged to join.

WHEN & WHERE?
Courses will take place via Zoom on Mondays from 12 - 1 p.m., Sept. 28 - Nov. 16.

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.

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.

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.

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:

The development of controlled polymerization methods has broadly expanded the chemical space accessible for polymerization and enabled the production of well-defined synthetic materials targeting numerous applications, including polyolefin thermoplastics and styrenebutadiene- styrene (SBS) rubber. These achievements represent decades of advancements in polymer technology; however, our ability to prepare materials with backbones comprised of functional groups beyond alkyl or ester chains remains limited. Well-defined materials featuring unique molecular structures, and by extension exhibiting unique properties, will be required to meet the demand for high-performance materials that address contemporary challenges. This presentation will discuss our recent eForts in post-polymerization modification, monomer design, and methods development towards achieving this goal.

Bio: 

Aaron earned a B.S. degree in Chemistry in 2012 from the University of Nevada, Reno prior to attending the University of Texas at Austin, where he completed a PhD in Chemistry in 2016. His dissertation work with Prof. Christopher Bielawski focused on the design and development of photoswitchable N-heterocyclic carbenes for enhanced control over organocatalyzed and transition metal catalyzed transformations. Aaron subsequently joined the lab of Prof. Frank Leibfarth at the University of North Carolina at Chapel Hill as a postdoctoral fellow. His work at UNC focused on the development of new stereoselective ionic polymerizations and subsequent exploration of the resulting polar thermoplastics. Aaron joined the faculty at the University of Kansas as an Assistant Professor in 2021, where his group is focused on the design and synthesis of new, functional synthetic macromolecules.

Radiation Worker Registration and X-Ray Safety Training Sign-Up

1. Complete a Form B, Radiation Worker registration before attending class (you may need to be connected to the GT VPN). After the training, the completed Form B will be sent to your Authorized User (AU - this is whoever manages the x-ray machine you'll be using) for signature via DocuSign.
2. Go to Genius LMS and use your GT Login
3. Click "View Course Catalog"
4. Search for X-Ray
5. Click "Register" for your preferred date
6. Click "Proceed to Checkout" and follow the instructions

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

Radiation Worker Registration and X-Ray Safety Training Sign-Up

1. Complete a Form B, Radiation Worker registration before attending class (you may need to be connected to the GT VPN). After the training, the completed Form B will be sent to your Authorized User (AU - this is whoever manages the x-ray machine you'll be using) for signature via DocuSign.
2. Go to Genius LMS and use your GT Login
3. Click "View Course Catalog"
4. Search for X-Ray
5. Click "Register" for your preferred date
6. Click "Proceed to Checkout" and follow the instructions

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

Radiation Worker Registration and X-Ray Safety Training Sign-Up

1. Complete a Form B, Radiation Worker registration before attending class (you may need to be connected to the GT VPN). After the training, the completed Form B will be sent to your Authorized User (AU - this is whoever manages the x-ray machine you'll be using) for signature via DocuSign.
2. Go to Genius LMS and use your GT Login
3. Click "View Course Catalog"
4. Search for X-Ray
5. Click "Register" for your preferred date
6. Click "Proceed to Checkout" and follow the instructions

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