{"693752":{"#nid":"693752","#data":{"type":"event","title":"CSIP Seminar | Yuheng Bu","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETalk Title:\u003C\/strong\u003E Rethinking LLM Watermarking: From Theory to In-Context Methods\u003Cbr\u003E\u003Cstrong\u003ESpeaker: \u003C\/strong\u003EYuheng Bu, Assistant Professor in the Department of Computer Science at the University of California, Santa\u003Cbr\u003EBarbara\u003Cbr\u003E\u003Cstrong\u003EDate:\u003C\/strong\u003E Friday, November 13, 2026\u003Cbr\u003E\u003Cstrong\u003ETime:\u003C\/strong\u003E 3:00 p.m. - 4:00 p.m.\u003Cbr\u003E\u003Cstrong\u003ELocation:\u003C\/strong\u003E CSIP Library \u2014 Room 5126, Centergy One, 5th Floor\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E LLM watermarking has emerged as a promising tool for attributing AI-generated text, but its practical deployment\u003Cbr\u003Eremains limited. In this talk, I will first present a unified theoretical framework that jointly optimizes watermark\u003Cbr\u003Edesign and detection, characterizing the fundamental trade-off between detectability, false positive control, and text\u003Cbr\u003Edistortion. While this formulation yields sharp optimality insights, it typically requires the LLM provider to implement\u003Cbr\u003Ethe watermark during generation, which has limited its deployment in practice. I will then discuss the reasons\u003Cbr\u003Ebehind this gap, emphasizing stakeholder incentives and real-world deployment constraints. Motivated by these\u003Cbr\u003Echallenges, I will present in-context watermarking, a model-agnostic approach that embeds detectable signals\u003Cbr\u003Ethrough prompts rather than decoding-time access. This perspective suggests that effective watermarking requires\u003Cbr\u003Enot only strong algorithms, but also incentive-aligned designs tailored to practical domains such as peer review\u003Cbr\u003Eand education.\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBio: \u003C\/strong\u003EYuheng Bu is an Assistant Professor in the Department of Computer Science at the University of California, Santa\u003Cbr\u003EBarbara. His research lies at the intersection of information theory, machine learning, and trustworthy AI. He\u003Cbr\u003Ereceived the NSF CAREER Award in 2026. Before joining UC Santa Barbara, he was an Assistant Professor in the\u003Cbr\u003EDepartment 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.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EJoin University of California, Santa Barbara Computer Science Assistant Professor Yuheng Bu for a talk on Rethinking LLM Watermarking.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Join University of California, Santa Barbara Computer Science Assistant Professor Yuheng Bu for a talk on Rethinking LLM Watermarking."}],"uid":"36558","created_gmt":"2026-11-03 22:46:48","changed_gmt":"2026-11-03 22:50:23","author":"zwiniecki3","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-11-13T15:00:00-05:00","event_time_end":"2026-11-13T16:00:00-05:00","event_time_end_last":"2026-11-13T16:00:00-05:00","gmt_time_start":"2026-11-13 20:00:00","gmt_time_end":"2026-11-13 21:00:00","gmt_time_end_last":"2026-11-13 21:00:00","rrule":null,"timezone":"America\/New_York"},"location":"CSIP Library \u2014 Room 5126, Centergy One, 5th Floor","extras":["free_food"],"groups":[{"id":"1255","name":"School of Electrical and Computer Engineering"}],"categories":[],"keywords":[{"id":"5611","name":"CSIP Seminar Series"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"194683","name":"Talk"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"693751":{"#nid":"693751","#data":{"type":"event","title":"CSIP Seminar | William Yuan","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETalk Title:\u003C\/strong\u003E Understanding and Improving Low-Step Discrete Diffusion Sampling\u003Cbr\u003E\u003Cstrong\u003ESpeaker: \u003C\/strong\u003EWilliam Yuan, Georgia Tech Ph.D. Student\u003Cbr\u003E\u003Cstrong\u003EDate:\u003C\/strong\u003E Thursday, November 19, 2026\u003Cbr\u003E\u003Cstrong\u003ETime:\u003C\/strong\u003E 3:00 p.m. - 4:00 p.m.\u003Cbr\u003E\u003Cstrong\u003ELocation:\u003C\/strong\u003E CSIP Library \u2014 Room 5126, Centergy One, 5th Floor\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E Discrete diffusion models achieve strong performance in text and image generation, but inference remains slow\u003Cbr\u003Eand requires balancing sampling efficiency with sample quality. We systematically study how the degree of\u003Cbr\u003Estochasticity in Markov transitions governs this tradeoff, finding that deterministic transitions converge quickly but\u003Cbr\u003Eaccumulate errors, while stochastic transitions converge more slowly but can achieve better final sample quality.\u003Cbr\u003EThrough an information-theoretic analysis, we show that redundant transitions that symmetrically exchange\u003Cbr\u003Eprobability mass between states can contract sampling errors, providing an error-correcting effect. Motivated by\u003Cbr\u003Ethis insight, we introduce Discrete Churn and Restart Sampling (DCRS), which injects controlled stochasticity by\u003Cbr\u003Ealternating between forward and reverse diffusion. Across synthetic, image, and language benchmarks, DCRS\u003Cbr\u003Eimproves the speed-quality tradeoff at low numbers of function evaluations, achieving up to a 10\u00d7 reduction in\u003Cbr\u003Eimage sampling steps while revealing more nuanced behavior for language generation.\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBio: \u003C\/strong\u003EWilliam Yuan is a Ph.D. student in Electrical and Computer Engineering at the Georgia Institute of Technology,\u003Cbr\u003Eadvised by Prof. Amirali Aghazadeh. His research focuses on developing methods to accelerate the inference in\u003Cbr\u003Edeep generative models. He received his B.S. from Cornell University.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EJoin Georgia Tech Ph.D. student William Yuan for a talk on Understanding and Improving Low-Step Discrete Diffusion Sampling.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Join Georgia Tech Ph.D. student William Yuan for a talk on Understanding and Improving Low-Step Discrete Diffusion Sampling."}],"uid":"36558","created_gmt":"2026-11-03 22:44:36","changed_gmt":"2026-11-03 22:46:28","author":"zwiniecki3","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-11-19T15:00:00-05:00","event_time_end":"2026-11-19T16:00:00-05:00","event_time_end_last":"2026-11-19T16:00:00-05:00","gmt_time_start":"2026-11-19 20:00:00","gmt_time_end":"2026-11-19 21:00:00","gmt_time_end_last":"2026-11-19 21:00:00","rrule":null,"timezone":"America\/New_York"},"location":"CSIP Library \u2014 Room 5126, Centergy One, 5th Floor","extras":["free_food"],"groups":[{"id":"1255","name":"School of Electrical and Computer Engineering"}],"categories":[],"keywords":[{"id":"5611","name":"CSIP Seminar Series"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"194683","name":"Talk"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"693750":{"#nid":"693750","#data":{"type":"event","title":"CSIP Seminar | Nakul Singh","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETalk Title:\u003C\/strong\u003E A Fast Broadband Beamspace Transformation\u003Cbr\u003E\u003Cstrong\u003ESpeaker: \u003C\/strong\u003ENakul Singh, Georgia Tech Ph.D. Student\u003Cbr\u003E\u003Cstrong\u003EDate:\u003C\/strong\u003E Thursday, November 12, 2026\u003Cbr\u003E\u003Cstrong\u003ETime:\u003C\/strong\u003E 3:00 p.m. - 4:00 p.m.\u003Cbr\u003E\u003Cstrong\u003ELocation:\u003C\/strong\u003E CSIP Library \u2014 Room 5126, Centergy One, 5th Floor\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E We present a new computationally efficient method for multi-beamforming in the broadband setting. Our \u201cfast\u003Cbr\u003Ebeamspace transformation\u201d forms B beams from M sensor outputs using a number of operations per sample that\u003Cbr\u003Escales linearly (to within logarithmic factors) with M when B\u223cM . While the narrowband version of this\u003Cbr\u003Etransformation can be performed efficiently with a spatial fast Fourier transform, the broadband setting requires\u003Cbr\u003Ecoherent processing of multiple array snapshots simultaneously. Our algorithm works by taking N samples off of\u003Cbr\u003Eeach of M sensors and encoding the sensor outputs into a set of coefficients using a special non-uniformly spaced\u003Cbr\u003EFourier transform. From these coefficients, each beam is formed by solving a small system of equations that has\u003Cbr\u003EToeplitz structure. The total runtime complexity is O(M log N + B log N ) operations per sample, exhibiting\u003Cbr\u003Eessentially the same scaling as in the narrowband case. Our method offers significantly higher accuracy and lower\u003Cbr\u003Ecomputational complexity than delay-and-sum filtering techniques and significantly higher accuracy and lower\u003Cbr\u003Elatency than standard frequency- domain techniques based on sub-band processing. An additional benefit of the\u003Cbr\u003Eproposed approach is that it allows for a careful error analysis; we give concrete formulas for the bias and variance\u003Cbr\u003Eof the beamformer given the array parameters. We also provide a host of numerical experiments demonstrating the\u003Cbr\u003Ealgorithm\u2019s favorable computational scaling and high accuracy. Finally, we demonstrate how tasks such as\u003Cbr\u003Einterpolating to \u201coff-grid\u201d angles and nulling an interferer are more computationally efficient when performed directly\u003Cbr\u003Ein beamspace.\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBio: \u003C\/strong\u003ENakul Singh received a bachelor\u2019s degree in electrical engineering from the Indian Institute of Technology, Kanpur,\u003Cbr\u003EIndia, in 2022. He is currently working toward the Ph.D. degree at the Georgia Institute of Technology, advised by\u003Cbr\u003EProf. Justin Romberg and Prof. Mark Davenport. His research interests include machine learning, signal\u003Cbr\u003Eprocessing, and massive MIMO.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EJoin Georgia Tech Ph.D. student Nakul Singh for a talk on a Fast Broadband Beamspace Transformation.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Join Georgia Tech Ph.D. student Nakul Singh for a talk on a Fast Broadband Beamspace Transformation."}],"uid":"36558","created_gmt":"2026-11-03 22:41:11","changed_gmt":"2026-11-03 22:44:26","author":"zwiniecki3","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-11-12T15:00:00-05:00","event_time_end":"2026-11-12T16:00:00-05:00","event_time_end_last":"2026-11-12T16:00:00-05:00","gmt_time_start":"2026-11-12 20:00:00","gmt_time_end":"2026-11-12 21:00:00","gmt_time_end_last":"2026-11-12 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