- talks - lists.cs.princeton.edu

[Robotics Seminar] TOMORROW FEB 20 @ 11AM Naira Hovakimyan
by Emily C. Lawrence 19 Feb '26

19 Feb '26

Join us TOMORROW (02/20) for our first Robotics Seminar of the semester. Please note the change of location to CS 105. Speaker: [ https://mechse.illinois.edu/people/profile/nhovakim | Naira Hovakimyan ] Date: Feb 20th, 2026 Time: 11:00 AM EST Location: CS 105 Event page: [ https://robotics.princeton.edu/seminars | https://robotics.princeton.edu/seminars ] The seminar will be followed by a lunch with the speaker in the F-Wing space. We are looking forward to seeing you there! Best wishes, Robotics Seminar Committee Title: Ontological Robustness for Certification of Layered Autonomy Architectures Abstract: Learning-based control paradigms have seen many success stories with autonomous systems in recent years. A typical architecture in these systems involves layers for perception, planning and control, wherein each of these layers uses different tools and metrics for assessing robustness and performance. For example, the planners -- that use vision-based sensors to update the navigation and motion planning -- operate largely relying on distributionally robust stochastic optimal control, whereas the low-level controller can be a deterministic controller with its conventional gain and phase (time-delay) margin. We present a new analysis framework for addressing this ontology challenge inherent to autonomous systems. We derive distributional robustness guarantees for deterministic L1 adaptive controllers that can be used by any stochastic planner without facing a language barrier. The combined planner-controller framework can serve as foundation for development of certificates for V&V of learning-enabled systems. An overview of different projects at our lab that build upon this framework will be demonstrated to show different applications. Bio: Naira Hovakimyan received her MS degree in Applied Mathematics from Yerevan State University in Armenia. She got her Ph.D. in Physics and Mathematics from the Institute of Applied Mathematics of Russian Academy of Sciences in Moscow. She is currently W. Grafton and Lillian B. Wilkins Professor of Mechanical Science and Engineering and the Director of AVIATE Center of UIUC. She has co-authored two books, eleven patents and more than 500 refereed publications. She is the 2011 recipient of AIAA Mechanics and Control of Flight Award , the 2015 recipient of SWE Achievement Award, the 2017 recipient of IEEE CSS Award for Technical Excellence in Aerospace Controls , and the 2019 recipient of AIAA Pendray Aerospace Literature Award . In 2014 she was awarded the Humboldt prize for her lifetime achievements. In 2015 and 2023 she was awarded the UIUC Engineering Council Award for Excellence in Advising . In 2024 she was recognized as the winner of the College Award for Excellence in Translational Research , and in 2025 she was recognized for Excellence in Graduate Student Mentoring . She is Fellow of AIAA, IEEE, ASME, IFAC, and senior member of National Academy of Inventors. She has been named a Distinguished Lecturer for IEEE CSS for 2026-2028. She is a co-founder and chief scientist of Intelinair. Her work in robotics for elderly care was featured in the New York Times, on Fox TV, CNBC, and her recent NASA ULI award on flying cars led her to a live interview on Cheddar Innovates and many other media platforms. Her research interests are in control and optimization, autonomous systems, machine learning, neural networks, game theory, and their applications in aerospace, robotics, mechanical, agricultural, electrical, petroleum, biomedical engineering, and elderly care.

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[Robotics Seminar] FRIDAY FEB 20 @ 11AM Naira Hovakimyan
by Emily C. Lawrence 13 Feb '26

13 Feb '26

Join us for our first Robotics Seminar of the semester next Friday (02/20) . Please note the change of location to CS 105. Speaker: [ https://mechse.illinois.edu/people/profile/nhovakim | Naira Hovakimyan ] Date: Feb 20th, 2026 Time: 11:00 AM EST Location: CS 105 Event page: [ https://robotics.princeton.edu/seminars | https://robotics.princeton.edu/seminars ] The seminar will be followed by a lunch with the speaker in the F-Wing space. We are looking forward to seeing you there! Best wishes, Robotics Seminar Committee Title: Ontological Robustness for Certification of Layered Autonomy Architectures Abstract: Learning-based control paradigms have seen many success stories with autonomous systems in recent years. A typical architecture in these systems involves layers for perception, planning and control, wherein each of these layers uses different tools and metrics for assessing robustness and performance. For example, the planners -- that use vision-based sensors to update the navigation and motion planning -- operate largely relying on distributionally robust stochastic optimal control, whereas the low-level controller can be a deterministic controller with its conventional gain and phase (time-delay) margin. We present a new analysis framework for addressing this ontology challenge inherent to autonomous systems. We derive distributional robustness guarantees for deterministic L1 adaptive controllers that can be used by any stochastic planner without facing a language barrier. The combined planner-controller framework can serve as foundation for development of certificates for V&V of learning-enabled systems. An overview of different projects at our lab that build upon this framework will be demonstrated to show different applications. Bio: Naira Hovakimyan received her MS degree in Applied Mathematics from Yerevan State University in Armenia. She got her Ph.D. in Physics and Mathematics from the Institute of Applied Mathematics of Russian Academy of Sciences in Moscow. She is currently W. Grafton and Lillian B. Wilkins Professor of Mechanical Science and Engineering and the Director of AVIATE Center of UIUC. She has co-authored two books, eleven patents and more than 500 refereed publications. She is the 2011 recipient of AIAA Mechanics and Control of Flight Award , the 2015 recipient of SWE Achievement Award, the 2017 recipient of IEEE CSS Award for Technical Excellence in Aerospace Controls , and the 2019 recipient of AIAA Pendray Aerospace Literature Award . In 2014 she was awarded the Humboldt prize for her lifetime achievements. In 2015 and 2023 she was awarded the UIUC Engineering Council Award for Excellence in Advising . In 2024 she was recognized as the winner of the College Award for Excellence in Translational Research , and in 2025 she was recognized for Excellence in Graduate Student Mentoring . She is Fellow of AIAA, IEEE, ASME, IFAC, and senior member of National Academy of Inventors. She has been named a Distinguished Lecturer for IEEE CSS for 2026-2028. She is a co-founder and chief scientist of Intelinair. Her work in robotics for elderly care was featured in the New York Times, on Fox TV, CNBC, and her recent NASA ULI award on flying cars led her to a live interview on Cheddar Innovates and many other media platforms. Her research interests are in control and optimization, autonomous systems, machine learning, neural networks, game theory, and their applications in aerospace, robotics, mechanical, agricultural, electrical, petroleum, biomedical engineering, and elderly care.

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ECE Seminar: Max Fishelson, MIT on Monday, Feb 16
by Emily C. Lawrence 12 Feb '26

12 Feb '26

ECE SEMINAR Speaker: Max Fishelson, Massachusetts Institute of Technology Title: Theoretical Foundations for Multi-Agent Learning Day: Monday, February 16, 2026 Time: 4:30 PM Location: B205 Engineering Quadrangle Host: Pramod Viswanath Abstract: As artificial intelligence becomes ubiquitous in complex, interactive systems, we need models that perform reliably under dynamic conditions. AI systems are moving beyond passive perception to active participation: controlling autonomous vehicles in mixed traffic, participating in markets, and managing decentralized networks. In these roles, an agent's decisions reshape the environment and invite strategic responses from others. This feedback loop invalidates a core assumption of classical machine learning: that data is independent and identically distributed. We require a new foundation for learning that is inherently robust to these adaptive, multi-agent dynamics. In this talk, I will demonstrate how we can approach this challenge using regret minimization and calibration. Instead of measuring performance against a static distribution, these measures allow us to derive provable guarantees against any sequence of outcomes. An algorithm with low regret effectively “learns to play” optimally, even against strategic counter-parties. Calibration guarantees that we can distill trustworthy forecasts from dynamic data. I will discuss my work pushing the boundaries of what is possible in this domain, resolving long-standing open questions on regret minimization in games, the scalability of algorithms for strong notions of regret, and the fundamental limits of trustworthy forecasting. Together, these results provide an algorithmic pathway toward AI systems that remain robust and reliable even in complex, interactive environments. Bio: Max Fishelson is a final-year PhD student in the Theory of Computation group at MIT CSAIL, advised by Constantinos Daskalakis. His research focuses on the theoretical foundations of machine learning, specifically at the intersection of learning theory and algorithmic game theory. He works on designing algorithms with provable guarantees for decision-making in adversarial and multi-agent environments.

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CS Distinguished Colloquium Speaker Jeff Dean: Tues. Feb 10 at 4:30
by Emily C. Lawrence 10 Feb '26

10 Feb '26

Distinguished Colloquium Speaker: Jeff Dean from Google Date & Time: Tuesday, February 10 - 4:30pm Location: CS 104 Host: Kai Li Title: Important Trends in AI: How Did We Get Here, What Can We Do Now, and Where are We Headed? Abstract: In this talk I'll highlight some of the key developments in artificial intelligence over the past 15 years, which serve as key ingredients in today's most advanced AI models. This will touch on development of new model architectures, advances in large-scale distributed training, ML accelerators like TPUs, and development of algorithms to improve training and serving efficiency. I'll discuss the Gemini effort at Google, both in terms of the capabilities of Gemini models but also in how we go about organizing a large-scale research and engineering effort like Gemini. Finally, I'll give a sense of the capabilities of today's models, and also highlight some areas that will advance rapidly in the next few years. Bio: Jeff Dean is the Chief Scientist for Google Research and Google DeepMind, and currently co-leads the Gemini project. He has done research in large-scale information retrieval, operating systems, distributed systems, compilers and in the field of AI, has done work on ML accelerators, vision models, low-level software and frameworks for machine learning, sparse model architectures, algorithms like distillation and neural architecture search, large language and multimodal models, and applications of machine learning to areas like ASIC design, healthcare, and translation. He is a recipient of the ACM Prize in Computing, and the IEEE John von Neumann medal, and a Fellow of the ACM, and a member of the US National Academy of Engineering, and the American Academy of Arts and Sciences. *This talk will not be live streamed.

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William Pierson Field Lecture: Auditing Automated Speech Recognition Systems for Disparities - Tues, Feb 17 at 1:30pm
by Emily C. Lawrence 10 Feb '26

10 Feb '26

William Pierson Field Lecture Date: Tuesday, Feb 17th Time: 1:30 – 2:30pm Location: Louis A. S. A71 Speaker: Allison Koenecke, Cornell Tech Host: Lydia Liu Title: [ https://www.cs.princeton.edu/events/auditing-automated-speech-recognition-s… | Auditing Automated Speech Recognition Systems for Disparities ] Bio: Allison Koenecke is an Assistant Professor of Information Science at Cornell Tech. Her research on algorithmic fairness applies computational methods, such as machine learning and causal inference, to study societal inequities in domains from online services to public health. Koenecke is regularly quoted as an expert on disparities in automated speech-to-text systems. She previously held a postdoctoral researcher role at Microsoft Research and received her PhD from Stanford's Institute for Computational and Mathematical Engineering. She is the recipient of several NSF grants and a Cornell CIS DEIB Faculty of the Year Award, and has been honored as a Sloan Fellow in Computer Science and Forbes 30 Under 30 lister in Science.

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PPH seminar: Sneha Goenka-Biology Needs Computer Architects: Keeping Up with Genomic-scale Data, Fri. Feb 6
by Emily C. Lawrence 04 Feb '26

04 Feb '26

Our next talk in the PPH seminar series is on Friday, February 6, at 12 pm , at 252 Nassau Street. Sneha Goenka will present, "Biology Needs Computer Architects: Keeping Up with Genomic-scale Data. " Professor Sneha Goenka is an Assistant Professor in the ECE department at Princeton University. Lunch will be provided. Abstract: The landscape of computing has undergone a significant transformation with the death of Dennard scaling and the slowing of Moore’s law: applications now drive innovations in computer systems architecture. At the same time, the advent of high throughput, low-cost sequencing technology has revolutionized genomics. The massive amount of data generated in genomics has uncovered acute compute challenges in biological inference due to limitations of software on traditional multi-core systems. To address this challenge, my research employs a hardware-software-algorithm co-design approach to significantly improve computational performance in key areas of comparative and clinical genomics. In this talk, I will present systems that accelerate pipelines in both these domains of genomics. First, I will present Darwin-WGA (FPGA/ASIC) and SegAlign (GPU) for cross-species whole genome alignment where co-design has yielded orders of magnitude increase in speed. Additionally, there are gains in accuracy while modifying the algorithm to improve the underlying hardware implementation. Next, I will talk about the ultra-rapid nanopore whole genome sequencing pipeline that can deliver a genetic diagnosis in under 8 hours, making it the fastest pipeline to date. The scalable, cloud-based distributed infrastructure overcomes system bottlenecks to enable near real-time computation and improved variant identification. This pipeline has been deployed in critical care units in Stanford hospitals and applied to 25 patients. Finally, I will share my vision and our current work on the continued advancements in computer systems applied to emerging fields such as pangenomics, single cell genomics, cancer and long read clinical sequencing. Upcoming talks include the following - please check our [ https://us.list-manage.com/fbV_-3DQ__6?e=691aac62ef&c2id=f584458f8dee0f3191… | website ] for more information and updates! Monday, Feb 9, at 10:00 am **NOTE THE SPECIAL DATE AND TIME** Stephany Duda (Vanderbilt) [ https://us.list-manage.com/x7_GHnGq-1c?e=691aac62ef&c2id=f584458f8dee0f3191… | Beyond Data Collection: REDCap as a Platform for Research Collaboration ] Friday, Feb 27, Aayush Visaria (Rutgers Robert Wood Johnson Medical School) ** AI and Precision Health series ** AI and Precision Health: [ https://us.list-manage.com/6_L2WELiCM8?e=691aac62ef&c2id=f584458f8dee0f3191… | Beyond Fat Mass: How Adiposity, Oxidative Stress, and Bilirubin Shape Metabolic Risk ] Friday, March 6, Florencia Torche (Princeton): [ https://us.list-manage.com/JR1vCff5h6C?e=691aac62ef&c2id=f584458f8dee0f3191… | Early Life Exposures and Social Inequality ] Friday, March 20, Ricky Bloomfield (Oura) ** AI and Precision Health series ** [ https://us.list-manage.com/_rvWqr__Bca?e=691aac62ef&c2id=f584458f8dee0f3191… | AI and Precision Health: From Episodic Care to Continuous Health: How Wearables and AI Are Reshaping Preventive Care ] Friday, April 3, David Pagliaccio (Columbia) Friday, April 10, René Vidal (UPenn) ** AI and Precision Health series ** Friday, April 24, Tom Silver (Princeton) Friday, May 8, Jessica Hamilton (Rutgers) [ https://us.list-manage.com/QW-l0J1AO1x?e=691aac62ef&c2id=f584458f8dee0f3191… | View all upcoming PPH events ] . Please note that getting to the seminar space at 252 Nassau Street currently requires that you climb a set of stairs. If an accommodation is needed, please contact PPH in advance at: [ mailto:princetonPPH@princeton.edu | princetonPPH(a)princeton.edu ] Thank you, Princeton Precision Health

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Shmuel Berman will present his General Exam "Beyond Long Context: Measuring Memory, Compression, and Calibration" on Friday, January 30, 2026 at 9:15 AM in CS 202 and via zoom.
by CS Grad Department 30 Jan '26

30 Jan '26

Shmuel Berman will present his General Exam "Beyond Long Context: Measuring Memory, Compression, and Calibration" on Friday, January 30, 2026 at 9:15 AM in CS 202 and via zoom. Zoom link: https://princeton.zoom.us/j/8227846962?omn=97734303297 Committee Members: Jia Deng (advisor), Zhuang Liu, Tom Griffiths Abstract: Humans reliably accumulate knowledge from experience over long time spans, but today’s LLMs and VLMs still struggle when the relevant evidence is distributed across long streams. Furthermore, even though humans frequently forget information, they are very cognizant of what they forget. We frame episodic memory as an online compression problem and investigate the memory capacity and compressive abilities of pretrained models and common architectures.. Our evaluation provides valuable information on how to improve the long-term memory of autonomous agents. Reading List: https://docs.google.com/document/d/1jsRPipttEXSFjk-tLyfAYpuZlztDuGEbaspba-x… Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.

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Berlin Chen will present his General Exam "Inference Efficiency for Sub-quadratic Models" on Friday, January 30, 2026 at 11:00 AM in Friend 108 and via zoom.
by CS Grad Department 29 Jan '26

29 Jan '26

Berlin Chen will present his General Exam "Inference Efficiency for Sub-quadratic Models" on Friday, January 30, 2026 at 11:00 AM in Friend 108 and via zoom. Zoom link: https://princeton.zoom.us/j/92265989878 Committee Members: Tri Dao (advisor), Elad Hazan, Kai Li Abstract: Recent progress in AI has witnessed a paradigm shift to test-time compute, where LLM inference commands an increasingly greater share of the compute budget. This shift presents a new opportunity for model design that tailors to the computational characteristic of inference. In this talk, I will present a recent work on improving the decoding efficiency of Mamba, which is a variant of subquadratic models based on State Space Models (SSMs). In particular, I will highlight a key challenge preventing subquadratic models from being hardware efficient during decoding. I will then propose an adjustment to the model that addresses the challenge, and demonstrate its key advantages from an inference-first perspective. I will further motivate the change by connecting it to classic SSMs. Drawing upon this connection, I will highlight two additional architectural adjustments to Mamba that are naturally motivated by classic SSM formulations and demonstrate their roles in improving model quality. Reading List: https://docs.google.com/document/d/1T2z9TI_StxTt9tMNawjSo5842qr8JSBP2zgl7oS… Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.

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Tim Kosfeld will present his General Exam "Computational modeling and integration of ATAC-seq" on Wednesday, January 28, 2026 at 12:00 PM in Bendheim House 103.
by CS Grad Department 23 Jan '26

23 Jan '26

Tim Kosfeld will present his General Exam "Computational modeling and integration of ATAC-seq" on Wednesday, January 28, 2026 at 12:00 PM in Bendheim House 103. Committee Members: Yuri Pritykin (advisor), Ben Raphael, Ellen Zhong Abstract: Assay for transposase-accessible chromatin with sequencing (ATAC-seq) has been widely used at single-cell (scATAC) and bulk levels to profile chromatin accessibility and explore the epigenomic properties in heterogeneous cell populations. Furthermore, transcription factors (TFs) bind to accessible sites, and scATAC-seq analysis could associate TF binding specificities, typically available as sequence motifs, with variance in chromatin accessibility. Here we present a new supervised TF motif regression method, MotifReg, for computational analysis and interpretation of scATAC-seq data. MotifReg can efficiently analyze large-scale scATAC-seq datasets and learn interpretable associations between TF motifs and accessible peaks. The resultant low-dimensional cell representations in the TF activity space facilitate downstream cell type-specific analyses of scATAC-seq data. The supervised formulation in MotifReg enables ranking TF motifs by significance of their association with chromatin accessibility, implicating cell type-specific TFs. Furthermore, by comparing MotifReg performance across data analysis settings, we develop guidelines and best practices for ATAC-seq peak calling, data preprocessing and normalization. We leverage these best practices to show that aggregation of biologically similar ATAC data allows for the discovery of chromosomal regions enriched for chromatin interaction, known motif binding, or ATAC co-accessibility. We provide a program to quickly and efficiently scrape, compile, and preprocess this data from online sources. These atlases improve per-nucleotide accessibility prediction in ATAC-seq through ChromBPNet and uniquely capture spacing preferences for known transcription factor complexes. Allowing a better understanding of transcription factor activity in accessible regions. Reading List: https://docs.google.com/document/d/1gnqGszJkHUIQisI3UL_mw-w_pCR2hD0dmL5N5OA… Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.

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Yebin Chon will present her FPO "LILAC: Enhancing Parallel Code Naturalness through Hybrid Compiler-LLM Translation" on Monday, January 26, 2026 at 1:30pm in CS 402.
by Gradinfo 20 Jan '26

20 Jan '26

Yebin Chon will present her FPO "LILAC: Enhancing Parallel Code Naturalness through Hybrid Compiler-LLM Translation" on Monday, January 26, 2026 at 1:30pm in CS 402. Readers: Margaret Martonosi, Zachary Kincaid, David August (adviser) Examiners: Sneha Goenka, Aarti Gupta, David August All are welcome to attend. Title: "LILAC: Enhancing Parallel Code Naturalness through Hybrid Compiler-LLM Translation" Abstract: Modern parallel hardware is becoming increasingly diverse, and there is a following increase in the heterogeneity of parallel programming models (PPMs), often tied to a particular parallel architecture. This brings a need for translation between different programming models to properly port and maintain code for various hardware systems. Source-to-source translators may automate this step and provide production compiler-level correctness guarantees. However, previous attempts to build translators that can go between different PPMs often result in unnatural code, hindering programmer comprehension and long-term maintenance. More recent Large Language Model (LLM) based methods produce highly natural, artful code, but lack the correctness guarantees of traditional compiler-based methods. One work provides a formal proof of correctness for the LLM-generated translation, but is severely limited in the code that it can process. This creates a critical trade-off: developers must choose between the correctness and applicability of compilers and the artfulness of LLMs, both of which are essential. This thesis provides a new way to bring the two methods together, creating cross-PPM translations that are both correct and natural. To achieve this, it introduces LILAC, a hybrid framework that combines the strengths of traceable and widely applicable compiler transformations and the stylistic capabilities of LLMs. LILAC builds on top of an existing compiler-based translator to provide functionally correct parallel translations and to simultaneously detect areas of potential unnaturalness. It then uses an LLM through an API to obtain suggestions for naturalizing the areas identified by the compiler. The compiler then checks these suggestions for correctness and applies the transformations to the code. In this way, LILAC performs several hybrid compiler-LLM passes to naturalize the code without compromising correctness. In a human study with 26 expert C/C++ programmers evaluating CUDA-to-OpenMP translations, LILAC-generated code was judged to be significantly more natural and maintainable than the output of the baseline compiler. The benchmarks used are unable to be translated by the formally verified LLM method. By building on a correct compiler translation, LILAC ensures that enhancements in naturalness do not come at the cost of correctness or applicability.

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