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DTSTART;TZID=America/Los_Angeles:20260327T100000
DTEND;TZID=America/Los_Angeles:20260327T110000
DTSTAMP:20260403T221856
CREATED:20260317T231250Z
LAST-MODIFIED:20260331T142721Z
UID:8222-1774605600-1774609200@tilos.ai
SUMMARY:TILOS-Optimization for ML and AI Seminar: Implicit bias results for Muon\, Adam\, and Friends
DESCRIPTION:Matus Telgarsky\, New York University \nAbstract: This talk will give both an empirical overview and a few simple bonds controlling the optimization path\, or implicit bias\, of modern optimization methods such as Adam and Muon (and Friends). The talk will begin with empirical results demonstrating the implicit bias phenomenon with shallow networks and also transformers combined with chain-of-thought. The talk will then briefly survey a few mathematical implicit bias analyses of nonlinear networks\, which unfortunately do not carry through to transformers. As such\, the talk concludes with a technical portion presenting another approach to analyzing these optimization methods in the linear case\, providing generic implicit bias results for them\, and empirically demonstrating hope that this particular methodology can carry over to the nonlinear case. \n\nMatus Telgarsky is an Associate Professor of Computer Science at the Courant Institute of Math at NYU\, specializing in deep learning theory. The highlight of his academic career was completing a PhD under Sanjoy Dasgupta at UC San Diego. Adventures since then include co-chairing the Midwest ML Symposium in 2017 with Po-Ling Loh\, and chairing two semester-long Simons Institute Programs at UC Berkeley. Accolades include a 2018 NSF Career Award and delivering a COLT 2025 keynote.
URL:https://tilos.ai/event/tilos-optimization-for-ml-and-ai-seminar-implicit-bias-results-for-muon-adam-and-friends/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series,TILOS Sponsored Event
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2026/03/telgarsky-matus-e1773789078482.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260325T110000
DTEND;TZID=America/Los_Angeles:20260325T120000
DTSTAMP:20260403T221856
CREATED:20260310T175540Z
LAST-MODIFIED:20260326T215133Z
UID:8191-1774436400-1774440000@tilos.ai
SUMMARY:TILOS-SDSU Seminar: Autopilots Need Parachutes: Reliability Lessons from LLM-Automated Embedded AI Systems
DESCRIPTION:Roberto Morabito\, EURECOM \nAbstract: Embedded AI systems are becoming increasingly complex to develop and maintain\, requiring specialized workflows that span data processing\, model conversion\, optimization\, and deployment across heterogeneous hardware platforms. Recently\, large language models have emerged as a promising tool to automate parts of this lifecycle. In this talk\, I present recent work investigating the use of generative AI models as orchestration agents for embedded machine learning pipelines. Using an automated system that leverages LLMs to generate and iteratively refine software artifacts for embedded platforms\, we evaluate the feasibility of automating key stages of the AI lifecycle. Our empirical results reveal both the promise and the limitations of this approach. Generative models can significantly accelerate development workflows. However\, they also introduce instability\, iterative failure modes\, and unpredictable operational costs. I will discuss the main failure patterns observed in practice and outline research directions aimed at improving reliability through hybrid reasoning frameworks and system-level feedback mechanisms. \n\nRoberto Morabito is an Assistant Professor in the Networked Systems group of the Communication Systems Department at EURECOM\, France\, and a Docent at the University of Helsinki. Before joining EURECOM\, he was a Senior Researcher in the Department of Computer Science at the University of Helsinki. Earlier in his career\, he spent eight years at Ericsson Research Finland\, where he worked on cloud platforms\, IoT systems\, and cyber-physical systems. He received his PhD in Networking Technology from Aalto University in 2019 and was a postdoctoral researcher at the EDGE Lab\, School of Electrical and Computer Engineering\, Princeton University. His research lies at the intersection of networked systems\, edge computing\, and distributed AI\, focusing on the design and lifecycle management of AI systems operating under computing and networking resource constraints.
URL:https://tilos.ai/event/tilos-sdsu-seminar-autopilots-need-parachutes-reliability-lessons-from-llm-automated-embedded-ai-systems/
LOCATION:Lamden Hall 341 (SDSU) and Virtual\, San Diego\, CA\, 92182\, United States
CATEGORIES:TILOS Seminar Series,TILOS Sponsored Event
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2026/03/morabito-roberto-e1773165764846.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260313T100000
DTEND;TZID=America/Los_Angeles:20260313T110000
DTSTAMP:20260403T221856
CREATED:20251014T200527Z
LAST-MODIFIED:20260313T183553Z
UID:7665-1773396000-1773399600@tilos.ai
SUMMARY:Optimization for ML and AI Seminar: Transformers Learn Generalizable Chain-of-Thought Reasoning via Gradient Descent
DESCRIPTION:Yuejie Chi\, Yale \nAbstract: Transformers have demonstrated remarkable chain-of-thought reasoning capabilities\, yet\, the underlying mechanisms by which they acquire and extrapolate these capabilities remain limited. This talk presents a theoretical analysis of transformers trained via gradient descent for symbolic reasoning and state tracking tasks with increasing problem complexity. Our analysis reveals the coordination of multi-head attention to solve multiple subtasks in a single autoregressive path\, and the bootstrapping of inherently sequential reasoning through recursive self-training curriculum. Our optimization-based guarantees demonstrate that even shallow multi-head transformers\, with chain-of-thought\, can be trained to effectively solve problems that would otherwise require deeper architectures. \n\nYuejie Chi is the Charles C. and Dorothea S. Dilley Professor of Statistics and Data Science at Yale University\, with a secondary appointment in Computer Science\, and a member of the Yale Institute for Foundations of Data Science. Before joining Yale\, Dr. Chi was the Sense of Wonder Group Endowed Professor of Electrical and Computer Engineering in AI Systems at Carnegie Melon University\, with affiliation in MLD and CyLab. She also spent some time as a visiting researcher at Meta’s Fundamental AI Research (FAIR). Dr. Yue’s research interests lie in the theoretical and algorithmic foundations of data science\, generative AI\, reinforcement learning\, and signal processing\, motivated by applications in scientific and engineering domains. Her current focus is on improving the performance\, efficiency and reliability of generative AI and decision making\, driven by data-intensive but resource-constrained scenarios.
URL:https://tilos.ai/event/optimization-for-ml-and-ai-seminar-transformers-learn-generalizable-chain-of-thought-reasoning-via-gradient-descent/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series,TILOS Sponsored Event
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/10/chi-yuejie-e1760472307997.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260227T110000
DTEND;TZID=America/Los_Angeles:20260227T120000
DTSTAMP:20260403T221856
CREATED:20251003T192706Z
LAST-MODIFIED:20260304T205819Z
UID:7637-1772190000-1772193600@tilos.ai
SUMMARY:Optimization for ML and AI Seminar: (De)regularized Wasserstein Gradient Flows via Reproducing Kernels
DESCRIPTION:Bharath Sriperumbudur\, Pennsylvania State University \nAbstract: Wasserstein gradient flows have become a popular tool in machine learning with applications in sampling\, variational inference\, generative modeling\, and reinforcement learning\, among others. The Wasserstein gradient flow (WGF) involves minimizing a probability functional over the Wasserstein space (by taking into account the intrinsic geometry of the Wasserstein space). In this work\, we introduce approximate/regularized Wasserstein gradient flows in two different settings: (a) approximate the probability functional and (b) approximate the Wasserstein geometry. In (a)\, we consider the probability functional to be chi^2-divergence\, whose WGF is difficult to implement. To this end\, we propose a (de)-regularization of the Maximum Mean Discrepancy (DrMMD) as an approximation of chi^2-divergence and develop an approximate WGF\, which is easy to implement and has applications in generative modeling. On the other hand\, in the setting of (b)\, we use Kullback-Leibler divergence as the probability functional and develop an approximation to the Wassertein geometry\, which allows for an efficient implementation than that of the exact WGF\, with applications in sampling. In both settings\, we present a variety of theoretical results that relate the approximate flow to the exact flow and demonstrate the superiority of the approximate flows via numerical simulations. \n\nBharath Sriperumbudur is a professor in the Department of Statistics (with a courtesy appointment in the Department of Mathematics) at the Pennsylvania State University. His research interests include non-parametric statistics\, machine learning\, statistical learning theory\, optimal transport and gradient flows\, regularization and inverse problems\, reproducing kernel spaces in probability and statistics\, functional and topological data analysis.
URL:https://tilos.ai/event/optimization-for-ml-and-ai-seminar-with-bharath-sriperumbudur-penn-state/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series,TILOS Sponsored Event
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/10/sriperumbudur-bharath-e1759519613665.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260220T100000
DTEND;TZID=America/Los_Angeles:20260220T110000
DTSTAMP:20260403T221856
CREATED:20251124T183900Z
LAST-MODIFIED:20260224T215057Z
UID:7904-1771581600-1771585200@tilos.ai
SUMMARY:TILOS-HDSI Seminar: Neuromorphic LLMs
DESCRIPTION:Jason Eshraghian\, UC Santa Cruz \nAbstract: This talk will show you what neuromorphic computing can do when an academic lab accidentally pulls $2-million of GPU-hours. We will showcase a series of frontier reasoning LLMs developed out of an academic lab\, from data curation and pre-training to post-training and alignment. These models surpass leading LLMs from Meta\, Google\, and other heavily-resourced labs in the ~10-billion parameter regime\, despite being 5x smaller. \nWe have deployed several models on neuromorphic hardware at just 2 watts\, bringing state-of-the-art reasoning from the datacenter to the edge. Along the way\, we dispel a series of widely-held assumptions about large-scale neuromorphic computation\, revealing how it fundamentally differs from conventional deep learning\, and why that difference matters. \n\nJason Eshraghian is an Assistant Professor and Fulbright Scholar in the Department of Electrical and Computer Engineering at the University of California\, Santa Cruz. He is the developer of snnTorch\, a Python library with over 500\,000 downloads for training spiking neural networks. He is a dual-appointed IEEE CAS and EMBS Distinguished Lecturer\, an Associate Editor of APL Machine Learning\, the Chair of the IEEE Neural Systems and Applications Technical Committee\, has been the recipient of seven IEEE Best Paper Awards\, a Scientific Advisory Board Member of BrainChip and leads the Neuromorphic Agents Team at Conscium.
URL:https://tilos.ai/event/tilos-hdsi-seminar-neuromorphic-llms/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series
ATTACH;FMTTYPE=image/png:https://tilos.ai/wp-content/uploads/2025/11/eshraghian-jason-e1764009503674.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260211T110000
DTEND;TZID=America/Los_Angeles:20260211T120000
DTSTAMP:20260403T221856
CREATED:20250828T192042Z
LAST-MODIFIED:20260227T212830Z
UID:7265-1770807600-1770811200@tilos.ai
SUMMARY:TILOS-HDSI Seminar: Kinetic Theory Perspective of Foundation Models for Physics
DESCRIPTION:Maarten de Hoop\, Rice University \nAbstract: We present a kinetic theory perspective of foundation models for physics. We begin with providing a mathematical framework for analyzing transformers. To uniformly address their expressivity\, we consider the case that the mappings are conditioned on a context represented by a probability distribution of tokens. That is\, transformers become mappings between probability measures. The relevant notion of smoothness then corresponds to continuity in terms of the Wasserstein distance between such contexts. We demonstrate that deep transformers are universal and can approximate continuous in-context mappings to arbitrary precision\, uniformly over compact token domains. We then characterize the conditions on mappings between measures that enable these to be represented in terms of in-context mappings as transformers. The solution map of the Vlasov equation\, which is of nonlocal transport type\, for interacting particle systems in the mean-field regime for the Cauchy problem satisfies the conditions; conversely\, we prove that the measure-theoretic self-attention has the properties that ensure that the infinite depth\, mean-field transformer can be identified with a Vlasov flow. Extending this framework from interactions to collisions leads to a further development of structured architectures inspired by Lattice Boltzmann Models\, while flow motivates a design based on self-warping. \n\nProfessor Maarten V. de Hoop\, Simons Chair in Computational and Applied Mathematics and Earth Science at Rice University\, is internationally recognized for his contributions to the mathematical foundations of seismology\, wave propagation\, and inverse problems. His research bridges microlocal and harmonic analysis\, scattering theory\, and structured numerical methods with applications to seismic imaging\, geophysical inversion\, and large-scale computational modeling of acoustic\, elastic\, and electromagnetic phenomena. De Hoop has been a pioneer in developing techniques to extract subtle information from massive\, complex seismic datasets\, advancing our ability to probe the Earth’s interior with unprecedented resolution\, and more recently has integrated deep learning and data-driven discovery with rigorous mathematical frameworks to open new frontiers in the analysis of multiscale wave phenomena and inverse spectral problems. He is the recipient of the J. Clarence Karcher Award from the Society of Exploration Geophysicists and the Young Scientists Award from the International Society for Analysis\, its Applications and Computation\, has been elected a Fellow of the Institute of Physics and an External Member of the Finnish Academy of Science and Letters\, and has served as associate editor for Inverse Problems\, Inverse Problems and Imaging\, and the International Journal on Geomathematics.
URL:https://tilos.ai/event/tilos-seminar-with-maarten-de-hoop/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series
ATTACH;FMTTYPE=image/webp:https://tilos.ai/wp-content/uploads/2025/08/dehoop-maarten-e1756406140690.webp
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260209T110000
DTEND;TZID=America/Los_Angeles:20260209T120000
DTSTAMP:20260403T221856
CREATED:20260202T183947Z
LAST-MODIFIED:20260304T205925Z
UID:8053-1770634800-1770638400@tilos.ai
SUMMARY:TILOS-MICS Seminar: AI-Driven Design Automation for Multi-Chip Integration in AI Chips
DESCRIPTION:Sung-Kyu Lim\, University of Southern California \nAbstract: Multi-chip integration has become a standard approach in AI training and is rapidly gaining traction in edge learning applications. Leveraging 2.5D and 3D IC architecture enables substantial improvements in energy efficiency and latency by optimizing inter chip data transfer. At the core of this transformation lies the automation of design and simulation for heterogeneous AI chips\, shifting from manual engineering to algorithm driven methodologies. This evolution is being accelerated by advanced electronic design automation (EDA) tools powered by AI. My research group develops novel AI driven algorithms that enhance or replace traditional design automation techniques\, with a focus on enabling next generation heterogeneous AI systems. In this talk\, I will present our recent innovations and explore the critical challenges that lie ahead in applying AI algorithms to EDA for high performance AI chip design. \n\nDr. Sung Kyu Lim is Dean’s Professor of Electrical and Computer Engineering at the University of Southern California\, joining in Fall 2025 after over two decades at Georgia Tech. He received his B.S.\, M.S.\, and Ph.D. in Computer Science from UCLA. His research focuses on the architecture\, design\, and electronic design automation (EDA) of 2.5D and 3D integrated circuits\, with over 450 publications. Dr. Lim is an IEEE Fellow and recipient of major awards including multiple Best Paper Awards (DAC 2023\, TCAD 2022)\, and several Georgia Tech teaching honors. From 2022 to 2024\, he served as a Program Manager at DARPA’s Microsystems Technology Office.
URL:https://tilos.ai/event/tilos-seminar-with-sung-kyu-lim-usc/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2026/02/lim-sungkyu-scaled-e1770057488135.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260206T110000
DTEND;TZID=America/Los_Angeles:20260206T120000
DTSTAMP:20260403T221856
CREATED:20251014T201307Z
LAST-MODIFIED:20260304T210204Z
UID:7668-1770375600-1770379200@tilos.ai
SUMMARY:Optimization for ML and AI Seminar: Extended Convex Lifting for Policy Optimization in Control
DESCRIPTION:Yang Zheng\, UC San Diego \nAbstract: Direct policy search has achieved great empirical success in reinforcement learning. Many recent studies have revisited its theoretical foundation for continuous control\, which reveals elegant nonconvex geometry in various benchmark problems. In this talk\, we introduce an Extended Convex Lifting (ECL) framework\, which reveals hidden convexity in classical optimal and robust control problems from a modern optimization perspective. Our ECL offers a bridge between nonconvex policy optimization and convex reformulations. Despite non-convexity and non-smoothness\, the existence of an ECL not only reveals that minimizing the original function is equivalent to a convex problem\, but also certifies a class of first-order non-degenerate stationary points to be globally optimal. This ECL framework encompasses many benchmark control problems\, including LQR\, LQG\, state-feedback\, and output-feedback H-infinity robust control. We believe that the ECL framework may be of independent interest for analyzing nonconvex problems beyond control. \n\nYang Zheng is an Assistant Professor in the ECE Department at UC San Diego. His research focuses on control theory\, convex and nonconvex optimization\, and their applications to autonomous vehicles and traffic systems. He received his DPhil (Ph.D.) in Engineering Science from the University of Oxford in 2019\, and his B.E. and M.S. degrees from Tsinghua University in 2013 and 2015\, respectively. His work has been recognized with several awards\, including the 2019 European Ph.D. Award on Control for Complex and Heterogeneous Systems\, the 2022 Best Paper Award from IEEE Transactions on Control of Network Systems\, the 2023 Best Graduate Teacher Award from UC San Diego’s ECE Department\, the 2024 NSF CAREER Award\, and the 2025 Donald P. Eckman Award from the American Automatic Control Council.
URL:https://tilos.ai/event/optimization-for-ml-and-ai-seminar-with-yang-zheng-uc-san-diego/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series,TILOS Sponsored Event
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/10/zheng-yang-scaled-e1769464299795.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260130T110000
DTEND;TZID=America/Los_Angeles:20260130T120000
DTSTAMP:20260403T221856
CREATED:20251014T200143Z
LAST-MODIFIED:20260304T210210Z
UID:7663-1769770800-1769774400@tilos.ai
SUMMARY:[CANCELED] Optimization for ML and AI Seminar: Fantastic Pretraining Optimizers and Where to Find Them
DESCRIPTION:Tengyu Ma\, Stanford \nAbstract: AdamW has long been the dominant optimizer in language model pretraining\, despite numerous claims that alternative optimizers offer 1.4 to 2x speedup. We posit that two methodological shortcomings have obscured fair comparisons and hindered practical adoption: (i) unequal hyperparameter tuning and (ii) limited or misleading evaluation setups. To address these two issues\, we conduct a systematic study of ten deep learning optimizers across four model scales (0.1B-1.2B parameters) and data-to-model ratios (1-8x the Chinchilla optimum). We find that fair and informative comparisons require rigorous hyperparameter tuning and evaluations across a range of model scales and data-to-model ratios\, performed at the end of training. First\, optimal hyperparameters for one optimizer may be suboptimal for another\, making blind hyperparameter transfer unfair. Second\, the actual speedup of many proposed optimizers over well-tuned baselines is lower than claimed and decreases with model size to only 1.1x for 1.2B parameter models. Thirdly\, comparing intermediate checkpoints before reaching the target training budgets can be misleading\, as rankings between two optimizers can flip during training due to learning rate decay. Through our thorough investigation\, we find that all the fastest optimizers such as Muon and Soap\, use matrices as preconditioners—multiplying gradients with matrices rather than entry-wise scalars. However\, the speedup of matrix-based optimizers is inversely proportional to model scale\, decreasing from 1.4x over AdamW for 0.1B parameter models to merely 1.1x for 1.2B parameter models. \n\nTengyu Ma is an assistant professor of computer science at Stanford University. His research interests broadly include topics in machine learning\, algorithms and their theory\, such as deep learning\, (deep) reinforcement learning\, pre-training / foundation models\, robustness\, non-convex optimization\, distributed optimization\, and high-dimensional statistics. \nZoom: https://bit.ly/Opt-AI-ML
URL:https://tilos.ai/event/optimization-for-ml-and-ai-seminar-with-tengyu-ma-stanford/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series,TILOS Sponsored Event
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/10/ma-tengyu-e1760473083457.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260128T110000
DTEND;TZID=America/Los_Angeles:20260128T120000
DTSTAMP:20260403T221856
CREATED:20251031T211533Z
LAST-MODIFIED:20260227T213734Z
UID:7725-1769598000-1769601600@tilos.ai
SUMMARY:TILOS-HDSI Seminar: Safety\, Representations\, and Generative Learning in Dynamical Systems
DESCRIPTION:Koushil Sreenath\, UC Berkeley \nAbstract: This talk explores the interplay between model-based guarantees and learning-based flexibility in the control of dynamical systems. I begin with safety-critical control using control barrier functions (CBFs)\, highlighting that while CBFs enforce state constraints\, they may induce unstable internal dynamics. I introduce conditions under which CBF-based safety filters ensure boundedness of the full system state. I then transition to learning representations of hybrid dynamical systems. I present a framework that learns continuous neural representations by exploiting the geometric structure induced by guards and resets\, enabling accurate flow prediction without explicit mode switching. Finally\, I discuss generative learning approaches for control\, emphasizing guided diffusion models that jointly represent states and actions. Through applications to agile humanoid locomotion\, motion synthesis\, and dynamic manipulation\, I demonstrate how generative models can produce versatile\, long-horizon behaviors while respecting physical constraints. Together\, these results highlight how structure\, geometry\, and learning can bridge safety guarantees and expressive control in complex dynamical systems. \n\nKoushil Sreenath is an Associate Professor of Mechanical Engineering\, at UC Berkeley. He received a Ph.D. degree in Electrical Engineering and Computer Science and a M.S. degree in Applied Mathematics from the University of Michigan at Ann Arbor\, MI\, in 2011. He was a Postdoctoral Scholar at the GRASP Lab at University of Pennsylvania from 2011 to 2013 and an Assistant Professor at Carnegie Mellon University from 2013 to 2017. His research interest lies at the intersection of highly dynamic robotics and applied nonlinear control. His work on dynamic legged locomotion was featured on The Discovery Channel\, CNN\, ESPN\, FOX\, and CBS. His work on dynamic aerial manipulation was featured on the IEEE Spectrum\, New Scientist\, and Huffington Post. His work on adaptive sampling with mobile sensor networks was published as a book. He received the NSF CAREER\, Hellman Fellow\, Google Faculty Research Award in Robotics\, and Best Paper Awards at Learning for Dynamics and Control (L4DC) and Robotics: Science and Systems (RSS).
URL:https://tilos.ai/event/tilos-hdsi-seminar-with-koushil-sreenath-uc-berkeley/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/10/sreenath-koushil-1-e1769450413875.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260109T110000
DTEND;TZID=America/Los_Angeles:20260109T120000
DTSTAMP:20260403T221856
CREATED:20251014T195932Z
LAST-MODIFIED:20260304T210221Z
UID:7661-1767956400-1767960000@tilos.ai
SUMMARY:Optimization for ML and AI Seminar: Randomized linear algebra with subspace injections
DESCRIPTION:Joel Tropp\, Caltech \nAbstract: To achieve the greatest possible speed\, practitioners regularly implement randomized algorithms for low-rank approximation and least-squares regression with structured dimension reduction maps. This talk outlines a new perspective on structured dimension reduction\, based on the injectivity properties of the dimension reduction map. This approach provides sharper bounds for sparse dimension reduction maps\, and it leads to exponential improvements for tensor-product dimension reduction. Empirical evidence confirms that these types of structured random matrices offer exemplary performance for a range of synthetic problems and contemporary scientific applications. \nJoint work with Chris Camaño\, Ethan Epperly\, and Raphael Meyer; available at arXiv:2508.21189. \n\nJoel A. Tropp is Steele Family Professor of Applied & Computational Mathematics at the California Institute of Technology. His research centers on applied mathematics\, machine learning\, data science\, numerical algorithms\, and random matrix theory. Some of his best-known contributions include matching pursuit algorithms\, randomized SVD algorithms\, matrix concentration inequalities\, and statistical phase transitions. Prof. Tropp attained the Ph.D. degree in Computational Applied Mathematics at the University of Texas at Austin in 2004\, and he joined Caltech in 2007. He won the PECASE in 2008\, and he was recognized as a Highly Cited Researcher in Computer Science each year from 2014–2018. He is co-founder of the SIAM Journal on Mathematics of Data Science (SIMODS)\, and he was co-chair of the inaugural 2020 SIAM Conference on the Mathematics of Data Science. Prof. Tropp was elected SIAM Fellow in 2019\, IEEE Fellow in 2020\, and IMS Fellow in 2024. He received the 2025 Richard P. Feynman Prize for Excellence in Teaching at Caltech. He is an invited speaker at the 2026 International Congress of Mathematicians (ICM).
URL:https://tilos.ai/event/optimization-for-ml-and-ai-seminar-with-joel-tropp-caltech/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series,TILOS Sponsored Event
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/10/tropp-joel-e1760471957302.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251208T100000
DTEND;TZID=America/Los_Angeles:20251208T110000
DTSTAMP:20260403T221856
CREATED:20251021T125343Z
LAST-MODIFIED:20260227T214449Z
UID:7677-1765188000-1765191600@tilos.ai
SUMMARY:TILOS-HDSI Seminar: Incentivizing Emergent Behaviors for LLMs via Reinforcement Learning
DESCRIPTION:Yi Wu\, Tsinghua University \nAbstract: Reinforcement Learning (RL) has become a powerful post-training method for eliciting advanced behaviors in large language models (LLMs). This talk presents recent results showing how RL can incentivize the emergence of LLM capabilities across three domains: (1) multi-player deduction game\, Werewolf\, where RL-trained LLM agents develop strategic behaviors and outperform strong human players; (2) agentic search\, where large-scale RL enables a 32B model to run multi-step search to answer non-trivial questions beyond commercial baselines; and (3) efficient reasoning\, where RL mitigates over-thinking and improves both reliability and compute efficiency. \nThe papers can be found at \n\nWerewolf: https://arxiv.org/abs/2310.18940 (ICML24)\, https://arxiv.org/abs/2502.04686 (ICML25)\nASearcher: https://arxiv.org/abs/2508.07976\nThinking Efficiency: https://www.arxiv.org/abs/2506.07104 (NeurIPS25)\n\nAll the projects are trained using our large-scale agentic RL system\, AReaL\, which is open-source at https://github.com/inclusionAI/AReaL with its paper at https://arxiv.org/abs/2505.24298 (NeurIPS25). \n\nYi Wu is an assistant professor at the Institute for Interdisciplinary Information Sciences (IIIS)\, Tsinghua University. He obtained his Ph.D. from UC Berkeley and was a researcher at OpenAI from 2019 to 2020. His research focuses on reinforcement learning\, multi-agent learning\, and LLM agents. His representative works include the value iteration network\, the MADDPG/MAPPO algorithm\, OpenAI’s hide-and-seek project\, and the AReaL project. He received the best paper award at NIPS 2016\, the best demo award finalist at ICRA 2024\, and MIT TR35 Asia Pacific 2025 award.
URL:https://tilos.ai/event/tilos-hdsi-seminar-with-yi-wu-tsinghua-university/
LOCATION:Qualcomm Conference Center Room B (Jacobs Hall first floor) and Virtual\, 9736 Engineers Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/10/wu-yi.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251205T110000
DTEND;TZID=America/Los_Angeles:20251205T120000
DTSTAMP:20260403T221856
CREATED:20251014T194842Z
LAST-MODIFIED:20260304T210702Z
UID:7652-1764932400-1764936000@tilos.ai
SUMMARY:Optimization for ML and AI Seminar: Stochastic-Gradient and Diagonal-Scaling Algorithms for Constrained Optimization and Learning
DESCRIPTION:Frank E. Curtis\, Lehigh University \nAbstract: I will motivate and provide an overview of recent efforts in my research group on the design and analysis of stochastic-gradient-based algorithms for solving constrained optimization problems. I will focus in particular on our motivation for informed supervised learning\, where constraints in the training problem can be used to impose prior knowledge on the properties that should be possessed by a trained prediction model. In addition\, I will provide a detailed look at our newest extensions of heavy-ball and Adam schemes from the unconstrained to the equality-constrained setting\, for which we have shown state-of-the-art convergence guarantees. I will demonstrate the impressive practical performance of our methods using a few informed supervised learning problems. \n\nFrank E. Curtis is a Professor in the Department of Industrial and Systems Engineering at Lehigh University\, where he has been employed since 2009. He received a bachelor’s degree from the College of William and Mary in 2003 with a double major in Computer Science and Mathematics\, received a master’s degree in 2004 and Ph.D. degree in 2007 from the Department of Industrial Engineering and Management Science at Northwestern University\, and spent two years as a Postdoctoral Researcher in the Courant Institute of Mathematical Sciences at New York University from 2007 until 2009. His research focuses on the design\, analysis\, and implementation of numerical methods for solving large-scale nonlinear optimization problems. He received an Early Career Award from the Advanced Scientific Computing Research (ASCR) program of the U.S. Department of Energy (DoE)\, and has received funding from various programs of the U.S. National Science Foundation (NSF)\, including through a TRIPODS Phase I grant awarded to him and his collaborators at Lehigh\, Northwestern\, and Boston University. He has also received funding from the U.S. Office of Naval Research (ONR) and DoE’s Advanced Research Projects Agency-Energy (ARPA-E). He received\, along with Leon Bottou (Meta AI) and Jorge Nocedal (Northwestern)\, the 2021 SIAM/MOS Lagrange Prize in Continuous Optimization. He was awarded\, with James V. Burke (U. of Washington)\, Adrian Lewis (Cornell)\, and Michael Overton (NYU)\, the 2018 INFORMS Computing Society Prize. He and team members Daniel Molzahn (Georgia Tech)\, Andreas Waechter (Northwestern)\, Ermin Wei (Northwestern)\, and Elizabeth Wong (UC San Diego) were awarded second place in the ARPA-E Grid Optimization Competition in 2020. He currently serves as Area Editor for Continuous Optimization for Mathematics of Operations Research and serves as an Associate Editor for Mathematical Programming\, SIAM Journal on Optimization\, Operations Research\, IMA Journal of Numerical Analysis\, and Mathematical Programming Computation. He previously served as the Vice Chair for Nonlinear Programming for the INFORMS Optimization Society\, and is currently very active in professional societies and groups related to mathematical optimization\, including INFORMS\, the Mathematics Optimization Society\, and the SIAM Activity Group on Optimization.
URL:https://tilos.ai/event/optimization-for-ml-and-ai-seminar-with-frank-e-curtis-lehigh-university/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series,TILOS Sponsored Event
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/10/curtis-frank-e1760471303881.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251203T130000
DTEND;TZID=America/Los_Angeles:20251203T140000
DTSTAMP:20260403T221856
CREATED:20250930T163903Z
LAST-MODIFIED:20260304T210653Z
UID:7627-1764766800-1764770400@tilos.ai
SUMMARY:Optimization for AI and ML Seminar: Training Neural Networks at Any Scale
DESCRIPTION:Volkan Cevher\, École Polytechnique Fédérale de Lausanne \nAbstract: At the heart of deep learning’s transformative impact lies the concept of scale–encompassing both data and computational resources\, as well as their interaction with neural network architectures. Scale\, however\, presents critical challenges\, such as increased instability during training and prohibitively expensive model-specific tuning. Given the substantial resources required to train such models\, formulating high-confidence scaling hypotheses backed by rigorous theoretical research has become paramount. \nTo bridge theory and practice\, the talk explores a key mathematical ingredient of scaling in tandem with scaling theory: the numerical solution algorithms commonly employed in deep learning\, spanning domains from vision to language models. We unify these algorithms under a common master template\, making their foundational principles transparent. In doing so\, we reveal the interplay between adaptation to smoothness structures via online learning and the exploitation of optimization geometry through non-Euclidean norms. Our exposition moves beyond simply building larger models–it emphasizes strategic scaling\, offering insights that promise to advance the field while economizing on resources. \n\nVolkan Cevher received the B.Sc. (valedictorian) in electrical engineering from Bilkent University in Ankara\, Turkey\, in 1999 and the Ph.D. in electrical and computer engineering from the Georgia Institute of Technology in Atlanta\, GA in 2005. He was a Research Scientist with the University of Maryland\, College Park from 2006-2007 and also with Rice University in Houston\, TX\, from 2008-2009. Currently\, he is an Associate Professor at the Swiss Federal Institute of Technology Lausanne and a Faculty Fellow in the Electrical and Computer Engineering Department at Rice University. His research interests include machine learning\, signal processing theory\, optimization theory and methods\, and information theory. Dr. Cevher is an ELLIS fellow and was the recipient of the Google Faculty Research award in 2018\, the IEEE Signal Processing Society Best Paper Award in 2016\, a Best Paper Award at CAMSAP in 2015\, a Best Paper Award at SPARS in 2009\, and an ERC CG in 2016 as well as an ERC StG in 2011.
URL:https://tilos.ai/event/optimization-for-ai-and-ml-seminar-with-volkan-cevher-epfl/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series,TILOS Sponsored Event
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/09/cevher-volkan-e1759250260485.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251203T110000
DTEND;TZID=America/Los_Angeles:20251203T120000
DTSTAMP:20260403T221856
CREATED:20250924T154049Z
LAST-MODIFIED:20260227T215023Z
UID:7606-1764759600-1764763200@tilos.ai
SUMMARY:TILOS-SDSU Seminar: 95 Percent: Bridging the Gap Between Prototype and Product
DESCRIPTION:Jeremy Schwartz\, Zoox \nAbstract: When transitioning from the academic world to the professional world of engineering\, one of the most common pitfalls is failing to understand the difference between a compelling prototype and a successful product. This talk will focus on that distinction. We will discuss the differences between them\, and the work required to evolve a good prototype into a real product. We will also discuss some common pitfalls encountered in product development\, and some of the practical software design considerations to keep in mind for development of robust\, mature code. The talk will include examples from my background developing robotic systems for air\, space\, and ground. \n\nJeremy Schwartz is a robotics engineer at Zoox with expertise in a wide variety of areas of mechanical and electrical engineering and computer science. His primary professional expertise is in autonomy and behavioral algorithms\, and he has worked in the aerospace industry as well as ground robotics\, specializing in autonomous systems of all kinds.
URL:https://tilos.ai/event/tilos-sdsu-seminar-with-jeremy-schwartz-of-zoox/
LOCATION:Lamden Hall 341 (SDSU) and Virtual\, San Diego\, CA\, 92182\, United States
CATEGORIES:TILOS Seminar Series,TILOS Sponsored Event
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/09/schwartz-jeremy-e1758728403382.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251119T110000
DTEND;TZID=America/Los_Angeles:20251119T120000
DTSTAMP:20260403T221856
CREATED:20251105T193505Z
LAST-MODIFIED:20260227T215217Z
UID:7735-1763550000-1763553600@tilos.ai
SUMMARY:TILOS-SDSU Seminar: Certifiably Correct Machine Perception
DESCRIPTION:David Rosen\, Northeastern University \nAbstract: Many fundamental machine perception and state estimation tasks require the solution of a high-dimensional nonconvex estimation problem; this class includes (for example) the fundamental problems of simultaneous localization and mapping (in robotics)\, 3D reconstruction (in computer vision)\, and sensor network localization (in distributed sensing). Such problems are known to be computationally hard in general\, with many local minima that can entrap the smooth local optimization methods commonly applied to solve them. The result is that standard machine perception algorithms (based upon local optimization) can be surprisingly brittle\, often returning egregiously wrong answers even when the problem to which they are applied is well-posed. \nIn this talk\, we present a novel class of certifiably correct estimation algorithms that are capable of efficiently recovering provably good (often globally optimal) solutions of generally-intractable machine perception problems in many practical settings. Our approach directly tackles the problem of nonconvexity by employing convex relaxations whose minimizers provide provably good approximate solutions to the original estimation problem under moderate measurement noise. We illustrate the design of this class of methods using the fundamental problem of pose-graph optimization (a mathematical abstraction of robotic mapping) as a running example. We conclude with a brief discussion of open questions and future research directions. \n\nDavid M. Rosen is an Assistant Professor in the Departments of Electrical & Computer Engineering and Mathematics and the Khoury College of Computer Sciences (by courtesy) at Northeastern University\, where he leads the Robust Autonomy Laboratory (NEURAL). Prior to joining Northeastern\, he was a Research Scientist at Oculus Research (now Meta Reality Labs) from 2016 to 2018\, and a Postdoctoral Associate at MIT’s Laboratory for Information and Decision Systems (LIDS) from 2018 to 2021. He holds the degrees of B.S. in Mathematics from the California Institute of Technology (2008)\, M.A. in Mathematics from the University of Texas at Austin (2010)\, and ScD in Computer Science from the Massachusetts Institute of Technology (2016). \n\nHe is broadly interested in the mathematical and algorithmic foundations of trustworthy machine perception\, learning\, and control. His work has been recognized with the IEEE Transactions on Robotics Best Paper Award (2024)\, an Honorable Mention for the IEEE Transactions on Robotics Best Paper Award (2021)\, a Best Student Paper Award at Robotics: Science and Systems (2020)\, a Best Paper Award at the International Workshop on the Algorithmic Foundations of Robotics (2016)\, and selection as an RSS Pioneer (2019).
URL:https://tilos.ai/event/tilos-sdsu-seminar-with-david-rosen-northeastern/
LOCATION:Lamden Hall 341 (SDSU) and Virtual\, San Diego\, CA\, 92182\, United States
CATEGORIES:TILOS Seminar Series,TILOS Sponsored Event
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/11/rosen-david-scaled-e1762371210779.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251112T110000
DTEND;TZID=America/Los_Angeles:20251112T120000
DTSTAMP:20260403T221856
CREATED:20251104T173955Z
LAST-MODIFIED:20260304T210641Z
UID:7730-1762945200-1762948800@tilos.ai
SUMMARY:TILOS-HDSI Seminar: AI safety theory: the missing middle ground
DESCRIPTION:Adam Oberman\, McGill University \nAbstract:  Over the past few years\, the capabilities of generative artificial intelligence (AI) systems have advanced rapidly. Along with the benefits of AI\, there is also a risk of harm. In order to benefit from AI while mitigating the risks\, we need a grounded theoretical framework. \nThe current AI safety theory\, which predates generative AI\, is insufficient. Most theoretical AI safety results tend to reason absolutely: a system is a system is “aligned” or “mis-aligned”\, “honest” or “dishonest”. But in practice safety is probabilistic\, not absolute. The missing middle ground is a quantitative or relative theory of safety — a way to reason formally about degrees of safety. Such a theory is required for defining safety and harms\, and is essential for technical solutions as well as for making good policy decisions. \nIn this talk I will: \n\nReview current AI risks (from misuse\, from lack of reliability\, and systemic risks to the economy) as well as important future risks (lack of control).\nReview theoretical predictions of bad AI behavior and discuss experiments which demonstrate that they can occur in current LLMs.\nExplain why technical and theoretical safety solutions are valuable\, even by contributors outside of the major labs.\nDiscuss some gaps in the theory and present some open problems which could address the gaps.\n\n\nAdam Oberman is a Full Professor of Mathematics and Statistics at McGill University\, a Canada CIFAR AI Chair\, and an Associate Member of Mila. He is a research collaborator at LawZero\, Yoshua Bengio’s AI Safety Institute. He has been researching AI safety since 2024. His research spans generative models\, reinforcement learning\, optimization\, calibration\, and robustness. Earlier in his career\, he made significant contributions to optimal transport and nonlinear partial differential equations. He earned degrees from the University of Toronto and the University of Chicago\, and previously held faculty and postdoctoral positions at Simon Fraser University and the University of Texas at Austin.
URL:https://tilos.ai/event/tilos-hdsi-seminar-with-adam-oberman-mcgill-ai-safety-theory-the-missing-middle-ground/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series
ATTACH;FMTTYPE=image/webp:https://tilos.ai/wp-content/uploads/2025/11/oberman-adam-e1762277416983.webp
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251024T110000
DTEND;TZID=America/Los_Angeles:20251024T120000
DTSTAMP:20260403T221856
CREATED:20250925T175700Z
LAST-MODIFIED:20260304T210610Z
UID:7611-1761303600-1761307200@tilos.ai
SUMMARY:Optimization for ML and AI Seminar: High-dimensional Optimization with Applications to Compute-Optimal Neural Scaling Laws
DESCRIPTION:Courtney Paquette\, McGill University \nAbstract: Given the massive scale of modern ML models\, we now only get a single shot to train them effectively. This restricts our ability to test multiple architectures and hyper-parameter configurations. Instead\, we need to understand how these models scale\, allowing us to experiment with smaller problems and then apply those insights to larger-scale models. In this talk\, I will present a framework for analyzing scaling laws in stochastic learning algorithms using a power-law random features model (PLRF)\, leveraging high-dimensional probability and random matrix theory. I will then use this scaling law to address the compute-optimal question: How should we choose model size and hyper-parameters to achieve the best possible performance in the most compute-efficient manner? Then using this PLRF model\, I will devise a new momentum-based algorithm that (provably) improves the scaling law exponent. Finally\, I will present some numerical experiments on LSTMs that show how this new stochastic algorithm can be applied to real data to improve the compute-optimal exponent. \n\nCourtney Paquette is an assistant professor at McGill University in the Mathematics and Statistics department\, a CIFAR AI Chair (MILA)\, and an active member of the Montreal Machine Learning Optimization Group (MTL MLOpt) at MILA. Her research broadly focuses on designing and analyzing algorithms for large-scale optimization problems\, motivated by applications in data science\, and using techniques that draw from a variety of fields\, including probability\, complexity theory\, and convex and nonsmooth analysis. Dr. Paquette is a lead organizer of the OPT-ML Workshop at NeurIPS since 2020\, and a lead organizer (and original creator) of the High-dimensional Learning Dynamics (HiLD) Workshop at ICML.
URL:https://tilos.ai/event/optimization-for-ml-and-ai-seminar-with-courtney-paquette-mcgill-university/
LOCATION:CSE 1242 and Virtual\, 3235 Voigt Dr\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series,TILOS Sponsored Event
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/09/paquette-courtney-scaled-e1758822988381.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251001T110000
DTEND;TZID=America/Los_Angeles:20251001T120000
DTSTAMP:20260403T221856
CREATED:20250828T192015Z
LAST-MODIFIED:20260304T210603Z
UID:7259-1759316400-1759320000@tilos.ai
SUMMARY:TILOS-HDSI Seminar: A New Paradigm for Learning with Distribution Shift
DESCRIPTION:Adam Klivans\, The University of Texas at Austin \nAbstract: We revisit the fundamental problem of learning with distribution shift\, where a learner is given labeled samples from training distribution D\, unlabeled samples from test distribution D′ and is asked to output a classifier with low test error. The standard approach in this setting is to prove a generalization bound in terms of some notion of distance between D and D′. These distances\, however\, are difficult to compute\, and this has been the main stumbling block for efficient algorithm design over the last two decades. \nWe sidestep this issue and define a new model called TDS learning\, where a learner runs a test on the training set and is allowed to reject if this test detects distribution shift relative to a fixed output classifier. This approach leads to the first set of efficient algorithms for learning with distribution shift that do not take any assumptions on the test distribution. Finally\, we discuss how our techniques have recently been used to solve longstanding problems in supervised learning with contamination. \n\nAdam Klivans is a Professor of Computer Science at the University of Texas at Austin and Director of the NSF AI Institute for Foundations of Machine Learning (IFML). His research interests lie in machine learning and theoretical computer science\, in particular\, Learning Theory\, Computational Complexity\, Pseudorandomness\, Limit Theorems\, and Gaussian Space. Dr. Klivans is a recipient of the NSF CAREER Award and serves on the editorial board for the Theory of Computing and Machine Learning Journal.
URL:https://tilos.ai/event/tilos-seminar-with-adam-klivans/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/08/klivans-adam-e1756405638325.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250523T110000
DTEND;TZID=America/Los_Angeles:20250523T120000
DTSTAMP:20260403T221856
CREATED:20250828T192125Z
LAST-MODIFIED:20260227T222820Z
UID:7272-1747998000-1748001600@tilos.ai
SUMMARY:TILOS Seminar: Optimal Quantization for LLMs and Matrix Multiplication
DESCRIPTION:Yury Polyanskiy\, MIT \nAbstract: The main building block of large language models is matrix multiplication\, which is often bottlenecked by the speed of loading these matrices from memory. A number of recent quantization algorithms (SmoothQuant\, GPTQ\, QuIP\, SpinQuant etc) address this issue by storing matrices in lower precision. We derive optimal asymptotic information-theoretic tradeoff between accuracy of the matrix product and compression rate (number of bits per matrix entry). We also show that a non-asymptotic version of our construction (based on nested Gosset lattices and Conway-Sloan decoding)\, which we call NestQuant\, reduces perplexity deterioration almost three-fold compared to the state-of-the-art algorithms (as measured on LLama-2\, Llama-3 with 8B to 70B parameters). Based on a joint work with Or Ordentlich (HUJI)\, Eitan Porat and Semyon Savkin (MIT EECS). \n\nYury Polyanskiy is a Cutten Professor of Electrical Engineering and Computer Science\, a member of IDSS and LIDS at MIT\, and an IEEE Fellow (2024). Yury received M.S. degree in applied mathematics and physics from the Moscow Institute of Physics and Technology in 2005 and Ph.D. degree in electrical engineering from Princeton University in 2010. His research interests span information theory\, machine learning and statistics. Dr. Polyanskiy won the 2020 IEEE Information Theory Society James Massey Award\, 2013 NSF CAREER award and 2011 IEEE Information Theory Society Paper Award.
URL:https://tilos.ai/event/tilos-seminar-optimal-quantization-for-llms-and-matrix-multiplication/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/04/polyanskiy-yuri.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250416T110000
DTEND;TZID=America/Los_Angeles:20250416T120000
DTSTAMP:20260403T221856
CREATED:20250828T192233Z
LAST-MODIFIED:20260227T222458Z
UID:7286-1744801200-1744804800@tilos.ai
SUMMARY:TILOS Seminar: Amplifying human performance in combinatorial competitive programming
DESCRIPTION:Petar Veličković\, Google DeepMind \nAbstract: Recent years have seen a significant surge in complex AI systems for competitive programming\, capable of performing at admirable levels against human competitors. While steady progress has been made\, the highest percentiles still remain out of reach for these methods on standard competition platforms such as Codeforces. In this talk\, I will describe and dive into our recent work\, where we focussed on combinatorial competitive programming. In combinatorial challenges\, the target is to find as-good-as-possible solutions to otherwise computationally intractable problems\, over specific given inputs. We hypothesise that this scenario offers a unique testbed for human-AI synergy\, as human programmers can write a backbone of a heuristic solution\, after which AI can be used to optimise the scoring function used by the heuristic. We deploy our approach on previous iterations of Hash Code\, a global team programming competition inspired by NP-hard software engineering problems at Google\, and we leverage FunSearch to evolve our scoring functions. Our evolved solutions significantly improve the attained scores from their baseline\, successfully breaking into the top percentile on all previous Hash Code online qualification rounds\, and outperforming the top human teams on several. To the best of our knowledge\, this is the first known AI-assisted top-tier result in competitive programming.
URL:https://tilos.ai/event/tilos-seminar-amplifying-human-performance-in-combinatorial-competitive-programming/
LOCATION:Virtual
CATEGORIES:TILOS Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/08/velickovic-petar-e1736275993608-TwwARw.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250402T110000
DTEND;TZID=America/Los_Angeles:20250402T120000
DTSTAMP:20260403T221856
CREATED:20250828T192344Z
LAST-MODIFIED:20260227T222401Z
UID:7287-1743591600-1743595200@tilos.ai
SUMMARY:TILOS Seminar: Foundational Methods for Foundation Models for Scientific Machine Learning
DESCRIPTION:Michael W. Mahoney\, ICSI\, LBNL\, and Department of Statistics\, UC Berkeley \nAbstract: The remarkable successes of ChatGPT in natural language processing (NLP) and related developments in computer vision (CV) motivate the question of what foundation models would look like and what new advances they would enable\, when built on the rich\, diverse\, multimodal data that are available from large-scale experimental and simulational data in scientific computing (SC)\, broadly defined. Such models could provide a robust and principled foundation for scientific machine learning (SciML)\, going well beyond simply using ML tools developed for internet and social media applications to help solve future scientific problems. I will describe recent work demonstrating the potential of the “pre-train and fine-tune” paradigm\, widely-used in CV and NLP\, for SciML problems\, demonstrating a clear path towards building SciML foundation models; as well as recent work highlighting multiple “failure modes” that arise when trying to interface data-driven ML methodologies with domain-driven SC methodologies\, demonstrating clear obstacles to traversing that path successfully. I will also describe initial work on developing novel methods to address several of these challenges\, as well as their implementations at scale\, a general solution to which will be needed to build robust and reliable SciML models consisting of millions or billions or trillions of parameters. \n\nMichael W. Mahoney is at the University of California at Berkeley in the Department of Statistics and at the International Computer Science Institute (ICSI). He is also an Amazon Scholar as well as head of the Machine Learning and Analytics Group at the Lawrence Berkeley National Laboratory. He works on algorithmic and statistical aspects of modern large-scale data analysis. Much of his recent research has focused on large-scale machine learning\, including randomized matrix algorithms and randomized numerical linear algebra\, scientific machine learning\, scalable stochastic optimization\, geometric network analysis tools for structure extraction in large informatics graphs\, scalable implicit regularization methods\, computational methods for neural network analysis\, physics informed machine learning\, and applications in genetics\, astronomy\, medical imaging\, social network analysis\, and internet data analysis. He received his PhD from Yale University with a dissertation in computational statistical mechanics\, and he has worked and taught at Yale University in the mathematics department\, at Yahoo Research\, and at Stanford University in the mathematics department. Among other things\, he was on the national advisory committee of the Statistical and Applied Mathematical Sciences Institute (SAMSI)\, he was on the National Research Council’s Committee on the Analysis of Massive Data\, he co-organized the Simons Institute’s fall 2013 and 2018 programs on the foundations of data science\, he ran the Park City Mathematics Institute’s 2016 PCMI Summer Session on The Mathematics of Data\, he ran the biennial MMDS Workshops on Algorithms for Modern Massive Data Sets\, and he was the Director of the NSF/TRIPODS-funded FODA (Foundations of Data Analysis) Institute at UC Berkeley. More information is available at https://www.stat.berkeley.edu/~mmahoney/.
URL:https://tilos.ai/event/tilos-seminar-foundational-methods-for-foundation-models-for-scientific-machine-learning/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series
ATTACH;FMTTYPE=image/png:https://tilos.ai/wp-content/uploads/2025/08/mahoney-michael-e1733251484543-1e6Odv.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250327T140000
DTEND;TZID=America/Los_Angeles:20250327T150000
DTSTAMP:20260403T221856
CREATED:20250828T192427Z
LAST-MODIFIED:20250828T192653Z
UID:7273-1743084000-1743087600@tilos.ai
SUMMARY:TILOS Seminar: Single location regression and attention-based models
DESCRIPTION:Claire Boyer\, Université Paris-Saclay \nAbstract: Attention-based models\, such as Transformer\, excel across various tasks but lack a comprehensive theoretical understanding\, especially regarding token-wise sparsity and internal linear representations. To address this gap\, we introduce the single-location regression task\, where only one token in a sequence determines the output\, and its position is a latent random variable\, retrievable via a linear projection of the input. To solve this task\, we propose a dedicated predictor\, which turns out to be a simplified version of a non-linear self-attention layer. We study its theoretical properties\, by showing its asymptotic Bayes optimality and analyzing its training dynamics. In particular\, despite the non-convex nature of the problem\, the predictor effectively learns the underlying structure. This work highlights the capacity of attention mechanisms to handle sparse token information and internal linear structures.
URL:https://tilos.ai/event/tilos-seminar-single-location-regression-and-attention-based-models/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/08/boyer-claire-e1742860147959-s8d3nW.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250312T110000
DTEND;TZID=America/Los_Angeles:20250312T120000
DTSTAMP:20260403T221856
CREATED:20250828T192527Z
LAST-MODIFIED:20250828T192602Z
UID:7295-1741777200-1741780800@tilos.ai
SUMMARY:TILOS Seminar: Synthetic Tasks as Testbeds for Attributing Model Behavior
DESCRIPTION:Surbhi Goel\, University of Pennsylvania \nAbstract: Understanding how different components of the machine learning pipeline—spanning data composition\, architectural choices\, and optimization dynamics—shape model behavior remains a fundamental challenge. In this talk\, I will argue that synthetic tasks\, which enable precise control over data distribution and task complexity\, serve as powerful testbeds for analyzing and attributing behaviors in deep learning. Focusing on the sparse parity learning problem\, a canonical task in learning theory\, I will present insights into: (1) the phenomenon of “hidden progress” in gradient-based optimization\, where models exhibit consistent advancement despite stagnating loss curves; (2) nuanced trade-offs between data\, compute\, model width\, and initialization that govern learning success; and (3) the role of progressive distillation in implicitly structuring curricula to accelerate feature learning. These findings highlight the utility of synthetic tasks in uncovering empirical insights into the mechanisms driving deep learning\, without the cost of training expensive models. This talk is based on joint work with a lot of amazing collaborators: Boaz Barak\, Ben Edelman\, Sham Kakade\, Bingbin Liu\, Eran Malach\, Sadhika Malladi\, Abhishek Panigrahi\, Andrej Risteski\, and Cyril Zhang. \n\nSurbhi Goel is the Magerman Term Assistant Professor of Computer and Information Science at the University of Pennsylvania. She is associated with the theory group\, the ASSET Center on safe\, explainable\, and trustworthy AI systems\, and the Warren Center for Network and Data Sciences. Surbhi’s research focuses on theoretical foundations of modern machine learning paradigms\, particularly deep learning\, and is supported by Microsoft Research and OpenAI. Previously\, she was a postdoctoral researcher at Microsoft Research NYC and completed her Ph.D. at the University of Texas at Austin under Adam Klivans\, receiving the UTCS Bert Kay Dissertation Award. She has also been a visiting researcher at IAS\, Princeton\, and the Simons Institute at UC Berkeley. Surbhi co-founded the Learning Theory Alliance (LeT‐All) and holds several leadership roles\, including Office Hours co-chair for ICLR 2024 and co-treasurer for the Association for Computational Learning Theory.
URL:https://tilos.ai/event/tilos-seminar-synthetic-tasks-as-testbeds-for-attributing-model-behavior/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/08/goel-surbhi-e1727126779765-U5P80t.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250212T110000
DTEND;TZID=America/Los_Angeles:20250212T120000
DTSTAMP:20260403T221856
CREATED:20250828T195559Z
LAST-MODIFIED:20250828T195908Z
UID:7299-1739358000-1739361600@tilos.ai
SUMMARY:TILOS-SDSU Seminar: Challenging Estimation Problems in Vehicle Autonomy
DESCRIPTION:Rajesh Rajamani\, University of Minnesota \nAbstract: This talk presents some interesting problems in estimation related to vehicle autonomy. First\, a teleoperation application in which a remote operator can intervene to control an autonomous vehicle is considered. Fundamental challenges here include the need to design an effective teleoperation station\, bandwidth and time-criticality constraints in wireless communication\, and the need for a control system that can handle delays. A predictive display system that uses generative AI to estimate the current video display for the teleoperator from fusion of delayed camera and Lidar images is developed. By estimating trajectories of the ego vehicle and of other nearby vehicles on the road\, realistic intermediate updates of the remote vehicle environment are used to compensate for delayed camera data. A different estimation application involving the driving of a vehicle with automated steering control on snow-covered and rural roads is considered next. Since camera-based feedback of lane markers cannot be used\, sensor fusion algorithms and RTK-corrected GPS are utilized for lateral position estimation. Finally\, the modification of target vehicle tracking methods utilized on autonomous vehicles for use on other low-cost platforms is considered. Applications involving protection of vulnerable road users such as e-scooter riders\, bicyclists and construction zone workers is demonstrated. The fundamental theme underlying the different estimation problems in this seminar is the effective use of nonlinear vehicle dynamic models and novel nonlinear observer design algorithms. \n\nRajesh Rajamani obtained his M.S. and Ph.D. degrees from the University of California at Berkeley and his B.Tech degree from the Indian Institute of Technology at Madras. He joined the faculty in Mechanical Engineering at the University of Minnesota in 1998 where he is currently the Benjamin Y.H. Liu-TSI Endowed Chair Professor and Associate Director (Research) of the Minnesota Robotics Institute. His active research interests include estimation\, sensing and control for smart and autonomous systems.\nDr. Rajamani has co-authored over 190 journal papers and is a co-inventor on 20+ patents/patent applications. He is a Fellow of IEEE and ASME and has been a recipient of the CAREER award from the National Science Foundation\, the O. Hugo Schuck Award from the American Automatic Control Council\, the Ralph Teetor Award from SAE\, the Charles Stark Draper award from ASME\, and a number of best paper awards from journals and conferences. Several inventions from his laboratory have been commercialized through start-up ventures co-founded by industry executives. One of these companies\, Innotronics\, was recently recognized among the 35 Best University Start-Ups of 2016 by the US National Council of Entrepreneurial Tech Transfer.
URL:https://tilos.ai/event/tilos-sdsu-seminar-challenging-estimation-problems-in-vehicle-autonomy/
LOCATION:San Diego State University\, 5500 Campanile Dr\, San Diego\, 92182\, United States
CATEGORIES:TILOS Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/08/rajamani-rajesh-e1725919938393-FsSjfr.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250129T110000
DTEND;TZID=America/Los_Angeles:20250129T123000
DTSTAMP:20260403T221856
CREATED:20250828T195813Z
LAST-MODIFIED:20250828T195813Z
UID:7301-1738148400-1738153800@tilos.ai
SUMMARY:TILOS Seminar: Unlearnable Facts Cause Hallucinations in Pretrained Language Models
DESCRIPTION:Adam Tauman Kalai\, OpenAI \nAbstract: Pretrained language models (LMs) tend to preserve many qualities present in their training data\, such as grammaticality\, formatting\, and politeness. However\, for specific types of factuality\, even LMs pretrained on factually correct statements tend to produce falsehoods at high rates. We explain these “hallucinations” by drawing a connection to binary classification\, enabling us to leverage insights from supervised learning. We prove that pretrained LMs (which are “calibrated”) fail to mimic criteria that cannot be learned. Our analysis explains why pretrained LMs hallucinate on facts such as people’s birthdays but not on systematic facts such as even vs. odd numbers.\nOf course\, LM pretraining is only one stage in the development of a chatbot\, and thus hallucinations are *not* inevitable in chatbots.\nThis is joint work with Santosh Vempala. \n\nAdam Tauman Kalai is a Research Scientist at OpenAI working on AI Safety and Ethics. He has worked in Algorithms\, Fairness\, Machine Learning Theory\, Game Theory\, and Crowdsourcing. He received his PhD from Carnegie Mellon University. He has served as an Assistant Professor at Georgia Tech and TTIC\, and is on the science team of the whale-translation Project CETI. He has co-chaired AI and crowdsourcing conferences and has numerous honors\, most notably the Majulook prize.
URL:https://tilos.ai/event/tilos-seminar-unlearnable-facts-cause-hallucinations-in-pretrained-language-models/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series,TILOS Sponsored Event
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20241120T110000
DTEND;TZID=America/Los_Angeles:20241120T120000
DTSTAMP:20260403T221856
CREATED:20250828T200101Z
LAST-MODIFIED:20250828T200101Z
UID:7294-1732100400-1732104000@tilos.ai
SUMMARY:TILOS Seminar: How Transformers Learn Causal Structure with Gradient Descent
DESCRIPTION:Jason Lee\, Princeton University \nAbstract: The incredible success of transformers on sequence modeling tasks can be largely attributed to the self-attention mechanism\, which allows information to be transferred between different parts of a sequence. Self-attention allows transformers to encode causal structure which makes them particularly suitable for sequence modeling. However\, the process by which transformers learn such causal structure via gradient-based training algorithms remains poorly understood. To better understand this process\, we introduce an in-context learning task that requires learning latent causal structure. We prove that gradient descent on a simplified two-layer transformer learns to solve this task by encoding the latent causal graph in the first attention layer. The key insight of our proof is that the gradient of the attention matrix encodes the mutual information between tokens. As a consequence of the data processing inequality\, the largest entries of this gradient correspond to edges in the latent causal graph. As a special case\, when the sequences are generated from in-context Markov chains\, we prove that transformers learn an induction head (Olsson et al.\, 2022). We confirm our theoretical findings by showing that transformers trained on our in-context learning task are able to recover a wide variety of causal structures. \n\nJason Lee is an associate professor in Electrical Engineering and Computer Science (secondary) at Princeton University. Prior to that\, he was in the Data Science and Operations department at the University of Southern California and a postdoctoral researcher at UC Berkeley working with Michael I. Jordan. Jason received his PhD at Stanford University advised by Trevor Hastie and Jonathan Taylor. His research interests are in the theory of machine learning\, optimization\, and statistics. Lately\, he has worked on the foundations of deep learning\, representation learning\, and reinforcement learning. He has received the Samsung AI Researcher of the Year Award\, NSF Career Award\, ONR Young Investigator Award in Mathematical Data Science\, Sloan Research Fellowship\, NeurIPS Best Student Paper Award and Finalist for the Best Paper Prize for Young Researchers in Continuous Optimization\, and Princeton Commendation for Outstanding Teaching.
URL:https://tilos.ai/event/tilos-seminar-how-transformers-learn-causal-structure-with-gradient-descent/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20241113T110000
DTEND;TZID=America/Los_Angeles:20241113T120000
DTSTAMP:20260403T221856
CREATED:20250828T200305Z
LAST-MODIFIED:20250828T200305Z
UID:7291-1731495600-1731499200@tilos.ai
SUMMARY:TILOS Seminar: Off-the-shelf Algorithmic Stability
DESCRIPTION:Rebecca Willett\, University of Chicago \nAbstract: Algorithmic stability holds when our conclusions\, estimates\, fitted models\, predictions\, or decisions are insensitive to small changes to the training data. Stability has emerged as a core principle for reliable data science\, providing insights into generalization\, cross-validation\, uncertainty quantification\, and more. Whereas prior literature has developed mathematical tools for analyzing the stability of specific machine learning (ML) algorithms\, we study methods that can be applied to arbitrary learning algorithms to satisfy a desired level of stability. First\, I will discuss how bagging is guaranteed to stabilize any prediction model\, regardless of the input data. Thus\, if we remove or replace a small fraction of the training data at random\, the resulting prediction will typically change very little. Our analysis provides insight into how the size of the bags (bootstrap datasets) influences stability\, giving practitioners a new tool for guaranteeing a desired level of stability. Second\, I will describe how to extend these stability guarantees beyond prediction modeling to more general statistical estimation problems where bagging is not as well known but equally useful for stability. Specifically\, I will describe a new framework for stable classification and model selection by combining bagging on class or model weights with a stable\, “soft” version of the argmax operator. This is joint work with Jake Soloff and Rina Barber. \n\nRebecca Willett is a Professor of Statistics and Computer Science and the Director of AI in the Data Science Institute at the University of Chicago\, and she holds a courtesy appointment at the Toyota Technological Institute at Chicago. Her research is focused on machine learning foundations\, scientific machine learning\, and signal processing. Willett received the inaugural Data Science Career Prize from the Society of Industrial and Applied Mathematics in 2024\, was named a Fellow of the Society of Industrial and Applied Mathematics in 2021\, and was named a Fellow of the IEEE in 2022. She is the Deputy Director for Research at the NSF-Simons Foundation National Institute for Theory and Mathematics in Biology\, Deputy Director for Research at the NSF-Simons Institute for AI in the Sky (SkAI)\, and a member of the NSF Institute for the Foundations of Data Science Executive Committee. She is the Faculty Director of the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship. She helps direct the Air Force Research Lab University Center of Excellence on Machine Learning. She received the National Science Foundation CAREER Award in 2007\, was a DARPA Computer Science Study Group member\, and received an Air Force Office of Scientific Research Young Investigator Program award in 2010. She completed her PhD in Electrical and Computer Engineering at Rice University in 2005. She was an Assistant and then tenured Associate Professor of Electrical and Computer Engineering at Duke University from 2005 to 2013. She was an Associate Professor of Electrical and Computer Engineering\, Harvey D. Spangler Faculty Scholar\, and Fellow of the Wisconsin Institutes for Discovery at the University of Wisconsin-Madison from 2013 to 2018.
URL:https://tilos.ai/event/tilos-seminar-off-the-shelf-algorithmic-stability/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20241107T160000
DTEND;TZID=America/Los_Angeles:20241107T170000
DTSTAMP:20260403T221856
CREATED:20250828T200431Z
LAST-MODIFIED:20250828T200431Z
UID:7292-1730995200-1730998800@tilos.ai
SUMMARY:TILOS Seminar: Data Models for Deep Learning: Beyond i.i.d. Assumptions
DESCRIPTION:Elchanan Mossel\, Professor of Mathematics\, MIT \nAbstract: Classical Machine Learning theory is largely built upon the assumption that data samples are independent and identically distributed (i.i.d.) from general distribution families. In this talk\, I will present novel insights that emerge when we move beyond these traditional assumptions\, exploring both dependent sampling scenarios and structured generative distributions. These perspectives offer fresh theoretical frameworks and practical implications for modern machine learning approaches. \n\nElchanan Mossel is a Professor of Mathematics at the Massachusetts Institute of Technology (MIT)\, specializing in probability theory\, combinatorics\, and theoretical computer science. His research explores a range of complex\, interdisciplinary problems\, including social choice theory\, inference in networks\, and the analysis of algorithms\, with applications across economics\, political science\, and genetics. Mossel completed his Ph.D. at the Hebrew University of Jerusalem and held postdoctoral positions at Microsoft Research and UC Berkeley before joining MIT. Recognized for his innovative work\, Mossel has received a Sloan fellowship\, NSF CAREER award\, and COLT best paper award\, and is a Fellow of the American Mathematical Society.
URL:https://tilos.ai/event/tilos-seminar-data-models-for-deep-learning-beyond-i-i-d-assumptions/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/08/mossel-elchanan-e1728935276435-milFYz.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20241002T110000
DTEND;TZID=America/Los_Angeles:20241002T120000
DTSTAMP:20260403T221856
CREATED:20250828T200544Z
LAST-MODIFIED:20250828T200612Z
UID:7297-1727866800-1727870400@tilos.ai
SUMMARY:TILOS-SDSU Seminar: AI/ML & NLP for UAS/Air Traffic Management
DESCRIPTION:Krishna Kalyanam\, NASA Ames Research Center \nAbstract: We introduce several Air Traffic Management (ATM) initiatives envisioned by NASA and FAA for a future airspace that combines conventional traffic and new entrants (e.g.\, drones) without sacrificing safety. In this framework\, we demonstrate the use of state-of-the-art AI/ML modeling and prediction tools that will enable efficient and safe traffic flow in the U.S. National Airspace System (NAS). For example\, Natural Language Processing (NLP) tools can help extract data (e.g.\, airspace constraints) that are currently contained in legacy text and audio format and convert them into digital information. The digitized information can be ingested by route planning\, arrival scheduling and other decision support tools both on the ground and in the flight deck. We show how historical data (track\, weather & events) can be preprocessed and utilized to create accurate models to predict flight trajectories and events of interest (e.g.\, Traffic Management Initiatives). We show several application areas within ATM that benefit from AI/ML including trajectory prediction\, airport runway configuration management and automatic speech to text. The overarching goal of the work is to accelerate the integration of package delivery drones\, air taxis and autonomous cargo aircraft into the NAS without impacting the safety and efficacy of current manned operations. As an example\, we also show a strategic deconfliction scenario and demonstrate scalable algorithms that provide conflict free schedules for package delivery drones in an urban setting. \n\nDr. Krishna Kalyanam is the Autonomy & AI/ML tech lead with the NASA Aeronautics Research Institute (NARI). In his current role\, he is focused on delivering state of the art AI/ML algorithms to enable scalable and efficient manned/unmanned operations in a mixed-use National airspace. Prior to joining NASA\, Dr. Kalyanam was with AFRL’s Autonomous Controls branch\, where he co-designed several multi-UAV cooperative control algorithms that were flight tested as part of the Intelligent Control & Evaluation of Teams (ICE-T) program. Dr. Kalyanam has published 100+ papers on stochastic control\, human machine teaming and multi-agent scheduling in IEEE\, ASME and AIAA venues. Dr. Kalyanam is a senior member of IEEE and an associate fellow of the AIAA.  He is a recipient of the prestigious Research associateship award sponsored by the National Academies. He was also part of the UAV Autonomy team that won the AFRL “Star Team” award for performing the most innovative in-house basic research in 2018.
URL:https://tilos.ai/event/tilos-sdsu-seminar-ai-ml-nlp-for-uas-air-traffic-management/
LOCATION:San Diego State University\, 5500 Campanile Dr\, San Diego\, 92182\, United States
CATEGORIES:TILOS Seminar Series
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