Optimization for ML and AI Seminar: High-dimensional Optimization with Applications to Compute-Optimal Neural Scaling Laws
2025-10-24 11:00 - 12:00 (America/Los_Angeles) | CSE 1242 and Virtual, 3235 Voigt Dr, La Jolla, CA, 92093, United States
Courtney Paquette, McGill University
Abstract: 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.

Courtney 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.

TILOS-HDSI Seminar: AI safety theory: the missing middle ground
2025-11-12 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Adam Oberman, McGill University
Abstract: 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.
The 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.
In this talk I will:

Review current AI risks (from misuse, from lack of reliability, and systemic risks to the economy) as well as important future risks (lack of control).
Review theoretical predictions of bad AI behavior and discuss experiments which demonstrate that they can occur in current LLMs.
Explain why technical and theoretical safety solutions are valuable, even by contributors outside of the major labs.
Discuss some gaps in the theory and present some open problems which could address the gaps.

Adam 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.

TILOS-SDSU Seminar: Certifiably Correct Machine Perception
2025-11-19 11:00 - 12:00 | Lamden Hall 341 (SDSU) and Virtual, San Diego, CA, 92182, United States
David Rosen, Northeastern University
Abstract: 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.
In 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.

David 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).

He 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).

TILOS-SDSU Seminar: 95 Percent: Bridging the Gap Between Prototype and Product
2025-12-03 11:00 - 12:00 | Lamden Hall 341 (SDSU) and Virtual, San Diego, CA, 92182, United States
Jeremy Schwartz, Zoox
Abstract: 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.

Jeremy 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.

Optimization for AI and ML Seminar: Training Neural Networks at Any Scale
2025-12-03 13:00 - 14:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Volkan Cevher, École Polytechnique Fédérale de Lausanne
Abstract: 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.
To 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.

Volkan 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.

Optimization for ML and AI Seminar: Stochastic-Gradient and Diagonal-Scaling Algorithms for Constrained Optimization and Learning
2025-12-05 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Frank E. Curtis, Lehigh University
Abstract: 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.

Frank 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.

TILOS-HDSI Seminar: Incentivizing Emergent Behaviors for LLMs via Reinforcement Learning
2025-12-08 10:00 - 11:00 | Qualcomm Conference Center Room B (Jacobs Hall first floor) and Virtual, 9736 Engineers Ln, La Jolla, CA, 92093, United States
Yi Wu, Tsinghua University
Abstract: 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.
The papers can be found at

Werewolf: https://arxiv.org/abs/2310.18940 (ICML24), https://arxiv.org/abs/2502.04686 (ICML25)
ASearcher: https://arxiv.org/abs/2508.07976
Thinking Efficiency: https://www.arxiv.org/abs/2506.07104 (NeurIPS25)

All 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).

Yi 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.

Optimization for ML and AI Seminar: Randomized linear algebra with subspace injections
2026-01-09 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Joel Tropp, Caltech
Abstract: 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.
Joint work with Chris Camaño, Ethan Epperly, and Raphael Meyer; available at arXiv:2508.21189.

Joel 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).

TILOS-HDSI Seminar: Safety, Representations, and Generative Learning in Dynamical Systems
2026-01-28 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Koushil Sreenath, UC Berkeley
Abstract: 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.

Koushil 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).

[CANCELED] Optimization for ML and AI Seminar: Fantastic Pretraining Optimizers and Where to Find Them
2026-01-30 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Tengyu Ma, Stanford
Abstract: 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.

Tengyu 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.
Zoom: https://bit.ly/Opt-AI-ML

Optimization for ML and AI Seminar: Extended Convex Lifting for Policy Optimization in Control
2026-02-06 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Yang Zheng, UC San Diego
Abstract: 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.

Yang 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.

TILOS-MICS Seminar: AI-Driven Design Automation for Multi-Chip Integration in AI Chips
2026-02-09 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Sung-Kyu Lim, University of Southern California
Abstract: 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.

Dr. 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.

TILOS-HDSI Seminar: Kinetic Theory Perspective of Foundation Models for Physics
2026-02-11 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Maarten de Hoop, Rice University
Abstract: 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.

Professor 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.

TILOS-HDSI Seminar: Neuromorphic LLMs
2026-02-20 10:00 - 11:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Jason Eshraghian, UC Santa Cruz
Abstract: 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.
We 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.

Jason 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.

Optimization for ML and AI Seminar: (De)regularized Wasserstein Gradient Flows via Reproducing Kernels
2026-02-27 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Bharath Sriperumbudur, Pennsylvania State University
Abstract: 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.

Bharath 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.

Optimization for ML and AI Seminar: Transformers Learn Generalizable Chain-of-Thought Reasoning via Gradient Descent
2026-03-13 10:00 - 11:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Yuejie Chi, Yale
Abstract: 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.

Yuejie 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.

TILOS-SDSU Seminar: Autopilots Need Parachutes: Reliability Lessons from LLM-Automated Embedded AI Systems
2026-03-25 11:00 - 12:00 | Lamden Hall 341 (SDSU) and Virtual, San Diego, CA, 92182, United States
Roberto Morabito, EURECOM
Abstract: 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.

Roberto 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.

TILOS-Optimization for ML and AI Seminar: Implicit bias results for Muon, Adam, and Friends
2026-03-27 10:00 - 11:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Matus Telgarsky, New York University
Abstract: 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.

Matus 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.

TILOS-HDSI Seminar: Engineering Interpretable and Faithful AI Systems
2026-04-08 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
René Vidal, University of Pennsylvania
Abstract: Large Language Models (LLMs) and Vision Language Models (VLMs) have achieved remarkable performance across a wide range of tasks. However, their growing deployment has exposed fundamental limitations in faithfulness, safety, and transparency. In this talk, I will present a unified perspective on addressing these challenges through principled model interventions and interpretable decision-making frameworks. I first introduce Information Pursuit (IP), an interpretable-by-design prediction framework that replaces opaque reasoning with a sequence of informative, user-interpretable queries, yielding concise explanations alongside accurate predictions. I then present Parsimonious Concept Engineering (PaCE), an approach that improves faithfulness and alignment by selectively removing undesirable internal activations, mitigating hallucinations and biased language while preserving linguistic competence. Results across text, vision, and medical tasks illustrate how these ideas advance transparency without sacrificing performance. Together, these contributions point toward a broader direction for building AI systems that are powerful, faithful, and aligned with human values.

René Vidal is the Penn Integrates Knowledge and Rachleff University Professor of Electrical and Systems Engineering and Radiology at the University of Pennsylvania, where he directs the Center for Innovation in Data Engineering and Science (IDEAS) and serves as Co-Chair of Penn AI. He is also an Amazon Scholar, Affiliated Chief Scientist at NORCE, and former Associate Editor-in-Chief of IEEE Transactions on Pattern Analysis and Machine Intelligence. Professor Vidal’s research advances the mathematical foundations of deep learning and trustworthy AI, with broad impact across computer vision and biomedical data science. His contributions have been recognized with major honors, including the IEEE Edward J. McCluskey Technical Achievement Award, the D’Alembert Faculty Award, the J.K. Aggarwal Prize, the ONR Young Investigator Award, the NSF CAREER Award, and best paper awards in machine learning, computer vision, signal processing, control, and medical robotics. He is a Fellow of ACM, AIMBE, IEEE, and IAPR, and a Sloan Fellow.

Optimization for ML and AI Seminar: A survey of the mixing times of the Proximal Sampler algorithm
2026-04-10 10:00 - 11:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Andre Wibisono, Yale University
Abstract: Sampling is a fundamental algorithmic task with many connections to optimization. In this talk, we survey a recent algorithm for sampling known as the Proximal Sampler, which can be seen as a proximal discretization of the continuous-time Langevin dynamics, and achieves the current state-of-the-art iteration complexity for sampling in discrete time. We survey the mixing time guarantees of the Proximal Sampler algorithm and show they match the guarantees for the Langevin dynamics. When the target distribution satisfies log-concavity or isoperimetry, the Proximal Sampler has rapid convergence guarantees. We illustrate the proof technique via the strong data processing inequality along the Gaussian channel and its time reversal under isoperimetry.

Andre Wibisono is an assistant professor in the Department of Computer Science at Yale University, with a secondary appointment in the Department of Statistics & Data Science. His research interests are in the design and analysis of algorithms for machine learning, in particular for problems in optimization, sampling, and game theory. He received his BS degrees in Mathematics and in Computer Science from MIT, his MEng in Computer Science from MIT, his MA in Statistics from UC Berkeley, and his PhD in Computer Science from UC Berkeley. He has done postdoctoral research at the University of Wisconsin-Madison and at the Georgia Institute of Technology.

TILOS-SDSU Seminar: A Modular AgenticAI Architecture for Commercially Scalable and Compliant Robotics
2026-04-29 11:00 - 12:00 | TBA
Sahil Rajesh Dhayalkar, Brain Corporation
Abstract: Autonomous navigation in dynamic environments faces immense challenges. Traditional rigid, rules-based systems often fail due to a lack of semantic understanding needed to adapt to continuous environmental shifts. Conversely, emerging end-to-end Vision-Language-Action (VLA) models introduce a critical “black box” dilemma; they inherently lack the explicit application context, deterministic guardrails, and data efficiency required for rigorous enterprise safety and compliance (e.g., SOC2). To address this, Brain Corp, in collaboration with UCSD, proposes a robust hybrid architecture underpinning the BrainOS platform. In this framework, visual inputs (via VLMs) and task commands (via LLMs) feed directly into a distinct Perception block anchored by a Contextual Grounding Layer with Semantic Mapping. This rich, grounded perception then informs a hybrid Action block, where the reasoning capabilities of VLA models operate safely alongside proven deterministic controls such as deep learning, reinforcement learning, model predictive control, etc. Crucially, an underlying Directed Safety Layer and strict Enterprise Infrastructure wrap this entire process. By isolating adaptable AI reasoning from hard-coded physical controls, this architecture provides a framework designed to securely manage the unpredictable realities of varied environments. Ultimately, this approach addresses the compliance bottleneck, laying the foundation to scale safely across diverse commercial applications and power the continuous, real-world data engine necessary to fuel next-generation physical AI.

Sahil Rajesh Dhayalkar is a Staff Autonomy Engineer and Perception Team Lead at Brain Corporation. He specializes in architecting real-time perception pipelines across LiDAR, RGB, and depth sensors, with his work currently deployed on production robots in dynamic commercial environments. During his tenure, he has pioneered the real-time computer vision pipeline for on-robot object detection at the edge, spearheaded “Localize From Anywhere,” a global localization system utilizing Vision-Language Models and RGB images, and auto-calibration, a targetless calibration of ranging sensors on robots. He holds a Master’s degree in Computer Science from Arizona State University. His research interests include robotic perception, large language models, deep learning, neuro-symbolic reasoning, and optimizations.

TILOS-HDSI Seminar: Machine learning for discrete optimization: Theoretical foundations
2026-05-06 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Ellen Vitercik, Stanford University
Abstract: Many of the most important optimization problems in practice are massive in scale, mathematically complex, and involve numerous unknown parameters. Machine learning offers a powerful way to address these challenges by uncovering hidden structure across problem instances, but integrating predictions into algorithms raises fundamental questions: which architectures align with combinatorial structure, and how can we ensure robustness to error? This talk presents two case studies. First, we show how graph neural networks can approximate the optimal dynamic program for online matching, yielding algorithms that generalize across graph sizes and achieve strong empirical performance. Second, we investigate calibration as a principled interface between machine learning and decision-making, demonstrating through rent-or-buy and job scheduling problems that calibrated predictions yield both theoretical guarantees and practical improvements. This is joint work with Alexandre Hayderi, Amin Saberi, Anders Wikum, and Judy Hanwen Shen.

Ellen Vitercik is an Assistant Professor at Stanford University with a joint appointment between the Management Science & Engineering department and the Computer Science department. Her research interests include machine learning, algorithm design, discrete and combinatorial optimization, and the interface between economics and computation. Before joining Stanford, she spent a year as a Miller Postdoctoral Fellow at UC Berkeley and received a PhD in Computer Science from Carnegie Mellon University. Her research has been recognized with a Schmidt Sciences AI2050 Early Career Fellowship, an NSF CAREER award, the SIGecom Doctoral Dissertation Award, and the CMU School of Computer Science Distinguished Dissertation Award, among other honors.

Optimization for ML and AI Seminar: Self-play Algorithms for Math Theorem Proving
2026-05-08 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Tengyu Ma, Stanford University
Abstract: I will discuss RL algorithms for automated theorem proving with LLMs, especially in the possible future regime where we run out of high-quality training data. To keep improving the models with limited data, we draw inspiration from mathematicians, who continuously develop new results, partly by proposing novel conjectures or exercises and attempting to solve them. We design the Self-play Theorem Prover (STP) that simultaneously takes on two roles, conjecturer and prover, each providing training signals to the other. At the end of the talk, I will mention a recent paper on extending the algorithm to include another role, Guide, which helps guide the conjecturer to generate clean and relevant conjectures, and a few other related works in using AI for math.

Tengyu 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.

TILOS-HDSI Seminar: ComPO: Preference Alignment via Comparison Oracles
2026-05-13 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Tianyi Lin, Columbia University
Direct alignment methods are increasingly used for aligning large language models (LLMs) with human preferences. However, these methods suffer from the likelihood displacement, which can be driven by noisy preference pairs that induce similar likelihood for preferred and dis-preferred responses. To address this issue, we consider doing derivative-free optimization based on comparison oracles. First, we propose a new preference alignment method via comparison oracles and provide convergence guarantees for its basic mechanism. Second, we improve our method using some heuristics and conduct the experiments to demonstrate the flexibility and compatibility of practical mechanisms in improving the performance of LLMs using noisy preference pairs. Evaluations are conducted across multiple base and instruction-tuned models with different benchmarks. Experimental results show the effectiveness of our method as an alternative to addressing the limitations of existing methods. A highlight of our work is that we evidence the importance of designing specialized methods for preference pairs with distinct likelihood margins.

Tianyi Lin is an assistant professor in the Department of Industrial Engineering and Operations Research (IEOR) at Columbia University. His research interests lie in generative artificial intelligence, optimization for machine learning, game theory, social and economic network, and optimal transport. He obtained his Ph.D. in Electrical Engineering and Computer Science at UC Berkeley, where he was advised by Professor Michael Jordan and was associated with the Berkeley Artificial Intelligence Research (BAIR) group. From 2023 to 2024, he was a postdoctoral researcher at the Laboratory for Information & Decision Systems (LIDS) at Massachusetts Institute of Technology, working with Professor Asuman Ozdaglar. Prior to that, he received a B.S. in Mathematics from Nanjing University, a M.S. in Pure Mathematics and Statistics from University of Cambridge and a M.S. in Operations Research from UC Berkeley.

Optimization for ML and AI Seminar: A non-equilibrium phase transition with broken ergodicity leads to double descent and benign overfitting in machine learning
2026-05-15 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Nigel Goldenfeld, UC San Diego Department of Physics and HDSI
Abstract: The remarkable ability of modern neural networks to generalize improves with increasing network capacity, even when the number of model parameters or effective degrees of freedom exceeds the number of training data points. This phenomenon is all the more surprising given that generalization error diverges when the number of model parameters approaches a critical value from below. Here we use dynamical mean field theory to show, in a simple setting of linear regression, that this so-called “double descent” behavior is the outcome of a phase transition in the stochastic field theory describing the training process. We calculate the critical exponents and scaling function of the double descent phase transition, and show that it is marked by a breakdown of the fluctuation-dissipation theorem associated with broken ergodicity. The corresponding response function has the same functional form as the simple London model of the superconducting transition, with the rigidity of the wave function corresponding to the neural network’s ability to generalize accurately. Our results are distinct from earlier work, because we calculate the time-dependence specifically, not just the equilibrium solutions. This is what enables us to identify the origin of the emergent behavior.
Work performed with Chan Li.

Nigel Goldenfeld holds the Chancellor’s Distinguished Professorship in Physics at UC San Diego, which he joined in Fall 2021 after 36 years at the University of Illinois at Urbana-Champaign (UIUC). Nigel’s research spans condensed matter theory, the theory of living systems, hydrodynamics and non-equilibrium statistical physics.
Nigel received his PhD in theoretical physics from the University of Cambridge (UK) in 1982, and from 1982-1985 was a postdoctoral fellow at the Institute for Theoretical Physics at UC Santa Barbara, where his work on the dynamics of snowflake growth helped launch the modern theory of pattern formation in nature. He joined the condensed matter theory group at the Department of Physics at UIUC in 1985, where his work was instrumental to the discovery of d-wave pairing in high temperature superconductors. Nigel’s interests in biology include microbial ecology, evolution and systems biology. He was a founding member of the Institute for Genomic Biology at UIUC, where he led the Biocomplexity Group and directed the NASA Astrobiology Institute for Universal Biology. During the COVID-19 pandemic, he pivoted from his experience in mathematical modeling of bacteria and viruses to computational epidemiology, advising the Governor of Illinois, and helping devise, set up and run the COVID saliva testing system at UIUC, which provided ~12 hour turnaround of PCR tests to the 50,000 people in the campus community and eventually to over 1700 schools and other institutions in Illinois and beyond. Nigel has served on the editorial boards of several journals, including The Philosophical Transactions of the Royal Society, Physical Biology and the International Journal of Theoretical and Applied Finance. Selected honors include: Alfred P. Sloan Foundation Fellow, University Scholar of the University of Illinois, the Xerox Award for research, the A. Nordsieck award for excellence in graduate teaching and the American Physical Society’s Leo P. Kadanoff Prize 2020. Nigel is a Fellow of the American Physical Society, a Fellow of the American Academy of Arts and Sciences, a Fellow of the Royal Society (UK) and a Member of the US National Academy of Sciences.

TILOS-HDSI Seminar: Inference-Time Algorithms: A Theoretical Lens on Tractability and Error Propagation
2026-05-20 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Andrej Risteski, Carnegie Mellon University
Abstract: Modern AI systems are increasingly built by placing trained models inside larger computational loops. Inference-time algorithms are a basic instance of this idea: they use one or more trained models at test time to incorporate new information, exploit pretrained models as priors, and trade computational effort for accuracy, sample quality, or control. Examples include generator-verifier search for reasoning, diffusion models for solving inverse problems, and reward-guided generation. Theoretically, this revisits a classical question from optimization and theoretical computer science: what can be done with access to an oracle? Here, however, the oracles are new and non-standard: they model the capabilities of large pretrained models, making them powerful, but also imperfect because they are learned. This combination leads to new questions about algorithm design and error propagation.
This talk studies two central aspects of this paradigm: computational efficiency and error propagation. The first vignette considers generator-verifier systems, and shows how stochastic backtracking can trade additional computation for accuracy, giving a principled version of test-time scaling even with imperfect learned oracles. The second vignette studies diffusion steering: when can we efficiently bias a pretrained diffusion model toward higher-reward samples while staying close to the original model? We show that tractability depends strongly on both the reward structure and the alignment objective, and that simple primitives—such as sampling from linear tilts—can be surprisingly useful for handling richer reward classes.
Based on https://arxiv.org/abs/2510.03149, https://arxiv.org/abs/2602.16570, https://arxiv.org/abs/2605.11361.

Andrej Risteski is an Associate Professor at the Machine Learning Department in Carnegie Mellon University. Prior to that, he was a Norbert Wiener Research Fellow jointly in the Applied Math department and IDSS at MIT. Dr. Risteski received his PhD in the Computer Science Department at Princeton University under the advisement of Sanjeev Arora.
Dr. Risteski’s research interests lie in the intersection of machine learning, statistics, and theoretical computer science, spanning topics like (probabilistic) generative models, algorithmic tools for learning and inference, representation and self-supervised learning, out-of-distribution generalization and applications of neural approaches to natural language processing and scientific domains. The broad goal of his research is principled and mathematical understanding of statistical and algorithmic problems arising in modern machine learning paradigms.
