TILOS-HDSI Seminar with Koushil Sreenath (UC Berkeley)
2026-01-28 11:00 - 12:00 (America/Los_Angeles) | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Title and abstract TBA…

Koushil Sreenath is an Assistant 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 on the bipedal robot MABEL 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 entitled Adaptive Sampling with Mobile WSN (IET). He received the Best Paper Award at the Robotics: Science and Systems (RSS) Conference in 2013, and the Google Faculty Research Award in Robotics in 2015.
Zoom: https://bit.ly/TILOS-Seminars

Optimization for ML and AI Seminar with Tengyu Ma (Stanford)
2026-01-30 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Title and abstract TBA…

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 with Yang Zheng (UC San Diego)
2026-02-06 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Title and abstract TBA…

Yang Zheng is an Assistant Professor in Electrical and Computer Engineering at UC San Diego, and is affiliated with the department of Computer Science and Engineering and the Contextual Robotics Institute. From March 2019 to August 2020, Dr. Zheng was a postdoctoral scholar in SEAS and CGBC at Harvard University, working with Prof. Na Li and Prof. Ali Malkawi. In 2021 he was a research associate in the Verification of Autonomous Systems group at Imperial College London. Dr. Zheng obtained his Ph.D. in Control from the University of Oxford for his work on Chordal Sparsity in Control and Optimization of Large-scale Systems under the supervision of Prof. Antonis Papachristodoulou. His research interests include convex and non-convex optimization for control, principled data-driven and learning-based control, structures and algorithms for scalable conic optimization, applications in mixed traffic control, and certifiable robustness of machine learning (e.g., deep neural networks).
Zoom: https://bit.ly/Opt-AI-ML

TILOS-HDSI Seminar with Maarten de Hoop (Rice University)
2026-02-11 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Title and abstract TBA…

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.
Zoom: https://bit.ly/TILOS-Seminars

TILOS-HDSI Seminar with Jason Eshraghian (UC Santa Cruz)
2026-02-20 10:00 - 11:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Title and abstract TBA…

Jason Eshraghian is an Assistant Professor at the Department of Electrical and Computer Engineering, University of California, Santa Cruz. He received the Bachelor of Engineering and the Bachelor of Laws degrees from The University of Western Australia, where he also obtained his Ph.D. degree. He currently serves as the secretary-elect of the IEEE Neural Systems and Applications Technical Committee and is a consultant to several medical-tech startups. Dr. Eshraghian was awarded the 2019 IEEE VLSI Systems Best Paper Award, the 2019 IEEE AICAS Best Paper Award, and the Best Live Demonstration Award at the 2020 IEEE International Conference on Electronics, Circuits, and Systems. He is a recipient of the Fulbright, Endeavour, and Forrest Research Fellowships. His current research focuses on brain-inspired circuit design to accelerate AI algorithms and spiking neural networks.
Zoom: https://bit.ly/TILOS-Seminars

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: TBA

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

Optimization for ML and AI Seminar with Yuejie Chi (Yale)
2026-03-13 10:00 - 11:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Title and abstract TBA…

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

TILOS-HDSI Seminar with Rene Vidal (University of Pennsylvania)
2026-04-08 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Title and abstract TBA…

René Vidal, a global pioneer of data science, is the Rachleff University Professor, with joint appointments in the Department of Radiology in the Perelman School of Medicine and the Department of Electrical and Systems Engineering in the School of Engineering and Applied Science. Dr. Vidal has been named a Penn Integrates Knowledge University Professor at the University of Pennsylvania.
René Vidal received his B.S. degree in Electrical Engineering (highest honors) from the Pontificia Universidad Catolica de Chile in 1997 and his M.S. and Ph.D. degrees in Electrical Engineering and Computer Sciences from the University of California at Berkeley in 2000 and 2003, respectively. He was a research fellow at the National ICT Australia in 2003 and joined The Johns Hopkins University in 2004 as a faculty member in the Department of Biomedical Engineering and the Center for Imaging Science.
Zoom: https://bit.ly/TILOS-Seminars

Optimization for ML and AI Seminar with Andre Wibisono (Yale)
2026-04-10 10:00 - 11:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Title and abstract TBA…

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. Dr. Wibisono received his MA in Statistics and PhD in Computer Science from UC Berkeley, advised by Michael I. Jordan. Before joining Yale in 2021, he was a postdoctoral researcher at the University of Wisconsin–Madison, working with Po-Ling Loh and Varun Jog, and at the Georgia Institute of Technology, working with Jacob Abernethy and Santosh Vempala.
Zoom: https://bit.ly/Opt-AI-ML

ICLR 2026 Workshop: Principled Design for Trustworthy AI - Interpretability, Robustness, and Safety across Modalities
2026-04-26 - 2026-04-27 | ICLR 2026, Riocentro Convention and Event Center, Rio de Janiero, Brazil
Modern AI systems, particularly large language models, vision-language models, and deep vision networks, are increasingly deployed in high-stakes settings such as healthcare, autonomous driving, and legal decisions. Yet, their lack of transparency, fragility to distributional shifts between train/test environments, and representation misalignment in emerging tasks and data/feature modalities raise serious concerns about their trustworthiness.
This workshop focuses on developing trustworthy AI systems by principled design: models that are interpretable, robust, and aligned across the full lifecycle – from training and evaluation to inference-time behavior and deployment. We aim to unify efforts across modalities (language, vision, audio, and time series) and across technical areas of trustworthiness spanning interpretability, robustness, uncertainty, and safety.

TILOS-HDSI Seminar with Ellen Vitercik (Stanford)
2026-05-06 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Title and abstract TBA…

Ellen Vitercik is an Assistant Professor at Stanford with a joint appointment between the Management Science and 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, Dr. Vitercik was a Miller fellow at UC Berkeley, hosted by Michael Jordan and Jennifer Chayes. She received a PhD in Computer Science from Carnegie Mellon University, advised by Nina Balcan and Tuomas Sandholm. Dr. Vitercik has been recognized by a Schmidt Sciences AI2050 Early Career Fellowship and an NSF CAREER award. Her thesis won the SIGecom Doctoral Dissertation Award, the CMU School of Computer Science Distinguished Dissertation Award, and the Honorable Mention Victor Lesser Distinguished Dissertation Award.
Zoom: https://bit.ly/TILOS-Seminars
