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

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.
Zoom: https://SDSU.zoom.us/j/82711917109

Optimization for AI and ML Seminar with Volkan Cevher (EPFL)
2025-12-03 13:00 - 14:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Title and abstract TBA…

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

Networking Lunch Reception at NeurIPS 2025
2025-12-04 12:00 - 14:00 | Mezé Greek Fusion, San Diego, CA, United States
TILOS will host a networking lunch reception during NeurIPS 2025 at Mezé Greek Fusion from 12:00-2:00pm on Thursday, December 4, 2025. This event is open to all NeurIPS attendees affiliated with any of the NSF AI Research Institutes, as well as industry partners. Join us to connect with colleagues across the network of NSF AI Institutes, share research interests, and explore opportunities for collaboration.
Space is limited; please RSVP here by Monday, November 24, to help us plan accordingly.
📅 Date: Thursday, December 4, 2025🕛 Time: 12:00 – 2:00pm PST📍 Location: Mezé Greek Fusion (3 blocks from the conference venue)

Optimization for ML and AI Seminar with Frank E. Curtis (Lehigh University)
2025-12-05 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Title and abstract TBA…

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

NeurIPS 2025 Workshop on Differentiable Learning of Combinatorial Algorithms
2025-12-06 | San Diego Convention Center, San Diego, CA, United States
Combinatorial algorithms are fundamental across a wide range of domains, owing to their ability to model optimization and decision-making tasks under complex constraints. These algorithms underpin practical applications such as vehicle routing, network and chip design, clustering and information retrieval. Combinatorial problems are also prominent in various areas of machine learning such as natural language processing and robotics. Recent research has focused on leveraging neural networks to design novel combinatorial algorithms and to come up with techniques that allow seamless integration of classic combinatorial algorithms in differentiable neural network architectures. Developments in this field, commonly referred to as neural combinatorial optimization, have raised several fundamental questions that span both theory and practice.
In this workshop, we take a broad perspective on designing differentiable algorithms for combinatorial optimization. The goal of the workshop is to explore novel ideas in the design and applications of neural combinatorial optimization, as well as to improve our theoretical understanding of existing methods.

NeurIPS 2025 Workshop on Optimization for Machine Learning
2025-12-06 | San Diego Convention Center, San Diego, CA, United States
Optimization lies at the heart of many machine learning algorithms and enjoys great interest in our community. Indeed, this intimate relation of optimization with ML is the key motivation for the OPT series of workshops. We aim to foster discussion, discovery, and dissemination of state-of-the-art research in optimization relevant to ML.
The focus of OPT 2025 is on “Statistics Meets Optimization”. Since its inception, stochastic optimization has been grounded in statistical principles. Today, many of the most pressing challenges in machine learning—such as generalization bounds, the training dynamics of overparameterized models, and the development of generative models—are directly inspired by statistical thinking. At the same time, the scale and complexity of modern datasets, along with the increasingly rich model classes used to represent them, pose new questions about how optimization algorithms interact with these structures—both computationally and statistically. For example, what role do data symmetries play in shaping optimization trajectories? How do statistical properties of the data affect the adaptivity and efficiency of learning algorithms? And how can optimization approaches be designed to scale with data while still preserving desirable statistical behavior? OPT 2025 will explore these questions with the goal of building bridges between the statistics and optimization communities, and highlighting their shared impact on the theory and practice of machine learning.

NeurIPS 2025 Workshop on Imageomics: Discovering Biological Knowledge from Images Using AI
2025-12-06 | San Diego Convention Center, San Diego, CA, United States
Imageomics is an emerging interdisciplinary field at the crossroads of machine learning (ML), computer vision (CV), and biological sciences. It leverages visual data—from microscopic images of single-cell species to videos of megafauna—to extract and analyze biological information, specifically traits. By grounding ML models in existing scientific knowledge, Imageomics aims to make traits computable from images, facilitating insights into the evolution and function of living organisms. Imageomics poses research problems that resonate with the broad machine-learning community: multimodal representation learning, object detection and tracking, few-shot learning, imbalanced-class learning, video understanding, 3D modeling, hierarchical learning, etc. When people leverage ML tools to solve biological questions, the foundational bridges between ML and biological sciences also provide opportunities to address key challenges in ML, creating a virtuous cycle between the two fields.
We welcome participation from anyone interested in learning about the field of Imageomics, including:

Biological Scientists who are interested in applying ML and CV to their research, or who want to learn how to use ML tools to analyze biological images.
Machine Learning Researchers who are interested in applying their expertise to biological image data, or who want to learn about the unique challenges and opportunities in this domain.

The workshop will feature keynote talks, paper presentations, and discussions on the latest research in Imageomics. We encourage participants to submit papers and demos related to the topics outlined in the Call For Papers. The workshop will also provide opportunities for networking and collaboration among researchers from diverse backgrounds.

NeurIPS 2025 Workshop on New Perspectives in Advancing Graph Machine Learning
2025-12-07 | San Diego Convention Center, San Diego, CA, United States
Graphs serve as a powerful representational framework for machine learning, and their integration has substantially advanced the field. Indeed, extensive studies have pushed forward graph machine learning (GML) in both theory and applications. Recently, new perspectives have been emerging in the machine learning community, including algebraic–topological analyses, foundation models, generative models, and large models in applications. Leveraging these ideas for core graph machine learning holds a lot of promise, including the dual benefit of deeper theoretical insight, new capabilities and more powerful, application-aligned algorithms and models. The aim of this workshop is to explore and connect these new perspectives on GML, and to identify overarching challenges and tools – in terms of theory, methodology, and modeling.

TILOS-HDSI Seminar with Yi Wu (Tsinghua University)
2025-12-08 11:00 - 12:00 | Qualcomm Conference Center Room B and Virtual, 9736 Engineers Ln, La Jolla, CA, 92093, United States
Title and abstract TBA…

Yi Wu is an Assistant Professor at Tsinghua University’s Institute for Interdisciplinary Information Sciences (IIIS). He received his Ph.D. from the University of California, Berkeley, advised by Professor Stuart Russell. Before joining IIIS, Dr. Wu was a researcher at OpenAI. His research interest include deep reinforcement learning, multi-agent learning, natural language grounding, and large-scale learning systems.
Zoom: https://bit.ly/TILOS-Seminars

Optimization for ML and AI Seminar with Joel Tropp (Caltech)
2026-01-09 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Title and abstract TBA…

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

TILOS-HDSI Seminar with Koushil Sreenath (UC Berkeley)
2026-01-28 11:00 - 12:00 | 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

Optimization for ML and AI Seminar with Bharath Sriperumbudur (Penn State)
2026-02-27 11:00 - 12:00 | HDSI 123 and Virtual, 3234 Matthews Ln, La Jolla, CA, 92093, United States
Title and 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 11:00 - 12:00 | 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 11:00 - 12:00 | 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

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
