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 […]
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 […]
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 […]
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 […]
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, […]
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 […]
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 […]
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 […]
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
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 […]
