Organizers

Judah Goldfeder is a PhD Candidate at the Creative Machines Lab at Columbia University, a Student Researcher at Google, and a member of the NSF AI Institute in Dynamic Systems. He is focused on several projects in the Deep Learning space, including developing an open source building and HVAC simulator, applying Reinforcement Learning to the HVAC systems of large commercial buildings, reconstructing neural network weights from only query access, Auxiliary Learning and Self Supervised Learning for Computer Vision, Machine Crystallography, Machine Learning for Biometrics, and Robotics.
Previously, he worked at Facebook AI Research, on applying Transformers to Graph Neural Networks at scale, and at Twitter, where he worked on improving production ads models. He also has helped develop AI educational resources at Learn Ventures, an innovative education startup, and has interned at Bar Ilan University, where he worked on using formal verification to predict gene interaction in cells. He also is a consultant for Dicta, an NLP research nonprofit focusing on Hebrew and related low-resource languages. Judah is organizing several workshops on Smart Buildings, including at ICML, ACM E-energy, ASHRAE, and Syracuse University.

Na (Lina) Li is the Winokur Family Professor of Electrical Engineering and Applied Mathematics at the Harvard School of Engineering and Applied Sciences (SEAS), a research affiliate at the Laboratory for Information and Decision Systems (LIDS) at MIT, and the co-founder and Scientific Advisor of Singularity Energy, Inc., which provides innovative tools and APIs for building the future of decarbonization solutions. Her research focuses on control, learning, and optimization of networked systems, integrating theory development and algorithm design with applications in real-world cyber-physical systems, including energy grids, buildings, multi-robot systems, and biomedical devices. The goal of her research is to develop foundational decision-making tools that enable reliable, efficient, and autonomous operation of complex systems by leveraging their underlying structures. She has received numerous awards, including the IFAC Thoma Medal, Donald P. Eckman Award, ONR Young Investigator Award, AFOSR Young Investigator Award, NSF CAREER Award, and Harvard Climate Change Solution Fund. She has co-authored over 150 publications, is an active mentor in her field, and frequently speaks at industry and academic events, driving forward conversations around AI and control systems.

Donna Vakalis is a postdoctoral fellow at Mila, focusing on the application of machine learning to sustainable buildings. With a background in engineering and architecture, and experience as a two-time Olympian, she brings unique insights into performance. Her research dives deep into causal reasoning, spatiotemporal forecasting, and model-based control.

Bianca Howard is a building energy systems researcher whose work focuses on modeling, optimization, and control techniques to estimate, forecast, and manipulate the timing of energy use in buildings and their systems. Her research aims to inform policy and improve building design and operation to meet net-zero emissions goals. She has led projects on model predictive control for coordinated demand reduction in electrically heated homes, the development of urban-informed building occupancy models, the evaluation of gaps in validated urban energy models, and the use of evolutionary algorithms to study cost-emissions tradeoffs in the English housing stock under fuel poverty constraints. Dr. Howard received her PhD (2016) and MS (2011) in Mechanical Engineering from Columbia University and a BS (2009) from the University of Nebraska–Lincoln.

Philippe Wyder is a postdoctoral scholar at the University of Washington under Professor Nathan Kutz in the Department of Applied Mathematics. Wyder is a contributor to Google’s Open Source Smart Buildings Project. He earned his Ph.D. in Mechanical Engineering from Columbia University under the guidance of Professor Hod Lipson at the Creative Machines Lab. His research interests lie at the intersection of developmental robotics, artificial life, and artificial intelligence. Wyder focuses on robots that can self-assemble, self-repair, and grow by incorporating materials from their surroundings. In his recent work, Wyder introduces the Robot Link, a modular robotic platform that can absorb more material, integrate new parts, and shed waste. Wyder shows how robots can enhance their physical structure and capabilities within their lifetime. He is also pioneering the Vibrating Particle Robot, which leverages modularity to create adaptive robotic systems. His broader research ambition is to extend modularity in robot bodies and intelligence, aiming to advance the development of robots that can evolve physically and cognitively. Wyder’s research has been published in PLOS One and The Royal Society Interface, and was recently presented at the ReMAR 2024 conference, contributing to the frontier of adaptable and resilient robotic systems. In addition to his robotics work, Philippe is working towards new learning methods that favor neural network modularity and organizing several workshops on Smart Buildings, including at ICML, ACM E-energy, and Syracuse University.

Jiong Lin is a PhD candidate at the Creative Machines Lab at Columbia University, advised by Professor Hod Lipson. He is also a Graduate Student Associate at the NSF AI Institute in Dynamic Systems. His research focuses on robot self-modeling, integrating 3D vision and representation learning to infer a robot’s kinematic and morphological structure from visual data. Jiong received his MS from Columbia University and BS from Huazhong University of Science and Technology. His recent work includes AutoURDF (CVPR 2025), a learning-based pipeline for reconstructing robot description files from point cloud sequences, and a single-view visual self-modeling system using action-conditioned NeRFs (Nature Machine Intelligence 2025). He has also contributed to a project on human–robot co-expression (Science Robotics 2024), and is currently developing self-modeling techniques for soft and continuum robots, as well as generative models for kinematic synthesis.

Bing Dong is a Professor in the Mechanical and Aerospace Engineering Department at Syracuse University. He also serves as the Associate Director of the New York State Center of Excellence in Environmental and Energy Systems. Dr. Dong’s research interests encompass the broad field of smart buildings and cities, including intelligent energy systems, occupant behavior modeling at both building and urban scales, buildings-to-grid integration, building controls and diagnostics, quantum computing for building and urban energy system optimization, as well as AI/machine learning applications for smart buildings. He has published over 120 peer-reviewed journal and conference papers and holds 7 U.S. patents. Professor Dong received numerous awards including the IBPSA-USA Emerging Contributor Award and NSF CAREER Award. He is the Associate Editor of the International Journal of Building Simulation and serves as the Vice Chair for ASHRAE TC7.10. He is also a member of the Board of Directors of the International Association of Building Physics. Professor Dong is an IBPSA Fellow.

Yoshua Bengio
Recognized worldwide as one of the leading experts in artificial intelligence, Yoshua Bengio is most known for his pioneering work in deep learning, earning him the 2018 A.M. Turing Award, “the Nobel Prize of Computing,” with Geoffrey Hinton and Yann LeCun. He is Full Professor at Université de Montréal, and Founder and Scientific Advisor of Mila – Quebec AI Institute. He co-directs the CIFAR Learning in Machines & Brains program as Senior Fellow and acts as Special Advisor and Founding Scientific Director of IVADO. In 2019, he was awarded the prestigious Killam Prize and for several years has been the computer scientist with the greatest impact in terms of number of citations and h-index. He is a Fellow of both the Royal Society of London and Canada, Knight of the Legion of Honor of France, Officer of the Order of Canada, Member of the UN’s Scientific Advisory Board for Independent Advice on Breakthroughs in Science and Technology since 2023, and a Canada CIFAR AI Chair.
Concerned about the social impact of AI, he actively contributed to the Montreal Declaration for the Responsible Development of Artificial Intelligence and currently chairs the International Scientific Report on the Safety of Advanced AI.