Thursday, May 15, 2025
7:15 am – 6 pm
The Grimes Engineering Center
Day of schedule
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7:15 – 8:15 AM |
Continental Breakfast– Raw Thrills Lounge |
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8:15 – 9:45 AM |
Business Session *NAE Members Only* – Room 164/166 |
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10 – 10:20 AM |
Welcome Session – Jarvis Auditorium |
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10:20 – 11:10 AM |
Keynote Speakers – Jarvis Auditorium |
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11:10 – 11:25 AM |
Break |
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11:25 AM – 12:30 PM |
Lightning Talks – Jarvis Auditorium |
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12:30 – 1:30 PM |
Lunch – Raw Thrills Lounge |
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1:30 – 2:40 PM |
Keynote Speakers – Jarvis Auditorium |
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2:40 – 3 PM |
Break |
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3 PM – 4:20 PM |
Panel Discussion – Jarvis Auditorium |
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4:20 – 5 PM |
Lab tour(s) – Separate Reservation Required |
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5 – 6 PM |
Reception – Raw Thrills Lounge |
Speakers
Pieter Abbeel
Pieter Abbeel is a professor of electrical engineering and computer sciences at UC Berkeley, as well as the director of the Berkeley Robot Learning Lab and co-director of the Berkeley Artificial Intelligence Research Lab. Abbeel’s research strives to build ever more intelligent systems, while exploring the influence of AI on society and ways AI could advance other science and engineering disciplines. Abbeel has founded three companies, including Covariant, which is focused on AI for robotic automation of warehouses and factories. In addition, he is the recipient of such prestigious awards as the Presidential Early Career Award for Scientists and Engineers and the National Science Foundation CAREER Award.
Tejas N. Narechania
Tejas N. Narechania is a Professor of Law at the University of California, Berkeley, School of Law, where he focuses on a range of technology law matters. He has a J.D. from Columbia Law School, and a B.S. (Electrical Engineering and Computer Science) and a B.A. (Political Science) from the University of California, Berkeley. His research projects have appeared in interdisciplinary venues, from law reviews to computer science proceedings, and they have been cited by policymakers and in the press.
Tianzhen Hong
Dr. Tianzhen Hong is Senior Scientist and Deputy Director for Research with the Building Technology and Urban Systems Division of LBNL. His research employs interdisciplinary approaches to studying building technologies, human factors, building performance modeling, and AI supporting the design and operation of energy efficient, demand flexible, and resilient buildings across scales. He is an IBPSA Fellow and an ASHRAE Fellow. He is a Highly Cited Researcher since 2021 and the Executive Editor of the Energy and Buildings journal.
Kristin Persson
Kristin Persson is the Daniel M. Tellep Distinguished Professor of materials science and engineering at UC Berkeley and a senior faculty scientist at Lawrence Berkeley National Laboratory. In addition, she is the director and founder of the Materials Project, a world-leading resource for materials data and design. Persson is the recipient of such honors as the DOE Secretary of Energy’s Achievement Award and the TMS Cyril Stanley Smith Award, and was recently named an Office of Science Distinguished Scientist Fellow. She also is a member of the Royal Swedish Academy of Science, as well as an MRS Fellow, a AAAS Fellow and an APS Fellow.
Kenichi Soga
Kenichi Soga is the Donald H. McLaughlin Chair in Mineral Engineering and a Distinguished Professor of Civil and Environmental Engineering at UC Berkeley. He serves as the director of the Berkeley Center for Smart Infrastructure and is a faculty scientist at the Lawrence Berkeley National Laboratory. Soga’s research areas include infrastructure sensing and modeling, performance-based design and maintenance of infrastructure, energy geotechnics, and geomechanics. He has published over 500 journal and conference papers, and is the co-author of the book “Fundamentals of Soil Behavior.” Throughout his career, Soga has received numerous notable awards, including the UC Berkeley Bakar Prize
Panelist speakers
Bryan Catanzaro
Bryan Catanzaro is Vice President of Applied Deep Learning Research at NVIDIA, where he leads a team working on multimodal language modeling, chip design, audio and speech, graphics and vision. Bryan helped create CUDNN, NVIDIA’s first AI product; DLSS, the most widely deployed neural rendering system, which uses AI to render more than 500 of the most advanced virtual worlds in gaming and design; and Megatron, which set speed records at scale for training large language models and forms the technical basis for many Generative AI projects around the industry. Bryan received his PhD in EECS from the University of California, Berkeley.
Ion Stoica
Ion Stoica is a Professor in the EECS Department at the University of California at Berkeley, where he holds the Xu Bao Chancellor’s Chair and is the Director of SkyLab. He is currently doing research on cloud computing and AI systems. Past work includes Ray, Apache Spark, Apache Mesos, Tachyon, Chord DHT, and Dynamic Packet State. He is an Honorary Member of the Romanian Academy, an ACM Fellow and has received numerous awards, including the Mark Weiser Award (2019), SIGOPS Hall of Fame Award (2015), and several “Test of Time” awards. He also co-founded three companies, Anyscale (2019), Databricks (2013) and Conviva (2006).
Dan Klein
Dan Klein is a Professor of Computer Science at UC Berkeley and CTO/Cofounder at Scaled Cognition. His research focuses on making AI/NLP systems reliable, truthful, and safe. Topics include grounding, reasoning, and multimodality in LLMs, particularly in decision-theoretic contexts where systems must act pursuant to goals while complying with restrictions. Prof. Klein’s academic honors include the ACM Grace Murray Hopper Award, the Sloan Fellowship, the Microsoft Faculty Fellowship, the Berkeley Distinguished Teaching Award, and others. His industry experience includes founding roles at Adap.tv and Semantic Machines, and as a Technical Fellow at Microsoft.
Tsu-Jae King Liu
In addition to her impactful and award-winning research contributions, Dr. Liu has demonstrated a strong commitment to enhancing the educational experience of students. As dean, she has bolstered programs to support the academic success and well-being of both undergraduate and graduate engineering students, including supporting the success of women and students from underrepresented minority groups and first-generation college students. Dr. Liu’s most recent initiative was to renovate and expand the Berkeley engineering student center to create more welcoming and inclusive spaces for students to learn, discover, and innovate together; the building is scheduled to open in early 2025. Dr. Liu also spearheaded a nationwide effort called the American Semiconductor Academy initiative to establish a collaborative and inclusive national network for microelectronics education. That network is designed to meet the workforce development needs of the US microelectronics industry. For her outstanding contributions in education, Dr. Liu received the IEEE Electron Devices Society Education Award.
Lightning speakers
Angjoo Kanazawa
Angjoo Kanazawa is an assistant professor in the Department of Electrical Engineering and Computer Sciences. Her research lies at the intersection of computer vision, computer graphics, and machine learning. We live in a 3D world that is dynamic, full of life with people and animals interacting with the environment. How can we build a system that can capture, perceive, and understand this 4D world from everyday photograph and video? How can we learn priors on the 4D world from image and video observations? The goal of her lab is to answer these questions.
Mary Scott
Mary Scott is the Ted van Duzer Associate Professor in the Materials Science and Engineering
department at University of California, Berkeley. She is also a Faculty Staff Scientist at the
National Center for Electron Microscopy, part of the Molecular Foundry at Lawrence Berkeley
National Lab. She received a B.S. in Aerospace Engineering and a B.S. in Physics, followed by
an M.S. in Physics, from North Carolina State University. She obtained her Ph.D. in Physics
from the University of California, Los Angeles. Prof. Scott’s research program seeks to combine
advanced electron microscopy with modern mathematical approaches for data handling and
interpretation. Examples of her work include atomic resolution electron tomography studies of
nanomaterials, machine learning approaches to interpret imaging and diffraction electron
microscopy data, scanning nanodiffraction studies of disordered materials, and multimodal
studies of interfaces in battery materials.
Aditi Krishnapriyan
Aditi Krishnapriyan’s research interests are focused on developing machine learning methods that are motivated by the opportunities and challenges in science and engineering, with particular interest in physics-inspired machine learning methods. Some of the areas of exploration include approaches to incorporate physical inductive biases into ML models to improve generalization, the advantages that ML can bring to classical physics-based numerical solvers (such as through end-to-end differentiable frameworks and implicit layers), and better learning strategies for distribution shifts in the physical sciences. Our foundational research is informed by and grounded in applications in physics, fluid and molecular dynamics, materials design, climate science, and other related areas. This work also includes interfacing with other fields including numerical methods, dynamical systems theory, quantum mechanical simulations, computational geometry, and optimization.
Emma Pierson
Emma Pierson is an assistant professor of computer science, affiliated with the Berkeley AI Research Lab, Computational Precision Health, and the Center for Human-Compatible AI. Her work develops data science and machine learning methods to study two broad areas: inequality and healthcare. Please see the “Publications” link for representative papers. Her work has been recognized by best paper awards at KDD and AISTATS, an NSF CAREER award, a Rhodes Scholarship, Hertz Fellowship, Rising Star in EECS, MIT Technology Review 35 Innovators Under 35, Forbes 30 Under 30 in Science, AI2050 Early Career Fellowship, and Samsung AI Researcher of the Year. She writes a statistics blog, Obsession with Regression, and has also written for The New York Times, FiveThirtyEight, The Atlantic, The Washington Post, Wired, and various other publications.
Alane Suhr
Alane Suhr is an assistant professor in the Division of Computer Science, EECS. Alane’s research focuses on building systems that use and learn language to interact with people in collaborative, situated interactions. This work spans natural language processing, machine learning, and computer vision. Alane received a PhD in Computer Science from Cornell University, and a BS in Computer Science and Engineering with a minor in Linguistics from Ohio State University.
Dani Ushizima
Dani Ushizima, Ph.D., is a SENIOR Scientist at Berkeley Lab, also Affiliated Faculty with the Bakar Institute at UC San Francisco, and Berkeley Institute for Data Science (BIDS) at UC Berkeley. She leads the Machine Learning (ML) team for the Center of Advanced Mathematics for Energy Research Applications (CAMERA) at LBNL. Since 1996, Ushizima has investigated, developed and deployed advanced algorithms to extract information from scientific data for decision making. Together with her team at Berkeley Lab, they develop computer vision software that exploits unique ML and datasets from microscopes (e.g. X-ray, electron, tomography) for quantitative analysis. She has led projects ranging from quality control of materials, plant growth to testing radioactive markers for biomedical imaging. In addition to high-resolution, high-throughput image sources, her research also includes ML such as transformers applied to natural language processing. Awarded DOE Early Career fellowship in 2016. Development of pyCBIR, an image recommendation software. Nominated the LBNL Director’s Award in 2017, followed by the LBNL Women@Lab in 2018, the Latina Scientist in 2021 and the PMWC Pioneer Award in 2023. Scientific diplomacy with the U.S. Dept. of State TechWomen. Cooperation with UC San Francisco in the design of algorithms to improve cell counting and analyses of biomedical imaging. Visit Ushizima’s website for more information.