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BIDMaP Leadership is excited to collaborate with our colleagues in Physics, Astronomy, and EPS, to include additional seminars in AI + Physical Sciences as part of this series.

We are adding speaker dates all the time - please check back frequently.

This seminar series is intended for UC Berkeley researchers, in particular, BIDMaP faculty, physical science affiliates, and trainees. Please send inquiries and registration requests to

Upcoming Seminars

Ben Nachman

Sept. 28, 2023 - Ben Nachman: Re-imagining the search for fundamental interactions with machine learning

I will describe a research program aimed at advancing the potential for discovery and interdisciplinary collaboration by approaching fundamental physics challenges through the lens of modern machine learning (ML). This research program has two complementary components. Ab initio simulations are a powerful tool of fundamental...
Jascha Sohl-Dickstein

Oct. 12, 2023 - Jascha Sohl-Dickstein

Jascha Sohl-Dickstein is a principal scientist in Google DeepMind. He is most (in)famous for inventing diffusion models. His recent work has focused on theory of overparameterized neural networks, meta-training of learned optimizers, and understanding the capabilities of large language models. Before working at Google, Jascha...
Abby Doyle

Nov. 2, 2023 - Abby Doyle: Enabling chemical synthesis with machine learning

Abby Doyle is a professor of chemical and biomolecular engineering at the University of California, Los Angeles (UCLA). Her research is focused on tackling unresolved issues in the field of organic synthesis by creating innovative catalysts, catalytic reactions, and synthetic techniques. Recently, the Doyle group...
Tess Smidt

Feb. 1, 2023 - Tess Smidt

Tess Smidt is an Assistant Professor of Electrical Engineering and Computer Science at MIT. Tess earned her SB in Physics from MIT in 2012 and her PhD in Physics from the University of California, Berkeley in 2018. Her research focuses on machine learning that incorporates...
omar yaghi

May 3, 2024 - Omar Yaghi

Omar Yaghi is the James and Neeltje Tretter Chair Professor of Chemistry at the University of California, Berkeley, the Founding Director of the Berkeley Global Science Institute, and an elected member of the US National Academy of Sciences as well as the German National Academy...

Past Seminars

Bingqing Cheng

Sep. 21, 2023 - Bingqing Cheng: Predicting material properties with the help of machine learning

A central goal of computational chemistry is to predict material properties using first-principles methods based on the fundamental laws of quantum mechanics. However, the high computational costs of these methods typically prevent rigorous predictions of macroscopic quantities at finite temperatures. In this talk, I will...
Alessandra Lanzara

Sep. 7, 2023 - Alessandra Lanzara: Quantum Operando - Switchable Quantum Materials

Alessandra Lanzara is a prominent physicist who has contributed significantly to the field of materials science. After obtaining her PhD from Universita’ di Roma La Sapienza in 1999, she joined the faculty of the physics department at UC Berkeley as an assistant professor. She has...
Fernando Pérez

May 4, 2023 - Fernando Pérez: Open Platforms, Open Science and Impact

Fernando Pérez's research focuses on creating tools for modern computational research and data science across domain disciplines, with an emphasis on high-level languages, interactive and literate computing, and reproducible research. Through tools like IPython and Project Jupyter, he builds foundational blocks that enable scientists to tackle all stages of computational research (from exploration through publication) with a coherent approach, thus improving scientific productivity, collaboration and reproducibility.
Théo Inizan Jaffrelot

Apr. 27, 2023 - Exploring the Frontiers of Large-Scale Molecular Dynamics with GPUs, Sampling, and Machine Learning

Molecular dynamics (MD) simulations offer valuable insights into the atomistic-level behavior of molecular systems. However, large-scale Molecular Dynamics simulations face some limitations. First, the high-dimensional nature of large simulation trajectories can make them difficult to interpret. Second, the accessible sampled timescale is often shorter than...
Yao Yang

Apr. 20, 2023 - Yao Yang: Operando Methods for Catalyst Discovery Driven by Machine Learning

Electrocatalysis lies at the interface between chemistry and physics and represents one of the most promising approaches for enabling renewable energy technologies to mitigate carbon emissions through the use of hydrogen fuel cells and the electrochemical reduction of CO2. One of the key challenges is...
Felix R. Fischer

Apr. 13, 2023 - Felix Fischer: A Materials Genome for 0D-, 1D-, and 2D-Nanographenes

The number of chemically unique arrangements of 15 annulated benzene rings into a planar two-dimensional molecular hydrocarbon exceeds 74,000,000. How does one pick the right target structure? We herein present the development of a complete materials genome library that predicts key fundamental physical properties for...
Shengchao Liu

Mar. 16, 2023 - Molecule Representation Learning: A Perspective from Topology, Geometry, and Textual Description

Recently, artificial intelligence (AI) for drug discovery has raised increasing interest in both the machine learning (ML) and computational chemistry/biology communities. The core task of AI for drug discovery is molecule representation learning, where the molecule knowledge can be naturally presented in different modalities: chemical...
Kristin Persson

Mar. 2, 2023 - The Era of Data-Driven Materials Innovation and Design

Kristin Persson is a professor in the Department of Materials Science and Engineering at UC Berkeley and runs a lab at the Lawrence Berkeley National Laboratory. The Persson group studies the physics and chemistry of materials using atomistic computational methods and high-performance computing technology, particularly...
Syrine Belakaria

Feb. 23, 2023 - BIDMaP Special Seminar: Syrine Belakaria

Adaptive Experimental Design for Multi-Objective Optimization: An Output Space Entropy Search Framework. Syrine Belakaria is a PhD candidate in Computer Science at Washington State University advised by Prof. Jana Doppa. Prior to her PhD, she received a Master's degree in Electrical Engineering from the University...
Colin White

Jan. 15, 2023 - Neural Architecture Search: The Next Frontier

Throughout the history of machine learning, we have seen more and more automation of tasks, such as using neural networks to replace the tedious process of manual feature design. One of the next major steps is to automatically design and tune neural networks themselves: neural...