BIDMaP is happy to be jointly hosting this year's seminar series with colleagues in computational physical sciences, including collaborators from the departments of Physics and Astronomy, and the Lawrence Berkeley National Laboratory.
Foundation models like GPT-4 have dramatically altered the modern work landscape for many industries reliant on language tasks, but no equivalent model exists yet for scientific applications. Incorporating foundation models into research workflows could enable unprecedented discoveries. However, mainstream foundation models trained on human-scale datasets...
Join us for a symposium exploring the transformative potential of AI to advance climate tech, hosted by Bakar Climate Labs and Bakar Institute of Digital Materials for the Planet. Objective: This symposium will highlight the appropriate use of AI to enable new climate technologies, showcasing...
Biography: Professor Garnett's primary research interest is Bayesian active learning, with a focus on applications in the natural sciences and engineering. A major theme in his research is automating scientific discovery, broadly interpreted to include both theory and practice and both policy design and modeling...
Biography: Zachary W. Ulissi joined Meta’s Fundamental AI Research lab in 2023 to work on AI for chemistry and climate applications and is based in the San Francisco Bay Area. He is particularly excited about how AI and machine learning methods can enhance various quantum...
Biography: Dr. Ching-Yao Lai and her group attack fundamental questions in ice-dynamics, geophysics, and fluid dynamics by integrating mathematical and machine-learned models with observational data. They use their findings to address challenges facing the world, such as advancing our scientific knowledge of ice dynamics under...
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...
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 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 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 of Idaho where she was advised by Professor Sameh Sorour and received...
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...
