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.

Upcoming Seminars

17
Mar
Frank

March. 17, 2025 - Frank Noé: Molecular Science in the Age of AI


Frank Noé will explore how AI-powered computational methods are transforming biomolecular research, emphasizing the role of AI in decoding complex biological functions. He will discuss how sophisticated machine learning techniques enhance the analysis of biomolecular dynamics, such as protein folding, offering unprecedented insights into molecular...
20
Mar
Young

March. 20, 2025 - Yousung Jung: Data-Enabled Synthesis Predictions for Molecules and Materials

Chemical and materials informatics leverages data to correlate a compound’s structure with its properties, enabling the exploration of vast chemical spaces through modeling. Although this approach has the potential to reveal novel materials with desired characteristics, it frequently generates designs that cannot be synthesized, leading...
03
Apr
Jesse

Apr. 3, 2025 - Jesse Thaler

Biography: Professor Thaler is a theoretical particle physicist who fuses techniques from quantum field theory and machine learning to address outstanding questions in fundamental physics. His current research is focused on maximizing the discovery potential of the Large Hadron Collider (LHC) through new theoretical frameworks...
10
Apr
Clemence

Apr. 10, 2025 - Clemence Corminboeuf

Biography: Professor Corminboeuf researches and focuses on electronic structure theory in the area of method development and conceptual work applied to the field of homogeneous catalysis and organic electronics. Her group has contributed to the establishment of quantum chemical approaches and is involved in injecting...
17
Apr
Shirley

Apr. 17, 2025 - Shirley Ho

Biography: Professor Ho joined the Physics Department as a Research Professor and as an Affiliated Faculty at Center for Data Science at NYU in 2021. Ho joined Simons Foundation in 2018 as leader of the Cosmology X Data Science group at CCA and in 2021...
24
Apr
Maria

Apr. 24, 2025 - Maria Chan

Biography: Dr. Chan is a scientist at the Center for Nanoscale Materials at Argonne National Laboratory who studies nanomaterials and renewable energy materials, including solar cells, batteries, thermoelectrics, and catalysts. Her particular focus is on using artificial intelligence/machine learning ( AI/ ML) for efficient materials...
01
May
Roman Garnett

May. 1, 2025 - Roman Garnett

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...
08
May
Klauss

May. 8, 2025 - Klauss Robert Muller

Biography: Klaus-Robert Müller has been a professor of computer science at Technische Universität Berlin since 2006; at the same time he is directing rsp. co-directing the Berlin Machine Learning Center and the Berlin Big Data Center and most recently BIFOLD . He studied physics in...
15
May
Yao

May. 15, 2025 - Ching-Yao Lai

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...

Past Seminars

13
Mar
Eva

March. 13, 2025 - Eva Silverstein: Hamiltonian Dynamics for precision optimization

After reviewing and updating the theory of energy-conserving Hamiltonian dynamics for optimization and sampling, I'll explain a new application of Energy Conserving Descent (ECD) optimization to precision scientific data analysis in which NN initialization variance has been a bottleneck. Specifically, we choose a particular ECD...
06
Mar
Matthew

March. 6, 2025 - Matthew Sigman: Developing Data Science Tools for Synthetic Chemists

The optimization of catalytic reactions for organic synthesis can be challenging as the interplay between the catalyst structure, reaction conditions, and substrates involved is a complex multidimensional problem. In other words, it is difficult to ascertain the pattern within the noise to offer a complete...
27
Feb
David Shih

Feb. 27, 2025 - David Shih: Shedding Light on Dark Matter with Modern Machine Learning and the Gaia Space Telescope

Dark matter is one of the greatest enduring mysteries of fundamental physics. Despite countless direct and indirect searches for dark matter, still, the only evidence we have for it is through its gravitational effects on astrophysical and cosmological scales. In this talk, I will describe...
20
Feb
Gabe

Feb. 20, 2025 - Gabe Gomes: Autonomous chemical research with large language models

Transformer-based large language models are making significant strides in various fields, such as natural language processing, biology, chemistry, and computer programming. Here, we show the development and capabilities of Coscientist, an artificial intelligence system that autonomously designs, plans, and performs complex experiments by incorporating large...
13
Feb
Test

Feb. 13, 2025 - Teresa Head-Gordon: Machine Learning and Artificial Intelligence for Chemistry (and Materials)

The size of chemical space is vast. This makes the application of the first principles of quantum mechanical and advanced statistical mechanics sampling methods to identify binding motifs, conformational equilibria, and reaction pathways extremely challenging, even when considering better physical models, algorithms, or future exascale...
06
Feb
Gershoni

Feb. 6, 2025 - Renana Gershoni-Poranne: Mission ImPASsible - Decoding Polycyclic Aromatic Systems with Deep Learning

Polycyclic aromatic systems (PASs) present a seemingly insurmountable challenge: vast chemical spaces, complex electronic structures, and elusive aromatic properties. Our mission, should we choose to accept it, is to harness the power of deep learning to decode these molecular mysteries. In this talk, we embark...
30
Jan
Daniel

Jan. 30, 2025 - Daniel Whiteson: Learning to find weird tracks

Finding particle tracks is a central component of searching for new phenomena, but is very a challenging combinatorial problem. Traditionally, track finding codes assume that tracks must be helical, which simplifies the task but also restricts power to discover new physics which might produce non-helical...
23
Jan
Fran

Jan. 23, 2025 - Franziska Bell: At-scale Human - AI Teams

At-scale Human - AI Teams In this talk, Dr. Franziska Bell will share the evolution of enterprise-scale human - AI teams, alongside corresponding examples and share her vision of the next generation of agentic-based AI. Biography Dr. Franziska Bell holds a PhD in theoretical chemistry...
09
Jan
Seminar

Jan. 9, 2025 - Shijing Sun: Teaching a Robot to Design Energy Materials

Artificial intelligence (AI) and robotics have emerged as transformative tools to accelerate materials research, however, challenges remain in realizing the full potential of computational designs in laboratory settings. With the rise of self-driving laboratories powered by automated experiments and AI-driven guidance, a paradigm shift in...
05
Dec
Vini

Dec. 5, 2024 - Vinicius Mikuni: Accelerating Discovery in High Energy Physics using AI

The past decade was marked by an exponential increase in the availability of experimental data in high energy physics, leading to unprecedented precision in the description of particle interactions. However, indirect evidence for new physics processes, such as the existence of dark matter, motivates the...
21
Nov
BARTOSZ

Nov. 21, 2024 - Bartosz Grzybowski: Algorithms for synthesis planning, reaction discovery and chemical industry.

After decades of rather unsuccessful attempts, computers are finally making impact on the practice of synthetic chemistry. This change is made possible by the combination of increased computing power and, above all, new algorithms to encode and manipulate synthetic knowledge at various levels, from sequences...
14
Nov
Lin

Nov. 14, 2024 - Lin Lin: Optimization and anti-symmetry in neural network variational Monte Carlo

Neural network wavefunctions optimized using the variational Monte Carlo method have been shown to produce highly accurate results for the electronic structure of atoms and small molecules, but the high cost of optimizing such wavefunctions prevents their application to larger systems. We propose the Subsampled...
07
Nov
Sherry Yang

Nov. 7, 2024 - Sherry Yang: Harnessing Generative Models for Scalable Materials Discovery

Generative models are increasingly used to produce novel scientific data, including crystal structures. In this talk, I will present two methods leveraging generative models for materials discovery. First, I will talk about UniMat, a unified crystal structure representation, which enables scalable generation of high-fidelity crystal...
31
Oct
Kieron Burke

Oct. 31, 2024 - Kieron Burke: Electronic structure calculations and the inexorable rise of machine learning

At least 50,000 papers each year report the results of Kohn-Sham density functional calculations for materials and molecular properties. This is a huge worldwide effort, growing rapidly with computer power and powerful machine-learning algorithms to search for desired properties. But all these calculations are limited...
24
Oct
Gerald2

Oct. 24, 2024 - Gerald Friedland: Information-driven Machine Learning

In this talk, I present a selection of ideas and algorithms that are presented in my recently published textbook of the same title [1]. The book introduces information measurement methodologies for machine learning that reduce the reliance on hyperparameters and model-type biases. This information-driven perspective...