Speakers

Schedule in Progress

We are actively building the MLCM-26 program. More speakers will be announced soon!

43
confirmed speakers so far

Confirmed Speakers

Evan Antoniuk
Lawrence Livermore National Laboratory
TBD
Government
Masha Babak
City University of Hong Kong
TBD
Academy
Sai Harshit Balantrapu
University at Buffalo - SUNY
Neutron and X-ray Diffraction Reveal the Limits of Long-Range Machine Learning Potentials for Medium-Range Order in Silica
Academy
Erika Barcelos
Case Western Reserve University
Semantic Data Management with MDS-Onto: Polymer Degradation and Formulation Science Studies
Academy
Samuel Blau
Lawrence Berkeley National Laboratory
TBD
Government
Rose Cersonsky
University of Wisconsin-Madison
TBD
Academy
Matthew Cox
Massachusetts Institute of Technology
Post-Training Alignment of Scientific Agents Through Composable Workflows
Academy
Megan Davis
Los Alamos National Laboratory
TBD
Government
Brian DeCost
NIST
Tools for Automating High Throughput Analysis of Multiphase Powder X-ray Diffraction Data
Government
Nikita Fedik
NVIDIA
Interactive Session / Tutorial — sign up
Industry
Josh Finkelstein
Los Alamos National Laboratory
TBD
Government
Roger French
Case Western Reserve University
TBD
Academy
Johannes Hachmann
University at Buffalo
TBD
Academy
Raphael Husistein
ETH Zürich
TBD
Academy
Jan Janssen
Max Planck Institute for Sustainable Materials
Towards Autonomous Materials Discovery
Academy
Hiroyuki Tsujimoto
ENEOS
From Atomistic Understanding toward High-Throughput Discovery: ENEOS's Journey with Matlantis and NVIDIA ALCHEMI
Industry
Wonseok Jeong
NIST
Bridging Atomic Simulations and Experimental Time Scales via Deep Reinforcement Learning
Government
Neerav Kaushal
Michigan Technological University
TBD
Academy
John Keith
University of Pittsburgh
Post-Semiempirical Methods
Academy
Maya Martirossyan
Lila Sciences
TBD
Industry
Aditi Krishnapriyan
UC Berkeley
TBD
Academy
Jeffrey Law
National Laboratory of the Rockies
Polymer Property Prediction via Automated Molecular Dynamics Pipeline and Transfer Learning
Government
Daniel Levine
Meta
Replacing DFT across the periodic table with machine-learning interatomic potentials
Industry
Fang Liu
Emory University
A multi-agent system for autonomous training of machine-learned exciton models
Academy
Arun Mannodi-Kanakkithodi
Purdue University
Defect Engineering in Semiconductors using First Principles Simulations and Machine Learning
Academy
Sakib Matin
Lawrence Berkeley National Laboratory
TBD
Government
Orlando Mendible Barreto
University of Notre Dame
MLIPs and Agentic AI as Enabling Tools for the Computational Study of Metal-Organic Framework Self-Assembly
Academy
Adesh Rohan Mishra
University of California, San Diego
TBD
Academy
Avanish Mishra
Los Alamos National Laboratory
TBD
Government
Matous Mrovec
Ruhr-Universität Bochum
TBD
Academy
Michele Pavanello
Rutgers University
TBD
Academy
Akhil Reddy Peeketi
Los Alamos National Laboratory
Fragment-Constrained Charge Equilibration Enables the Electrochemical Double Layer in MLIPs
Government
Yulia Pimonova
Los Alamos National Laboratory
TBD
Government
Owen Price Skelly
Globulus Labs / University of Chicago
TBD
Academy
Josh Rackers
Achira
TBD
Industry
Patrick Sahrmann
Los Alamos National Laboratory
Transferable Machine-Learned Coarse-Grained Interaction Potentials via a Thermodynamic Force-Matching Principle
Government
Nathan Szymanski
University of California, Los Angeles
TBD
Academy
Thomas Swinburne
University of Michigan
TBD
Academy
Henry Tischler
Los Alamos National Laboratory
Generating Molecules with Physics-Constrained Graph Diffusion
Government
Steven Torrisi
Toyota Research Institute
TBD
Industry
Quỳnh Trần
Case Western Reserve University
Data-Centric Approach in Molecular Machine Learning: Representation Learning on Massive Multi-Modal Datasets
Academy
Hao Wan
University of Toronto
TBD
Academy
Jack Weber
Schrödinger
TBD
Industry