Publications
Neural potentials of proteins extrapolate beyond training data
A Perspective on Explanations of Molecular Prediction Models
TALKS
Extracting human interpretable structure-property relationships in chemistry using XAI and large language models
AGI Leap Summit; Research Talk
February 29, 2024
Extrapolating with CG neural potentials
Summer of Chemical Theory @WashU 2023; invited talk
May 18, 2023
Can CG neural potentials extrapolate beyond training data?
ACS Spring 2023; invited talk (on behalf of Dr. Andrew White)
March 28, 2023
Model Agnostic Counterfactual Explanations for Molecular Property Predictions
EPFL ISIC ML Seminar; invited talk
November 22, 2022
Tools for Materials Science; Molecular model agnostic counterfactual explanations (MMACE) in explainable AI
ACS NERM 2022
October 04, 2022
Model Agnostic Molecular Counterfactual Explanations for Molecules
ICLR 2022: invited talk
April 29, 2022
Generating Model Agnostic Molecular Counterfactual Explanations with MMACE
M2D2 Talk Series: invited talk
February 1, 2022
Predicting coarse-grained (CG) mappings using graph neural networks: Applications in CG molecular dynamics
ACS Fall 2021: Division of Computers in Chemistry
August 22, 2021
Applications of Machine Learning in Coarse-Grained (CG) Molecular Dynamics (MD)
MTSM Spring 2021:Applications of Machine Learning, Contributed Speech
June 16, 2021
Developing Coarse-Grained Models Using Machine Learning
Third Year talk: University of Rochester, Department of Chemistry
April 26, 2021
Theory and application of graph neural networks for molecular modeling
AIChE Fall 2020: Computational Molecular Science and Engineering Plenary Forum
November 17, 2021