Stanford University
Showing 461-470 of 34,929 Results
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Changzhi Ai
Postdoctoral Scholar, Photon Science, SLAC
BioChangzhi Ai is a Postdoctoral Researcher at the SUNCAT Center for Interface Science and Catalysis at Stanford University and SLAC National Accelerator Laboratory. He specializes in developing machine learning models for surface and interfacial chemistry, with broader expertise in atomistic modeling for materials science and chemistry. His research also explores agentic AI for scientific discovery, automation of active learning workflows, global optimization algorithms, and high-throughput materials screening. He obtained his PhD from the Technical University of Denmark.
His current research focuses on the development of scalable, physically informed machine learning potentials, particularly equivariant neural network architectures, for accurately modeling complex chemical environments. His work spans heterogeneous catalysis, multi-metallic alloy design, reaction kinetics, and surface and interfacial chemistry, with an emphasis on uncovering structure–property relationships at the atomic scale.
In addition, he has extensive experience integrating machine learning models into simulation pipelines and deploying them in large-scale computational environments. His technical expertise includes deep learning frameworks such as PyTorch, distributed training (DDP and multi-node GPU systems), and scientific computing tools including LAMMPS, ASE, and TorchScript/LibTorch for production-level deployment. He also develops end-to-end automated workflows for data generation, model training, and adaptive sampling in materials discovery.
Keywords:
Machine Learning Potentials (Equivariant GNNs), Atomistic Simulations, Molecular Dynamics, Active Learning & Workflow Automation, High-Throughput Screening, Global Optimization Algorithms, Scientific Machine Learning, Distributed GPU Computing, PyTorch & TorchScript, LAMMPS Integration, ASE, HPC Systems, Data-Driven Materials Discovery
Code & Projects:
GitHub: https://github.com/changzhiai -
Agnideep “Agni” Aich, PhD
Postdoctoral Scholar, Emergency Medicine
Current Research and Scholarly InterestsAgni's research develops interpretable statistical machine learning methods for clinical, biomedical, and population health data, with interests spanning dependence-aware learning and copula-based methods, extreme-event modeling, predictive modeling, supervised feature selection, and AI in healthcare. At the HEAL Lab, he analyzes clinical workflows and AI implementation to advance understanding of the human experience in healthcare, emphasizing practical, interpretable, human-centered outcomes.
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Meghali Aich
Postdoctoral Scholar, Neonatal and Developmental Medicine
BioMy research interest lies in understanding how environmental factors contribute to neurodevelopmental disorders and translating those insights into therapies. Aligned with this, my current research in Dr. Anca Pasca’s lab at Stanford focuses on how reductive stress associated with maternal metabolic syndrome affects fetal brain development.
