Stanford University
Showing 31-40 of 2,695 Results
-
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 statistical machine learning methods for analyzing complex, high-dimensional clinical, biomedical, and population health data. His work centers on predictive modeling, AI in healthcare, supervised feature selection, and dependence-aware methods, including copula-based approaches. At the HEAL Lab, his current focus is on analyzing clinical workflows and AI implementation in healthcare systems, with an emphasis on practical, interpretable, and human-centered outcomes.
-
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.
-
Ali Akbarisehat
Postdoctoral Scholar, Radiology
Current Research and Scholarly InterestsBiosensing, electrochemical sensing
-
Franz Ake
Postdoctoral Scholar, Pathology
BioDr. Ake is a computational biologist specializing in single-cell transcriptomics, alternative polyadenylation, and isoform regulation. His research focuses on developing computational methods and bioinformatic pipelines to characterize post-transcriptional regulation and differential isoform usage at single-cell resolution. During his PhD under the mentorship of Dr. Mireya Plass at the University of Barcelona and the IDIBELL Research Institute in Barcelona, Spain, he developed approaches for isoform quantification and the analysis of differential isoform usage in single-cell datasets.
Currently, his research at Stanford University focuses on multi-omics data integration, with a particular emphasis on spatial transcriptomics and cancer genomics to study tumor biology, tissue heterogeneity, and disease-associated molecular programs from high-dimensional sequencing data.
