Stanford University
Showing 441-450 of 34,508 Results
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Neera Ahuja
Professor of Medicine (Hospital Medicine)
Current Research and Scholarly InterestsClinical inpatient trials, Quality improvement, Assessing interventions with operations on throughput. SDOH/Health equity
Medical education research; Intergenerational teaching/learning; Analysis of effects of duty hour regulations on housestaff training and ways to improve the system -
Gavin Ahumada
Undergraduate, Vice Provost for Undergraduate Education
BioHello! My name is Gavin Ahumada, and I am from Spokane Valley, Washington. I look forward to studying Political Science as a member of the Stanford University Class of 2030, and I am beyond excited to be involved in extracurricular activities like the Stanford Running Club, Debate, Student Leadership, and more. In my free time, I enjoy running, reading, playing pickleball, hiking, watching movies, and experiencing food runs with friends. Please do not hesitate to contact me, and come say Hello if you see me on campus!
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Changzhi Ai
Postdoctoral Scholar, Photon Science, SLAC
BioChangzhi Ai is a Postdoctoral Researcher 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 (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
