Stanford University
Showing 11-20 of 83 Results
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Zeeshan Ahmed
Associate Professor of Particle Physics and Astrophysics
BioI am an observational cosmologist, and an experimental physicist. I build ultra-low-noise detectors using superconducting and quantum sensing techniques, and use them in experiments and instrumentation for cosmology. I currently spend most of my time investigating the inflation paradigm of standard cosmology, using the cosmic microwave background (CMB). Recently, I've become interested in using the weak lensing of the CMB in conjunction with galaxy surveys to study the growth of large-scale structure in the universe.
I received my PhD in particle astrophysics from Caltech in 2012, working on direct detection of WIMP dark matter with the CDMS-II experiment. I then shifted my effort to searching for inflation with the CMB. I was a postdoctoral scholar at Stanford through 2015 before being appointed as a Wolfgang Panofsky Fellow at SLAC National Accelerator Laboratory. In 2017, I won a DOE Office of Science Early Career Award to work on new signal transduction and superconducting multiplexing techniques for next-generation CMB cameras. In 2020, I was appointed as a Lead Scientist at SLAC, and in 2023, I was appointed Associate Professor of Particle Physics and Astrophysics at Stanford and SLAC. I serve as CMB department head in the Fundamental Physics Directorate at SLAC. I also serve as scientific project manager for the bring up of SLAC's Detector Microfabrication Facility for the development of superconducting and quantum sensors and devices. -
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 -
Alex Aiken
Alcatel-Lucent Professor of Communications and Networking, Professor of Particle Physics and Astrophysics, and of Photon Science
BioAlex Aiken is the Alcatel-Lucent Professor of Computer Science at Stanford. Alex received his Bachelors degree in Computer Science and Music from Bowling Green State University in 1983 and his Ph.D. from Cornell University in 1988. Alex was a Research Staff Member at the IBM Almaden Research Center (1988-1993) and a Professor in the EECS department at UC Berkeley (1993-2003) before joining the Stanford faculty in 2003. His research interest is in areas related to programming languages.
