School of Engineering
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Ph.D. Student in Management Science and Engineering, admitted Autumn 2017
BioLin Fan is a Ph.D. student in the Department of Management Science & Engineering at Stanford University.
Research interests: statistical methods for stochastic processes, stochastic simulation, online learning and decision-making
Ph.D. Student in Materials Science and Engineering, admitted Autumn 2019
Masters Student in Materials Science and Engineering, admitted Winter 2021
BioUndergraduate, Zhejiang University 2015-2019
Visiting Student Researcher, Aaron Lindenberg's group, Dept. of Materials Science and Engineering, Stanford 07.2018-09.2018
Ph.D Student, Dept. of Materials Science and Engineering, Stanford 09.2019-
Director, Edward L. Ginzton Laboratory, Professor of Electrical Engineering, Senior Fellow at the Precourt Institute for Energy and Professor, by courtesy, of Applied Physics
BioFan's research involves the theory and simulations of photonic and solid-state materials and devices; photonic crystals; nano-scale photonic devices and plasmonics; quantum optics; computational electromagnetics; parallel scientific computing.
Ph.D. Student in Bioengineering, admitted Autumn 2018
BioI work on understanding the statistical physics and optimization principles of organized biological systems. Specifically, I use planarian as model system to study cell collective behavior and the molecular mechanisms of adaption.
I am interested in a lot of things: development, evolution, ecology, statistical physics, dynamic systems, and biophysics. I also spend time on sequencing and fluorescence imaging technology required for depicting concrete biological systems.
Vivian Church Hoff Professor of Aircraft Structures, Professor of Mechanical Engineering and Director of the Army High Performance Computing Research Center
Current Research and Scholarly InterestsCharbel Farhat and his Research Group (FRG) develop mathematical models, advanced computational algorithms, and high-performance software for the design and analysis of complex systems in aerospace, marine, mechanical, and naval engineering. They contribute major advances to Simulation-Based Engineering Science. Current engineering foci in research are on the nonlinear aeroelasticity and flight dynamics of Micro Aerial Vehicles (MAVs) with flexible flapping wings and N+3 aircraft with High Aspect Ratio (HAR) wings, layout optimization and additive manufacturing of wing structures, supersonic inflatable aerodynamic decelerators for Mars landing, and the reliable automated carrier landing via model predictive control. Current theoretical and computational emphases in research are on high-performance, multi-scale modeling for the high-fidelity analysis of multi-physics problems, high-order embedded boundary methods, uncertainty quantification, probabilistic machine learning, and efficient projection-based model order reduction as well as other forms of physics-based machine learning for time-critical applications such as design, active control, and digital twins.