Tianxiang Dai
Representations for physical systems
PhD Student in Electrical Engineering
Stanford University
Email: txdai@stanford.edu
Phone: (650) 460-9218
Representations for physical systems
PhD Student in Electrical Engineering
Stanford University
Email: txdai@stanford.edu
Phone: (650) 460-9218
I believe the right way to solve a problem emerges from a good representation of the problem itself. My work is about finding the optimal way to express systems: physical systems as differentiable computing structures, neural networks as physical systems, physical systems as neural networks, and physical systems as text. Across nanophotonics, scientific machine learning, optical instrumentation, and autonomous design, I look for representations that make complex systems easier to optimize, interpret, and build.
PhD in Electronic Engineering
Relevant coursework: Deep Generative Models, Reinforcement Learning, Computational Imaging, Nanophotonics, Electromagnetic Waves
Bachelor of Science in Physics and Computer Sciences (dual degree)
GPA: 3.8/4.0
Python (Advanced), Mathematica (Advanced), C/C++ (Intermediate)
PyTorch, Jax
Meep, Tidy3D
Deep Neural Networks, Convolutional Neural Networks, Natural Language Processing, Multi-modal Models, Agentic System Building
Metasurface Design, Nanophotonics, Optical Neural Networks
* denotes equal contribution
Awarded to top 1.3% of graduating class (50 out of 3,826 students)
Awarded to top 3.4% in Department of Physics (7 out of 207 students)
Final competition
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