Wenda Chu 储闻达

Wenda Chu

储闻达

PhD Student

Caltech

Biography

I am currently a first year PhD student at CMS department, Caltech, where I am very fortunate to be advised by Yisong Yue and Yang Song. My primary research interests lies in both practical and theoretical aspects of machine learning. To elaborate, I enjoy making machine learning algorithms more robust, efficient and powerful.

I like playing piano and I especially enjoy playing the songs from animations. I am also a fan of chess and photography.

Download my curriculum vitae. (Last update in Jan, 2023)

Interests
  • Generative Models
  • Inverse Problems
  • Trustworthy Machine Learning
  • Federated Learning
  • Optimization
Education
  • California Institute of Technology, 2023-Present

    Computing + Mathematical Science

  • Tsinghua University, 2019-2023

    Yao Class, Institute for Interdisciplinary Information Sciences

  • Shenzhen Middle School, 2016-2019

News
Sep, 2023 I joined Caltech CMS as a PhD student!
June, 2023 I graduated from Tsinghua University!

Experience

 
 
 
 
 
Secure Learning Lab, University of Illinois at Urbana-Champaign
Research Intern
Nov 2021 - June 2023 Illinois, US

I was fortunate to be advised by Prof. Bo Li. Projects include:

  • Certified robustness for point cloud models
  • Certified robustness for multi-sensor fusion systems
  • Fair federated learning by EM clustering
  • Distributed differentially private generative models with heterogeneous data
  • Personalized federated learning with knowledge distillation
 
 
 
 
 
Tsinghua University
Bachelor of Engineering, Computer Science
Jan 2019 - June 2023 Beijing China
  • Yao Class, IIIS.
  • I was fortunate to be advised by Prof. Xiaolin Hu and researched on launching physically realizable adversarial attacks on object detection models.
 
 
 
 
 
Shenzhen Middle School
High School Education
Jan 2016 – Jan 2019 Shenzhen, China

Selected Projects

Distributed Robust Principal Component Analysis
The first distributed robust principal component analysis algorithm with some convergence guarantees.
Distributed Robust Principal Component Analysis
Comprehensive and distinguishable graph-linked embedding for multi-omics single-cell data integration
It solve the indistinguishability of aggregating multi-omics data on the graph for the graph-linked embedding. Besides, it enriches the multi-omics information of graph embedding by using multiple aggregators in the GNN
Comprehensive and distinguishable graph-linked embedding for multi-omics single-cell data integration
Traffic at Peak Hours - A Game Theory View
Analyze an illustrative traffic game using game theory and queuing theory. We show how excessive competition to limited resources could lead to a dramatic decrease of social welfare.
Traffic at Peak Hours - A Game Theory View
Ray Tracing Renderer
A simple C++ implementation of ray tracing rendering, based on Monte Carlo path tracing.
Ray Tracing Renderer
A survey on Differential Privacy
Surveys over basic building blocks of Differential Privacy algorithms as well as their applications. They guard sensitive data from exposure without harming its usage too much.
A survey on Differential Privacy
Diversifying Options in Option-Critic Framework of Hierarchical Reinforcement Learning
Implement the Option-Critic architecture and diversify its choice of options.
Diversifying Options in Option-Critic Framework of Hierarchical Reinforcement Learning