education
2016 - Dec. 2022
Ph.D. in Electrical Engineering
Bionet Group, Columbia University, New York, New York, USA
GPA: 4.05/4.33Honors:
- Presidential Distinguished Fellowship (awarded anually to 1-2 Ph.D. students in Electrical Engineering)
- The Professional Development and Leadership (PDL) Graduate Student Award (Awarded anually by the School of Engineering and Applied Sciences to one student who has demonstrated an exceptional level of professionalism; leadership; teamwork; integrity; and commitment to become engineering leaders.)
- Graduate Student Service Award (Awarded by the Faculty of Electrical Engineering to an Electrical Engineering graduate student who has made significant contributions to the Department and community at large)
2016 - 2018
M.S. in Electrical Engineering
Bionet Group, Columbia University, New York, New York, USA
GPA: 4.03/4.33Honors:
- Master of Science Award of Execellence (awarded anually to <5% M.S. candidates in Electrical Engineering.)
2013 - 2016
B.S. in Electrical Engineering
Rice University, Houston, Texas, USA
GPA: 4.01/4.33Honors:
- magna cum laude
- Louse J. Walsh Scholarship in Engineering
- President's Honor Roll
2008 - 2012
GCE A-Level
Hwa Chong Institution/Junior College, Singapore
Honors:
- Senior-Middle 1 (SM1) Scholar
Relevant graduate-level coursework
- Computational Neuroscience: Circuits in the Brain (with Prof. Aurel A. Lazar)
- Brain-Computer Interfaces (with Prof. Paul Sajda)
- Neural Networks & Deep Learning (with Prof. Zoran Kostic)
- Foundations of Graphical Models (with Prof. David Blei)
- Machine Learning for Data Scientists (with Prof. John Paisley)
- Sparse Representation and High-dimensional Geometry (with Prof. John Wright)
- Statistical Learning for Biological and Information Systems (with Prof. Predrag R Jelenkovic)
- Statistical Signal Processing (with Prof. Don H. Johnson)
- Introduction to Random Processes and Applications (with Prof. Behnaam Aazhang)
- Convex Optimization (with Prof. Donald Goldfarb)
- Applied Math III: Dynamical Systems (with Prof. Marc W. Spiegelman)
- Bandits and Reinforcement Learning (with Prof. Alekh Agarwal and Prof. Alex Slivkins)
- Applied Functional Analysis (with Prof. Guillame Bal while he was at Columbia)