Coursework

 
 
 
 
 
June 2018 – June 2019
California

UC Berkeley & Stanford

Concurrent Enrollment

Courses Taken (courses taken and audited at Stanford are mentioned to differentiate courses taken at Berkeley and at Stanford) from summer of 2018 to June of 2019

  • STAT 325 Multivariate and Random Matrix Theory (Graduate level; Audited at Stanford)
  • STAT 318 Modern Markov Chains (Graduate level; Audited at Stanford)
  • STAT 300B Theory of Statistics (Graduate level; Audited at Stanford)
  • CS 228 Probabilistic Graphical Model (Graduate level; Stanford)
  • STAT 210B Theoretical Statistics (Graduate level)
  • STAT 210A Theoretical Statistics (Graduate level)
  • STAT 153 Time Series (Audited)
  • EECS 227C Optimization for Large Scare Data Analysis (Graduate level)
  • EECS 227B Convex Optimization and Approximation (Graduate level)
  • EE 290S Machine Learning for Sequential Decision Making Under Uncertainty (Graduate level)
  • CS 189 Machine Learning (Summer 2018)
  • MATH 228A Numerical Solution of Differential Equations (Graduate Level)
 
 
 
 
 
August 2015 – May 2017
California

UC Berkeley

Concurrent Enrollment

Courses Taken for Graduate School Preparation

  • MATH 118 Fourier Analysis, Wavelets and Signal Processing
  • MATH 126 Introduction to Partial Differential Equation
  • MATH 202A Introduction to Topology and Analysis (Graduate Level)
  • MATH 202B Introduction to Topology and Analysis (Graduate Level)
  • MATH 204 Ordinary Differential Equation (Graduate Level)
  • MATH 228B Numerical Solution of Differential Equation (Graduate Level)
 
 
 
 
 
June 2015 – Present
California

Stanford

Summer School

Courses Taken (EE364A was taken during the summer of 2018)

  • EE 364A Convex Optimization (Graduate level)
  • STAT 200 Introduction to Statistical Inference (Master Level)
  • STAT 217 Introduction to Stochastic Process (Master Level)
 
 
 
 
 
September 2008 – June 2012
California

UC Irvine

Undergraduate Institution

Courses Taken

  • MATH 240A Graduate Real Analysis (Graduate level)
  • MATH 121AB Linear Algebra
  • MATH 112ABC Introduction to Partial Differential Equation

Experience

 
 
 
 
 
September 2018 – April 2019
Berkeley

Research Student under the supervision of Doctor John Wu

Lawrence Berkeley National Lab

Research areas are:

  • Conducted research on high frequency trading via reinforcement learning

Applied Mathematics

This section contains all aspects of applied mathematics ranging from numerical linear algebra to computational mathematical modeling such as fluid dynamics. I’m working on solving every problems from Linear Algebra and Learning from Data written by Prof. Gilbert Strang.

Machine Learning & Mathematical Optimization

This section focuses mostly on machine learning. It may upset some people, but I think machine learning is a subset of statistics. To avoid confusion, I created this section to specifically focus on machine learning. Disclaimer: I don’t have anything related to deep learning and this is intentional.

Statistics

This section contains notes, commentaries, solutions to problems on statistics (excluding machine learning). I hope that whoever visits this website can find these materials to be helpful.

Projects

Contact

  • Lawrence Berkeley National Lab, One Cyclotron Road, MS50B Berkeley, CA 94720, USA