Hello, welcome to my academic and professional webpage. My name is Jung Yoon (friends call me Christian) and I’m an applied mathematicians with PhD level of understanding in statistics and optimization. From September 2018 to May 2019, I have done research with Dr. John Wu at Lawrence Berkeley National Lab and Prof. Miguel Alonso at Columbia on applying “Deep Reinforcement Learning” to high frequency trading. While I was not able to write a peer-reviewed quality of research paper, it taught me valuable skills (i.e. how not to do research).
From April 2018 to June 2019, I have taken 9 courses total in PhD - level statistics, optimization and computer science at Berkeley and Stanford. Before that, I was admitted to Rutguers University’s Master for Financial Mathematics program, but I chose to leave the program voluntarily due to poorly structured curriculums, professors not genuinely interested in teaching students, students not interested in learning but total compensation after graduation, and the outrageous cost of tuition.
As of January 2020 (till June 2020), I’m taking courses in computational photography, digital photography, applications of partial differential equations in medical images such as tomography, and photolithography. As someone who loves making great videos, I find advance in computational photography very appealing.
I’ve also done several consulting in applying statistics (or machine learning) over the past several months with marketing and analytics firms. For consulting, please send an email to firstname.lastname@example.org. I’m happy to show portion of my work. Disclosure: I’m not interested in “Big Data” problem and anything that uses Deep Learning.
Also, I’ve uploaded several class notes that I took, while I at Berkeley and Stanford. For example, the note for Stat 300B at Stanford is uploaded here with my detailed commentary. I’ll also upload solutions to homework for Stat 210A and Stat 210B (the first PhD - level statistics classes at Berkeley), and numerical linear algebra (Stanford’s CME 302 or Math 228A and 228B at Berkeley) every week.
Thank you, and I hope you find this information useful.
Master in Financial Engineering - voluntarily dropped out in September 2017, 2017
B.S in Mathematics, 2012
University of California, Irvine
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.
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.
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.