As of Aug 2024, I am a VP quantitative researcher/alpha modeler working for Two Sigma Investments. My day-to-day responsibility is related to machine learning research and alpha modeling, specifically for US and global equity market.
I joined Two Sigma as a quant research intern in Summer of 2019 and returned back as a full-time after completing my PhD in 2020. Before I joined, I worked at Argonne National Laboratory's Mathematics and Computer Science division as a Given's associate (2018) and a research intern (2017).
I received Ph.D. in statistics at University of Chicago (2015-2020). My Ph.D. advisors are Chao Gao and Matthew Stephens. I was fortunate enough to work also with Mihai Anitescu as well as with both advisors. I am supported by Kwanjeong Educational Fellowship for my Ph.D. study. I was a member of Helios group at UChicago, and was a member of Matthew Stephens lab.
During my PhD, main research interests lie at the intersection of high dimensional statistics, graph theory, Bayesian statistics and optimization. With Chao Gao, I work on statistical models on graphs. With Matthew Stephens, I work on several topics in the fields of Bayesian statistics and human genetics. With Mihai Anitescu, I work on large scale optimization on statistical problems.
Before coming to University of Chicago, I worked in signal processing and neuromarketing at Biosensor Laboratories, Inc. Prior to this, I completed undergraduate studies in mathematics at Seoul National University, supported by President’s Scholarship for Science. I also served in military for two years.
I was IMO finalists (2005, 2006) but I failed to become 6 participants out of 10 finalists. This was one of biggest failures in my life but I thought I was able to learn from this failure.
Two Sigma Investments
Vice President, Quant Researcher (2024 - present)
Full-time Quantitative Researcher (2020 - 2024)
Quantitative Research Intern (Summer 2019)
Argonne National Laboratory, Given's Associate (Summer 2018)
Argonne National Laboratory, Research Intern (Summer 2017)
University of Chicago, PhD in Statistics (2015-2020)
Seoul National University (2007-2015)
Seoul Science High School (2004-2007)
Bayesian Model Selection with Graph Structured Sparsity
K and Gao, Journal of Machine Learning Research 21(109):1-61, 2020
A Fast Algorithm for Maximum Likelihood Estimation of Mixture Proportions Using Sequential Quadratic Programming
K, Carbonetto, Stephens and Anitescu, Journal of Computational and Graphical Statistics 1-13, 2020
A Flexible Empirical Bayes Approach to Multiple Linear Regression and Connections with Penalized Regression
K, Wang, Carbonetto and Stephens, accepted to Journal of Machine Learning Research, 2024
Scale Invariant Power Iteration
Kim, K, Klabjan, Journal of Machine Learning Research 24(321):1−47, 2023
Stochastic Scale Invariant Power Iteration for Kullback-Leibler Divergence Nonnegative Matrix Factorization.
Kim, K, Klabjan, preprint, 2023
I was lucky enough to serve as a teaching assistant for the following courses with the following professors/lecturers.
University of Chicago
Spring 2018
STAT 27850/30850: Multiple Testing, Modern Inference, and Replicability
Lecturer: Rina Foygel Barber
Winter 2018
STAT 24510/30040: Statistical Theory and Method 2
Lecturer: Michael Stein
Spring 2017
STAT 3771/CMSC 35400: Machine Learning
Lecturer: Risi Kondor
Winter 2017
STAT 24400: Statistical Theory and Method 1
Lecturer: Stephen Stigler
Winter 2016, Spring 2016
STAT 23400: Statistical Models and Method 1