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David Suh

My name is David Suh. I was born in Austin, Texas on December 17, 2001. After attending school until first grade while my dad completed his PhD, our family returned to South Korea for a while. While there, I attened Seoul International School from Grade 1 through 12. During this time, I would slowly but surely discover my passion for programming and mathematical thinking. I participated in a number of tournaments at the national/international level, such NAQT, IYPT/KYPT, Empire mock trial, and more. I was accepted into the University of Chicago in 2020. The first year I attended remotely from Korea, and for the second year onwards I have attended in person. I am currently a Mathematics and Computer Science major, and I am also part of the joint Masters program in Computer Science, thus I am also purusing a MS in Computer Science at the moment. My personal interests are in optimization, probabilistic algorithms, and machine learning. In my free time I go to the gym and play video games.

📖 Education

2020-2024

University of Chicago

M.S. Computer Science,
B.A. Computer Science,
B.A. Mathematics

🏢 Work Experience

Jan. 2023 - Present

Computer Vision Engineering Intern

@Edlight, PBC

Developed a browser based document scanning system utilizing computer vision techniques. Existing market solution SDKs and a homebrew CNN combined using a SqueezeNet backbone were used to amplify detection performance and accuracy to >90%. Further improved computational performance through implementation with TensorFlow Lite to provide users a real time edge preview of documents. This was then wrapped into a React.JS component.

Jan. 2023 - Present

Advanced Research Assistant

@Voltage Research Group, UChicago

Worked as a research assistant in maintain and developing research related software and tools, including the maintenance and migration of the research group’s website using Django, react, and other data visualization tools such d3. Furthermore, implemented an optimized data pipeline uploaded papers and implemented an optimized segment tree system for searching.

Summer 2018, 2019

Data Science Intern

@Yonsei Graduate School of Civil Engineering

Assisted researchers (M.S, Ph.D students) in research concerning the utilization of machine learning in the realm of civil engineering. Included wrangling data into usable form for machine learning purposes through R and pandas, as well as optimizing the distributed computation system through Hadoop for faster training.

🏆 Competitions/Extracurriculars

2023 Citadel Datathon Midwest Regional

🥇1st Place

Came in first in the Midwest Regional for Citadel’s 2023 Datathon. Winning paper was written on the environmental (and consequently health-related) effects of greenspaces and bluespaces, in particular on surface temperatures and air quality index through programmatic time series analysis, using metrics such as the Dicky-Fuller test, and providing strong evidence of data correlation through K-means segmentation and heuristic minima discovery. Winning prize money was $10,000 for the team. Link to code. Download writeup.

2022 University of Chicago Mathematics Research Experience for Undergrads (REU)

Paid Full Program Attendee

Attended the UChicago Mathematics REU, a competitive program for undergraduates to spend 8 weeks emulating the graduate mathematics research process under the supervision of a mentor. The final paper, Mean Field Games and Weak Solutions, written on the topic of stochastic mean field games, a natural topic that forms from optimal control of the Fokker-Planck equation, which concerns the optimization of the actions of a small agent compared to a large population.

2022 William Lowell Putnam Math Competition

75th percentile score (9/120)

Scored a 9/120 on the 2022 Putnam Exam, a score which is just under the top 1000, about the 75th percentile overall. For reference, the typical median score on this competition is 0, and the mean is usually 1 or 2.