Resume

Education

• UC San Diego, 2015–2022.
  • Ph.D. in Electrical Engineering, June 2022.
    • Advisors: Young-Han Kim and Sanjoy Dasgupta.
    • Thesis title: “From Information Theory to Machine Learning Algorithms: A Few Vignettes” (defended on 06/03/2022; [slides])
  • M.S. in Electrical Engineering, December 2018.
• Seoul National University, 2008–2015.
  • B.S. in Electrical and Computer Engineering and Mathematical Sciences (double major), August 2015.
  • Honors: summa cum laude (GPA: 4.11/4.3).
• Seoul Science High School, 2006–2008.

Employment

• MIT, 2022–
  • Postdoctoral Associate, August 2022–present
  • Host: Gregory W. Wornell

Honors and awards

• Departmental Fellowship, Department of ECE, UCSD, 2015–2016.
• Kwanjeong Scholarship, Kwanjeong Educational Foundation.
  • for graduate study (5 years), 2015–2020.
  • for undergraduate study (2 years), 2010–2013.
• University Students Contest of Mathematics, Korean Mathematical Society.
  • Bronze Prize (2013) (math majors).
  • Gold Prize (2010), Honorable Mention (2009) (non-math majors).

Reviewer service

• Journal: TransIT, JSAIT.
• Conference:
  • ISIT {2017, 2023}
  • ITW 2022
  • AISTATS {2022, 2023, 2024}
  • ICML {2022, 2023, 2024}
  • NeurIPS {2022, 2023 (top reviewer), 2024}
  • ICLR 2024.

Industry experience

• Research intern, Qualcomm AI Research, San Diego, CA.
  • Source Compression Group, Jun. 2019–Dec. 2019.
  • Mentor: Yang Yang.
• Research intern, Samsung Semiconductor Inc., San Diego, CA.
  • Deep Learning Group, Jun. 2018–Sep. 2018.
  • Mentor: Yoojin Choi.

Teaching experience

• MIT: as a postdoctoral instructor, I was in charge of the following course.
  • 6.7800 Inference and Information (Spring 2024)
    • highlight: designed and lectured bonus sessions on advanced topics of the course.
    • topics: minimax optimal bit prediction, large-deviation bounds, plug-and-play universal prediction, variational techniques in generative models.
• UCSD: I was a teaching assistant for the following courses.
  • ECE 269 Linear Algebra and Applications (Winter 2019)
  • ECE 225B Universal Probability and Applications in Data Science (Spring 2018)
    • highlight: designed hands-on programming assignments for the class based on Python.
    • topics: Lempel–Ziv probability assignment, context-tree weighting, and universal portfolio.
  • ECE 250 Random Processes (Winter 2017)
  • ECE 154C Communication Systems (Spring 2017)
    • highlight: designed hands-on programming assignments for the class based on Julia.
    • topics: basic source coding and channel coding algorithms.

Selected graduate coursework

• ECE: Information Theory, Universal Information Processing, Network Information Theory, Algebraic Coding Theory, Probabilistic Coding Theory, Random Processes, Dynamical Systems under Uncertainty, Image and Video Restoration, Semidefinite Optimization and Sum-Of-Squares.
• CSE: Probabilistic Reasoning and Learning, Distribution Learning and Testing, Randomized Algorithms, Advanced Optimization, Online Learning, Unsupervised Learning.
• MATH/STAT/DS: Probability Theory (A,B,C), Mathematical Statistics (A,B,C), Applied Statistics (A,B), Markov Chains and Mixing Times, Convex Analysis and Optimization (A,B,C), High-dimensional Statistics, Multivariate Analysis, Probabilistic Combinatorics and Algorithms (A), Geometry of Data.

Miscellaneous

• Military service, Republic of Korea Army, Mar. 2011–Dec. 2012.