Education/Experience

Education

Ph.D. in Electrical Engineering, KAIST, Feb. 2022 (Thesis: Fast and Robust Distributed Machine Learning).

M.S. in Electrical Engineering, KAIST, Feb. 2018 (Thesis: Combined Window-Filter Waveform Design for Ultra-Low Latency Communications).

B.S. in Mathematics & Electrical Engineering, KAIST, Feb. 2016.

Work Experience

Postdoctoral Researcher, Purdue University (Jan. 2023 - Present)

• Hosts: Prof. Christopher G. Brinton and Prof. Mung Chiang

Postdoctoral Researcher, KAIST (Mar. 2022 - Dec. 2022)

• Host: Prof. Jaekyun Moon

Honors and Awards

Best Ph.D. Dissertation Award from KAIST EE, 2022 (Dissertation title: "Fast and Robust Distributed Machine Learning")

Qualcomm-KAIST Innovation Award, 2019 (Paper title: "Hierarchical Broadcast Coding: Expediting Distributed Learning at the Wireless Edge") 

Academic Services

Technical Program Committee

• IEEE INFOCOM 2025

• IEEE MASS 2024

• WiOpt 2024

Projects

FL-NTN: Fog Learning Orchestration of Heterogeneous Model Training across Hybrid Terrestrial and Non-Terrestrial Network Systems (Jan. 2023 – Present)

• DARPA

• Topic: AI/ML over hybrid terrestrial and satellite networks

Developing Distributed Storage System Solutions for Dynamic Network Environments via Exploration of Fundamental Tradeoffs Involving Storage Resources in Hyper-Connected Networks (Jun. 2019 – Dec. 2022) 

• National Research Foundation of Korea 

• Topic: Developing distributed machine learning solutions for hyper-connected networks

SK Hynix - KAIST Next Generation Artificial Intelligence Semiconductor System Research Center (Jun. 2021 – Feb. 2022)

• SK Hynix

• Topic: Training multi-exit architectures for anytime predictions in low-latency applications

Cloud Storage Coding (Mar. 2018 – May 2019)

• National Research Foundation of Korea 

• Topic: Developed coding schemes to speed up distributed computing in wireless networks

Control & Command Data Link for Unmanned Aerial Vehicle (Jun. 2016 – May 2017) 

• Hanwha Systems 

• Topics: 

  • Developed a physical layer simulator using MATLAB

  • Designed waveforms highly suited for asynchronous scenarios

  • Designed a RNN-based channel prediction scheme for Doppler fading channels

Teaching Experience

Teaching Assistant, Seongnam-KAIST next generation ICT research center Machine Learning Course: Sep. 2020 - Nov. 2020 (8 days), Apr. 2021 (2 days) 

Teaching Assistant, KAIST

• KAIST EE488: Special Topics in Electrical Engineering <Machine Learning Basics and Practices> (Fall 2020, Spring 2021) 

• KAIST EE528: Engineering Random Processes (Spring 2020)

• KAIST EE681: Nonlinear Systems (Fall 2018)

• KAIST EE405: Electronic Design Lab. (Spring 2018)

• KAIST EE210: Probability and Introductory Random Processes (Fall 2017)

• KAIST EE305: Introduction to Electronic Design Lab. (Spring 2017)

• KAIST MAS102: Calculus II (Spring 2014)