Short Bio.:
Sung-eok Jeon received B.S. from EE at Yonsei University, Seoul, Korea; M.S. from EE at KAIST, Daejeon, Korea; and Ph.D. from the School of ECE at Georgia Institute of Technology, Atlanta GA.
He worked at Microsoft, Seattle, WA about Windows system reliability (large system reliability analysis and probabilistic modeling; applying machine learning models on data analysis and modeling), user-mode debugging, backend Windows services, at Facebook from 2015 to 2017 (filed over 10 patents), also at Uber and a startup, having worked on ML, backend of services and backend of platform. And he is now at Meta (Facebook), working as a software engineer with ML focus since Feb/2022.
His research interests are machine learning, feature engineering, deep-learning variations; statistical modeling of large and complex systems based on the probabilistic graphical models in machine learning; message-passing algorithms over graphical models; data modeling/analysis/mining and information retrieval with machine learning approaches.
Career Experience:
Meta (Facebook), Feb/2022~now
Startup (series F), Jun/2020~Feb/2022
Uber, Oct/2017~Jun/2020
Facebook, Aug/2015~Oct/2017
Microsoft, Jan/2008-Aug/2015
Graduate Research and Teaching Assistant, Georgia Institute of Technology, Atlanta, GA
Graduate Research and Teaching Assistant, KAIST, Daejeon, Korea
Awards and Recognition:
5 Times Academic Excellence Awards from Yonsei University President
Upper 4.0/4.0, Early Graduation in 3.5 years, Yonsei University
Listed in Marquis Who's Who in America since 2007 (Computer Scientist)
Filed over 10 patents at Facebook
Skills:
Go, Python, C#/C++, SQL/HQL, R, Matlab, Java
Pandas/Sklearn/Numpy, Tensorflow/Keras, Deep learning
Data modeling (predictive modeling, and statistical modeling), analysis, and mining with machine learning approaches (with Python, R, SQL)
Feature Engineering and ML modeling (Pandas/Sklearn, Tensorflow/Keras, lightGBM, R)
Learning based on probabilistic Graphical Models (MRF, MLP, RBM), and message-passing over probabilistic graphical models
Debugging (mostly about user-mode)
Computer Network Theories (Queuing, Routing, Scheduling)/Protocols/Test-beds/Discrete Event Driven Network Traffic Simulations/Resource Optimization
Statistical/Deterministic Optimization
Selected Publications:
S. Jeon, and C. Ji, "Distributed Configuration Management of Wireless Networks: Markov Random Field and Near-Optimality," IEEE Trans. Signal Processing, vol. 58(9), pp. 4859-4870, Sep., 2010
S. Jeon, and C. Ji, "Nearly-Optimal Distributed Configuration Management Using Probabilistic Graphical Models," in Proc. of IEEE MASS, RPMSN Workshop, 2005
S. Jeon, and C. Ji, "Graphical Models for Self-Configuration of Ad-hoc Wireless Networks," in Proc. of Snowbird Learning Workshop, 2005
S. Jeon, and C. Ji, "Role of Machine Leaning in Self-Configuration of Ad-hoc Wireless Networks," in Proc. of ACM SIGCOMM, MineNet Workshop, 2005
S. Jeon, "Topology Aggregation Method for Multiple Link Parameters," Journal of Computer Communications, Elsevier, Feb. 2006