Jeonghoon Kim

adkn1e@r.postjobfree.com adkn1e@r.postjobfree.com 626. 390. 5890 Linkedin/in/JeonghoonKim EDUCATION

UC DAVIS

PHD IN APPLIED MATHEMATICS

Expected June 2022 Davis, CA

ADVISOR: PROF. XIN LIU

GPA: 3.683/4.0

MS IN APPLIED MATHEMATICS

Expected Dec 2020 Davis, CA

SEOUL NAT’L UNIVERSITY

MS IN COMPUTATIONAL SCIENCE

February 2018 Seoul, S. Korea

ADVISOR: PROF. MYUNGJOO KANG

Thesis: Nonconvex TGV-Shealet Based

Model for Compressive Sensing

GPA: 4.15/4.3

CHUNG-ANG UNIVERSITY

BS IN MATHEMATICS

February 2016 Seoul, S. Korea

Class Rank: 2nd in Dept. of Math.

Major GPA: 3.97/4.0

Upper Division GPA: 4.0/4.0

SCHOLARSHIP, AWARDS

GRADUATE

•Winter 2021: Departmental fellowship

•Fall 2016: Merit-based scholarship

UNDERGRADUATE

•2015: 1st place at the Annual Academic

Seminar, CAUMathematicsDepartment

•Fall 2015: Departmental Honor

Scholarship, CAUMathDepartment

•Fall 2015: The National Science and

Engineering Scholarship, Korea Scholarship

Foundation

•Fall 2014: Volunteer Scholarship, CAU

Mathematics Department

•Fall 2014, Fall 2015: Scholarship for

Intership, CAU mathematics Department

•Fall 2010, Fall 2014: Departmental

Secondary Honor Scholarship, CAUMath

•Spring 2010, Fall 2010: Admission with

2nd rank in the department, CAUMath

SKILLS

• Professional proficiency in Python,

MATLAB, LATEX

• High motivation for new problems

• Capacity to think mathematically

• Hard working, self-starting, open to

collaborative work

PAPERS

• S. An

, J. Kim

, M. Kang, S. Rezaei, and X. Liu. ”OAAE: Adversarial Autoencoder for Novelty Detection in Multi-modal Normality Case via Orthogonalized Latent Space”, the 35th AAAI Workshop (2021).

• Application of Machine Learning on Amino Acid Scores of ORF5 Gene for Classification of PRRS Virus [To be submitted]

WORK EXPERIENCE

RESEARCH INTERN HYUNDAI MOTOR GROUP

Summer 2020 Big Data Team, Chief Data Office

- Mentor: Hong, Hyun-wook

• Conducted exploratory data analysis on time series data from more than 40 sensors in multiple different engine factories.

• Researched deep generative model algorithms (e.g., autoencoder(AE), GAN) for anomaly detection.

• Analyzed outputs of different models of anomaly detection based on CNN/AE algorithms with time-series sensor data from engine manufacture factories. RESEARCH PROJECTS

GENERATIVE ADVERSARIAL NETWORKS FOR NETWORK

SECURITYWITHMULTIPLEATTACKERSANDDEFENDERS

Dec 2020 – Aug 2021 UC Davis

- Collaborative work with Army Research Laboratory (ARL)

- PIs: Prof. P. Mohapatra, Prof. C. Gonzalez, Prof. X. Liu

• Determined if the incoming attack (sequential traffic data) is new (unseen) or not using BiGAN in order to decided if it needs to be shared with other defenders or not.

• Let defenders share the least amount of data by sharing intermediate layer(s) or the latent vector(s) to keep privacy and to enhance overall defense simultaneously. MACHINE LEARNING APPROACH FOR ANALYSIS OF INFECTIOUS DISEASE IN THE SWINE INDUSTRY

Jan 2020 – Dec 2022 UC Davis

- PIs: Prof. López, Beatriz Martínez, Prof. Liu, Xin

• Examined different machine learning models such as RF, SVM, and etc and explored diverse features to predict and detect PRRS virus outbreaks more effectively.

• Conducted models based on the farm location, pig movements, pig production parameters, weather information, and testing/diagnostic data of the farm. DEEP LEARNING APPROACHES ON YIELD PREDICTION IN

SEMICONDUCTOR MANUFACTURING USING FDC SENSOR

Apr 2017 – Aug 2017 Seoul National University

- Collaborative work with SK Hynix

- PI: Prof. Kang, Myungjoo

• Predicted semiconductor productivity using CNN and LSTM.

• Used raw sensor data without the statistical selection or dimension reductions.

• Outputted CD data for some chips on critical locations and interpolated it over all chips using a combination of cubic and radial methods. PERSONAL ENGAGEMENT

KGSA SECRETARY & TREASURER UC DAVIS

Board member of Korean Graduate Student Association at UC Davis TEACHING ASSISTANT UC DAVIS

MAT 16AB, (Short Calculus I), MAT 17A/B/C (Calculus for Biology Medicine), MAT 22B (Differential Equations)

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