Jung hoon Kim


joseph.junghoon.kim (at) gmail.com

Room 515, Bldg #301
Seoul National University

I am a M.S. student majoring in Computer Science and Engineering at Seoul National University, advised by Professor U Kang.

My research interests include reinforcement learning and deep learning.

  • Finanacial AI
    • Stock Price Movement Prediction
    • Portfolio Management
  • Time Series Prediction
  • Reinforcement Learning
  • Machine Learning & Deep Learning

Master's Degree Student
Advisor: U Kang
Computer Science
Seoul National University
Seoul, Republic of Korea
05/2019 - PRESENT
Bachelor of Arts
Minor: Web Programming and Application
New York University
New York, USA
09/2010 - 05/2017

Decision Making Problem: Efficient Portfolio Management
01/2020 - PRESENT
    • Using reinforcement learning methods (e.g. policy gradient and temporal difference) to efficiently allocate the weights of investment to minimize risk and maximize profit
    • Integrating reinforcement learning with deep learning method
Time Series Prediction using Deep Learning Methods
01/2019 - PRESENT
    • Participating in a project utilizing time series prediction to predict stock prices
    • Implementing deep learning methods to improve the prediction performance
Object Detection with LiDAR Sensor
03/2019 - 03/2020
    • Participated in a project aimed at improving the accuracy of human detection given LiDAR sensory data.
    • Implemented machine learning methods such as random forest for a classification.
Anomaly Detection with Imbalanced Dataset for CNC Machines
09/2018 - 02/2019
    • Participated in a project for an anomaly detection where the data was extremely imbalanced.
    • Conducted research on handling imbalanced datasets and implemented an algorithm to solve the imbalanced problem.
    • Implemented machine learning models to classify anomalies.
Optimization for Balancing the Sputter Uniformity of LCD
06/2018 - 08/2018
    • Implemented a deep learning method (multi-layer perceptron) to find the best state that the target matters are uniformly distributed on a substrate.

Introduction and Practice of IoT, AI, and Big Data
Teaching Assistant
Seoul National University
Spring 2020
Data Structure
Teaching Assistant
Seoul National University
Fall 2019
Samsung DS^2 (Deep learning)
Teaching Assistant
Samsung Electronics
05/2018 - 12/2019
Hana Finance DxP (Deep learning)
Teaching Assistant
Hana Financial Investment
Samsung Action Learning (Deep learning)
Teaching Assistant
Samsung electronics

  • Founder's Day Award (May 2017)
    • Top 35% of school's graduating class
  • University Honors Scholar (May 2017)
  • Dean's List (2015 - 2016)
  • Outstanding Performance in Urban Economics (Fall 2015)
    • 1st. rank among 132 students in Urban Economics

  • Machine Learning & Deep Learning
    • Decision Making: Reinforcement learning methods
    • Time Series Prediction: Deep learning methods (e.g. LSTM, GRU, etc.)
    • Anomaly Detection and Imbalanced Data Classification
    • Vision Problem: CNN, GAN, etc.
  • Programming Skills
    • ML/DL Frameworks: Tensorflow, Pytorch, Scikit-Learn
    • Data Analysis Tools: Pandas, Numpy, Pyplot from Matplotlib, and Plotly
    • Web Related Programming: Django, Nodejs, MongoDB, HTML, and CSS
    • Database: MySQL
    • Languages: Python, Java, and JavaScript

  • Korean: Native Language
  • English: Fluent in English