Hyunseok (Peter) Jang

Data Scientist
ML Researcher

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About me

I'm currently a Data Scientist at Paran Energy and a National Researcher under SK Telecom Consortium funded by the South Korean Ministry of Trade, Industry and Energy.

I'm also an undergraduate student at the University of Toronto studying Mathematics and Physics with a minor in Statistics.

At Paran Energy, I have been working on machine learning and deep learning-based models for non-intrusive load monitoring (NILM), electricity trading, and demand response advised by Dr. Seong-cheol Kim.

I have been very fortunate to work with brilliant mentors/co-workers and gain lots of hands-on experience.


My research interests lie on the application of machine learning to sequential data.
e.g. time-series analysis, signal processing, audio processing, and natural language processing.

HyunYong Lee Hyunseok Jang Seung-Hun Oh Nac-Woo Kim Seong-cheol Kim Byung-Tak Lee

IEEE Access, 2021

We propose a method to indirectly calculate the incentive for each customer not using the customer’s data but using the data of other customers of the same DR group (cluster). Our method solves the non-equal incentive problem in most cases.

Demand Response (DR) Customer Baseline Load (CBL)
Hyunseok Jang Taehyun Lim Seong-cheol Kim

EDACOM, 2021

Online learning and ensemble machine learning-based energy consumption forcasting technique for residential customers.
🏆 Won Grand Prize @ EDACOM 2021
🏆 Won the Minister of Trade, Industry and Energy Award
🏆 Presented @ BIXPO 2021

Ensemble Learning Online Learning Forecasting
Hyunseok Jang Seong-cheol Kim

Pending for submission, 2022

Time-series modelling technique using Gaussian Mixture Models for Energy Consumption data in limited environments.

Machine Learning Mixture Models Forecasting Missing Data Analysis
Hyunseok Jang Taehyun Lim Seong-cheol Kim

Pending for submission, 2022

Applied research on time-series clustering and forecasting for voluntary demand response using energy consumption data of factories.

Machine Learning Deep Learning Clustering Forecasting
  • Python
  • R
  • SQL
  • C
  • Octave
  • TensorFlow
  • PyTorch
  • scikit-learn
  • Keras
  • XGBoost
  • LightGBM
  • Prophet
  • Airflow
  • Pandas
  • NumPy
  • SciPy
  • tidyverse
  • Git
  • Beautiful Soup
  • Selenium
  • AWS
  • Docker
  • Heroku
  • Postman
  • PostgreSQL
  • InfluxDB
  • Grafana
  • Zeppelin
  • matplotlib
  • ggplot2
  • Tableau
  • Plotly/Dash
  • Shiny
Jan 2021 - Present
National Researcher
June 2020 - Present
Data Scientist
June - Nov 2020
Machine Learning Engineer (Part-time)
June - August 2019
Data Analyst Intern

A web image scraper for machine learning datasets.

Python Selenium bs4 requests

An automated real‑time web monitoring service.

Python XML bs4 SMTP

Analyzed data provided by non‑profit agency ‘Findhelp’ and the City of Toronto using R.

R RStudio ggplot2 tidyverse

Computer vision surveillance application that can be used to look after your device in a public space.

Python OpenCV SMTP

Automatically and efficiently organizes dropped files into a file system.

C# Windows Forms Visio (UML)

Data analysis on the quality of plumbing pipes using statistics.

Minitab Excel

A modified version of Galaga, an arcade game developed and published by Namco.

Processing 3 (Java)