CV

Education

• University of Wisconsin-Madison, Ph.D in Electrical and Computer Engineering, Dec 2023 (expected)
• University of Wisconsin-Madison, M.S. in Electrical and Computer Engineering, May 2019
• Fudan University, B.Eng. in Communication Engineering, June 2017

Work experience

• Summer 2022: Data Science Intern
  • Riot Games
  • As a Data Science Intern with the League Data Central team, I gained valuable experience in data analytics, machine learning engineering, and communication. Specifically:
    • Data Analytics: Utilized Apache Spark on Databricks platform to query and analyze large-scale player data, enabling data-driven decision-making.
    • Machine Learning Engineering: Designed and built a decision-making pipeline using MLFlow and Airflow, optimizing data processing and model training workflows.
    • Communication: Effectively communicated with technical mentors and stakeholders, presenting results using visualization tools such as Tableau.
  • Supervisor: Harry Papadimitriou
• Summer 2021: Machine Learning Intern
  • Text IQ (now Relativity)
  • As a Machine Learning Intern in R&D team in Text IQ, I have successfully shipped a product Unconsious Bias Detector as well as learned the following things:
    • Data Scraping: Developed an annotated dataset for a project using data scraping and augmentation techniques.
    • Data Science: Designed and analyzed NLP models for Unconscious Bias Detection, incorporating requirements and feedback from the project manager.
    • Project Management: Collaborated with team members to successfully ship the Unconscious Bias Detector, demonstrating my ability to work effectively in a team setting.
  • Supervisor: Jasneet Sabharwal

Skills

• Machine Learning: Natural Language Processing (main interest), Image Processing, Time Series Data Analysis
• Languages: Python, SQL, Mathematica, C++
• Libraries and Platforms: PyTorch, Pandas, scikit-learn, github, AWS, Databricks, Airflow, Tableau
• Natural Languages: Chinese (native), English (fluent), Japanese (N2-certificated), German (A2-certificated)

Publications

RNN-Based Online Anomaly Detection in Nuclear Reactors for Highly Imbalanced Datasets with Uncertainty

Kim, M., Ou, E., Loh, P., Allen, T., Agasie, R., and Liu, K. (2020), “RNN-Based Online Anomaly Detection in Nuclear Reactors for Highly Imbalanced Datasets with Uncertainty”, Nuclear Engineering and Design, 364, 110699

Automatic recognition system for document digitization in nuclear power plants

Ou, E., Kim, M., Loh, P., Allen, T., Agasie, R., and Liu, K. (2022), “Automatic recognition system for document digitization in nuclear power plants”, Nuclear Engineering and Design, 398, 111975.

Projects

Watermark image denoising

Drunk message detection

Real-time Formant Tracking