Education

• 2019 - 2023

  Sc.B., Applied Mathematics & Computer Science, Brown University

  • GPA: 4.0 / magna cum laude / CS Honors / Sigma Xi

  • resume (updated Feb 2023)
  • Curriculum Vitae (updated June 2023)

  • CS courses:
    Advanced Deep Learning (grad level) Machine Learning Computer Vision Collaborative Robotics (grad level) Computer Systems Software Engineering Multiprocessor Syncrhonization

  • Applied Math courses:
    Recent Applciations of Computational Probability and Statistics Pattern Theory Honors Statistical Inference Computational Linear Algebra Applied ODE/PDE Honors Calculus and Linear Algebra

Honors and Awards

• • Berkeley College of Engineering Fellowship
  • Brown University magna cum laude, CS Honors, CS Senior Prize, Sigma Xi
  • Placed 227th (top 5%) in Putnam 2019, top 3 at Brown
  • 2nd Place in Hartshorn-Hypatia Math Contest
  • Brown UTRA research scholarship
  • only recipient of Yongren Full Fellowship at PROMYS (2018)
  • Provincial Top 1% in Chinese Physics Olympiad
  • Regional Top 10 & International Top 100 in Physics Bowl
  • Finalist in High School Mathematical Contest in Modeling (HiMCM)

Experience

• Jan 2021 - Present

  Undergraduate Researcher, Brown University

  Research Reinforcement Learning Machine Learning/AI Python Pytorch

  • Worked on lifelong RL, hierarchical RL, and action generalization in the Intelligent Robot Lab under Prof. George Konidaris.
  • Worked on behavior specification and reward design in RL under Prof. Michael Littman.
  • please refer to my research page for details of my work.
• Jan 2022 - May 2022

  Head Teaching Assistant, Brown University

  Autograder Python Management Course Development

  • Managed a team of 20 teaching assistants and organized the course logistics for 200 students, and handled communication between the professor, teaching assistants, and students.
  • Built auto-grading pipeline for 12 coding assignments on Gradescope and enabled students to see code correctness shortly after handin.
  • Answered questions through weekly TA hours and the online discussion platform Edstem.
• July 2021 - Aug 2021

  Natural Language Processing Engineer Intern, Zencastr Inc.

  Machine Learning NLP MongoDB Python C++

  • Engineered and deployed a web app with websockets and FastAPI that allows users to edit (faulty) audio-to-text automatic transcriptions and provides a faster editing experience by intelligently recommending potentially incorrect segments: the recommendations are made by finding similar occurrences of user-made edits throughout the audio file with Keyword Spotting using language and acoustic models from Kaldi and Vosk-api.
  • Sped up Keyword Spotting 2x using multithreaded offline-decoding in Python and Shell; sped up automatic speech recognition 5x using WeNet architecture (written in C++) and Speech Activity Detection models from Kaldi; model is pushed to production.
  • Implemented a thread-safe MongoDB store with asyncio and motor to store user-made edits in the backend.
• Dec 2020 - Jan 2021

  Machine Learning Engineer Intern, Zencastr Inc.

  Machine Learning NLP data augmentation CNN Keras Python

  • Built a CNN in Keras that classifies audio files into speech, music, laughter, or noise with 93% accuracy; trained using audio data crawled from YouTube using youtube-dl and augmented by adding noise, changing pitch, and stretching time.
  • Aligned audio-to-text transcriptions from DeepSpeech and Webspeech API using dynamic time warping and grapheme confusion.
  • Built a private Python package of machine learning utility scripts hosted on GitHub with Continuous Integration.

Techinical Skills

• Python PyTorch TensorFlow/Keras C Docker MongoDB Java JavaScript React MATLAB Assembly x86-64 Scala ReasonML C#