About Me
Hi! I am a master student in the Department of Bioengineering at University of Pennsylvania, My research interests include Causal Discovery, Causal Representation Learning and Machine Learning Applications in all kinds of real-world problem. My ultimate question thinking now is, can we develop a world model based on causality?
Currently, I am doing research about Learning Causal Discovery in large complex system in KordingLab, advised by Dr. Konrad Kording. Prior to UPenn, I earned a BEng degree in Biomedical Engineering from Shenzhen University and conducted the Neural Feedback System development in MINDLab, Advised by Dr. Gan Huang. Meanwhile, I am a data science enthusiast and excited about applying machine learning to solve real-world problems, have won multiple medals on Kaggle.
I will join Dr. Biwei Huang’s lab at University of California, San Diego as a HDSI PhD student in 2023 Fall.
- Causal Discovery
- Causal Representation Learning
- Machine Learning Applications
MSE in Bioengineering, 2023
University of Pennsylvania
BEng in Biomedical Engineering, 2021
Shenzhen University
Exchange Student, 2020
University of Pennsylvania
Experience
Responsibilities include:
- Conduct large complex system simulations and perturbations studies on NMOS6502 microprocessor.
- Design meta-learning algorithms to infer the causal relationships between system parameters.
- Collaborate with the lab to do research on Theoretical Deep Learning and Causal Inference.
Responsibilities include:
- Led and mentored a pod of 15 students in the twelve-week Deep Learning study.
- Helped three groups of students design and develop their Deep Learning projects.
- Designed and organized homework and tutorials.
Responsibilities include:
- Led and mentored students in the three-week Deep Learning tutorials study.
- Helped students design and develop their Deep Learning projects based on Computer Vision.
- Managed and organized tutorials learning and communications with mentors and students.
Responsibilities include:
- Conduct data process pipeline development to analyze and model the EEG data.
- Assisted recruit subjects and collected EEG data for following neural feedback analysis.
Responsibilities include:
- Developed a Python based real-time brain wave visualization tool for host computer
- Developed a multi-module C++ based real-time Neural Feedback System on OpenBCI
- Designed and conducted tests to quantify the intensity and depth of the system’s modulation of brain wave
Accomplishments
Participated and won more than two silver medals in the Kaggle Competitions:
- 17th place Sartorius - Cell Instance Segmentation (Top 1%)
- 32nd place UW-Madison GI Tract Image Segmentation (Top 2%)
- 38th place chaii - Hindi and Tamil Question Answering (Top 4%)
- 50th place VinBigData Chest X-ray Abnormalities Detection (Top 4%)
- 73rd place Tabular Playground Series - Nov 2021 (Top 5%)
Fellowships and Awards during acquisition of Bachelor’s Degree:
- Twice Learning Star
- Twice Innovation and Invention Star
- Outstanding Bachelor Thesis
- Honor Prize in the 2019 International Mathematical Contest in Modeling
- Third Place in the 2019 National Biomedical Engineering Design Competition
Projects
Could a Neural Network Understand Microprocessor
Exploring causal relationships inside a large complex system with deep learning
Real-time Phase Locked Neural Feedback System Development
A millisecond-level phase locked neural feedback system based on OpenBCI
Sartorius Neuronal Cells Segmentation
A robust deep learning pipeline for segmenting neuronal cells in microscopy images.
Recent Publications
Contact
- wsinyue@seas.upenn.edu
- 669 649 04 24
- 3700 Hamilton Walk, Philadelphia, PA 19104
- Book an appointment
- DM Me
- Zoom Me