CV

You can also download my single-paged Resume here.

Education

• B.S. in Engineering, Zhejiang University, 2014
• M.S. in Electrical Engineering, University of California, Los Angeles, 2016
• Ph.D in Electrical and Computer Engineering, University of California, Los Angeles, 2020

Research Experience

• Rare disease detection using Generative Adversarial Network (GAN) (07/18-09/18)
  • Work on developing a semi-supervised rare disease detection framework using a generative adversarial network (GAN). The model performance beats common classifier (logistic regression, neural network, and random forest) by 5% in terms of precision-recall AUC score. Read More
• Medical image segmentation based on multitask learning (11/17-06/18)
  • Design a two-branch deep learning “Path R-CNN” architecture which decouple the classification and segmentation task. Region proposal network inspired by Mask R-CNN was later added to our model. The new architecture boosts the segmentation performance around 7% in mIOU. Read More
• Collective neural dynamics: for better mental disease prediction and faster neuromorphic computing (06/15-09/17)
  • Design and emulate neural dynamics on Neuromorphic Circuits – Spikey; a brain phase diagram was constructed for achieving better mental disease prediction and faster neuromorphic computing. Read More
• Skyrmion tracking by optical flow method (12/14-04/15)
  • Matlab programing to track Skyrmion moving using optical flow algorithm. Read More

Intern Experience

UCLA-CSST Research Intern

IQVIA Machine Learning Research Intern

Facebook Search Software (Machine Learning) Intern

Skills

• Programming
  • Python, Tensorflow, Scikit-learn, Pandas
  • Java
  • C++
  • Matlab
  • Labview
• Machine Learning
  • Random Forest
  • Logistic Regression
  • SVM
  • Monte Carlo Methods
  • Deep Learning
    • Convolutional Neural Network (CNN)
    • Region Based CNN (R-CNN)
    • Generative Adversarial Network

Publications

Comparative study on graphene–based artificial magnetic conductor (AMC)

Wang XC, Li WY, Zhao WS, Hu J, Xu YY, Huang QS. (2013). "Comparative Study on Graphene–Based Artificial Magnetic Conductor (AMC)." PIERS Proceedings, Sweden. Aug 12:496-9.

Room-temperature creation and spin–orbit torque manipulation of skyrmions in thin films with engineered asymmetry

Guoqiang Yu, Pramey Upadhyaya, Xiang Li, Wenyuan Li, Se Kwon Kim, Yabin Fan, Kin L Wong, Yaroslav Tserkovnyak, Pedram Khalili Amiri, Kang L Wang (2016). "Room-temperature creation and spin–orbit torque manipulation of skyrmions in thin films with engineered asymmetry. " Nano letters. 16(3), 1981-1988.

Analysis of light-emission enhancement of low-efficiency quantum dots by plasmonic nano-particle

Jinxi Huang, Hao Hu, Zhewei Wang, Wenyuan Li, Ji Cang, Jianqi Shen, Hui Ye. (2016). "Analysis of light-emission enhancement of low-efficiency quantum dots by plasmonic nano-particle." Optics Express. 24(8), 8555-8573.

A Basic Phase Diagram of Neuronal Dynamics

Wenyuan Li , Igor V. Ovchinnikov , Honglin Chen , Zhe Wang , Albert Lee , Houchul Lee , Carlos Cepeda , Robert N. Schwartz , Karlheinz Meier and Kang L. Wang(2018). "A Basic Phase Diagram of Neuronal Dynamics" Neural Computation. 30(9), 2418-2438

Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images

Wenyuan Li, Jiayun Li, Karthik V. Sarma, King Chung Ho, Shiwen Shen, Beatrice S. Knudsen, Arkadiusz Gertych, and Corey W. Arnold (2018). "Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images." IEEE transactions on medical imaging.

Semi-supervised Rare Disease Detection Using Generative Adversarial Network

Wenyuan Li, Yunlong Wang, Yong Cai, Corey Arnold, Emily Zhao, and Yilian Yuan (2018). "Semi-supervised Rare Disease Detection Using Generative Adversarial Network." NIPS Machine Learning for Healthcare Workshop.

Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approach

Wenyuan Li, Zichen Wang, Jiayun Li, Jennifer Polson, William Speier and Corey Arnold (2019). " Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approach." CVPR LID Workshop.

A Social Search Model for Large Scale Social Networks

Yunzhong He, Wenyuan Li, Liang-Wei Chen, Gabriel Forgues, Xunlong Gui, Sui Liang, and Bo Hou (2020). " A Social Search Model for Large Scale Social Networks. " arXiv preprint arXiv:2005.04356.

Semi-supervised Learning using Adversarial Training with Good and Bad Samples

Wenyuan Li, Zichen Wang, Yuguang Yue, Jiayun Li, William Speier, Mingyuan Zhou and Corey Arnold (2020). " Semi-supervised Learning using Adversarial Training with Good and Bad Samples ." Machine Vision and Application 31.6, 1-11..

Talks

Talk: Neural Dynamics Emulation Using Neuromorphic Chips

March 06, 2017

Talk at 5th Neuro Inspired Computational Elements Workshop (NICE 2017), IBM Research-Almaden, USA

Talk: Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images

June 17, 2018

Talk at GRS: Advanced Health Informatics, Hong Kong, China

Talk: Gleason grading of biopsies using an attention-based multi-resolution model ensembled with LGBM and XGBoost

October 08, 2020

Talk at MICCAI 2020 PANDA Challenge Workshop, Virtual

Teaching

Physics Instructor at New Oriental

AI Teaching Fellow and Associate Academic Director at Embark China

TA: LS1 Evolution, Ecology, and Biodiversity

Activity, Membership, and Service

• IEEE student membership
• Member of UCLA-C3S (Chinese-American Students and Scholars Seminar)
• Member of UCLA-CSBD (Chinese Students Ballroom Dance Team)
• Volunteer at China Rainbow Network (CRN)