Working Experience
Research Assistant, Spring 2017 - Present
Themes: Deep Learning, Machine Learning, Computational Biology
Developing deep learning approaches for pixel-level predictions on biological images
Key techniques: U-Nets, CNNs, GANs, Attention mechanism, Skip connections
Investigating the interpretability of deep neural networks for both images and text models
Key techniques: Feature visualization, Optimization, Saliency map, Policy gradient, Discriminative learning, Reinforcement learning
Machine Learning Engineer Intern, Facebook, Summer 2020
Topic: Building Efficient Lightweight Baselines for Offline Explorations
Key techniques: Data sampling, Feature reduction
Summer Research Intern, NEC Labs America, Summer 2019
Topic: Multi-modal Information Retrieval via Deep Active Canonical Correlation Analysis
Key techniques: Deep CCA, Active learning, spectral clustering
Teaching Experience
Guest lectures at Texas A&M University CSCE 636, Fall 2018, Fall 2019, Fall 2020
Topic: Deep Learning interpretation by visualization
Service Experience
Journal Reviewer
IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
The ACM Transactions on Knowledge Discovery from Data (TKDD)
IEEE Transactions on Knowledge and Data Engineering (TKDE)
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
ACM Computing Surveys
IEEE Computational Intelligence Magazine
Bioinformatics
Program Committee Member
AAAI Conference on Artificial Intelligence (AAAI) , 2019
International Conference on Machine Learning (ICML) , 2020, 2021
International Conference on Learning Representations (ICLR) , 2020, 2021, 2o22
Conference on Neural Information Processing Systems (NeurIPS) , 2020, 2021
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) , 2020, 2021