Dr. Angela Y. Lee

Bio

I am an Assistant Professor in the School of Journalism and Mass Communication at the University of Wisconsin-Madison.

Prior to joining Stanford, I completed my PhD in Computer Science at MIT, where I was advised by Professor Michael Chen. My dissertation explored novel approaches to neural network interpretability and received the ACM SIGAI Doctoral Dissertation Award. I also hold a Master's degree from Carnegie Mellon University and a Bachelor's degree from UC Berkeley.

My work has been published in top-tier venues including NeurIPS, ICML, ICLR, and CVPR. I am particularly interested in bridging the gap between theoretical advances in machine learning and practical applications in healthcare, autonomous systems, and scientific discovery. I serve on the program committees of several major AI conferences and am an associate editor for the Journal of Machine Learning Research.

PhD Students

• Alex Martinez - Reinforcement learning for robotics (co-advised with Prof. Emily Zhang)
• Priya Patel - Interpretable deep learning for medical imaging
• James Liu - Adversarial robustness in neural networks
• Sofia Andersson - Causal inference in machine learning systems

Postdoctoral Researchers

• Dr. David Kim - Previously PhD at University of Toronto, working on federated learning (Google Scholar)
• Dr. Maria Rodriguez - Previously PhD at ETH Zurich, focusing on neural architecture search (LinkedIn)

Prospective Student FAQs

• Are you accepting new PhD students? Yes, I am looking for 1-2 motivated PhD students to join my lab in Fall 2025. Please apply through the Stanford CS PhD program.
• What background should I have? Strong foundations in machine learning, linear algebra, and probability. Programming experience in Python and deep learning frameworks (PyTorch/TensorFlow) is essential.
• Should I email you before applying? Feel free to reach out if you have specific questions about research fit. Please include your CV and briefly describe your research interests.
• Do you accept visiting students? Occasionally, depending on research alignment and funding availability. Visits are typically 3-6 months.

Teaching

• CS 229: Machine Learning - Graduate-level course covering supervised and unsupervised learning (Fall 2024, Fall 2023)
• CS 330: Deep Multi-Task Learning - Advanced topics in transfer learning and meta-learning (Spring 2024)
• CS 109: Introduction to Probability - Undergraduate probability theory with applications to data science (Winter 2024)
• CS 231N: Convolutional Neural Networks - Deep learning for computer vision (Spring 2023)