Bobby Yan

Education

• Ph.D. candidate in Computer Science; research in compiler-based tensor systems for sparse machine learning and scalable evaluation of LLMs.
• Advisor: Prof. Fredrik Kjolstad.

• B.S. in Electrical Engineering and Computer Science (EECS).

Experience

• Investigated how training-data quality affects algorithmic fairness; designed and implemented dataset reweighting methods that reduce fairness violations in multi-class classification.

• Built systems for machine learning data management, model-debugging instrumentation, experiment replay, reproducibility, and explainability with Rolando Garcia, advised by Prof. Joe Hellerstein and Prof. Joseph Gonzalez.

• Head TA for CS 162: Operating Systems; TA for CS 188: Artificial Intelligence.
• Led instruction, assessment, and course infrastructure for upper-division computer science courses.
• Maintained course materials and rebuilt the CS 188 website.
• Teaching rating: 6.32/7.00, 0.55 above department average.

• Studied safe deep reinforcement learning and efficient policy optimization under safety constraints with Ashwin Balakrishna and Brijen Thananjeyan, advised by Prof. Ken Goldberg.
• Developed methods to improve the scalability and stability of model-based reinforcement learning algorithms based on model predictive control.

• Applied domain randomization to train domain-invariant object recognition and robot grasping models for surface declustering, increasing simulated variability to improve transfer to real-world scenes.

Honors & Awards

Service & Leadership

Projects

• Record-replay infrastructure for efficient hindsight logging during model training.

• Course infrastructure and website for CS 188: Artificial Intelligence.