Teaching & mentoring
Parallel computing,
in the classroom and beyond.
Teaching and advising from my time on the UC Berkeley EECS adjunct faculty and at Berkeley Lab.
UC Berkeley · 2017–2025
CS267
Applications of Parallel Computers
I modernized CS267 with multiple lectures on parallel algorithms for machine learning and expanded coverage of distributed-memory programming, graph algorithms, sorting and searching, and computational biology.
I co-taught the course with Jim Demmel, Katherine Yelick, and Laura Grigori across different years.
Course materials
- Spring 2025 With Jim Demmel
- Spring 2024 With Jim Demmel
- Spring 2023 With Jim Demmel and Laura Grigori
- Earlier course offerings 2017–2022
- OpenMP mergesort example Teaching code on GitHub
Ph.D. co-advising
Destinations as recorded in the September 2026 CV| Name | University | Ph.D. | Destination |
|---|---|---|---|
| Alok Tripathy | UC Berkeley | 2025 | NVIDIA |
| Vivek Bharadwaj | UC Berkeley | 2025 | Apple |
| Giulia Guidi | UC Berkeley | 2022 | Cornell |
| Benjamin Brock | UC Berkeley | 2022 | Intel Labs |
| Carl Yang | UC Davis | 2019 | Waymo |
Other recent advisees include Tianyu Liang, Yen-Hsiang Chang, and Gabriel Raulet.
Postdoctoral supervision & co-supervision
Affiliations follow the September 2026 CV.
- Helen XuGeorgia Tech
- Can KizilkaleUC Berkeley
- Prashant PandeyNortheastern University
- Israt NisaAmazon
- Oguz SelvitopiLBNL
- Saliya Ekanayaked-Matrix
- Ariful AzadTexas A&M
- Sang-Yun OhUC Santa Barbara
- Harsha Vardhan SimhadriMicrosoft Research
PASSION Lab
At Berkeley Lab, I led PASSION Lab: a research team of UC Berkeley Ph.D. students and LBNL postdoctoral fellows, research scientists, and engineers, working on parallel algorithms for scalable sparse computations.
Visit the lab archive ↗