Rohan Yadav - Computer Science PhD Student at Stanford University

Rohan Yadav

Computer Science PhD Student at Stanford University

Pittsburgh, Pennsylvania, United States

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Summary

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Rockstar

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Top School

Rohan Yadav is a Computer Science PhD student at Stanford with a decade of software engineering experience building high-performance, parallel systems and SQL execution engines. He has production experience at Cockroach Labs and internships at Facebook, Uber ATG, and CMU research groups, pairing systems-level engineering with research-led optimization. Rohan contributes to notable open-source projects like the Tensor Algebra Compiler (taco) and Legion, where he improved compiler robustness, deduplicated critical checks, fixed transformation bugs, and extended OpenMP runtime support. He teaches and mentors extensively—former head TA for CMU’s flagship algorithms course—bringing clear communication to complex algorithmic and parallelization problems. Known for pragmatic fixes that improve maintainability and correctness, he blends low-level systems work with academic rigor to push performant, reliable software.

10 years of coding experience

3 years of employment as a software developer

Bachelor’s Degree, Computer Science, Minor in Machine Learning, Bachelor’s Degree, Computer Science, Minor in Machine Learning at Carnegie Mellon University

High School, High School at North Allegheny Senior High School

English, Hindi

Github Skills (21)

c-language10

parallel10

parallelization10

algebra10

sparse-matrix10

mappings10

openmp10

parallel-processing10

parallel-computing10

sparse-data10

tensor10

cprogramming-language10

runtimes9

run-time9

sys9

Programming languages (14)

JavaC++RustCMakefileTeXGoHTML

Github contributions (5)

tensor-compiler/taco

Jan 2021 - May 2022

The Tensor Algebra Compiler (taco) computes sparse tensor expressions on CPUs and GPUs

Role in this project:

Back-end Developer

Contributions:18 reviews, 303 commits, 51 PRs in 1 year 4 months

Contributions summary:Rohan primarily contributed to fixing compilation warnings and improving the code's robustness by addressing issues related to variable references within the tensor algebra compiler. They also deduplicated dimension-checking routines, improving code maintainability and reducing redundancy. Additionally, they addressed a bug in the fuse transformation, resolving undefined variable issues during compilation. Finally, they added and improved test cases.

cudatacolinear-algebratensor-algebra-compilergpu

StanfordLegion/legion

Aug 2021 - Dec 2022

The Legion Parallel Programming System

Role in this project:

Back-end Developer

Contributions:13 commits, 262 comments, 83 issues in 1 year 4 months

Contributions summary:Rohan primarily contributed to the Legion Parallel Programming System, focusing on improvements within the default mapper, example programs, and the realm/openmp modules. They fixed error messages in the default mapper and added an example for backpressuring task execution using a custom mapper. Furthermore, the user implemented `omp_get_num_places` and `omp_get_num_procs` runtime calls in Realm's OpenMP API, and fixed local task registrations.

system-programminglegionparallel-programmingparallel

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