Raghav Chakravarthy
Ellicott City, Maryland, United States
Summary
🤩
Rockstar🎓
Top SchoolRaghav Chakravarthy is a University of Maryland Honors computer science student with four years of hands-on experience building cloud-native, AI-driven systems and contributing to prominent open-source projects like Apache TVM. He has delivered low-latency microservices and REST APIs for an AI contracting analytics platform that processed billions in contract data, implemented GenAI layers with GPT-4, and automated CI/CD and provisioning to dramatically cut deployment time. Raghav’s open-source work at AMD/TVM emphasized code quality and runtime improvements—adding executor instrumentation and fixing build/lint issues—showing a rare focus on maintainability in performance-critical ML tooling. Comfortable across the full stack (FastAPI, PostgreSQL, MongoDB, Docker, AWS) and experienced in research-grade simulations and data analysis, he blends academic curiosity with product-minded engineering. Based in Ellicott City, MD, he brings a pragmatic drive to use AI/ML and open-source software to create measurable societal impact.
4 years of coding experience
1 year of employment as a software developer
Bachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at University of MarylandHigh School Diploma, Computer Science, High School Diploma, Computer Science at Centennial High School
Github Skills (12)
tvm10
compiler10
compiler-compiler10
performance-monitor10
performance-analysis10
python10
cprogramming-language9
deeplearning-ai9
machine-learning9
c-language9
deep-learning9
javascript4
Programming languages (3)
Github contributions (5)
apache/tvm
Jul 2021 - Jun 2022
Open deep learning compiler stack for cpu, gpu and specialized accelerators
Role in this project:
Software Engineer
Software EngineerContributions:2 reviews, 6 commits, 13 PRs in 11 months
Contributions summary:Raghav primarily contributed to fixing typos, resolving deprecation warnings, and improving code style within the TVM repository. Their work includes addressing linting errors, fixing build issues, and ensuring code conventions are followed. Furthermore, they added an interface to track the number of global inputs for the pipeline executor, enhancing the runtime functionality. These contributions indicate a focus on code quality, maintainability, and improving the performance of the pipeline executor.
metalvulkancompilertensoropencl
Raghav-Chakravarthy/tvm
Jul 2021 - Jun 2023
Open deep learning compiler stack for cpu, gpu and specialized accelerators
Contributions:36 pushes in 1 year 11 months
cpugpu-programminggpu-accelerationtvmdeep-learning
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