Raayan Dhar
I am was an undergraduate studying Computer Science with a concentration in Electrical Engineering at the University of California, Los Angeles. I'm currently on a leave of absence.
Most recently, I was at Magic, where I worked on inference supporting our RL systems for our O(trillion) parameter long-context models.
Prior to that I interned at
- AMD, working on the IREE ("eerie") ML compiler and Torch-MLIR project,
- NVIDIA, on their disaggregated serving efforts in TensorRT-LLM,
- Amazon, where I wrote the killswitch for their low-latency risk evaluation service.
Interests
I am most interested in deep learning systems for the real world, across the entire stack (theory to architecture to low-level optimization). Recently, I've been working on the LLM inference problem.
While at UCLA, I was advised by and worked with Yuchen Cui on imitation learning at URIL:
- Collecting dexterous robot data using an exoskeleton (DexEXO)
- Data quality algorithms for learning (RINSE)
UCLA
For the winter 2026 quarter, I was the head learning assistant for our upper-division robotics class. Previously, I was a learning assistant the spring prior and a board member and ran fellowships for AI Safety at UCLA.
Academically, most of my coursework thus far has been in the electrical engineering and computer science departments. Some personal favorites include:
- PHIL31: Logic, First Course
- CS180: Introduction to Algorithms and Complexity
- ECE236B: Convex Optimization
Writing
Sometimes, I will write about something that I find interesting, which will typically be related to what I am currently learning about or working on. For example,
Contact
You can reach me at raayan.dhar@g.ucla.edu. Check out my projects on my Github and see what I'm up to on Twitter.
Credit
Credit for the inspiration and design of this website goes to Brandon Wang who himself was inspired by Ankur Moitra's website. The picture above the navigation bar is a detail of a tile mosaic at La Alhambra, located in Granada, Spain. The other images are due to M.C. Escher. Check out my friends Sidd, Sean, Nate, Haoji and Konst too!