Anirud (Ani) Aggarwal

I'm an undergraduate studying Computer Science and Mathematics at the University of Maryland, College Park. I'm part of Abhinav Shrivastava’s Perception and Intelligence Lab.

I'm currently applying to PhD programs in Computer Science (Computer Vision & Deep Learning). Super open to research opportunities—faculty, collaborators, and anyone else please email me!

Previously, I interned at Amazon AWS EC2 Nitro, building tools for detecting server issues across millions of machines, and at Anello Photonics, working on data compression pipelines and automated photonic gyroscopes testing. I've also explored real-time ML applications in surgical wearables and helped build autonomous drones.

My interests include computer vision, robotics, and deep learning. Outside of research, I love reading sci-fi and fantasy novels (favorites include Dune and The Way of Kings) or training in Brazilian Jiu-Jitsu, Muay Thai, and MMA.

Note: This website is still under construction.

Email aggarwal.anirud@gmail.com
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Publications

Coming Soon!

This paper is coming soon, and is available upon request.

Evolutionary Caching to Accelerate Your Off-the-Shelf Diffusion Model

We introduce ECAD, an evolutionary algorithm to automatically discover efficient caching schedules for accelerating diffusion-based image generation models. ECAD achieves faster than state-of-the-art speed and higher quality among training-free methods and generalizes across models and resolutions.

Projects

Coursework, side projects, and unpublished (failed?) research work.

Real-Time Face Blur for Privacy-Preserving Video

We build a real-time face-blurring system that redacts faces in live and recorded video while keeping chosen identities visible. The modular pipeline combines YuNet/SCRFD detection, SORT tracking, SFace recognition, and configurable blur options for flexible speed–accuracy tradeoffs. On crowded IRL Twitch footage, it runs faster than real time on CPU and favors privacy by accepting a few extra blur boxes rather than missing a face.

Learning to Settle: Reinforcement Learning in Catan

We build a custom PettingZoo environment and training stack for learning to play the board game Catan with reinforcement learning. Starting from a refactored Settlers-RL codebase, we explore both multi-agent methods (via MARLlib) and a single-agent PPO baseline with dense reward shaping. Our experiments show agents that learn to play shorter, higher-scoring games, while highlighting the remaining gap to robust multi-agent performance in non-stationary, multi-player settings.

Design and source code from Jon Barron's website and Leonid Keselman's website.