Rushil Desai

About me

I'm a passionate and driven computer scientist with a deep interest in various critical areas of computer science. My background includes developing large-scale applications that integrate seamlessly with cutting-edge systems. I've engineered complex machine learning algorithms to tackle modern challenges and enhanced computer systems for speed and security using advanced tools and practices. Known for my strong work ethic, I'm dedicated to delivering high-quality results efficiently.

Currently, I'm an undergraduate at the University of California, Berkeley, majoring in Electrical Engineering and Computer Science, with a focus on Machine Learning, Artificial Intelligence, Reinforcement Learning, and Computer Vision.

Testimonials

EECS @ UC Berkeley

Electrical Engineering and Computer Science Major
AI Researcher

Computer Vision Focus
ML Engineer

Develop Tech For Companies
Startup Entrepreneur

Work In Progress For Multiple Ventures

Resume

Education

University of California, Berkeley
2022 — Present
I'm affiliated with Sigma Nu Fraternity (Beta Psi Chapter) and have experience at Growth Equity at Berkeley. Some of the courses I've taken include Structure and Interpretation of Computer Programs, Data Structures, Efficient Algorithms and Intractable Problems, Artificial Intelligence, Designing Information Devices and Systems I & II, Great Ideas in Computer Architecture and Machine Structures, as well as Discrete Mathematics and Probability Theory, and Probability and Random Processes.
Santa Teresa High School
2018 — 2022
I've been involved with CyberPatriot for cybersecurity and hacking, Robotics, and MESA (Math, Engineering, Science Achievements). In terms of courses, I've taken Database Design and SQL Programming, AP Computer Science Principles, and AP Computer Science A.

Experience

Undergraduate Researcher, Berkeley Artificial Intelligence Research
2023 — Present
Currently, I'm working on advancing self-supervised learning with PhD Assaf Shocher under Professor Alexei Efros. I lead the development of continual learning models that are applied to various computer vision tasks, allowing them to dynamically adapt to new inputs. Additionally, I'm enhancing uncertainty methods for Neural Radiance Fields to improve efficiency and quality in 3D reconstruction. Alongside PhD Marissa Isabella Ramirez de Chanlatte and Professor Trevor Darrell, I'm pioneering the integration of Signed Distance Functions, aiming to revolutionize current methodologies and frameworks. In my research, I'm focused on developing algorithms for real-time action prediction from videos. This involves transforming human objects into 3D meshes, collaborating with PhD candidate Jathushan Rajasegaran and Professor Jitendra Malik.
Machine Learning / Full Stack Intern, PipeIQ
2023 — 2023
I orchestrated the development of a comprehensive production application tailored to real-world user needs, combining frontend and backend engineering expertise with cutting-edge industry tools to deliver a seamless user experience. I led initiatives to integrate essential features, including role-based access control and multi-tenancy capabilities, throughout the application. I also designed complex API integrations and optimized data retrieval from databases for enhanced efficiency. Deploying high-performance PostgreSQL databases and leveraging AWS services like Lambda, API Gateway, CloudWatch, and SageMaker, I established a robust architecture for production deployment. Integrating advanced LLM models, autonomous AI agents, and large-scale machine learning capabilities, I empowered the application to predict sales analytics, automate personalized communications, schedule critical meetings, facilitate intelligent chatbot interactions, and transform our enterprise's sales pipeline.
Undergraduate Researcher, Berkeley Sensor & Actuator Center
2023 — 2023
I designed and built advanced reinforcement learning and deep neural network models to enable microbots, powered by Single Chip Micro Motes (SCUM), to make decisions autonomously. I used widely recognized machine learning tools such as PyTorch, Tensorflow, Keras, Ray Tune, and RLlib. I developed functional models by creating a strong reinforcement learning environment and implementing a sophisticated algorithm, which successfully trained the microbots. I collaborated closely with Professor Kristofer Pister and PhD student Yichen Liu during this project.