Nikhil Raghuraman

CS @ Stanford

Nikhil Raghuraman is a prominent Deep Learning Researcher currently affiliated with the Stanford Medical AI and Computer Vision Lab. He is pursuing a combined Bachelor’s and Master’s degree in Computer Science at Stanford University, where he has been actively involved in various research projects related to artificial intelligence and computer vision.

Academic Background

Education: Nikhil is a BS/MS student at Stanford University, specializing in Computer Science. His research interests include neurosymbolic AI and computer vision, particularly within the Stanford Geometric Computation Group.1
Research Experience: He has held positions as both an undergraduate and graduate researcher at the Stanford Artificial Intelligence Laboratory (SAIL), focusing on projects such as semi-supervised learning for tumor segmentation in surgical videos using convolutional neural networks (CNNs) and developing AI systems for disease detection.1

Professional Experience

Internships: Nikhil has interned at several prestigious organizations, including:
- Matroid, Inc.: Worked on deep learning approaches for anomaly detection.
- Jane Street: Engaged in machine learning infrastructure and software engineering tasks.
- Facebook: Contributed to backend API development for Instagram and implemented messaging features.1

Teaching Roles

Nikhil has also been involved in teaching at Stanford, serving as a co-lecturer for CS 106L (Standard C++) and as a teaching assistant for various computer science courses.1

Achievements

Nikhil received the Stanford Bioengineering Ethics Writing Prize and was recognized for achieving a perfect score on the AP Calculus BC Exam in 2017, an accomplishment shared by only two other students globally out of approximately 133,000 test takers.1

Online Presence

For more information about his work and contributions to the field of AI and computer vision, Nikhil maintains a professional profile on LinkedIn under the username nikhilraghuraman, where he outlines his projects and experiences.1