Santhosh Ramakrishnan
Research Scientist at Apple
United States
Summary
🤩
Rockstar🎓
Top SchoolSanthosh Ramakrishnan is a research scientist specializing in computer vision, deep learning, and reinforcement learning with eight years of experience spanning academia and industry. He holds a PhD from UT Austin and has transitioned from doctoral research and postdoctoral roles into applied research positions at Apple, bringing a strong track record in embodied AI and visual navigation developed during collaborations with Meta and large-scale dataset efforts (HM3D, Ego4D). His work blends foundational research with practical system-building, including contributions to occupancy anticipation and exploration projects that bridge simulation and real-world perception. Based in the United States, he combines academic rigor with product-focused research at scale, often working on problems that require marrying learning-based perception with decision-making. Notably, he has moved quickly through roles at top labs, suggesting an ability to deliver impactful research outcomes in high-paced, cross-disciplinary teams.
8 years of coding experience
4 years of employment as a software developer
Indian Institute of Technology MadrasDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at The University of Texas at AustinP S Senior Secondary School
English, Tamil
Github Skills (106)
python10
simulator10
deep-learning10
robotics10
modular10
ai10
computer-vision10
deep-reinforcement-learning10
high-performance10
artificial-intelligence10
reinforcement-learning10
adapter10
occupancy10
simulation10
machine-learning10
Programming languages (7)
Github contributions (5)
facebookresearch/OccupancyAnticipation
Aug 2020 - Jul 2021
This repository contains code for our publication "Occupancy Anticipation for Efficient Exploration and Navigation" in ECCV 2020.
Contributions:16 commits, 10 PRs, 11 pushes in 10 months
eccvoccupancycomputer-visionanticipationeccv-2020
srama2512/PONI
Jun 2022 - Dec 2022
PONI: Potential Functions for ObjectGoal Navigation with Interaction-free Learning. CVPR 2022 (Oral).
Contributions:13 commits, 6 PRs, 8 pushes in 6 months
navigationinteractionpotential-functionsscene-understandingcvpr2022
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