Sagnik Majumder
Research Engineer at Google
Austin, Texas, United States
Summary
👤
Senior🎓
Top SchoolSagnik Majumder is a research engineer specializing in computer vision and machine learning with nine years of experience spanning academia and industry. He completed a PhD at UT Austin under Prof. Kristen Grauman, focusing on embodied and active audio-visual learning for service robotics and AR/VR, and now develops vision models for Google XR. His background includes work on continual and meta-learning for image recognition at Goethe University and neuroscience-inspired visual modeling at the Frankfurt Institute for Advanced Studies. Comfortable bridging theory and product, he has a track record of moving academic ideas toward real-world AR/VR systems. Trained in electrical engineering at BITS Pilani and experienced with ISRO-affiliated research early in his career, he brings both hardware-aware intuition and algorithmic depth. His personal webpage captures ongoing projects and publications, reflecting a habit of keeping research practically grounded and publicly accessible.
9 years of coding experience
8 years of employment as a software developer
BITS Pilani, Birla Institute of Technology and ScienceDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at The University of Texas at AustinPassed AISSCE (high school diploma), Science stream, Passed AISSCE (high school diploma), Science stream at South Point High School, Kolkata
English, Hindi, Bengali
Github Skills (39)
feature-extraction10
flops10
video-understanding9
multi-target9
efficientnet9
computer-vision9
visual-recognition8
transfer-learning8
bob8
visualize8
meta8
digit8
pytorch7
deep-learning6
localization6
Programming languages (3)
Github contributions (5)
SAGNIKMJR/active-AV-dynamic-separation
Jul 2022 - Feb 2023
Contributions:17 commits, 14 pushes, 2 branches in 6 months
SAGNIKMJR/CODEBRIM_MetaQNN
May 2019 - Mar 2021
Open-source code for Q-learning based architecture search for our paper: Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset
Contributions:35 commits, 13 PRs, 16 pushes in 1 year 10 months
meta-learningmetaq-learningclassificationconcrete
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