Abhijit Roy
Staff Research Engineer at Google
London, England, United Kingdom
Summary
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Senior🎓
Top SchoolAbhijit Roy is a Staff Research Engineer at Google in London with eight years of experience building and leading ML teams focused on improving large language model factuality and multi-turn dialogue. He currently leads the Gemini Multi-Turn pillar, having driven measurable gains in AI Overviews and AI Mode products by tackling entity conflation and reducing unhelpful hedging. Previously he co-led dermatology ML efforts that powered Google Lens and a CE-marked clinical device, and has published AI safety and OOD detection research with Google Brain. Comfortable operating at the intersection of research, product, and clinical expertise, he has led cross-disciplinary teams of 20+ contributors to scale high-quality health content on platforms like YouTube. His background in biomedical imaging and signal processing underpins a pragmatic approach to translating advanced research into production-safe health AI.
8 years of coding experience
7 years of employment as a software developer
Informatik Masters Thesis on Computational Biomedical Imaging, Informatik Masters Thesis on Computational Biomedical Imaging at Technical University of MunichBachelor of Engineering (B.E.) Electrical Engineering, Bachelor of Engineering (B.E.) Electrical Engineering at Jadavpur UniversityMaster of Technology (M.Tech.) Instrumentation and Signal Processing, Master of Technology (M.Tech.) Instrumentation and Signal Processing at Indian Institute of Technology, Kharagpur
English, German
Stackoverflow
101reputation
0reached
0answers
0questions
Github Skills (10)
convolutional-neural-networks10
mri9
segmentation9
bayesian9
uncertainty9
pytorch8
semantic-segmentation7
deep-learning7
fluid5
pre-trained-model4
Programming languages (1)
Github contributions (5)
ai-med/quickNAT_pytorch
May 2018 - Mar 2020
PyTorch Implementation of QuickNAT and Bayesian QuickNAT, a fast brain MRI segmentation framework with segmentation Quality control using structure-wise uncertainty
Contributions:32 commits, 4 PRs, 26 pushes in 1 year 9 months
pytorchbrainquality-controldeep-learningmri
ai-med/squeeze_and_excitation
Oct 2018 - Jun 2020
PyTorch Implementation of 2D and 3D 'squeeze and excitation' blocks for Fully Convolutional Neural Networks
Contributions:20 commits, 3 PRs, 21 pushes in 1 year 8 months
pytorchdeep-learningsqueezeconvolutionalneural-networks
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