Anirban Santara
Research Software Engineer at Google
Kolkata, West Bengal, India
Summary
👤
Senior🎓
Top SchoolAnirban Santara is a Research Software Engineer at Google Research India with a decade of experience at the intersection of deep learning, robotics, and safe reinforcement learning. He completed a PhD at IIT Kharagpur as a Google India PhD Fellow, leading work on safe RL for autonomous driving and hybrid continuous control during internships at Google Brain and Intel Labs. He spearheaded MADRaS, the world’s first open-source multi-agent autonomous driving simulator, and has applied multimodal deep learning to hyperspectral imaging, medical diagnosis, and industrial optimization in collaborations with ISRO, Apollo Gleneagles, and Tata Steel. Anirban is also an educator who contributes machine learning tutorials to the NPTEL MOOC ecosystem and represents India’s AI community internationally. His blend of production-facing research, open-source leadership, and applied domain work distinguishes him as a practitioner who moves ideas from papers to simulators and real-world collaborations.
10 years of coding experience
School leaving certificate Pure Science, School leaving certificate Pure Science at Bidhannagar Municipal SchoolDoctor of Philosophy (PhD) Computer Science, Doctor of Philosophy (PhD) Computer Science at Indian Institute of Technology, Kharagpur
English, Hindi, Bengali, German
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (8)
scikit-learn10
machine-learning10
naive-bayes10
python10
scikit10
data-analysis9
k-means-clustering8
mnist7
Programming languages (5)
Github contributions (5)
Santara/ML-MOOC-NPTEL
May 2016 - Oct 2022
This repository contains the Tutorials for the NPTEL MOOC on Machine Learning.
Role in this project:
Data Scientist
Data ScientistContributions:19 commits, 4 PRs, 17 pushes in 6 years 5 months
Contributions summary:Anirban contributed to a machine learning project by adding several tutorials. These tutorials cover a range of topics, including linear regression, k-nearest neighbors classification, support vector machines (SVMs), and a spam detection model using Naive Bayes. The user's work demonstrates a focus on developing and implementing various machine learning algorithms and concepts.
machine-learning
KGPML/Hyperspectral
Jul 2016 - Mar 2017
Deep Learning for Land-cover Classification in Hyperspectral Images.
Contributions:52 commits, 48 pushes, 2 branches in 8 months
classificationdeep-learning
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