Software Engineer @ Google Research | 2x Research/SWE Intern at Google Brain | Interests: Deep Learning, Reinforcement Learning, Robotics
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Google Research
Data Scientist
June 16, 2026 – Present
bisimulation-transfer
April 9, 2019 – May 9, 2019
Implementation of the paper Castro et al. "Using bisimulation for policy transfer in MDPs." AAAI-2010
View ProjectRAIL
March 5, 2018 – January 15, 2022
Codebase of Santara et. al., RAIL: Risk Averse Imitation Learning, Published in AAMAS 2018
View Projectstochastic_value_gradient
April 9, 2017 – January 15, 2022
Implementation of (Learning Continuous Control Policies by Stochastic Value Gradients)[https://arxiv.org/abs/1510.09142]
View Projectdeeptesla
January 16, 2017 – January 7, 2017
End-to-End Learning from Tesla Autopilot Driving Data
View ProjectBASS-Net
November 29, 2016 – January 27, 2019
Band-Adaptive Spectral-Spatial Feature Learning Deep Neural Network for Hyperspectral Image Classification
View ProjectQuality-Optimization-of-Steel
November 2, 2016 – March 14, 2017
The repository contains code associated with the project "Optimization of Properties of Hot-rolled Steel" supervised by Prof. Pabitra Mitra and Itishree Mohanty, Research and Development, Tata Steel
View ProjectML-MOOC-NPTEL
May 27, 2016 – October 18, 2022
This repository contains the Tutorials for the NPTEL MOOC on Machine Learning.
View ProjectCultural Fit Analysis
The candidate's profile shows a strong inclination towards research-oriented projects in Machine Learning and Deep Learning. The current role at Google Research aligns with this. However, the projects are predominantly personal and academic, which might indicate a preference for individual contribution over team-based product development, depending on the specific team culture. The lack of diverse project types (e.g., business intelligence, A/B testing, data engineering) suggests a focused, but potentially narrow, skill set for a broader Data Scientist role.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions indicate a strong technical focus, but collaboration, communication, and problem-solving approaches are not detailed.