Sanuj Sharma
Software Engineer at Google
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolSanuj Sharma is a software engineer with 11 years of experience, currently building features for YouTube Premium at Google in San Francisco. He combines academic rigor—from an MS in Computer Science at Texas A&M and a thesis on leveraging conversational history for dialogue systems—with hands-on ML engineering, contributing to projects like the well-known Shogun toolbox where he improved data pipelines, feature engineering, and model training workflows. His published work includes an NAACL 2019 oral on dialogue state tracking and an IEEE TMI paper on computational pathology, reflecting a rare blend of language-vision grounding and biomedical imaging expertise. Passionate about model-based RL and multimodal reasoning, he focuses on techniques that make dialog systems more grounded and intelligent. A practical tinkerer who moves seamlessly between research and production, he also maintains a personal website highlighting his projects and papers.
11 years of coding experience
Bachelor's Degree, Electrical, Electronics and Communications Engineering, Bachelor's Degree, Electrical, Electronics and Communications Engineering at Indian Institute of Technology, GuwahatiMaster of Science - MS, Computer Science, Master of Science - MS, Computer Science at Texas A&M University
Hindi, English
Github Skills (16)
data-preprocessing10
machine-learning10
jupyter-notebook10
artificial-intelligence10
c-language10
cprogramming-language10
machine-learning-models10
python10
data-science10
data-analysis9
cplus9
cpp9
data-visualization8
data-visualisation8
data-visualizations8
Programming languages (9)
Github contributions (5)
shogun-toolbox/shogun
Dec 2014 - Dec 2017
Shōgun
Role in this project:
Data Scientist & ML Engineer
Data Scientist & ML EngineerContributions:61 commits, 66 PRs, 242 comments in 3 years
Contributions summary:Sanuj's commits primarily focused on moving datasets, updating existing notebooks, and modifying the underlying code related to those notebooks. This work involves adjustments to the data loading process, feature engineering steps, model training, and evaluation in various machine learning models. The modifications were made to several notebooks related to the usage of UCI datasets and also involve work on the models such as the implementation of a tree ensemble and support vector machines.
cmakedata-sciencegunc-plus-plusmachine-learning
sanuj/shogun
May 2015 - Aug 2016
Contributions:20 commits, 213 pushes, 54 branches in 1 year 3 months
toolboxmachine-learning-toolboxshogunmachine-learning
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