Rishit Sheth
Machine Learning Researcher at D-Wave
Arlington, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolRishit Sheth is a machine learning researcher with a decade of experience building efficient, empirically robust algorithms across industry and academia. He holds a PhD in Computer Science from Tufts and has led automated ML and representation-learning efforts at Microsoft, Lila Sciences, Flagship Labs 97, and now D-Wave. His work blends Bayesian modeling, approximate inference, and deep learning with practical tooling (PyTorch, cloud, NumPy/Pandas) to make hyperparameter optimization, feature selection, and meta-learning practically useful. Rishit has a background in sensor and systems analysis from a long tenure at MIT Lincoln Laboratory, which informs his focus on rigorous evaluation and real-world data transformations. He’s drawn to concrete representations of abstract objects—models, datasets, and pipelines—and favors theory when it yields clear practical gains. Based in Arlington, MA, he pairs research depth with production-minded engineering to move ideas from concept to scalable experiments.
10 years of coding experience
18 years of employment as a software developer
Bachelor's degree Electrical Engineering, Bachelor's degree Electrical Engineering at University of DelawareDoctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at Tufts UniversityMaster of Science (MS) Electrical Engineering, Master of Science (MS) Electrical Engineering at University of Illinois Urbana-Champaign
Gujarati, Spanish
Github Skills (18)
missing-data8
resampling7
factorization7
density-estimation6
predictive-modeling6
pattern-recognition6
feature-selection6
probabilistic5
ensembles5
matrix5
machine-learning4
ensemble-learning4
matrix-factorization4
probabilistic-models4
fairness-ml3
Programming languages (1)
Github contributions (5)
rsheth80/dfs
Feb 2020 - Jun 2020
Contributions:3 commits, 2 pushes, 1 branch in 3 months
rsheth80/pmf-automl
Oct 2018 - Dec 2018
Data and code related to the paper "Probabilistic matrix factorization for automated machine learning", NIPS, 2018.
Contributions:6 commits, 5 pushes, 3 branches in 1 month
fairness-mlautoencodersmixture-of-expertsmatrixpattern-recognition
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