Tanay Wakhare
Co-Founder & CEO at Prompt Inversion
Cambridge, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolTanay Wakhare is a computer scientist and entrepreneur pursuing a PhD at MIT while co-founding and leading Prompt Inversion, an applied AI lab that turns state-of-the-art deep learning research into secure, enterprise-grade systems that have produced multi-million dollar cost savings. With 11 years of experience spanning theoretical math research in number and graph theory to hands-on ML engineering, he bridges rigorous theory and production-scale ML—evident in his contributions to Ray’s RLlib where he improved reinforcement learning components and added a Curiosity exploration module. A University Medalist and multiple award-winning researcher, he has a track record of translating advanced mathematical ideas into practical algorithms and scalable software. Based in Cambridge, MA, he combines academic depth with startup execution, specializing in prompt engineering to control LLM outputs and bespoke AI solutions for complex enterprise needs.
11 years of coding experience
1 year of employment as a software developer
PhD Computer Science, PhD Computer Science at Massachusetts Institute of TechnologyDouble BSc Mathematics (High Honors) and Computer Science Archaeology Minor, Double BSc Mathematics (High Honors) and Computer Science Archaeology Minor at University of Maryland
Github Skills (12)
pytorch10
machine-learning10
deeplearning-ai10
ray10
deep-learning10
python10
rllib10
reinforcement-learning10
optimization7
data-science6
distribute5
tensorflow4
Programming languages (2)
Github contributions (5)
ray-project/ray
Jun 2020 - Aug 2020
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:
ML Engineer
ML EngineerContributions:6 commits, 13 PRs, 2 comments in 2 months
Contributions summary:Tanay's commits primarily involve bug fixes, tests, and the implementation of new features within the RLlib library, a core component of the Ray AI compute engine. These changes specifically target reinforcement learning algorithms, including DiagGaussian, AttentionNet, and MADDPG, demonstrating a focus on improving model accuracy and resolving integration issues. The contributions also include the integration of a new exploration module, Curiosity, illustrating a focus on innovative methods for improving RL performance.
pythonconsistsruntimetensorflowserving
09wakharet/ray
May 2020 - Aug 2020
A fast and simple framework for building and running distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.
Contributions:22 pushes, 13 branches in 2 months
scalableraydistributed-applicationshyperparametersimple-framework
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