Yash Mehta

Baltimore, Maryland, United States

Join Prog.AI to see contacts

Summary

👤

Senior

🎓

Top School

Yash Mehta is a PhD student in Computational Cognitive Science at Johns Hopkins University with a decade of experience building bridges between neuroscience and machine learning. His work spans academic labs (HHMI Janelia, UCL, Freiburg) and industry (Amazon, Sakana AI, Microsoft), focusing on representations in biological and artificial neural networks and autonomous scientific discovery. He co-authored ICLR work on efficient AutoML / NAS, contributed DrNAS implementation to the widely used NASLib, and applies probabilistic and evolutionary methods to LLM-driven idea generation. Equally comfortable writing production code and designing biologically grounded models, Yash blends rigorous empirical research with hands-on engineering to push scalable, interpretable AI systems.

10 years of coding experience

4 years of employment as a software developer

BITS Pilani, Birla Institute of Technology and Science

Johns Hopkins University

High School Diploma, Science, High School Diploma, Science at National Public School

Research Thesis , Natural Language Processing, Deep Learning, Research Thesis , Natural Language Processing, Deep Learning at Nanyang Technological University Singapore

Japanese, Hindi, English

Stackoverflow

Stats

1reputation

0reached

0answers

0questions

Github Skills (10)

algorithm10

algorithms10

pytorch10

machine-learning10

deep-learning10

python10

nas10

neural-architecture-search10

implement10

git8

Programming languages (3)

JavaJupyter NotebookPython

Github contributions (5)

automl/NASLib

Nov 2020 - Mar 2022

NASLib is a Neural Architecture Search (NAS) library for facilitating NAS research for the community by providing interfaces to several state-of-the-art NAS search spaces and optimizers.

Role in this project:

Back-end Developer & ML Engineer

Contributions:1 review, 145 commits, 38 PRs in 1 year 4 months

Contributions summary:Yash primarily contributed to the development and implementation of the DrNAS optimizer, which is part of the neural architecture search (NAS) library. Their work involved modifying the DARTS optimizer, incorporating Dirichlet distributions (DrNAS), and integrating a KL divergence regularization term. They also added functions for discretizing the architecture, demonstrating a focus on implementing and refining NAS algorithms.

nasneural-architecture-searchautoml

yashsmehta/MetaLearnPlasticity

Aug 2022 - Jan 2023

A deep learning based method to uncover brain learning rules from behavior or neural experimental data.

Contributions:116 commits, 8 PRs, 236 pushes in 5 months

deep-learningneural-networksneuroscienceplasticityjax

Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.

Request Free Trial