Volodymyr Kuleshov
Joan Eliasoph, M.D. Assistant Professor at Cornell Tech
San Francisco, California, United States
Summary
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Rockstar🎓
Top SchoolVolodymyr Kuleshov is an applied machine learning researcher and educator with 12 years of experience building generative AI and ML systems and teaching masters and PhD-level courses. As a faculty member at Cornell Tech he runs a research group on generative modeling while advising students, and he has a long academic pedigree from Stanford (PhD and postdoc) and McGill. He is also a serial AI entrepreneur and technologist—co-founding Afresh where his teams deployed AI to optimize fresh food operations across a significant fraction of U.S. supermarkets. His open-source work ranges from audio super-resolution models to maintaining widely used ML teaching materials, reflecting a blend of production ML engineering and pedagogy. Known for translating research into real-world impact, he pairs rigorous academic foundations with startup execution.
12 years of coding experience
7 years of employment as a software developer
Bachelor’s Degree Mathematics and Computer Science, Bachelor’s Degree Mathematics and Computer Science at McGill UniversityPost-Doctoral Researcher Computer Science, Post-Doctoral Researcher Computer Science at Stanford University
Github Skills (18)
learn-ruby-on-rails10
python10
classification10
user-manual10
machine-learning10
audio-processing10
basics10
deep-learning10
tensorflow10
supervised-learning10
neural-network10
jupyter-notebook10
keras9
documentation9
logistic-regression9
Programming languages (5)
Github contributions (5)
kuleshov/audio-super-res
Mar 2017 - Dec 2020
Audio super resolution using neural networks
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 49 commits, 2 PRs in 3 years 9 months
Contributions summary:Volodymyr primarily contributed to the implementation and modification of a machine-learning model for audio super-resolution. Their work involved adding source code for the model, including its architecture and training procedures. They also added instructions and modified documentation related to the training process and evaluation, enhancing the usability of the repository. Moreover, the user included sample audio files for demonstration of the model performance.
resolutionneural-networksaudiosuper-resolutionnoise-reduction
kuleshov/cornell-cs5785-2024-applied-ml
Aug 2021 - Dec 2022
Lecture materials for Cornell CS5785 Applied Machine Learning (Fall 2024)
Role in this project:Data Scientist
Data ScientistContributions:21 reviews, 112 commits, 21 PRs in 1 year 3 months
Contributions summary:Volodymyr contributed lecture slides covering topics in applied machine learning, with a focus on introducing core concepts and practical applications. Their work included creating and modifying slides for different lectures, specifically covering supervised learning, including topics like linear regression, classification with logistic regression, and more complex concepts like kernels and kernel methods. The user's contributions demonstrate experience in presenting and explaining fundamental machine learning topics relevant to the course.
appliedfallmachine-learningcornellapplied-machine-learning
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