Abhibhav Garg
Design Analyst at Accenture in India
Pune District, Maharashtra, India
Summary
👤
Senior🎓
Top SchoolAbhibhav Garg is a Human–AI Design Analyst with 10 years of experience blending UX/UI design sensibilities and machine learning engineering to craft intuitive interactions for intelligent systems. Based in Pune, he currently works at Accenture where he has contributed to XR design and broader human-centered AI product work. His open-source contributions include refactoring tutorials in the well-known CleverHans adversarial ML library to integrate the Adam optimizer and improve training and testing workflows, signaling hands-on experience with ML training pipelines. Trained in UX/UI design at Chitkara University, Abhibhav pairs visual and interaction design strengths with practical ML tooling know-how to bridge design and engineering teams. He brings a pragmatic curiosity—equally comfortable iterating on prototypes and diving into model-training details—to deliver designs that respect both human needs and technical constraints.
10 years of coding experience
bachelor's in Design, Ux/Ui design, bachelor's in Design, Ux/Ui design at Chitkara University
Github Skills (12)
machine-learning10
optimizer10
tensorflow10
python10
optimizers10
optim10
adam10
testing9
mnist9
benchmarking9
benchmark9
security8
Programming languages (2)
Github contributions (5)
cleverhans-lab/cleverhans
Jun 2017 - Jul 2017
An adversarial example library for constructing attacks, building defenses, and benchmarking both
Role in this project:
ML Engineer
ML EngineerContributions:7 commits, 3 PRs, 19 comments in 24 days
Contributions summary:Abhibhav primarily focused on updating and refactoring the CleverHans library tutorials to work with the Adam optimizer. They modified various tutorial files related to MNIST examples, updating the learning rate parameter and integrating the Adam optimizer within the training process. Furthermore, the user made changes to incorporate testing and training error calculations into the tutorial workflows, and made updates to the test suites. These changes demonstrate a focus on improving the training process and assessing model performance.
benchmarkingrobustnessadversarial-machine-learningsecurityadversarial
abhibhav14/CS_203_Notes
May 2017 - Aug 2017
Contributions:21 commits, 2 PRs, 15 pushes in 2 months
notespythonfalliitsemester
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