Farhan Ahmed
Research Software Engineer at IBM
San Jose, California, United States
Summary
🤩
Rockstar🎓
Top SchoolFarhan Ahmed is a Research Software Engineer based in San Jose with eight years of experience at the intersection of research, software engineering, and data science. Currently at IBM, he applies rigorous academic training (MS and BS from Stony Brook, both with top grades) to build robust ML systems and tooling. His open-source work includes implementing Mixup and CutMix augmentation across PyTorch and TensorFlow in the widely used Adversarial Robustness Toolbox, showing a focus on improving model robustness and reproducibility. Prior internships at organizations from Bloomberg to Peloton and Barclays highlight his ability to translate research ideas into production-quality code across domains. Colleagues would describe him as a technically deep engineer who brings research-grade methods into practical, test-covered implementations.
8 years of coding experience
1 year of employment as a software developer
Advanced Regents Diploma, Computer Science, Advanced Regents Diploma, Computer Science at Brooklyn Technical High SchoolMaster of Science - MS, Computer Science, 3.91/4.0, Master of Science - MS, Computer Science, 3.91/4.0 at Stony Brook University
Github Skills (8)
unit-testing10
pytorch10
adversarial-machine-learning10
machine-learning10
adversarial-attacks10
tensorflow10
data-augmentation10
python10
Programming languages (8)
Github contributions (5)
Trusted-AI/adversarial-robustness-toolbox
Sep 2022 - Jan 2023
Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
Role in this project:
ML Engineer
ML EngineerContributions:32 reviews, 49 commits, 36 PRs in 4 months
Contributions summary:Farhan implemented and tested the Mixup data augmentation technique, along with unit tests. The code modifications involved implementing Mixup functionality for different frameworks, including PyTorch and TensorFlow, and also for CutMix functionality. The contributions focused on improving data augmentation capabilities within the Adversarial Robustness Toolbox (ART).
extractionpythonfairness-mlrobustnessadversarial-machine-learning
Ethos-lab/adversarial-robustness-toolbox
Nov 2022 - Sep 2023
Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
Contributions:4 PRs, 80 pushes, 35 branches in 10 months
extractionpythonfairness-mlrobustnessadversarial-machine-learning
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