Arjun Subramonian is a research scientist based in New York with 11 years of experience at the intersection of machine learning, NLP, and responsible AI. Currently at Meta FAIR, he combines deep academic training (PhD, UCLA) with hands-on impact from internships and research roles at AI2, Microsoft, Snap, and UCLA, focusing on fairness, bias mitigation, and privacy-aware systems. He has contributed to widely used open-source tooling—most notably enhancing AllenNLP’s fairness components and scheduler integrations—making fairness metrics and mitigation more accessible to practitioners. His work spans both applied production features (privacy-preserving safety systems, Teams harassment reporting) and foundational research (reinforcement learning environments, causal schema, and biomedical ML for rapid antibiotic resistance detection). Colleagues describe him as a pragmatic researcher who translates complex ethical concerns into deployable algorithms and clear documentation for broader adoption. Beyond code and papers, he blends technical depth with communication—authoring guides and blogs to demystify fairness for diverse audiences.
11 years of coding experience
7 years of employment as a software developer
Concurrent Enrollment, Mathematics, GPA: 4.00, Concurrent Enrollment, Mathematics, GPA: 4.00 at De Anza College
Concurrent Enrollment, Mathematics, GPA: 4.00, Concurrent Enrollment, Mathematics, GPA: 4.00 at Foothill College
Concurrent Enrollment, Mathematics, Concurrent Enrollment, Mathematics at University of Illinois Urbana-Champaign
An open-source NLP research library, built on PyTorch.
Role in this project:
ML Engineer
Contributions:57 reviews, 16 commits, 23 PRs in 2 months
Contributions summary:Arjun's contributions primarily revolve around updating and enhancing AllenNLP, an open-source NLP library built on PyTorch. Their work includes documenting and updating components related to softmax loss, setting transformers to evaluation mode within the library, and porting Huggingface LambdaLR-based schedulers. Additionally, the user has worked on fairness metrics, and implemented bias mitigation techniques and methods, suggesting a focus on improving and extending NLP capabilities.
Contributions:1 PR, 119 pushes, 1 branch in 4 years 7 months
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