Usama Muneeb
Education
- Ph.D. in Electrical and Computer Engineering, University of Illinois Chicago, USA (2017–2025) — Thesis: Induced Model Matching: Learning from Restricted Models
- B.S. in Electrical Engineering, LUMS School of Science and Engineering, Lahore, Pakistan (2013–2017)
Research Interests
- Machine learning and deep learning theory
- Natural language processing and reinforcement learning
- Scientific computing and numerical optimization
Biography
Dr. Usama Muneeb is a researcher in machine learning and the theoretical foundations of deep learning, working at the intersection of ML theory, NLP, reinforcement learning, and scientific computing. His Ph.D. research at the University of Illinois Chicago introduced Induced Model Matching, a framework leveraging restricted models to improve the training of full-featured models — work accepted as a Spotlight presentation (top 3% of submissions) at NeurIPS 2024.
Alongside his academic research, he has substantial industry experience, including software engineering roles at NVIDIA (2020, contributing to cuDNN v8 development) and Seagate Technology (2019, model efficiency and data augmentation for resource-constrained environments). He is an active open-source contributor and maintainer, leading multiprecision semidefinite programming tools used within CVXPY and the broader Python ecosystem, including SDPA Multiprecision and SDPA for Python.
Selected Publications
- Muneeb, U., and Ohannessian, M. I. “Induced Model Matching: Restricted Models Help Train Full-Featured Models.” NeurIPS, 2024. (Spotlight presentation, top 3% of submissions)
- Muneeb, U., Koyuncu, E., Keshtkarjahromi, Y., Seferoglu, H., Erden, M. F., and Cetin, A. E. “Robust and Computationally Efficient Anomaly Detection using Powers-of-Two Networks.” IEEE ICASSP, 2020.
Open-Source Leadership & Service
- Maintainer and contributor, SDPA and CVXPY open-source communities (2022–present)
- Creator and maintainer, SDPA Multiprecision and SDPA for Python
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