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Navid NaderiAlizadeh

Assistant Research Professor of Biostatistics & Bioinformatics
Biostatistics & Bioinformatics, Division of Integrative Genomics
2424 Erwin Road, 2721, Durham, NC 27705
2424 Erwin Road, 2721, Durham, NC 27705

Overview


Navid NaderiAlizadeh is an Assistant Research Professor in the Department of Biostatistics and Bioinformatics at Duke University. Prior to that, he was a Postdoctoral Researcher %in the Department of Electrical and Systems Engineering at the University of Pennsylvania. Navid’s current research interests span the foundations of machine learning, artificial intelligence, and signal processing and their applications in developing novel methods for analyzing biological data. Navid received the B.S. degree in electrical engineering from Sharif University of Technology, Tehran, Iran, in 2011, the M.S. degree in electrical and computer engineering from Cornell University, Ithaca, NY, USA, in 2014, and the Ph.D. degree in electrical engineering from the University of Southern California, Los Angeles, CA, USA, in 2016. Upon graduating with his Ph.D., he spent four years as a Research Scientist at Intel Labs and HRL Laboratories.

Current Appointments & Affiliations


Assistant Research Professor of Biostatistics & Bioinformatics · 2023 - Present Biostatistics & Bioinformatics, Division of Integrative Genomics, Biostatistics & Bioinformatics

Recent Publications


Learning State-Augmented Policies for Information Routing in Communication Networks

Journal Article IEEE Transactions on Signal Processing · January 1, 2025 This paper examines the problem of information routing in a large-scale communication network, which can be formulated as a constrained statistical learning problem having access to only local information. We delineate a novel State Augmentation (SA) strat ... Full text Cite

Aggregating residue-level protein language model embeddings with optimal transport.

Journal Article Bioinform Adv · 2025 MOTIVATION: Protein language models (PLMs) have emerged as powerful approaches for mapping protein sequences into embeddings suitable for various applications. As protein representation schemes, PLMs generate per-token (i.e. per-residue) representations, r ... Full text Link to item Cite
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Education, Training & Certifications


University of Southern California · 2016 Ph.D.
Cornell University · 2014 M.S.E.E.
Sharif University of Technology (Iran) · 2011 B.S.E.E.