https://orcid.org/0000-0002-4891-6726
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 Duke Appointments & Affiliations
Recent Scholarly Works
Context-Aware Protein Representations Using Protein Language Models and Optimal Transport
Preprint · 2026 Proteins have different functions in different contexts. As a result, representations that take into account a protein’s biological context would allow for a more accurate assessment of its functions and properties. Protein language models (PLMs) generate ... Full text CiteEvoPool: Evolution-Guided Pooling of Protein Language Model Embeddings
Preprint · 2026 Protein language models (PLMs) encode amino acid sequences into residue-level embeddings that must be pooled into fixed-size representations for downstream protein-level prediction tasks. Although these embeddings implicitly reflect evolutionary constraint ... Full text CiteStochastic Unrolled Neural Networks
Conference Proceedings of Machine Learning Research · January 1, 2026 This paper develops stochastic unrolled neural networks as learned optimizers for empirical risk minimization (ERM) problems. We view a fixed-depth unrolled architecture as a parameterized optimizer whose layers define a trajectory from an initial random m ... CiteRecent Grants
Protein Language Models: Representation Learning Innovations for Design and Functional Prediction
ResearchCo Investigator · Awarded by National Institutes of Health · 2026 - 2030View All Grants