Amarda Shehu

Mason CS professor Amarda Shehu
Titles and Organizations

Professor, Department of Computer Science; Associate Dean for AI Innovation in the CEC, Associate VP of Research (IDIA)

Contact Information

Phone: 703-993-4135
Campus: Fairfax
Building: Nguyen Engineering Building
Room 4452
Mail Stop: 4A5

Personal Websites

In the News


Amarda Shehu's research advances foundational investigations in Artificial Intelligence (AI) and Machine Learning (ML). Her team is driven by a passion to push the barriers of their understanding of the physical and biological world. She says, "It is real-world, complex, wicked problems that prompt us to design novel AI and ML frameworks and algorithms. This is nowadays abbreviated as AI4Sience."

Shehu is an accomplished administrator, teacher, and scholar. She serves as an associate vice president for research for the Institute of Digital InnovAtion (IDIA). She also serves as an associate dean for AI Innovation in the College of Engineering and Computing (CEC), where she is also a professor in the Department of Computer Science. Shehu is currently a co-director of the George Mason University Center of Excellence in Government Cybersecurity Risk Management and Resilience. She is also the inaugural Founding Co-Director of George Mason University’s Transdisciplinary Center for Advancing Human-Machine Partnerships (CAHMP). Shehu served as an NSF Program Director in the Information and Intelligent Systems Division of the Directorate for Computer and Information Science and Engineering during 2019-2022. She maintains an active AI research laboratory in CEC.

She is a fellow of the American Institute for Medical and Biological Engineering (AIMBE) and has received several awards, including the 2022 Outstanding Faculty Award from the State Council of Higher Education for Virginia, the 2021 Beck Family Presidential Medal for Faculty Excellence in Research and Scholarship, the 2018 Mason University Teaching Excellence Award, the 2014 Mason Emerging Researcher/Scholar/Creator Award, the 2013 Mason OSCAR Undergraduate Mentor Excellence Award, and the 2012 National Science Foundation (NSF) CAREER Award.

Her research is regularly supported by various NSF programs, the Department of Defense, as well as state and private research awards.

Research Interests:

Artificial Intelligence, Stochastic Optimization, Machine Learning, Deep Learning, Optimization for Deep Learning, Generative Models, Language Models, Bioinformatics, and Computational Biophysics.


  • 2023-2026: Learning Protein-ish: Foundational Insight on Protein Language Models for Better Understanding, Democratized Access, and Discovery. Funded by the National Science Foundation.
  • 2023-2026: Bioinformatics: Linking Chemical and Biological Space: Deep Learning and Experimentation for Property-Controlled Molecule Generation. Funded by the National Science Foundation.
  • 2023-2026:  Furthering U.S. Government Cybersecurity and IT Modernization Leadership and Governance. A Congressionally-funded Community Project.
  • 2022-2025: The cultural, economic, and institutional determinants of AI infrastructures and their consequences in global contexts. Funded by the Department of Defense Minerva Program.
  • 2022-2023: Detection of Malware through Side Channel Analysis. Funded by the Commonwealth of Virginia Cyber Initiative.
  • 2019-2023: Graph Generative Deep Learning for Protein Structure Prediction. Funded the National Science Foundation.
  • 2019-2023: Automated Analysis and Exploration of High-dimensional and Multimodal Molecular Energy Landscapes. Funded by the National Science Foundation.
  • 2021-2022: Mechanisms of Amyloid Interaction and Signaling through the Nicotinic Receptor. Funded by the Commonwealth of Virginia, Alzheimer’s and Related Diseases Program.
  • 2018-2022: Guiding Exploration of Protein Structure Spaces with Deep Learning. Funded by the National Science Foundation.
  • 2018-2021: Statistical Inference for Molecular Landscapes. Funded by the National Science Foundation.
  • 2019-2020: Evaluation of Molecular Structures via Deep Learning. Funded by the Jeffress Trust Awards Program in Interdisciplinary Research. 


  • PhD, Computer Science, Rice University
  • MS, Computer Science, Rice University
  • BS, Computer Science and Mathematics, Clarkson University