Summary
Amarda Shehu is the Associate Vice President of Research for the Institute of Digital InnovAtion (IDIA) and a Professor in the Department of Computer Science in the School of Computing in the College of Engineering and Computing at George Mason University.
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.
Research Interests:
Artificial Intelligence, Stochastic Optimization, Machine Learning, Deep Learning, Optimization for Deep Learning, Generative Models, Language Models, Bioinformatics, Computational Biophysics.
OnAir Post: Amarda Shehu
About
Amarda Shehu 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.
Degrees
- PhD, Computer Science, Rice University
- MS, Computer Science, Rice University
- BS, Computer Science and Mathematics, Clarkson University
Contact
Web Links
Research
- 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.
Shehu Lab
Our laboratory bridges two worlds. We advance foundational research in Artificial Intelligence (AI) and Machine Learning (ML). What drives us is our passion to push the barriers of our understanding of the physical and biological world. 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.
Over the years, we have contributed novel stochastic optimization algorithms for exploring high-dimensional, non-linear variable spaces and modeling the complex, spatio-temporal dynamics of physical and biological systems, as well as the analysis of multi-basin energy/fitness landscapes. We continue to make contributions in both the foundations and applications of ML and deep learning in diverse disciplines, from the life sciences to engineering. Highlights of our recent work include optimization for deep learning, deep generative (latent variable) models in generative AI for modeling small and large molecules. Lately, we are advancing large language models for bio & health informatics.
We always ask two questions: how are we advancing foundational research; what scientific breakthroughs can we make with this foundational research? In answering these questions, we bridge worlds and communities. We are also deeply passionate about science for all and, in particular, broadening participation in computing to historically-minoritized groups of students and researchers.
Videos
Machine Learning Workshop
August 9, 2022
Speaker: Amarda Shehu, George Mason University Talk: Very little data, some data, lots of data Workshop: Foundations of Machine Learning and Its Applications for Scientific Discovery in Physical and Biological Systems June 2022 | Washington, DC Metro
More Information: http://wp.me/P3qDa4-QP Hosted by Rice University Funded by NSF
KGML2020 Amarda Shehu Presentation
(28:23)
By: KGML Workshop
Amarda Shehu, George Mason University: “A Data-driven Journey in Macromolecular Structure, Dynamics, and Function”
Protein Modeling Research in the Shehu Computational Biology Laboratory
August 13, 2017 (01:20)
A summary of research in the Shehu Computational Biology laboratory in the department of computer science at George Mason University.
