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University research directory
Research labs at
George Washington University.
Compare 5 listed faculty-led profiles, explore their research interests, and follow the evidence in their selected work. These listings are a starting point for discovery and do not establish recruiting availability.
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Review each lab’s official website and recent publications. Compare the methods used, the questions being asked, and the practical requirements of any published opening. Unclaimed profiles are independently curated; the university has not approved or endorsed them.
How to compare research labs ↗Official university website ↗5 research-led lab profiles
Browse labs by their listed college affiliationBiofluid Dynamics Lab
George Washington UniversityMichael W. Plesniak · Department Chair & Professor
How can understanding fluid motion help explain human speech and the circulation of blood? The Biofluid Dynamics Lab investigates phonation and cardiovascular flows, with computer simulations intended to help assess possible outcomes of surgical procedures. Research connects fundamental fluid mechanics with the behavior of biological systems. A complementary research program uses wind-tunnel facilities to investigate turbulence and flows relevant to transportation and wind energy. Together these directions examine how fluid movement interacts with complex geometry in biomedical and engineering problems.
Independently curated · UnclaimedJames Hahn Research Group
George Washington UniversityJames Hahn · Professor
What concrete three-dimensional motion and virtual-reality problems improve medical training and body-surface assessment? The Motion Capture and Analysis Laboratory develops optical motion-capture hardware and software to analyze full-body motion for healthcare research. The group builds virtual-reality simulators — for example, a neonatal endotracheal intubation simulator — combining physics-based models and interactive VR for procedural training. The lab develops optical scanning and machine-learning pipelines to assess physiological features in highly obese subjects, targeting hepatic steatosis and fibrosis. Researchers run interdisciplinary projects that pair motion capture with clinical collaborators to translate sensing and ML into health-focused studies.
Lorena A. Barba Research Group
George Washington UniversityLorena A. Barba · Professor
How can faster computational methods make complex fluid and molecular systems practical to study? The group develops approaches in computational fluid dynamics, combining fluid mechanics with applied mathematics and computer science. Its work extends into biomolecular physics, including computer methods for problems involving protein electrostatics. Researchers use GPU accelerators and parallel algorithms to support large-scale scientific calculations. The program also examines reproducibility and open science, connecting the design of numerical methods with transparent computational workflows that other researchers can examine and reuse.
Timothy Wood Research Group
George Washington UniversityTimothy Wood · Professor
How can virtualization and new network programmability improve cloud performance and reliability? The GW Cloud Systems Lab develops systems-level tools that use virtualization and programmable network elements to improve performance, efficiency, and reliability in cloud data centers. Researchers build and evaluate serverless and edge platforms (Mu Serverless, EdgeOS) to tackle autoscaling, placement, and fast startup for edge/cloud functions. The group combines programmable switches, SmartNICs, and hosts to design terabit-scale network monitoring and NFV-aware load balancing. Experimental work uses implementation, benchmarking, and deployment studies to measure performance and isolation tradeoffs in real systems.
Xiaodong Qu Research Group
George Washington UniversityXiaodong Qu · Assistant Professor of Practice
Can machine learning remain reliable and interpretable across diverse human neural and behavioral data? The group develops machine-learning methods for noisy, temporal EEG and multimodal human data to improve generalization across people and sessions. Researchers build human-in-the-loop and interactive EEG/BCI systems, including real-time personalization and accessible EEG game control, to study model behavior under user adaptation. The lab evaluates temporal and multimodal modeling techniques (convolutional and transformer approaches) for EEG tasks such as gaze prediction and motor imagery. Projects examine AI for learning by measuring EEG markers during learning and testing generative-AI effects on higher-education tasks.
Showing 1–5 of 5 labs