Graduate Students

I study how our sense of touch is used to explore the physical world, and ways this knowledge can improve technology, healthcare, and human connection

I am conducting research with Dr. Sara Riggs to advance multimodal display design by addressing the strengths and limitations of human haptic perception. I integrate functional brain imaging techniques with conventional behavioral methods to gain a deeper understanding of human perception than psychophysical data alone can provide.

I am supporting a CCALS-led project funded by the VTRRC to identify and analyze hydrogen and hydrogen-hybrid use cases in Southern Virginia

Ekim's research focuses on human factors engineering, human performance modelling, and inclusive design. Her dissertation focuses on modeling touchscreen interactions with Fitts Law for people with tremor. 

I'm working to better understand the delivery of therapeutic touch, by improving methods of sensing the delivery and analysis.

Beatrice Li is a Ph.D. candidate in Systems Engineering working in Dr. Arsalan Heydarian's BRAin Lab and a resident of Link Lab, a multidisciplinary center for the research of Cyber-Physical Systems (CPS). 

Deep learning for 3D medical image segmentation; multimodal frameworks fusing imaging and clinical text to advance precision health.

My research focuses on Haptics. My current project studies skin deformation during massage therapy using computer vision to better quantify the effectiveness of these treatments. 

My research develops data-driven methods for modeling risk and resilience, informing robust decision-making in complex systems.

I develop AI-enabled cyber‚ physical systems using ML, NLP, and computer vision to automate infrastructure inspection & enable precision agriculture.

My research focuses on human factors approach to improving the quality and accessibility of healthcare for individuals with disabilities and chronic medical conditions. 

I’m broadly interested in machine learning-based information systems for solving real-world problems in diverse industries. My research particularly focuses on (1) developing recommender systems, (2) evaluating AI performance in terms of the information quality, and (3) designing human-in-the-loop frameworks to enhance systems' results.

Skye's dissertation research focuses on improving the design and reliability of subjective measures of human workload, namely the NASA-TLX.

My work uses graph neural networks to predict subsurface damage in structures.