Yasra Chandio
University of Massachusetts, Amherst
ychandio@umass.edu
Bio
Yasra Chandio is a final year Ph.D. student in Electrical and Computer Engineering at the University of Massachusetts Amherst. She is a Human-Centered Computing researcher focused on developing adaptive frameworks that identify, measure, and adjust key system parameters in Mixed Reality (MR). Yasra contributes to HCI, ML, and Systems, creating immersive, adaptive, and sustainable MR systems focused on user well-being and alignment with broader societal goals. Her research achievements include being named a CPS Rising Star and Heidelberg Laureate Forum young researcher, winning Best Presentation at the ACM SenSys PhD forum, and being a finalist at UMASS 3MT and AIxVR. Her work has been highlighted by BBC and ScienceDaily. Committed to mentorship, Yasra received the College of Engineering DEI Award and is a CRA-E Fellow, Grace Hopper Scholar, and Google CSRMP Scholar. She plans to pursue a tenure-track faculty position and will submit her dissertation in 2025.
Areas of Research
- Human-Computer Interaction
A Human-Centered Approach to Immersive and Adaptive Mixed Reality
Mixed Reality (MR) technology has rapidly evolved from recreational use to critical applications like surgery and therapy, raising concerns about potential physical and cognitive consequences as the technology advances. This research focuses on optimizing MR systems by developing adaptive frameworks that identify, measure, and adjust key system parameters to improve user comfort, safety, and overall experience. The work is organized into three key thrusts: experiential, systemic, and collaborative. The experiential aspect addresses the challenge of accurately sensing and maintaining a high level of presence in MR environments. Traditional methods, typically reliant on subjective questionnaires, fall short in capturing real-time user responses. This research utilizes underexplored system data, such as reaction time, as an adaptable, objective metric for measuring presence. This metric is sensitive to various influences, including the system’s fidelity and the user’s cognitive and emotional states. By leveraging this data, this research provides a more accurate understanding of user immersion, enabling more effective adjustments to maintain or improve the MR experience. The systemic thrust focuses on improving tracking systems, which are essential for accurately synchronizing virtual and physical elements in MR environments. Traditional tracking methods often struggle with the complexities introduced by human factors, such as unpredictable movements. This research develops tools for adaptive tracking frameworks that incorporate neurosymbolic architectures and predictive algorithms, improving the accuracy and adaptability of MR systems even in complex and unpredictable conditions. The collaborative thrust investigates how immersive technologies shape group dynamics. In this work, we use sensors like audio, proximity, and depth sensing to allow for the real-time interpretation of individual behaviors in a group to infer group dynamics, optimizing collaboration and ensuring effective and equitable interactions. Additionally, this work addresses broader fairness, privacy, and sustainability challenges, ensuring that collaborative MR systems are carbon and resource-efficient.