Yasra Chandio is a fifth-year Ph.D. student in Electrical and Computer Engineering at the University of Massachusetts Amherst. Her current research is at the intersection of security, data-driven ML, and HCI, and it is mainly focused on identifying fundamental design issues in mixed reality that might harm humans physically or cognitively. She has published her research in computing venues such as IEEE VR, IEEE TVCG, IEEE AIxVR, ACM FaCCT, ACM Sensys/Buildsys, ACM e-Energy, EWSN, IEEE TOSN, and IEEE IGSC. Her research achievements include receiving the Best Presentation award at the ACM SenSys PhD Forum, being a finalist at the UMASS 3MT, and Best Paper finalist at AIxVR; her work has also been highlighted by international media outlets such as BBC and ScienceDaily. She values mentorship and was recently awarded the College of Engineering DEI award for her contributions to increasing DEI in STEM both on campus and nationally. She is a CRA-E graduate fellow, a young researcher at the Heidelberg Laureate Forum, a Grace Hopper scholar, part of the CRA-WP grad and CRA IDEALS cohort, and a Google CSRMP scholar. Yasra’s goal is to join an academic institute as a tenure-track faculty member, and she plans to submit her dissertation in 2025.
Mixed Reality (MR) technology has evolved in recent years, transitioning from primarily recreational use to a tool in critical applications such as surgery and therapy. While these technological advancements have undoubtedly enhanced human lives, there is a growing concern that the rapid pace of change might outstrip our understanding of the potential consequences — a concern rooted in the lessons learned from past technological adoptions. In my research, I uncover the fundamental design challenges in MR systems and devices, focusing on the potential sources of physical and cognitive discomfort for users. First, my research explores stealthy attack surfaces that malicious entities can exploit to cause physical harm due to a lack of adaptiveness in current tracking. This insight leads to exploring SLAM tracking vulnerabilities and developing domain-specific adaptive techniques, such as neurosymbolic feature extraction, to strengthen MR systems against internal and external threats. Next, my research explores cognitive safety by investigating how user interaction dynamics affect experience. With a fundamental question: Does an individual experiencing heightened \emph{presence} in MR environments consistently exhibit faster reaction times? This inquiry seeks to provide a systemic metric, reaction time, that objectively quantifies presence in a non-intrusive and unbiased manner. In summary, my research ensures that integrating MR into our lives enhances experiences without compromising the safety and well-being of humans who are at the center of this technology.