Stanford Ocean Acidification VR Experience
Stanford University,
I am a third-year PhD student advised by James Fogarty in Computer Science & Engineering at the University of Washington. I am grateful to be an ARCS Foundation Scholar and a GEM Fellow. My research interest is in
I recieved my Bachelor's degree from the University of Maryland, Baltimore County (UMBC) in May 2021 where I majored in Computer Science and minored in Psychology. At UMBC, I was a McNair Scholar , LSAMP Scholar and a Center for Women in Technology (CWIT) Affiliate.
Deceptive design patterns (interchangeably named "dark patterns") are user interface design choices implemented to manipulate people’s behaviors for the optimization of shareholders’ desires. The existence of these patterns across interface modalities continue to be largely studied. Yet, little is known about how deceptive design patterns impact people who use screen readers and related accessibility tools when using online services. To address this, our study connects discourse of deceptive design patterns and accessibility barriers to articulate the exacerbated consequences they have on people who use screen readers and related accessibility tools. Through an interview and subsequent diary study with 16 participants, we reveal six deceptive design patterns that our participants encounter when using online services and the associated disproportionate impacts. We apply an existing taxonomy of harm and discuss how our analysis contributes to theories of consequence based accessibility. We offer design considerations for well-intentioned designers to consider when developing online services to prevent from inadvertently manipulating people who use screen readers and related accessibility tools.
This web application assists cyber analysts in detecting anomalous activity on machines. This research served to eliminate the difficulty cyber analysts experience when observing and detecting large amounts of data across computer systems so they can identify and prevent malicious machines more efficiently. To combat the difficulty experienced, machine learning was used to help analysts prioritize which events to focus on. The interactive tool implemented to evaluate these anomalous events was divided into three interactive visulations (i.e., filtering system, high-level treemap, low-level collapsible tree) to assist analysts in detecting anomalous patterns.
Christina N. Harrington, Aashaka Desai,
Aashaka Desai, Venkatesh Potluri,
Stanford University,
Cornell University,
2023 Speaker at the Paul G. Allen School of CSE Accessibility Colloquium
2023 Awarded UW CREATE's Race, Disability & Technology Grant
2023 Attended the CRA-WP Grad Cohort Workshop for Inclusion, Diversity, Equity, Accessibility, and Leadership Skills (IDEALS)
2022 Guest speaker at the 2022 LSAMP Conference
2020, 2018 Speaker at the annual LSAMP Summer Bridging Conference
2019 Speaker at BlackcomputeHER Conference