Stanford Ocean Acidification VR Experience
Stanford University,
Hello,
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.
Research Interests
- Accessibility
- Human-Computer Interaction
- Responsible AI
- Inclusive Design
Selected Projects
Digital Code-Switching and Masking in AI Use for Multilingual and Multicultural People with Disabilities
This project explores how multilingual and multicultural people with disabilities engage in digital code-switching and digital masking in the context of AI use, examining the experiences, challenges, and advantages they encounter in each practice and at their intersection.
Advancing AI Learning Technologies for Scalable Early Screening and Ability-based Intervention for Children’s Speech and Language Development
Speech-language pathologists (SLPs) are professionals who provide support to children with speech and language difficulties through delivering evaluation, assessment, and interventions. Despite growing research on how Artificial Intelligence (AI) can support SLPs, there is limited research examining how AI can assist SLPs in delivering equitable care to culturally and linguistically diverse (CLD) children with disabilities. Through interviews with 15 SLPs and a two-part survey study with 13 SLPs, we report SLP perceptions of challenges in delivering responsive care to CLD children with disabilities (i.e., insufficient representative materials, inaccurate and inefficient translation, and insufficient support for language variations), areas for AI-based support, evaluations of how available AI performs in addressing these challenges, and bias assessments of AI-generated materials. We discuss implications of the range of care in AI-prompting, tensions and tradeoffs of AI-based support, and honoring diverse representations in AI-generated materials. Finally, we offer considerations for AI-based support in speech-language pathology.
Examining Experiences of Speech Recognition Systems with African American English Speakers with Speech Disabilities
The purpose of this project is to examine the experiences, perceptions and amplified challenges of African American English Speakers who have speech disabilities when using speech recognition systems. In this, we aim to understand the sociocultural factors and tensions that shape the adoption and utilization of speech recognition systems.
Deceptive Design Patterns and Online Accessibility Barriers
Deceptive design patterns are interface designs that manipulate people into actions to which they would otherwise object. Despite growing research on deceptive design, limited research examines its interplay with accessibility and visual accessibility technology (e.g.,~screen readers, screen magnification, braille displays). We present an interview and subsequent diary study with 16 people who use visual accessibility technology to better understand their experiences with accessibility and deceptive design. We report participant experiences with six deceptive design patterns, including both designs that are intentionally deceptive and designs where participants describe accessibility barriers manifesting as deceptive, together with direct and indirect consequences of deceptive patterns. We discuss intent versus impact in accessibility and deceptive design, how access barriers exacerbate harms of deceptive design, and impacts of deceptive design from a perspective of consequence-based accessibility. We propose that accessibility tools could help address deceptive design patterns by offering higher-level feedback to well-intentioned designers.
Past Projects
Developing Interactive Tool to Assist Cyber Analysts in Detecting Anomalous Behavior on Machines
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.
Publications and Workshops
Working at the Intersection of Race, Disability, and Accessibility
Christina N. Harrington, Aashaka Desai,
Towards Intersectional CUI Design Approaches for African American English Speakers with Dysfuencies
Using Fiber Arts and Sonification to Improve Data Accessibility of Maker Spaces
Aashaka Desai, Venkatesh Potluri,
Conflict Mediation at Scale: Leveraging Big Data to Mediate Online Conflicts
Cornell University,
Highlights
2024 I moderated a talk with Patty Berne on Disability Justice: Centering Intersectionality and Liberation
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