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 Accessibility and Human-Computer Interaction (HCI), particularly developing more inclusive and accessible technology to empower and improve the quality of life for people with disabilities. Within this, I am interested in examining the intersection of race and disability as it pertains to the engagement with speech technology, particularly for people of color with disabilities that have associated English ethnolects. In exploring this research, I hope to advance the design and development of inclusive and equitable speech technologies that honor intersectionality.

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

Current Projects

Advancing AI Learning Technologies for Scalable Early Screening and Ability-based Intervention for Children’s Speech and Language Development

The purpose of this project is to create advanced AI technologies to support Speech Language Pathologists (SLP) in early screening and individualized interventions for children who require speech and language services. In this work, I am condicting a mixed-methods approach to address how we can design and develop culturally and linguistically responsive AI technologies in order to support SLP's in their practice with children with speech and language difficulties from diverse backgrounds.

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 (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.

Past Projects

Developing Interactive Tool to Assist Cyber Analysts in Detecting Anomalous Behavior on Machines

Aaleyah Lewis, Dave Richardson, John Goodall
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.


Working at the Intersection of Race, Disability, and Accessibility

Christina N. Harrington, Aashaka Desai, Aaleyah Lewis, Sanika Moharana, Anne Spencer Ross, Jennifer Mankoff. ASSETS ‘23: SIGACCESS Conference on Computers and Accessibility. [PDF]

Towards Intersectional CUI Design Approaches for African American English Speakers with Dysfuencies

Aaleyah Lewis, Orevaoghene Ahia, Jay L. Cunningham, James Fogarty. [Workshop Paper] ACM CHI 2023. CUI@CHI: Inclusive Design of CUIs Across Modalities and Mobilities. [PDF]

Using Fiber Arts and Sonification to Improve Data Accessibility of Maker Spaces

Aashaka Desai, Venkatesh Potluri, Aaleyah Lewis*, Daniel Campos Zamora*, Jayne Everson*, Jennifer Mankoff, Richard E. Ladner. [Workshop Paper] ACM CHI 2022. Reimagining Systems for Learning Hands-On Creative and Maker Skills. [PDF]

Stanford Ocean Acidification VR Experience

Aaleyah Lewis, Anna Queiroz, Jeremy Bailenson.
Stanford University, 2020

Conflict Mediation at Scale: Leveraging Big Data to Mediate Online Conflicts

Aaleyah Lewis, Ru Zhao, Susan Fussell.
Cornell University, 2019


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

Stay In Touch