Brianna is a Ph.D. student in Computer Science and Engineering at the University of Notre Dame and a visiting researcher at the University of Washington. She’s advised by Dr. Ronald Metoyer (Notre Dame) and Dr. Jennifer Mankoff (Washington). Brianna earned her Bachelor’s in Computer Science from the University of Alabama in 2021, advised by Prof. Chris Crawford. She is also a Google Ph.D. Fellow.
Her research centers on improving data visualizations for accessibility, particularly for those with visual impairments. She works on identifying accessibility challenges and crafting more user-friendly interactive visualization experiences.
Kate is a PhD student in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. She is advised by Professor Jennifer Mankoff. She completed her undergraduate studies at USC, where she double-majored in Computer Science and Business Administration, as well as received her master’s degree in Computer Science. She is an NSF CSGrad4US fellow.
She is interested in studying the intersection of digital and physical technologies that empower those with disabilities or illnesses. Her recent research focuses on generative AI and accessibility, seeking to gain a deeper understanding of the opportunities for improving access as well as identifying areas for improvement.
Aashaka is a PhD student in the Paul G. Allen School of Computer Science and Engineering. She is advised by Dr. Jennifer Mankoff and Dr. Richard Ladner. In 2020, she graduated from University of Delaware with Bachelors of Science in Computer Science and Cognitive Science. Her research interests are in the fields of accessibility and language — specifically how we can use technology to make the world more accessible. She firmly believes communication should not be a privilege — so she hopes to use her background in computer science and cognitive science to think of integrative approaches to multifaceted problems.
Jerry is a PhD student in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. In the past, he served as an Undergraduate Research Leader with the Undergraduate Research Program at UW and a Mary Gates Scholar.
His research focuses on utilizing fabrication and computer science to make healthcare technologies more affordable and accessible to the general populous. His current projects include generating optimized 3D-printable tactile maps and designing a cheap, unobtrusive continuous blood pressure monitor.
Han is a PhD student in the Paul G. Allen School of Computer Science & Engineering. She is advised by Prof Jennifer Mankoff (Computer Science) and Prof Anind K. Dey (Information School).
Han’s research interests span the interdisciplinary areas of human-computer interaction, human-centered machine learning, and fairness, responsibility, accountability, transparency, and ethics in AI (FATE). She is passionate about designing responsible technologies to improve human performance and wellbeing. Her research focuses on uncovering nuanced human performance behavioral patterns through explainable machine learning and data science. Additionally, she researches comprehending human needs and perceptions of AI-learned patterns, informing the design and development of interactive tools to support humans in proactively shaping their behaviors.
If you share similar research interests with her or simply want to have a chat, please feel free to reach out via email: micohan [at] cs [dot] washington [dot] edu.
Daniel is a first-year PhD student in the Paul G. Allen School of Computer Science and Engineering. He is advised by Drs. Jennifer Mankoff (Computer Science) and Jeffrey Lipton (Mechanical Engineering). He graduated from Texas A&M University with a BS in Electrical Engineering (2012) and an MS in Electrical Engineering from Georgia Tech (2016) and afterwards worked at Texas Instruments Kilby Research Labs (2016-2019).
Daniel’s research interests lie at the intersection of inverse design, additive manufacturing, and accessibility of fabrication. His prior work focused on industrial scale additive manufacturing applications; however, he has since turned his focus toward software solutions to enable the design of intricate digital models with minimal effort.
Avery is a Phd Student in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. They are advised by Prof. Jennifer Mankoff. They completed their bachelors in Computer Science at the University of Illinois at Urbana-Champaign in 2019, where Prof. Aditya Parameswaran and Prof. Karrie Karahalios advised them. They are an NSF Fellow and an ARCS Scholar.
Their research focuses on applying computer science to create or improve technologies that serve people with disabilities. Their current work focuses on 1) representation of people with disabilities in digital technologies like avatars and generative AI tools, and 2) how to support people with fluctuating access needs like neurodiverse people and people with chronic or mental health conditions.
Venkatesh Potluri is a Ph.D. student at the Paul G. Allen Center for Computer Science & Engineering at University of Washington. He is advised by Prof Jennifer Mankoff and Prof Jon Froehlich. Venkatesh believes that technology, when designed right, empowers everybody to fulfill their goals and aspirations. His broad research goals are to upgrade accessibility to the ever-changing ways of our interactions with technology, and, improve the independence and quality of life of people with disabilities. These goals stem from his personal experience as a researcher with a visual impairment. His research focus is to enable developers with visual impairments perform a variety of programming tasks efficiently. Previously, he was a Research Fellow at Microsoft Research India, where his team was responsible for building CodeTalk, an accessibility framework and a plugin for better IDE accessibility. Venkatesh earned a master’s degree in Computer Science at International Institute of Information Technology Hyderabad, where his research was on audio rendering of mathematical content.
