Dhruv is a freshman at the University of Washington studying Computer Science. In the past, he has began to build a coding expertise in full-stack development and data analytics. He hopes to delve more in the latter through strengthening his Machine Learning skills and utilizing them in meaningful real-world applications, with an emphasis on tackling accessibility. Outside of Computer Science, he maintains an active lifestyle – enjoying weight lifting, playing soccer, and running. Currently, he is working on the Academic Performance Prediction project here at the Make4All Lab.
Srihari Krishnaswamy is a first-year undergraduate student studying Computer Science at the University of Washington. His programming experience in the past consists of work on VR simulations and cross-platform apps. He’s looking to learn more about Machine Learning, AI and Signal Processing in the near future, and looks forward to working on more projects. In his free time, he enjoys music production and playing sports.
Hello! My name is Emma and I’m a third year computer science major at UW. My hobbies include crocheting and trying out new foods. I love talking to other people so feel free to reach out anytime!
Yusuf Shahpurwala is a freshman studying computer science at the University of Washington. Most of his coding experience so far has been through making web applications. He is hoping to learn more about human-computer interaction, machine learning, and embedded systems over the next couple of years. To relax he enjoys playing sports and reading. He is excited to currently be working on the Accessible Knitting project with Make4all.
Zelin is a first-year Master’s student majoring in Mechanical Engineering with Data Science at the University of Washington. He completed his bachelor’s degree in Mechanical Engineering at Colorado State University where he also engaged in research in the field of manufacturing high-performance polymers by 3D printing and robotics.
His current research, led by Momona, focuses on modeling and enhancing biosignals-based human-machine interaction to support accessibility and health. He presently works on data collection and analysis of biosignals.
Venkatesh Potluri, John Thompson, James Devine, Bongshin Lee, Nora Morsi, Peli De Halleux, Steve Hodges, and Jennifer Mankoff. 2022. PSST: Enabling Blind or Visually Impaired Developers to Author Sonifications of Streaming Sensor Data. In Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology (UIST ’22). Association for Computing Machinery, New York, NY, USA, Article 46, 1–13. https://doi.org/10.1145/3526113.3545700
We present the first toolkit that equips blind and visually impaired (BVI) developers with the tools to create accessible data displays. Called PSST (Physical Computing Streaming Sensor data Toolkit), it enables BVI developers to understand the data generated by sensors from a mouse to a micro: bit physical computing platform. By assuming visual abilities, earlier efforts to make physical computing accessible fail to address the need for BVI developers to access sensor data. PSST enables BVI developers to understand real-time, real-world sensor data by providing control over what should be displayed, as well as when to display and how to display sensor data. PSST supports filtering based on raw or calculated values, highlighting, and transformation of data. Output formats include tonal sonification, nonspeech audio files, speech, and SVGs for laser cutting. We validate PSST through a series of demonstrations and a user study with BVI developers.
Evan is an undergraduate at University of Washington, majoring in Computer Science. He is passionate about computer graphics and the huge potential of combining graphical programming techniques with fabrication such as 3D printing, machine embroidery, and so on. In the meantime, he is also a member of the UW Reality Lab. He learned advanced knowledge on how to design interactive, efficient, and accessible applications that run in virtual reality, but he also wants to make them physically touchable and perceivable and bring those models to real life. Since started discovering the vast potential in computer fabrication, he has decided to become a part of the pioneers in this field and contribute to the goal of making designs for everybody.
The U.S. National Institute of Health (NIH) 3D Print Exchange is a public, open-source repository for 3D printable medical device designs with contributions from clinicians, expert-amateur makers, and people from industry and academia. In response to the COVID-19 pandemic, the NIH formed a collection to foster submissions of low-cost, locally-manufacturable personal protective equipment (PPE). We evaluated the 623 submissions in this collection to understand: what makers contributed, how they were made, who made them, and key characteristics of their designs. We found an immediate design convergence to manufacturing-focused remixes of a few initial designs affiliated with NIH partners and major for-profit groups. The NIH worked to review safe, effective designs but was overloaded by manufacturing-focused design adaptations. Our work contributes insights into: the outcomes of distributed, community-based medical making; the features that the community accepted as “safe” making; and how platforms can support regulated maker activities in high-risk domains.
Christina Zhang is a senior at University of Washington, majoring in Computer Science and Informatics, her research interests are mainly HCI, mHealth, behavioral health, accessibility and social computing. Her current work involves supporting early identification of mental health issues in adolescents, and software-based solutions to accessible communication in higher education.
In the past, she has worked on a research project that studies how online tests could be leveraged to bridge the gap in the support system of people with cognitive and mental disabilities, the paper she co-authored won the Best Paper Award on ASSETS 2021.
Claris Winston is a third-year undergraduate studying Computer Science at the Paul. G. Allen Center for Computer Science and Engineering at the University of Washington. She is interested in human-computer interaction and accessibility research, and the applications of machine learning and computer vision to improve accessibility in the field of healthcare/sports medicine. She recently participated in and presented a paper that she co-authored at ICSE 2022. Apart from being involved as a teaching assistant in the Computer Science department, she loves graphic design. One of her designs can be found on the cover of the May 2022 ACS SynBio journal. In her spare time, she loves to bake cakes, compose music, and play the piano.
Currently, in the Make4all lab, she is excited to be working on the embroidered tactile graphics project.
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