Angela D. R. Smith is an Assistant Professor in UT Austin’s School of Information (iSchool). Her research sits at the intersection of Critical Computing, Social Computing, and Anti-racist Design within Human-Computer Interaction, Computer-Supported Cooperative Work, and Information Sciences. Grounded in Critical Race Theory, Black Feminist Thought, and Intersectionality, her work investigates how community-driven participatory methods can disrupt dominant technological narratives and create more equitable socio-technical systems. This theoretical foundation enables her to produce analytic, theoretical, and pragmatic contributions that deepen HCI's understanding of how information and communication technologies may foster or inhibit social change. Through qualitative, participatory design methods, she seeks to co-construct knowledge and conscientization, and to design interventions that support historically marginalized groups in pursuing emancipatory transformations. Her work has been supported by Google Research Awards and the National Science Foundation. For more information, please visit https://angeladrsmith.com/. 

Research Areas: human-computer interaction; racial equity in computing; flourishing & liberatory design; critical consciousness

Hanlin Li is an Assistant Professor in UT Austin’s School of Information (iSchool). Her research aims to inform policy and design interventions to incentivize responsible data collection and use. She examines the societal and economic impact of data generated by the public, from rating data to social media comments. Her work sits at the intersection of data governance and human-computer interaction. Li holds a Ph.D. in Technology and Social Behavior from Northwestern University. She publishes in ICWSM, ACM CHI, ACM CSCW, and ACM FAccT. Her work has been covered by national and international media outlets, including NY Times, The Washington Post, and MIT Tech Review. For more information, please visit https://www.hanlinli.com/. 

Research Areas: Human-Computer Interaction, Data Governance, Peer production, online communities 

Nathan TeBlunthuis is an Assistant Professor  in UT Austin’s School of Information (iSchool). Nathan is driven to understand and improve how people design and organize to create quality public information goods on platforms such as Wikipedia, Reddit, and alternative social media. Theoretically, he draws from Organizational Sociology and Communication. Methodologically, he uses data science and computational methods, grounding measurement and interpretation in qualitative research and engagement with communities of practice. He has contributed new understandings of online community lifecycles, why people build ecosystems of overlapping online communities, non-experimental methods for evaluating how AI systems change content moderation practice, and statistical methods that ground AI classification of data in human interpretations. His ongoing projects investigate how Wikipedia’s policies became institutionalized, Wikipedia’s relationships with external information sources, critical mass dynamics in online collaboration, and the sociotechnical co-evolution of AI and online communities. For more information, please visit https://teblunthuis.cc/. 

Research Areas: Data science, Peer production, Human-Computer Interaction, Online Communities, Communication

Matthew Lease is a Professor in UT Austin’s School of Information (iSchool), a Distinguished Member of the Association for Computing Machinery (ACM), and a Senior Member of the Association for the Advancement of Artificial Intelligence (AAAI). He also serves as CosmicAI Co-Director and is a faculty founder and leader of Good Systems. Recent honors include the 2024 AAAI HCOMP Test of Time Award, the 2024 IEEE/ACM ASE Most Influential Paper Award, and a ACM CSCW 2024 Honorable Mention. Beyond scholarship, Lease has also informed responsible AI policy at state and national levels:  https://bridgingbarriers.utexas.edu/news/good-systems-great-policy. For more information, please visit https://mattlease.com/.

Research Areas: Artificial Intelligence (AI), AI4Science, Human-Computer Interaction (HCI)

Brian McInnis is an Assistant Professor  in UT Austin’s School of Information (iSchool). Brian investigates the capability of computing systems to coordinate groups of people to problem-solve civic issues through informed discussion. This work involves a broad range of research methods, from system design and evaluation to expert interviews, data science, design speed-dating, as well as public policy analysis. Brian really enjoys asking students, “what civic issues matter to you?” and then working with them to explore socio-technical systems questions, like: (1) Where do people go to share experiences of this issue? (2) How have technologies helped to advance conversation about the issue? (3) What human labor and other resources are necessary to facilitate these conversations? For more information, please visit https://bmcinnis.github.io/. 

Research Areas: civic engagement; online deliberation; crowdsourcing; education policy

Earl Huff Jr. is an Assistant Professor in UT Austin’s School of Information (iSchool). He researches the social and technological barriers to equitable and accessible technology use by underserved and marginalized communities, in particular, individuals with disabilities and older adults. Leveraging participatory and interaction design methods, Earl designs, develops, and evaluates solutions addressing identified barriers. His latest research focuses on developing and evaluating AI-powered chatbots to promote Generative AI literacy among adults aged 65 and older. Additionally, Earl is investigating the use of AI as assistive technology for people with disabilities in education, assessing the potential benefits and pitfalls of its application. His secondary research looks at the design, implementation, and deployment of interventions for increasing the awareness, interest, and self-efficacy of K-16 students in pursuing careers in user experience (UX) design and research. Earl’s interested in teaching students inclusive design principles to inform the development of more inclusive products, with a primary focus on accessible user experiences. For more information, please visit https://earlhuffjr.com 

Research Areas:  Human-Centered Computing, Accessibility, Computing Education, AI for Education

Stephen C. Slota is an Assistant Professor  in UT Austin’s School of Information (iSchool). Stephen’s research in Social Informatics bridges critical infrastructure studies, data and information ethics, and information policy. His research focuses on advancing our shared understanding of how to leverage technology for social good by critically engaging with the role of information technology in policy, in the development of knowledge infrastructures for the sciences, and in the ethics of data and information in people’s personal and professional lives. Through his focus on infrastructural dynamics, Stephen is able to engage with a broad diversity of research sites. He has explored NSF-funded efforts to develop cyberinfrastructure for the geosciences, nationwide work to collaborate around and support the development of data science, and the data work and practices of social workers and local government related to homelessness in Austin. Stephen's current research explores how technology mediates and infrastructures the relationships between human and non-human life, from environmental management, to conservation work, to agriculture. For more information, please visit https://ischool.utexas.edu/profiles/stephen-slota/. 

Research Areas: Knowledge Infrastructures; Information Ethics and Policy; Science Studies

Dr. Ying Ding is Bill & Lewis Suit Professor in UT Austin’s School of Information (iSchool). She co-chairs AI in Health Lab at School of Information and Dell Medical School at UT Austin. She has collaborated widely with researchers in healthcare and drug discovery. Together with her colleague at Dell Medical School, the AI Health Lab aims to build human-centered AI approaches to deliver better health. Her lab is actively developing and applying chatbot to facilitate medical communication and counseling, LLM on drug combination synergy prediction, visual question answer for pathology images, medical annotation for social determinants of health, and LLM hallucination and uncertainty quantification for agentic AI in healthcare. Furthermore, her lab also focuses on developing deep learning method for medical imaging diagnosis , applying Natural Language Processing (NLP) for medical note annotation and summary, utilizing explainable AI and knowledge graph for health risk prediction, and understanding AI fairness in healthcare. She has published 350+ papers, chaired 40+ workshops, and served as a Program Committee member for 280+ international conferences. Her lab is supported by NIH, NSF, and major industry partners. For more information, please visit https://yingding.ischool.utexas.edu/. 

Research Areas: AI Healthcare