Jeff is an Associate Professor in the Human–Computer Interaction Institute and the Language Technologies Institute at CMU’s School of Computer Science. His research focuses on building interactive, human-centered AI systems that work reliably in real-world contexts. He works at the intersection of machine learning and human–computer interaction to create systems that are more useful, adaptable, and aligned with human needs, with interests spanning responsible AI, contextual reasoning, and collaborative intelligence.

In addition to his role at CMU, Jeff serves as Director of Human-Centered Machine Intelligence & Responsible AI under the Machine Learning Research org at Apple , where he leads research and applied work on AI/ML design, system behavior, accessibility technologies, learning sciences, and computational understanding of user interfaces.

Jeff received his B.S.E. in Computer Science from Princeton University and his Ph.D. in Computer Science and Engineering from the University of Washington, advised by Richard E. Ladner. He has been recognized with an Alfred P. Sloan Foundation Fellowship, a National Science Foundation CAREER Award, and the MIT Technology Review TR35 Award. Before joining CMU, he was a faculty member at the University of Rochester, where he led the ROC HCI Group.

Chris Maury is a Special Faculty Researcher at CMU’s Human-Computer Interaction Institute. His work focuses on reimagining civic engagement by strengthening the relationship between local news organizations, elected officials, and the communities they serve. His research spans both academic inquiry and applied infrastructure-building, including ongoing efforts through informup.org. Prior to joining CMU, Chris spent seven years at Apple  as an Engineering Manager, where he shipped key features across Accessibility and Siri, such as Personal Voice, Live Captions in FaceTime, and Siri support for HealthKit. Earlier in his career, he built voice-based applications supporting blind and low-vision users.

Eric is an Administrative Assistant in the Human–Computer Interaction Institute at CMU. He supports multiple labs including the BIG Lab, managing finances, purchasing, travel arrangements, reimbursements, and grant-related budgeting and coordination. He joined CMU in 2018 and plays a key role in ensuring smooth day-to-day operations for the research teams he works with. Eric holds a Master’s degree in Music Theory and Composition from Duquesne University and a Bachelor of Music in Performance from the Eastman School of Music in New York.

Atieh is a Presidential Postdoctoral Fellow in the Human-Computer Interaction Institute at CMU, where she works with Professors Jeffrey Bigham and Patrick Carrington. She recently earned her PhD in Electrical and Computer Engineering at UC Santa Barbara under the supervision of Professor Misha Sra in the Human-AI Integration Lab. Her research sits at the intersection of Human-Computer Interaction and Accessibility, with a focus on designing meaningful technologies that improve the lives of individuals with disabilities. Grounded in participatory design, her work emphasizes solutions that address both functional needs and broader quality-of-life considerations—an aspect often underexplored in assistive technology research. Atieh spent time at Google, Apple , and Magic Leap.

Atieh’s contributions have been recognized at leading HCI and accessibility venues, reflecting her commitment to inclusivity and to creating technologies that offer holistic, user-centered experiences. She is passionate about interdisciplinary collaboration and is always eager to engage with others on innovative projects advancing accessibility and human-centered design.

Saelyne is a Postdoctoral Fellow in the Human-Computer Interaction Institute at CMU, where she works with Jeff Bigham. She earned her Ph.D. in the School of Computing at KAIST, advised by Juho Kim, with support from the NAVER PhD Fellowship and the Kwanjeong Scholarship. Her research sits at the intersection of Human-AI Interaction, Human-Agent Collaboration, and Multimodal Understanding, with a focus on building collaborative and proactive AI agents that help people learn, understand, and perform complex tasks.

Saelyne’s work examines how AI systems can leverage diverse learning materials, including videos, documents, software tutorials, GUIs, and interaction traces, to support more effective knowledge capture and sharing. Her projects include systems and benchmarks for procedural task learning, instructional video understanding, software tutorial question answering, accessible video editing, and AI assistance for open-ended GUI tasks.

Before joining CMU, Saelyne completed research internships at Adobe Research, Autodesk Research, and LG AI Research. Her work has appeared at leading HCI and AI venues including CHI, UIST, IUI, AAAI, ICCV, and CVPR, and has been recognized through honors such as the Outstanding Ph.D. Thesis Award at KAIST, the Outstanding M.S. Thesis Award at KAIST.

Jessica is a Ph.D. student in the Language Technologies Institute at CMU, advised by Jeff Bigham and Fernando Diaz. Her research focuses on evaluating dialogue systems and understanding how large language models act as judges, with particular attention to how user characteristics, communication styles, and preferences shape conversational outcomes. She is broadly interested in dialog systems, personalized evaluation, and the nuances of human communication. Jessica received her B.S. in Computer Science from Columbia University. She has previously worked at Google, Apple , Amazon, and Vertex Pharmaceuticals.

Yi-Hao is a Ph.D. student in the Human–Computer Interaction Institute at CMU, advised by Jeffrey P. Bigham and Amy Pavel (UC, Berkeley - EECS). His research focuses on human-centered AI with an emphasis on interactive and accessible AI agents. He studies how these agents can robustly perceive multimodal context, collaborate proactively while preserving user agency, and adapt continually to support creative and expressive work in domains such as design and education. His work draws from multimodal machine learning, vision–language modeling, and agentic interaction to develop systems that understand, respond to, and learn from users in situ. Yi-Hao is an Adobe Research Fellow and received his B.S. in Computer Science and Information Engineering from National Taiwan University. He has previously held research roles at Google, Microsoft, and Adobe.

Peya is a Ph.D. student in the Robotics Institute at CMU, co-advised by Jeffrey P. Bigham and Aaron Steinfeld. Prior to starting her Ph.D. Peya earned her M.S. in Robotics at CMU. Her research focuses on applying machine learning to address challenges in visual accessibility, with an emphasis on building interactive systems that support people with visual impairments in navigating visual information and environments. She is broadly interested in the intersection of artificial intelligence and human–computer interaction, particularly how computational systems can be designed to meaningfully expand access and agency. Before joining CMU, Peya was a Research Fellow at Microsoft Research in the Technology and Empowerment group, and she earned her undergraduate degree with distinction from Delhi Technological University.

Hamza is a Ph.D. student at CMU advised by Jeff Bigham. His research bridges machine learning, robotics, and human–computer interaction to enable multimodal human–system collaboration for human augmentation.

His work spans both perception and assistance. He develops methods for structuring speech, visual, and temporal signals so digital and embodied systems can represent human goals and adapt over time, and he designs collaborators that translate this understanding into intuitive, actionable support that extends cognitive, sensory, and physical capabilities in situ. A central focus of his work is reliable human–AI collaboration and integration in high-risk/mission-critical domains such as public safety (EMS, fire, and S&R). Hamza is a volunteer firefighter, EMT, and S&R operator.

During his Ph.D., Hamza has spent time as a visiting scientist at Sony CSL working with Jun Rekimoto. Before joining CMU, he was a Member of Technical Staff (AI/ML) at Oracle in the Fusion AI team. He received a B.S. in Computer Science, with the highest honors, working with Violetta Cavalli-Sforza on NLP for low-resource settings.

Christine is a Ph.D. student in the Human–Computer Interaction Institute at CMU, advised by Jeff Bigham and Anat Caspi (University of Washington - ECE). She is an NSF GRFP fellow. Her research focuses on accessibility and the design of sociotechnical systems that support empowerment, bridge-building, and equitable participation in education and everyday life. She is particularly interested in how technology can meaningfully expand agency for people with disabilities, and how advocacy and community knowledge can be incorporated into computational systems and design processes. Christine earned her B.S. in Computer Science from the University of North Carolina at Chapel Hill, graduating with Highest Honors and Highest Distinction. Before beginning her doctoral studies, she spent time at SAS Institute, where she developed automation tools for content delivery and code generation in the Education Division.

Alex is currently pursing B.S. degree in Artificial Intelligence at CMU. His research focuses on Computer-Use Agents with an emphasis on enhancing the controllability during Human-AI Interaction. He is broadly interested in human-centered AI, ML, and robotics, especially in how intelligent systems can perceive real-world environments and provide useful support to people.

Sara earned her Ph.D. from the Human–Computer Interaction Institute at CMU, advised by Beibei Li and Jeff Bigham. Her research investigated the national-level economic, security, and cybersecurity risks posed by modern AI and machine learning systems, with a particular focus on auditing and evaluating foundation models. Her dissertation work combined empirical evaluation, red-team-inspired methods, and novel assessment techniques to characterize vulnerabilities, misuse pathways, and systemic risks in generative AI systems.

During her Ph.D., Sara developed auditing tools, redesigned evaluation pipelines for AI security, and studied how transformer-based models behave under adversarial or high-stakes conditions. She also worked with industry AI red teams and helped design both online and in-person “AI risk bounty” competitions to stress-test real-world AI systems.

After completing her Ph.D., Sara joined Carnegie Mellon University as Teaching Faculty, where she continues to work at the intersection of AI risk, cybersecurity, and responsible AI evaluation.

Carla completed a joint Ph.D. in Language Technologies at CMU (advised by Jeff Bigham) and the Universidade NOVA de Lisboa (advised by João Magalhães). Her research lies at the intersection of affective computing, multimodal interaction, and conversational AI, with a focus on moving beyond basic emotion categories toward fine-grained emotional and linguistic facial expressions that support more engaging human–machine dialogue. Her dissertation introduced a data bootstrapping methodology combining automated annotation and human evaluation to reduce the cost of building large-scale expression datasets. This framework enabled quantitative and statistical analysis of facial action units for expressions such as stress, enthusiasm, and adjective-driven expressions in sign language, and provided actionable insights into emotional, conversational, and linguistic facial signals.

Her work contributes to the development of emotionally intelligent dialogue systems by providing scalable methods to collect, analyze, and model nuanced facial expressions, advancing affective computing beyond Ekman’s basic emotion categories.

Kundan earned his Ph.D. from the Language Technologies Institute at CMU advised by Zachary Chase Lipton and Jeffrey Bigham. His dissertation focused on improving the reliability of neural abstractive summarization systems, addressing failure modes such as hallucinations, brittleness to noisy input, unsafe or biased model behaviors, and challenges in evaluating factuality. His work introduced new methods for robust evaluation, modular generation pipelines for long or noisy inputs, lightweight noise-detection probes, safety-aware generation controls, and new approaches to pretraining that reduce reliance on large web corpora.

After completing his Ph.D., Kundan joined Apple  as a Research Scientist in the Machine Learning Research org, where he works on AI safety, reliability, and trustworthy model behavior. Before graduate school, he earned his B.Tech in Computer Science and Engineering from the Indian Institute of Technology - Kanpur. He has previously worked at Google, Amazon, Adobe, and Abridge, contributing to research and applied machine learning across NLP and large-scale language technologies.

Jason earned his Ph.D. from the Human–Computer Interaction Institute at CMU, advised by Jeff Bigham. His dissertation developed the technical foundations for making user interfaces dynamically malleable through machine learning. His work showed how systems can infer UI structure and functionality directly from pixels, sense user context and ability, and generate or adapt interfaces automatically—enabling more accessible, personalized, and context-aware computing experiences. At CMU, Jason advanced research in user and UI understanding, including ML-driven methods for predicting UI semantics, systems that recommend accessibility settings based on behavior, and generative models that reconfigure interfaces for different users, devices, and modalities. His work received multiple awards, including Best Paper and Honorable Mention at CHI and W4A, and IUI.

After completing his Ph.D., Jason joined Apple  as a Research Scientist in the Machine Learning Research org, where he worked on Generative UI, UI understanding, and applied AI/ML for interactive systems. Prior to that, he spent time at Google, Meta, and the University of Southern California, and earned his B.S. in Computer Science from Georgia Tech. He is now an Assistant Professor in the Department of Computer Science at Purdue University, where he continues to explore how computational models can expand how humans interact with software.

Stephanie completed her Ph.D. in the Human–Computer Interaction Institute at CMU, advised by Jeff Bigham and Henny Admoni. Her dissertation examined how augmentative and alternative communication (AAC) systems can increase conversational agency for users, introducing new participatory design methods and AI-driven interaction techniques that support more adaptive, expressive, and personalized communication. Her research at CMU explored how design materials—including AI models, interactive templates, and embodied expressive objects—can be leveraged to help AAC users better shape conversations, articulate intent, and participate more naturally. She built and deployed AAC systems with real users and conducted empirical studies to understand their impact on communication experiences and autonomy. Stephanie is now an Assistant Professor in the College of Information at the University of Maryland, College Park, where she continues to work at the intersection of participatory design, AI, and assistive communication technology, developing agency-promoting AAC systems for diverse communicators.

Anhong earned his Ph.D. from the Human-Computer Interaction Institute at CMU, advised by Jeff Bigham. His dissertation introduced Human-AI Systems for Visual Information Access, which developed hybrid human–AI interactive systems for accessibility and real-world environmental sensing. His work demonstrated how human insight can bootstrap AI models with minimal training data, enabling systems that are nearly as robust as human perception and nearly as scalable as automated AI.

After completing his Ph.D., Anhong joined the University of Michigan, where he is now a Morris Wellman Assistant Professor in Computer Science & Engineering, leading the Human-AI Lab (HAIL). His research continues to leverage the synergy between human and machine intelligence to create interactive systems for accessibility, collaboration, and context-aware assistance. His work has received multiple Best Paper and Honorable Mention awards at CHI, UIST, ASSETS, and MobileHCI. He is a recipient of the NSF CAREER Award, a Google Research Scholar Award, a Snap Inc. Research Fellowship, and has been named to Forbes 30 Under 30 in Science.

Anhong previously worked with Microsoft Research, Snap Research, and Google. He also holds an M.S. in HCI from Georgia Tech and a B.Eng. from Beijing University of Posts and Telecommunications.

Cole earned his Ph.D. from the Human–Computer Interaction Institute at CMU, where he was advised by Jeff Bigham and Kris Kitani. His dissertation focused on enabling blind and low-vision users to access and contribute to user-generated visual content on social platforms, combining computer vision, accessibility, and social computing. His work examined how people with visual impairments navigate digital and physical environments, what information they need during navigation, and how systems can support richer, non-visual experiences.

Before joining CMU, Cole earned a B.S. in Computer Science from the University of Illinois at Urbana–Champaign. During his graduate studies, he also held research and engineering roles at Meta, Microsoft, Hulu, and CoreOS, working across accessibility, computer vision, and large-scale systems.

After completing his Ph.D. in 2020, Cole joined Apple , where he continues to work at the intersection of accessibility, machine learning, and user experience, building technologies that support navigation, understanding, and interaction for people with visual impairments.

Ting-Hao (Kenneth) earned his Ph.D. from the Language Technologies Institute at CMU, advised by Jeff Bigham. His dissertation explored how combining real-time human computation with AI can create more capable, open-domain conversational assistants. His work developed systems that integrate crowd input, automation, and chatbot components to support richer, more flexible human–computer conversations. After completing his Ph.D., Kenneth joined the College of Information Sciences and Technology at The Pennsylvania State University, where he is now an Associate Professor, directing the Crowd-AI Lab. His research continues to span natural language processing, human–computer interaction, and creativity support tools, with applications such as writing assistance and collaborative problem-solving.

Yu completed his Ph.D. in Computer Science at the University of Rochester, advised by Jeff Bigham and Jiebo Luo, where his research focused on improving non-visual access to complex and spatial information on mobile devices for blind and low-vision users. During his Ph.D., he was a Visiting Scholar in the Human–Computer Interaction Institute at CMU, contributing to research in accessibility and assistive technology.

After graduating, Yu joined Google’s Accessibility Engineering team, where he has spent nearly a decade building large-scale accessibility products used worldwide. He is currently a Senior Software Engineer and Tech Lead for Action Blocks, a system designed to simplify mobile interactions for users with cognitive disabilities, age-related conditions, and others who benefit from streamlined access to routine actions. He previously worked on Voice Access, Switch Access, Accessibility Scanner, and other initiatives within Google Research, alongside outreach efforts for accessibility at Google I/O and Google Developer Days.

Before pursuing his Ph.D., Yu earned his B.S. and M.S. degrees in Computer Science from Tsinghua University, where he conducted research on smartphones, smart homes, and interaction design.

Erin earned her Ph.D. in Computer Science at the University of Rochester, advised by Jeff Bigham. Her dissertation and early research focused on improving access to complex and spatial information for blind and low-vision users, using interactive systems, crowdsourcing, and computational methods. During her Ph.D., she was a Visiting Researcher at CMU’s Human-Computer Interaction Institute, where she collaborated closely with the accessibility research community and continued publishing work on non-visual interaction.

Her research examines accessibility, disability, and how technology—including social media and collaborative systems—shapes the experiences and agency of disabled people. Recent work includes real-time crowd assistance for inaccessible situations, online disability activism and representation, designing kitchens for cooks with cognitive impairments, assessing remote identification of accessibility barriers, and studying how people with bipolar disorder and schizophrenia engage in technology-mediated advocacy.

After completing her Ph.D., Erin joined Indiana University in the School of Informatics, Computing and Engineering, where she is now an Associate Professor. She previously spent time at Google and Microsoft Research. Before graduate school, she earned a B.S. in Computer Science and Mathematics from the University of Richmond.

Prakhar earned his Ph.D. from the Language Technologies Institute at CMU, advised by Jeff Bigham. His dissertation, Improving Reliability in Dialogue Systems, examined how to make neural conversational models more dependable in real-world settings. His work tackled key challenges in dialogue reliability, including robust evaluation and ranking, fine-grained and interpretable control over model outputs, and safety mechanisms to prevent harmful or inaccurate responses. He developed methods leveraging multiple references, adversarial evaluation, improved factuality metrics, and intuitive control strategies using templates, instructions, and developer guidelines.

After completing his Ph.D., Prakhar joined Google, where he is now a Senior Research Scientist working on Google DeepMind’s Gemini models. His current work focuses on agentic reasoning, tool use, and building more reliable and controllable large-scale generative models. Before graduate school, he completed his B.Tech in Computer Science at the Indian Institute of Technology - Roorkee and spent time at Adobe Research. Prakhar’s interests span the full lifecycle of conversational AI—from foundational research to practical deployment.

Yasmine Kotturi was a Postdoctoral Scholar in the Human–Computer Interaction Institute at CMU, where she worked with Jeff Bigham. Her postdoctoral research focused on human-centered AI, worker resilience, and the future of work, examining how AI systems and organizational infrastructures shape the lived experiences, agency, and support networks of frontline and independent workers. At CMU, she advanced community-engaged research methods for understanding and designing technologies that support workers navigating rapid technological change. Before her postdoc, Yasmine completed her Ph.D. in Human–Computer Interaction at CMU, advised by Chinmay Kulkarni. Her dissertation developed a community-based approach to building peer-support systems for independent workers, combining sociotechnical system design with participatory action research to create sociotechnical, educational, and in-person supports for local worker communities.

After her postdoctoral fellowship, Yasmine joined the University of Maryland, Baltimore County as an Assistant Professor in the Department of Information Systems, with an affiliate appointment in Computer Science and Electrical Engineering. Her current work continues to explore human-centered AI, community-engaged design, and the future of work, with a focus on supporting worker resilience and equity. Yasmine had also held research positions at Microsoft Research and the Massachusetts Institute of Technology.

Cynthia was a Postdoctoral Fellow in the Human–Computer Interaction Institute at CMU where she worked with Jeff Bigham. Her research at CMU examined the sociotechnical impact of emerging AI systems on people with disabilities, focusing on how algorithmic bias, automation, and new AI-powered experiences reshape accessibility, agency, and equity. Her work brought together empirical research, disability studies, and critical computing to articulate how AI should be designed, evaluated, and governed to support disabled communities ethically and safely. She has received Best Paper/Honorable Mention Awards at ACM ASSETS, CHI, and DIS conferences.

After completing her postdoc, Cynthia joined Google Research, where she is now a Staff Research Scientist in the Responsible AI and Human-Centered Technology org. Her work continues to shape public and academic discourse around AI bias, disability, and accessibility—spanning position papers, technical reports, and public scholarship, including talks, podcasts, and practitioner guidance. Before CMU, she earned her Ph.D. in Human Centered Design & Engineering from the University of Washington. She previously held research positions at Apple  and Microsoft Research.

Yang Zhang was a Postdoctoral Fellow in the Human–Computer Interaction Institute at CMU, where he worked with Jeff Bigham on accessibility-driven sensing and interaction technologies. His research at CMU focused on creating smartphone-based XR techniques that enable people with limited mobility to visually explore and navigate physical environments with accessibility barriers. His work combined human-centered design, computer vision, and mobile sensing to open new pathways for inclusive spatial understanding. After completing his postdoctoral work at CMU, Yang joined the University of California, Los Angeles as an Assistant Professor in the Department of Electrical and Computer Engineering. He now directs the Human-Centered Computing and Intelligent Sensing Lab (HiLab), where his research advances ambient and ubiquitous computing through novel sensing, interaction, and energy-harvesting technologies. His group develops practical, sustainable, and inclusive intelligent systems for assistive technologies, personal informatics, autonomous environments, and next-generation IoT.

Yang earned his Ph.D. in Human–Computer Interaction from CMU, where he worked with Chris Harrison, as well as an M.S. in Computational Design, also from CMU. He previously completed a B.Eng. in Automation Science at Beihang University. Yang has also conducted research at Apple , Microsoft Research, and Disney Research.

Amy was a Postdoctoral Fellow in the Human–Computer Interaction Institute at CMU, where she worked with Jeff Bigham. Her research spans accessibility, multimodal interaction, and video understanding, developing systems that make complex media more usable and understandable for people with disabilities. During her time at CMU, she produced influential work on interactive video navigation, audio descriptions, and machine-learning–augmented tools that support non-visual access to rich visual content. Before joining CMU, Amy earned both her Ph.D. in Computer Science and B.S. in Electrical Engineering and Computer Science from the University of California, Berkeley.

After completing her postdoctoral work at CMU, Amy joined the University of Texas at Austin as an Assistant Professor in Computer Science (2022–2025), while also holding a Research Scientist position at Apple  (AI/ML). She later became a Visiting Faculty Researcher at Google Research and, in 2025, joined the faculty at UC Berkeley as an Assistant Professor in Electrical Engineering and Computer Sciences. Across these roles, she continues to advance research at the intersection of accessibility, interactive media, and AI, building systems that help people more easily access, interpret, and create video content.

Patrick Carrington was a Postdoctoral Fellow in the Human–Computer Interaction Institute at CMU, where he worked with Jeff Bigham on accessibility, wearable computing, and assistive technologies. His postdoctoral research focused on understanding human abilities and designing systems that support empowerment, independence, and everyday quality of life for people with disabilities. Patrick’s work at CMU contributed to foundational directions in ability-based design, sensing for accessibility, and user-centered assistive technologies.

After completing his postdoc, Patrick joined the CMU faculty, where he is now an Associate Professor in the School of Computer Science and the Human-Computer Interaction Institute, and directs the AXLE Lab. His research continues to span accessibility, wearable and mobile sensing, and the design of inclusive interactive systems. He previously earned his Ph.D. and M.S. in Human-Centered Computing from the University of Maryland, Baltimore County (UMBC), and a B.S. in Information Systems, also from UMBC.

Kotaro was a Postdoctoral Researcher jointly in the Human-Computer Interaction Institute and the Robotics Institute at CMU, working with Jeff Bigham and Aaron Steinfeld. At CMU, his research examined the intersection of accessibility and crowdsourcing, including influential work on the accessibility of the physical world and the working conditions of people on platforms such as Amazon Mechanical Turk. His projects used large-scale data collection, computer vision, and participatory methods to understand and improve access for people with disabilities. Before joining CMU, Kotaro received his PhD in Computer Science from the University of Maryland, College Park, where he was advised by Jon E. Froehlich.

After completing his postdoctoral work at CMU, Kotaro joined the School of Computing and Information Systems at Singapore Management University, where he is now an Assistant Professor. His research continues to focus on the design and evaluation of interactive and assistive technologies that support people with disabilities, combining HCI, computer vision, and crowd-powered systems. He also mentors undergraduate, graduate, and visiting researchers interested in building future technologies for accessibility.

Hernisa was a Postdoctoral Fellow in the Human–Computer Interaction Institute and Robotics Institute at CMU, where she worked with Jeff Bigham, Chieko Asakawa, and Kris Kitani. Her research at CMU centered on data-driven assistive technologies that combine machine learning and human-centered design to support blind and low-vision users. She studied how AI systems can sense human behavior, model communication patterns, and assess accessibility in real-world contexts, across projects involving sign language animation, independent mobility, and adaptive assistance.

After CMU, she joined the University of Maryland, College Park, where she is now an Associate Professor in the College of Information. Her work continues to examine how AI can address accessibility challenges, emphasizing rigorous evaluation, inclusive design, and modeling of human communication and behavior. She integrates machine learning with participatory and user-centered methods to create technologies supporting diverse abilities. Her research has earned Best Paper and Honorable Mention awards at CHI and ASSETS.

Hernisa holds a Ph.D. and M.Phil. in Computer Science from the City University of New York, an M.S. in Advanced Computer Systems and a B.S. in Computer Science from the National and Kapodistrian University of Athens. She also spent time at IBM and the Lawrence Berkeley National Laboratory.

Luz was a Postdoctoral Researcher at the Human–Computer Interaction Institute at CMU, where she worked with Jeff Bigham on accessibility, natural language processing, and technology to support people with dyslexia. She later continued at CMU as a Systems Scientist, collaborating across HCI, machine learning, and linguistics to design tools that improve text accessibility and learning outcomes.

Her research focuses on dyslexia, computational linguistics, and educational technology, combining AI, cognitive science, and language modeling to better detect and support reading and writing difficulties. Her interdisciplinary work has led to large-scale deployments, patented technology, and international collaborations spanning academic institutions and industry. Luz had also worked as Assistant Professor at IE University.

Currently, she is the Founder and CEO of Change Dyslexia, a social enterprise that develops scientifically validated, AI-driven tools to detect and support dyslexia, including the platform Dytective, used by hundreds of thousands of learners. She has also served as an accessibility consultant at Apple  and participates in advisory roles related to technology, inclusion, and democratic participation.

Saiph Savage was a Visiting Faculty member in the Human–Computer Interaction Institute at CMU, collaborating with Jeffrey Bigham and colleagues on worker-centered AI, crowd work, and civic technologies. Her work at CMU examined how AI and platform design shape the conditions of digital labor, and explored new tools that help gig and crowd workers better understand, negotiate, and improve their working environments and wages. Saiph is now an Assistant Professor of Computer Science at Northeastern University, where she directs the Civic A.I. Lab. Her research designs and studies AI systems that empower workers, governments, journalists, and NGOs, with a focus on the future of work, disinformation, and equitable AI policy. She has received over $1.5M in research funding, including an NSF CAREER Award, and has been recognized as one of MIT Technology Review’s 35 Innovators Under 35 and among Forbes’ top AI leaders in Mexico. Her work has won awards at venues such as CHI, CSCW, and The Web Conference, and has been widely covered in international media.

Saiph holds a Ph.D. and M.S. in Computer Science from the University of California, Santa Barbara, and a B.S. in Computer Engineering from the Universidad Nacional Autónoma de México. Previously, she held academic positions at West Virginia University and the University of Washington, and industry roles at Intel Labs and Microsoft.

Yue Jiang was a visiting Ph.D. student in the BIG Lab in 2024, working with Jeff Bigham during her doctoral studies at Aalto University and the Finnish Center for Artificial Intelligence. Her research focuses on computational user interface understanding and eye-tracking, developing modeling and inference techniques that enable systems to interpret UI structure, predict user attention, and support adaptive interface behavior. During her time at CMU, Yue collaborated on projects at the intersection of UI understanding, accessibility, and intelligent interface adaptation, contributing expertise in computational modeling of user behavior.

Yue’s doctoral work has been recognized with several prestigious honors, including the Meta PhD Fellowship, Google Europe Students with Disabilities Scholarship, Nokia Scholarship, and selection as a Heidelberg Laureate Forum Young Researcher and UCSD Data Science Rising Star. She previously spent time at Apple , Adobe Research, Intel, the University of Toronto, the Max Planck Institute for Informatics, and Shenzhen University.

Currently, Yue is an an Assistant Professor of Computer Science in the Kahlert School of Computing at the University of Utah, where she continues to advance research in computational interaction, intelligent UIs, and human-centered AI.

Yaxin Hu was a visiting Ph.D. student in the BIG Lab, working with Jeff Bigham in 2023. During her time at the Lab, she led research on telepresence robots for homebound older adults, exploring how remote robotic embodiments can create social connection, autonomy, and meaningful experiences for people with accessibility needs.

Yaxin is a Ph.D. candidate in Computer Science at the University of Wisconsin–Madison, advised by Bilge Mutlu in the People and Robots Lab. Her research focuses on human–robot interaction in physical spaces, with an emphasis on accessibility, leisure, and social experiences. She designs, builds, and studies robots that support diverse users—including blind and low-vision communities, older adults, and people with cognitive disabilities—through embodied, contextually grounded interaction. Before her Ph.D., Yaxin earned her M.Sc. in Computational Design at CMU, advised by Henny Admoni, where she began her work in social robotics. She previously received her B.Sc. in Computer Science from the Chinese University of Hong Kong.

Amanda completed her B.S. and M.S. degreees in Computer Science at CMU, where she was advised by Jeff Bigham. Her master’s thesis, WaybackUI: A Dataset to Support the Longitudinal Analysis of User Interfaces, introduced a dataset of nearly 100,000 historical user interfaces spanning a decade of online archives and paired rendered screenshots with programmatically extracted semantics. This work enabled data-driven analysis of UI evolution in visual design, accessibility, and robustness of automated UI understanding models.

At CMU, Amanda served as Head TA for 15-122 (Principles of Imperative Computation), leading a large team of teaching assistants and supporting systems-focused computer science education. She also completed multiple engineering internships at Apple , working across ML-driven photographic editing, SwiftUI-based interface design, and embedded systems and firmware development using Swift for microcontrollers.

Amanda is now pursuing her Ph.D. in Computer Science at Stanford University.

Yi-Ting earned a Master of Language Technologies from CMU, where he worked with Jeff Bigham and Maxine Eskenazi on dialog systems, natural language generation, and machine learning for interactive language technologies. His research focused on improving conversational agents using data-driven modeling and evaluation, building on prior work in question answering and natural language generation.

Before CMU, Yi-Ting completed both a B.S. and M.S. degree in Computer Science at National Taiwan University. He also held engineering roles at Yahoo! and Taiwan AI Labs, working on news generation, social chatbots, and personalized marketplace experiences.

Yi-Ting is currently a Software Engineer at Google, where he works on Search Quality, ranking, and AI-driven search experiences.

Junhan (Judy) worked with Jeff Bigham as an undergraduate and master’s researcher at CMU, where she completed her B.S. and M.S. in Computer Science with an additional major in Human–Computer Interaction. During her time in the lab, Judy contributed to projects at the intersection of accessibility, user modeling, and adaptive interfaces, focusing on how computing systems can better understand and respond to individual differences in user abilities. Judy also spent time at Meta Reality Labs and Adobe Research.

After graduating from CMU in 2020, Judy joined the University of Washington’s Information School as a Ph.D. student, advised by Jacob O. Wobbrock. Her current research continues to advance ability-based design, personalized interaction, and accessible computing.

Yash earned his Masters in Language Technologies from CMU, advised by Jeff Bigham and Florian Metz, where he worked on automatic speech recognition, machine learning, crowdsourcing, and human computation. His research focused on building data-driven speech systems and advancing end-to-end modeling approaches.

After graduating, he joined Baidu USA as a researcher pushing deep learning approaches for large-scale automatic speech recognition. He later became a Principal Scientist at Microsoft in Azure AI, working on speech and language models for production deployment.

Yashesh is now a Staff Software Engineer at Meta in the Superintelligence Labs, working on multimodal foundation models, including training systems that enable LLaMA models to see, hear, and interact across modalities.

Nyah Rene is a computer science student at Emory University with a strong interest in human–computer interaction research. In Summer 2025, she worked under the mentorship of Atieh Taheri on disability-led design, examining how people with mobility disabilities conceptualized their disability identity in Virtual Reality spaces and what movement preferences shaped their virtual experiences.

Given that current locomotion systems often overlook mobility-disabled users, this work aimed to advance accessibility by focusing on creating movement experiences that better matched users’ expectations and offer a more believable sense of what these motions could feel like if the abilities were available to them. Through co-design and empirical evaluation of current VR locomotion techniques, the project sought to bridge the gap between existing limitations and inclusive design possibilities.

Quentin joined the Human–Computer Interaction Institute as an NSF REU student and later returned as an undergraduate research assistant, working with Jeff Bigham and Yasmine Kotturi at the intersection of generative AI and local entrepreneurship. At CMU, he built BizChat, an LLM-powered tool that scaffolds business planning for small business owners across different levels of digital skill, and co-authored work on how social support and human-centered AI can better fit small business workflows.

Beyond CMU, Quentin has pursued human-centered AI from both research and product angles. He spent a summer in UC Berkeley’s SUPERB program designing mixed-initiative interfaces for debugging RAG pipelines, and worked on the Product Engineering team at Character.AI, contributing to large-scale consumer AI experiences.

Quentin is currently Co-Founder and CEO of Inspector (YC F25), an AI IDE for front-end engineering that connects the browser and codebase so developers can visually select UI elements, prompt changes, and iterate where their users actually interact with the product. He earned his B.S. in Computer Science from the University of Pittsburgh.

Mitchell Gordon worked with Jeff Bigham as an undergraduate researcher at the University of Rochester, where he contributed to early work at the intersection of human–computer interaction and intelligent systems. After graduating with honors and high distinction in Computer Science from the University of Rochester, Mitchell completed both his M.S. and Ph.D. in Computer Science at Stanford University, advised by Michael Bernstein and James Landay. His doctoral work in human–AI interaction, model evaluation, and alignment received multiple distinctions, including Stanford’s Arthur Samuel Award for Best Ph.D. Dissertation in Computer Science, as well as Best Paper awards at CHI and CSCW and an oral presentation at NeurIPS. Mitchell interned at at Apple , Google, and CMU’s Human-Computer Interaction Institute. After his PhD, Mitchell was a Postdoctoral Scholar in the Paul G. Allen School at the University of Washington, hosted by Jeffrey Heer and Yejin Choi.

He is now an Assistant Professor of Computer Science at MIT in the Electrical Engineering and Computer Science Department, and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). Concurrently, he is a Member of Technical Staff at OpenAI, where he works on interactive systems, evaluation methods, and AI alignment.