Marcia O’Malley is the Thomas Michael Panos Family Professor in Mechanical Engineering, Computer Science, Electrical and Computer Engineering, and Bioengineering at Rice University’s George R. Brown School of Engineering. She chairs the Department of Mechanical Engineering and directs the Mechatronics and Haptic Interfaces (MAHI) Lab. Her research focuses on haptics and robotic rehabilitation, particularly wearable robotic systems for training and rehabilitation in virtual environments. She holds adjunct roles at Baylor College of Medicine and the University of Texas Medical School. Educated at Purdue University (B.S., 1996) and Vanderbilt University (M.S./Ph.D., 1999/2001), Dr. O’Malley has been recognized with prestigious awards, including the ONR Young Investigator Award, NSF CAREER Award, and multiple fellowships. She has twice won Rice’s George R. Brown Award for Superior Teaching. Her work bridges engineering and medicine, addressing human-robot interaction challenges in surgical training, workforce safety, and neurorehabilitation. The MAHI Lab develops devices like the hBracelet and Rice Haptic Rocker to enhance human-robot collaboration. She co-founded Houston Medical Robotics, Inc., applying her innovations to real-world medical applications. Research Interests: Haptics, wearable robotics, neural interfaces, surgical training metrics, and rehabilitation robotics. Labs/Teams: MAHI Lab (Biosciences Research Collaborative), collaborations with medical institutions. Grants/Awards: Extensive funding from NSF, ONR, and industry partnerships; leadership in editorial roles for IEEE Transactions on Haptics.
Enamul Hoque Prince is an Associate Professor and Director of the School of Information Technology at York University. He leads the Intelligent Visualization Lab, funded by the Canada Foundation for Innovation (CFI) and Ontario Research Funds (ORF). He holds a PhD in Computer Science from the University of British Columbia and completed postdoctoral work at Stanford University. His research integrates information visualization, human-computer interaction (HCI), and natural language processing (NLP) to address information overload challenges. Dr. Prince's educational background includes a PhD from UBC, an MSc from Memorial University of Newfoundland, and a BSc from Chittagong University of Engineering & Technology. He has conducted research at institutions like Tableau Software and the Qatar Computing Research Institute and serves on committees for top conferences like ACL and IEEE Vis. His work is supported by grants from NSERC, CFI, and others. Research interests focus on NLP-driven visual analytics, user-adaptive visualization, and accessible interfaces. Notable projects include Evizeon (natural language interfaces for visual analytics), ConVisIT (topic modeling for online conversations), and CIDER (concept-based image search). Publications highlight trends in multimodal systems, chart comprehension, and accessibility. Key awards include the NSERC Discovery Grant (2019) and a Best Paper Honorable Mention at DIS 2021. He supervises graduate and undergraduate students in areas like visualization, NLP, and HCI. Labs and collaborations emphasize interdisciplinary approaches, with the Intelligent Visualization Lab advancing tools for data exploration and user-centered design. Teaching includes courses on design principles and information visualization.
Navid Azizan is the Alfred H. (1929) and Jean M. Hayes Career Development Assistant Professor at Massachusetts Institute of Technology (MIT), holding dual appointments in the Department of Mechanical Engineering (in Control, Instrumentation & Robotics) and the Schwarzman College of Computing's Institute for Data, Systems & Society (IDSS). He is also a Principal Investigator in the Laboratory for Information & Decision Systems (LIDS), and a faculty member of the MIT Statistics and Data Science Center, the Center for Computational Science and Engineering, and the Operations Research Center. Dr. Azizan received his PhD in Computing and Mathematical Sciences from the California Institute of Technology (Caltech) in 2020, his MSc in Electrical Engineering from the University of Southern California in 2015, and his BSc in Electrical Engineering with a minor in Physics from Sharif University of Technology in 2013. Prior to joining MIT, he completed a postdoc at Stanford University's Autonomous Systems Laboratory and was a research scientist intern at Google DeepMind. His research spans the intersection of machine learning, systems and control, mathematical optimization, and network science. Dr. Azizan's work focuses on developing principled learning and optimization algorithms for reliable intelligent systems, with applications to autonomy and sociotechnical systems. His research has significant implications for creating trustworthy AI systems that can operate effectively in complex, uncertain environments. Dr. Azizan's recent publications demonstrate a strong focus on uncertainty quantification, reliable AI systems, constrained optimization, and control-oriented learning. His work bridges theoretical foundations with practical applications, particularly in autonomous systems where safety and reliability are paramount. His research group has made notable contributions to areas including neural network verification, multi-agent reinforcement learning, and adaptive inference techniques for large language models, with several papers featured on MIT News and selected for oral presentations at top conferences. Alfred H. (1929) and Jean M. Hayes Career Development Professorship (2025-present) Frank E. Perkins Award for Excellence in Graduate Advising (2025) List of Outstanding Academic Leaders in Data from the CDO Magazine (2024, 2023) Amazon Science Hub Research Award (2023) Outstanding UROP Faculty Mentor (2023) Esther and Harold E. Edgerton (1927) Career Development Chair (2022-2025) Information Theory and Applications (ITA) Gold Graduation Award (2020) Dr. Azizan has been recognized for his excellence in graduate advising, receiving the Frank E. Perkins Award for Excellence in Graduate Advising in 2025. During the pandemic, he founded and co-organized the 'Control meets Learning' virtual seminar series, connecting researchers across disciplines. His work has attracted significant research funding from industry partners including Google, Amazon, and MathWorks, supporting both fundamental research and practical applications in reliable intelligent systems. The Azizan Lab at MIT brings together researchers from mechanical engineering, computer science, and applied mathematics to tackle challenges at the intersection of learning and control. The lab emphasizes both theoretical foundations and practical implementations, with a particular focus on developing algorithms that provide guarantees of performance and safety. Current research directions include uncertainty quantification in AI systems, constrained optimization for neural networks, and control-oriented learning for autonomous systems, with applications spanning robotics, transportation, and complex sociotechnical systems.
Dr. Emily Wall is an Assistant Professor in the Department of Computer Science at Emory University, leading the CAV Lab (Cognition and Visualization). She earned her Ph.D. in Computer Science from Georgia Tech (2020) and completed a postdoctoral fellowship at Northwestern University before joining Emory. Ph.D., Computer Science, Georgia Tech (2020) Postdoctoral Researcher, Northwestern University Current: Assistant Professor, Emory University Her research focuses on enhancing human decision-making through data visualization and visual analytics by addressing cognitive limitations and biases. Key areas include metacognition promotion, computational strategies for bias mitigation, and human-AI collaboration in data analysis. She combines cognitive psychology principles with technical implementation using tools like D3.js and Tableau. Recent publications highlight her work on LLM applications in visual analytics, cognitive bias interventions, and trust calibration in AI systems. Current students like Mengyu Chen, Shiyao Li, and Zhongzheng Xu are presenting at top conferences such as CHI and IEEE VIS. She also explores educational applications through interactive visualization pedagogy and innovative assessment methods. Dr. Wall actively engages in academic service through graduate mentorship, conference judging, and diversity initiatives. Her lab emphasizes both technical proficiency and social justice implications in visualization design, reflected in course projects that merge artistic expression with data-driven storytelling.
Ming Yin is an Associate Professor in the Department of Computer Science at Purdue University. Her research bridges human-computer interaction, applied artificial intelligence, computational social science, and behavioral sciences. She focuses on leveraging human behavior data to design intelligent systems that balance machine efficiency with human understanding, trust, and engagement. Education : PhD in Computer Science (Harvard University, 2017), B.E. in Computer Software (Tsinghua University, 2011) Previous Roles : Postdoctoral researcher at Microsoft Research New York City (2017–2018) Teaching : Courses on AI, Human-AI Interaction, Data Mining, and Human-Centered Computing Research Interests center on social computing, crowdsourcing, human-AI interaction, and ethical AI. She employs experimental and computational methods to study how human behavior can improve AI systems' design, fairness, and user trust. Her work has significant implications for gig economy platforms, decision support systems, and algorithmic accountability. Scientific Contributions include over 15 recent articles in top venues like CHI, IJCAI, and ACL. These works explore topics such as LLM-driven trust calibration, adversarial social influence, and ethical AI design. Her research has been recognized with the NSF CAREER Award Siebel Scholar (Class of 2017) Multiple Best Paper and Honorable Mention Awards at CHI, CSCW, and HCOMP Teaching Expertise spans courses like Introduction to Artificial Intelligence (CS 471), Human-AI Interaction (CS 592-HAI), and Data Mining (CS 573). She emphasizes project-based learning and designing systems for real-world problems, such as "learning in a new era" in her 2025 HCI course.
Dr. Muhammad Abdul-Mageed is an Associate Professor in the School of Information at The University of British Columbia, with joint appointments in Linguistics and an associate membership in Computer Science. He holds the Canada Research Chair in Natural Language Processing and Machine Learning. His research focuses on deep learning, socio-pragmatics, and speech/language technologies, particularly for Arabic and African languages. He leads the UBC Deep Learning & NLP Group and co-directs SSHRC-funded grants like I Trust AI and Ensuring Full Literacy. He is a founding member of the Center for Artificial Intelligence Decision making and Action and a member of the Institute for Computing, Information, and Cognitive Systems. His work spans automatic speech recognition, machine translation, computational socio-pragmatics, and low-resource language technologies. Notable projects include developing Arabic speech recognition systems, multidialectal Arabic benchmarks, and tools for African language processing. He has authored over 100 peer-reviewed papers and leads initiatives like the NADI Arabic Dialect Identification shared task and the NileChat project for culturally-aware LLMs. His research aims to create equitable, socially-aware AI systems for health, social media, and information management.
Arvind Narayanan is a Professor of Computer Science at Princeton University and Director of the Center for Information Technology Policy (CITP). His research focuses on the societal impact of digital technologies, particularly artificial intelligence, with emphasis on policy implications, fairness, and privacy. He leads interdisciplinary efforts connecting technical research with real-world policy challenges. Dr. Narayanan earned his Ph.D. from the University of Texas, Austin in 2009. His academic journey has established him as a leading voice in the critical examination of AI systems and their societal consequences. Narayanan's research spans multiple domains where technology intersects with society. His work on AI includes critical analysis of AI capabilities versus marketing claims (AI Snake Oil), fairness in machine learning systems, and the reproducibility crisis in ML-based science. In privacy research, he led the Princeton Web Transparency and Accountability Project which uncovered how companies track users online, developing the OpenWPM tool used in over 100 studies. His early work demonstrated fundamental limits of de-identification techniques and how machine learning reflects cultural stereotypes. His recent publications reveal a consistent focus on demystifying AI capabilities while identifying genuine opportunities and risks. Narayanan's work bridges technical computer science with policy relevance, emphasizing the importance of evidence-based approaches to AI governance. His research increasingly addresses the limitations of prediction systems, the challenges of evaluating AI systems, and the need for transparency in foundation models. Presidential Early Career Award for Scientists and Engineers (PECASE) Privacy Enhancing Technologies Award (twice recipient) Privacy Papers for Policy Makers Award (three-time recipient) TIME's inaugural list of 100 most influential people in AI 2025 Graduate Mentoring Award Narayanan is recognized as an exceptional mentor, receiving Princeton's Graduate Mentoring Award in 2025. His policy engagement extends to congressional testimony, advisory roles, and frequent media commentary. He has secured significant research funding supporting his work on web transparency, AI policy, and cryptocurrency analysis. His research group has produced influential tools like OpenWPM for web privacy studies and contributed to foundational textbooks on cryptocurrencies and fairness in machine learning. At Princeton, Narayanan leads the Web Transparency and Accountability Project, a major research initiative that has conducted large-scale measurements of online tracking across millions of websites. He also co-founded and directs the CITP's AI Policy Initiative, which brings together researchers from multiple disciplines to address pressing AI governance questions. His work frequently involves collaboration with social scientists, legal scholars, and policymakers to develop practical solutions to technology governance challenges.
Matthew Kay is an Associate Professor in the Department of Communication Studies at Northwestern University's School of Communication, with a secondary appointment in Computer Science. He serves as Co-Director of Graduate Studies for the PhD in Technology and Social Behavior program. His research focuses on human-computer interaction and information visualization, specializing in uncertainty communication, usable statistics, and personal informatics. He employs mixed-method approaches including behavioral analysis, interactive system development, and visualization technique evaluation to address real-world data interpretation challenges. Analysis of his recent publications reveals dominant themes in visualization literacy development, uncertainty representation for decision-making, and health informatics applications. His work consistently bridges theoretical frameworks with practical implementations, particularly in educational assessment tools and election forecast visualizations. Professor Kay co-directs the Midwest Uncertainty Collective (MU collective), a research group advancing uncertainty communication methodologies. Previously faculty at the University of Michigan School of Information, he maintains active contributions to visualization tool development including the ggdist R package for uncertainty visualization.
Marta Kwiatkowska is a Professor of Computing Systems at the University of Oxford and a Fellow of Trinity College. Her research focuses on probabilistic verification , quantitative model checking , and formal methods for complex systems including autonomous robots, medical devices, and biological systems. She leads the development of the PRISM and PRISM-games probabilistic model checkers. Key research areas: Probabilistic systems, formal verification, autonomous robotics, medical device analysis, systems biology Grants: ERC Advanced Grant VERIWARE, EPSRC Programme Grant Mobile Autonomy Awards: 2024 ETAPS Test-of-Time Tool Award for PRISM Students: Current and former advisees in topics spanning formal methods, robotics, and quantitative verification The PRISM-games extension enables verification of stochastic multi-player games with applications in network protocols, autonomous systems, and game theory. Her work bridges theory, algorithms, and practical implementation, with real-world applications in ubiquitous computing and nanotechnology.
Dr. George Stamou is a Professor at the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA), serving as Director of the Artificial Intelligence and Learning Systems Laboratory (AILS). His expertise spans knowledge representation, machine learning, neural networks, and semantic technologies. He leads interdisciplinary initiatives such as the postgraduate program 'Data Science and Machine Learning' (2018–2022). Research Interests: Focuses on knowledge graphs, interpretable AI, semantic web applications, and multimodal learning. His work integrates formal logic systems (e.g., description logics) with modern deep learning techniques, addressing challenges in explainability, bias detection, and ethical AI applications. Publications: Over 150 articles in AI journals/conferences with an h-index of 34 (Google Scholar). Notable contributions include datasets like CHORDONOMICON (music analysis), GOSt-MT (gender bias in MT), and methodologies for counterfactual explanations in machine learning. Awards & Committees: Active in W3C and RuleML standardization bodies. Co-organized major AI conferences. Recognized for contributions to semantic interoperability and knowledge-based systems. Labs & Teams: Directs AILS-NTUA lab and collaborates with CISRI (Computer & Information Systems Research Institute). Engages in EU projects like CultureLabs (cultural heritage digitalization) andsmarty4covid (health data analysis).
Shai Ben-David is a Professor and University Research Chair at the Department of Computer Science, University of Waterloo. He is affiliated with the Cheriton School of Computer Science and can be reached at shai@uwaterloo.ca . His office is located in DC 2643. Education: Ph.D., Hebrew University, Jerusalem, Israel (1987) M.Sc., Hebrew University Jerusalem, Israel (1979) B.Sc., Hebrew University Jerusalem, Israel (1978) Research interests focus on foundational aspects of machine learning theory, including unsupervised learning (clustering), domain adaptation, fairness, interpretability, and alternative approaches to worst-case computational complexity. He also explores logic applications in computer science theory. His research trends emphasize theoretical challenges in machine learning, particularly clustering, fairness in representations, and the interplay between computational feasibility and learnability. He investigates how unlabeled data and sample compression techniques impact learning robustness and efficiency. No scientific awards are listed. His advising record shows no formal advisees listed here. Grants and funding details are not provided in the text. He has contributed to organizing events like the Dagstuhl Seminar on Foundations of Unsupervised Learning (2017) and co-edited MFCS 2016 proceedings. His work addresses both theoretical questions and practical gaps in ML implementation.
Ranjay Krishna is an Assistant Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where he co-directs the RAIVN lab and leads the computer vision team at the Allen Institute for AI (Ai2). His research intersects computer vision , natural language processing , robotics , and human-computer interaction . PhD in Computer Science from Stanford University (2021) Bachelor's and Master's degrees from Stanford and Cornell His work has received best paper , outstanding paper , and orals at top conferences like CVPR, ACL, CSCW, NeurIPS, UIST, and ECCV. Media outlets including Science , Forbes , and PBS NOVA have covered his research. He has been supported by grants from Google , Apple , NFS , and others. Ranjay advises a diverse group of 15 PhD and postdoctoral researchers , including Jieyu Zhang, Benlin Liu, and Cheng-Yu Hsieh. His teams have developed benchmarks like MemoryBench and The Colosseum , and his PathFinder framework achieved 74% accuracy in skin melanoma diagnosis—surpassing human experts by 9%. Notable contributions include: Perception Tokens for visual reasoning in MLMs SAM2Act for robotic manipulation with memory Synthetic Visual Genome dataset with 5.6M relationships
Bryan A. Plummer is an Assistant Professor in the Department of Computer Science at Boston University, affiliated with the IVC Group and the Artificial Intelligence Research (AIR) initiative at the Rafik B. Hariri Institute. He holds a PhD from the University of Illinois at Urbana-Champaign, specializing in computer vision. His research focuses on multimodal machine learning, efficient neural architectures, explainable AI, and robust ML systems. Plummer's work bridges vision and language, addressing challenges in domain generalization, synthetic data utilization, and model efficiency. Notable contributions include the Flickr30K Entities dataset and advancements in vision-language model robustness against web artifacts. He has advised over 20 students, with several securing roles at top institutions like NVIDIA and Google. His recent awards include the 3M Foundation Fellowship and NSF GRFP honorable mention. Plummer actively serves on conference committees (NeurIPS, CVPR, ICCV) and leads initiatives like the 1st Findings Workshop at ICCV'25.
Mark Lee is an Adjunct Professor in the People Analytics department at NYU’s Tandon School of Engineering, specializing in Technology Management and Innovation. He holds a Ph.D. in Engineering Psychology from Georgia Institute of Technology (1996). Currently, he serves as Head of Research, Analytics, and Business Development at UL ComplianceWire, focusing on pharmaceutical and medical device manufacturing training. His research leverages large datasets to improve healthcare safety through regulatory compliance and best practices. Courses taught include Human Factors Engineering, Workplace Design, and Predictive Analytics. Education: Ph.D. in Engineering Psychology, Georgia Tech (1996) Key Roles: Adjunct Professor, Head of Research at UL ComplianceWire Research Focus: Human Factors, Training Systems Design, Healthcare Compliance His work spans auditory display systems for aviation (e.g., 3D audio cockpit interfaces) and ergonomic design for industrial products. Recent projects emphasize data-driven solutions for regulatory challenges in life sciences. Publications highlight studies on visual search strategies, age-related cognitive performance, and application of signal detection theory in decision-making. He actively collaborates with industry and government entities, exemplified by the FDA-UL Cooperative Research Agreement.
Toby Jia-Jun Li is an Assistant Professor in the Department of Computer Science and Engineering at the University of Notre Dame, where he leads the SaNDwich Lab. He also serves as the Director of the Human-Centered Responsible AI Lab in the Lucy Family Institute for Data & Society and is a Faculty Fellow at the Institute for Educational Initiatives (IEI). Previously, he was affiliated with Carnegie Mellon University's Human-Computer Interaction Institute (HCII) and GroupLens Research. Dr. Li's research spans the intersection of Human-Computer Interaction (HCI), End-User Software Engineering, Machine Learning (ML), and Natural Language Processing (NLP), with recent work focusing on addressing societal challenges in the future of work through human-AI collaborative approaches. His work has resulted in over 40 publications at premier venues including CHI, UIST, CSCW, ACL, and ICSE, with 8 papers winning Best Paper or Honorable Mention awards. His recent publications demonstrate a strong focus on human-AI collaboration across various domains, including code understanding, privacy, accessibility, and creative tools. The work shows a trajectory toward increasingly sophisticated integration of human-centered design with AI capabilities, particularly using large language models to enhance human productivity and address societal challenges. Google Research Scholar Award recipient Recipient of Yahoo! Fellowship ($100,000/year) Best Paper Award at UIST 2020 Best Paper Honorable Mention Award at CHI 2021 Best Paper Award at CSCW 2024 Best Paper Award at CHI 2025 Dr. Li actively mentors Ph.D. students and has established collaborations with Google, Microsoft Research, IBM Research, Adobe, Verizon, and J.P. Morgan. His research has been supported by NSF, Google Research Scholar Program, AnalytiXIN Initiative, Yahoo! InMind project, and J.P. Morgan. He is currently recruiting Ph.D. students and undergraduate researchers for his SaNDwich Lab, which focuses on developing interactive systems to empower individuals to create, configure, and extend AI-powered computing systems.
