Matthew Louis Mauriello is an Assistant Professor in the Department of Computer and Information Sciences at the University of Delaware , where he directs the Sensify Lab . He holds a PhD in Computer Science from the University of Maryland (2018) and completed postdoctoral work at Stanford University (School of Medicine, 2020; School of Public Policy & Environmental Engineering Department, 2019). Research interests span Human-Computer Interaction (HCI), Ubiquitous Computing, and User-Centered Design with applications in: Sustainability Human-Building Interaction Wearable Technology Personal Informatics Educational Game Design Mental Health Interventions Recent publications highlight trends in stress monitoring (skin-like biosensors, workplace wellbeing), energy auditing (thermography systems), and educational technology (block-based programming tools for teachers). His work appears in ACM CHI, ACM Human-Computer Interaction, and Building and Environment. Scientific awards include: Best Paper Honorable Mention, CHI 2016 Best Paper Honorable Mention, CHI 2015 Best of WebSci'18 Teaching at University of Delaware encompasses courses like Educational Game Design Operating Systems Advanced Web Technologies Computing for Social Good with a focus on project-based learning and systems thinking.
Maja J Matarić is the Chan Soon-Shiong Chaired and Distinguished Professor of Computer Science at the University of Southern California's Viterbi School of Engineering, with courtesy appointments in Neuroscience and Pediatrics. She serves as founding director of the USC Robotics and Autonomous Systems Center, co-director of the USC Robotics Research Lab, and Principal Scientist at Google DeepMind. Previously, she held leadership roles as USC's interim Vice President of Research (2020-2021) and Vice Dean for Research (2006-2019). Her educational background includes: PhD in Computer Science and Artificial Intelligence from MIT (1994) MS in Computer Science from MIT (1990) BS in Computer Science from University of Kansas (1987) Matarić pioneers Socially Assistive Robotics (SAR) , a field her lab named, focusing on human-robot interaction that provides assistance through social rather than physical support. Her research targets critical health and wellness challenges including post-stroke rehabilitation, autism spectrum disorder therapy, cognitive exercises for Alzheimer's patients, ADHD academic support, and mental health interventions. She develops systems modeling user engagement, personality, and motivation, with extensive real-world deployments in schools, rehabilitation centers, and homes. Analysis of her recent publications reveals dominant themes in cognitive health robotics (2025 CHI paper on LLM-powered elder care), pediatric assistive technology (2025 IDC speech therapy review), and adaptive preference modeling (2025 HRI contrastive learning work). Her research consistently bridges machine learning with human-centered design for vulnerable populations. Major scientific recognition includes: ACM Athena Lecturer Award (2024) ACM Eugene L. Lawler Humanitarian Award (2024) ACM Fellow (2020) Presidential Mentoring Award (2011) Multiple society fellowships (AAAS, IEEE, AAAI) As a dedicated mentor, Matarić has championed underrepresented groups through CRA-W, placing numerous women in faculty positions. She leads USC Viterbi's K-12 STEM Outreach Program serving low-income Los Angeles schools and authored The Robotics Prime for student education. Her research has secured significant funding enabling real-world technology transfer, with documented impact in rehabilitation centers and homes through deployable SAR systems. Her Robotics Research Lab at USC drives innovation in embodied AI, with current projects spanning LLM-integrated elder care robots, ADHD academic companions, and autism therapy systems. The lab emphasizes co-design with end-users and rigorous real-world validation across diverse populations.
Dr. Camila Aquino serves as Assistant Professor in both the Department of Clinical Neurosciences and Department of Community Health Sciences at the University of Calgary's Cumming School of Medicine, and is a Full Member of the Hotchkiss Brain Institute. As Medical Director of the Deep Brain Stimulation Program, she specializes in Parkinson's disease, essential tremor, dystonia, and chorea management, including EMG-guided botulinum toxin injections. Her educational background includes: M.D. in Medicine from Escola Superior de Ciencias da Santa Casa (2005) M.S. in Neurosciences from Federal University of Sao Paulo (2012) Ph.D. in Neurosciences from Federal University of Sao Paulo (2019) M.S. in Health Research Methods, Clinical Epidemiology from McMaster University (2020) Dr. Aquino's research addresses unmet needs in movement disorders through innovative clinical trial designs for Parkinson's disease, focusing on precision-medicine approaches, early intervention, and outcome optimization. She actively evaluates neuromodulation therapies including Deep Brain Stimulation, Spinal Cord Stimulation, and MRgFUS, with emphasis on expanding indications and improving patient outcomes. As an International Parkinson and Movement Disorder Society member, she chairs the web-based learning committee and serves on evidence-based medicine and outcome measure committees. Analysis of her 15 most recent publications (2021-2025) reveals a cohesive research trajectory centered on Parkinson's disease management, with dominant themes in motor fluctuation treatments, deep brain stimulation methodology, and clinical trial innovation. Her work bridges fundamental clinical practice guidelines with advanced neuromodulation technologies, demonstrating consistent commitment to evidence-based solutions for movement disorders. Her scientific recognition includes: Parkinson Canada research grant Mohammed Al Zaibak Foundation scholarship CAPES scholarship from Brazilian Ministry of Education Dr. Aquino has secured significant research funding supporting clinical trials in Parkinson's disease neuroprotection and neuromodulation therapies. While specific student advisees aren't listed, her leadership of the DBS Program and active clinical trial portfolio indicate substantial mentorship responsibilities. Her grant portfolio emphasizes translational research with direct clinical impact. She directs the University of Calgary's Deep Brain Stimulation Program and contributes to the MRI-guided focused ultrasound initiative. Through her International Parkinson and Movement Disorder Society committee roles, she shapes global educational standards and evidence-based practices in movement disorders.
Maya Ramanath is an Associate Professor in the Department of Computer Science and Engineering at Indian Institute of Technology (IIT) Delhi. She joined IIT Delhi in 2011 after a postdoctoral research stint at the Max-Planck Institute for Informatics in Germany. Her research interests focus on database systems, information retrieval, semantic web technologies, and knowledge graph construction and applications. Education: PhD in Computer Science, Indian Institute of Science, Bangalore M.Sc.(Engg.) in Computer Science, Indian Institute of Science, Bangalore B.E. in Computer Science and Engineering, Bangalore University, Bangalore Her recent work emphasizes efficient query processing over large-scale graphs, knowledge graph applications, and natural language interfaces for semantic data. Notable contributions include algorithms for reachability approximation in web-scale graphs, speculative query planning for knowledge graphs, and exploratory querying techniques. She has collaborated extensively on projects like NAGA, ESTHETE, and KlusTree, advancing the state of the art in graph-based data management and semantic search. Publications span conferences such as ICDE, ECIR, EDBT, and VLDB, reflecting a strong focus on database systems, graph algorithms, and semantic web applications. Her work bridges theoretical foundations with practical implementations, addressing scalability and efficiency challenges in modern data management systems. Research and advising activities include supervision of projects on distributed graph processing, query optimization, and knowledge representation. She has contributed to open-source tools like LegoDB and StatiX, and her lab focuses on interdisciplinary approaches to data-centric AI.
Derek W Hoiem is a Professor in the Siebel School for Computing and Data Science at the University of Illinois Urbana-Champaign, where he has been a faculty member since 2009. His research focuses on computer vision and related areas, and he is also the co-founder and Chief Science Officer of Reconstruct, an AI-based construction technology company. His educational background includes: PhD in Robotics, Carnegie Mellon University (2007) Beckman Postdoctoral Fellowship (2008) Prof. Hoiem's research spans computer vision, with a focus on object recognition, scene understanding, and graphics. His work also extends to mobile robotics and 3D scene reconstruction. He has made significant contributions in areas such as visual recognition, 3D modeling, and the application of computer vision in construction monitoring. His recent publications (2023-2025) demonstrate a strong focus on advancing multimodal understanding, particularly in region-based representations, 3D vision, and neural radiance fields. There is a clear trend towards integrating language and vision, improving efficiency in neural networks, and applying computer vision to real-world problems such as construction progress monitoring. His scientific awards and honors are extensive and include: IEEE Fellow (2022) University Scholar (2022) Koendrink Prize (2022) Dean's Award for Excellence in Research, Associate Professor (2021) Campus Distinguished Promotion Award (2015) Best Paper Award: IEEE Winter Conference on Applications in Computer Vision (WACV) (2015) CW Gear Junior Faculty Award (2014) IEEE PAMI Young Researcher Award (2014) Dean's Award for Excellence in Research, Assistant Professor (2014) Sloan Research Fellowship (2013) Intel Early Career Faculty Honor Program Award (2012) NSF CAREER Award (2011) ACM Doctoral Dissertation Award, Honorable Mention (2008) Carnegie Mellon University SCS Distinguished Dissertation Award (2008) Best Paper Award: IEEE Computer Vision and Pattern Recognition (CVPR) (2006) Prof. Hoiem has secured significant research funding, including an NSF CAREER award and an Intel Early Career Faculty award. He is also actively involved in technology transfer, having co-founded Reconstruct where he serves as Chief Science Officer. His teaching excellence is reflected in multiple "List of Teachers Ranked as Excellent" awards spanning from 2010 to 2021. Prof. Hoiem leads a research group at UIUC focused on computer vision and 3D scene understanding. Additionally, he co-founded and serves as Chief Science Officer at Reconstruct, which develops AI-based solutions for construction monitoring.
Philip S. Yu is a Distinguished Professor in the Department of Computer Science at the University of Illinois at Chicago and holds the Wexler Chair in Information Technology. Previously, he led the Software Tools and Techniques department at IBM Thomas J. Watson Research Center. Education: B.S. in Electrical Engineering, National Taiwan University M.S. and Ph.D. in Electrical Engineering, Stanford University M.B.A., New York University His research spans data mining , big data , social networks , privacy-preserving data publishing , graph/network mining , recommender systems , and deep learning . He has authored over 970 papers with 74,500+ citations and an H-index of 127. Recent work focuses on heterogeneous graph representation, quantum walks in network analysis, and federated unlearning. Scientific Honors: ACM SIGKDD 2016 Innovation Award IEEE Computer Society 2013 Technical Achievement Award IEEE ICDM 2003 Research Contributions Award IEEE Region 1 Award (1999) UIC Research of the Year (2013) IBM Master Inventor with 300+ patents AI 2000 Most Influential Scholar Honorable Mentions (2024-2025) He served as Editor-in-Chief for ACM Transactions on Knowledge Discovery from Data and IEEE Transactions on Knowledge and Data Engineering , and on steering committees for ACM KDD and IEEE Data Mining. His work bridges theoretical advances in graph neural networks , deep learning , and privacy-preserving systems with applications in healthcare, social media, and enterprise analytics.
Prof. Dr. Thomas Brinkhoff is Chair of the Institute Board and Chair of Geoinformatics at Oldenburg University of Applied Sciences. He leads the Institute for Applied Photogrammetry and Geoinformatics (IAPG) and contributes to institutions like the Association for the Promotion of Geoinformatics in Northern Germany (GiN e.V.) and the Oldenburg Research and Development Institute for Computer Science (OFFIS e.V.). Education: Diploma in Computer Science (Informatik), Universität Bremen (1990) Doctorate in Computer Science (Dr. rer. nat.), Ludwig Maximilian University of Munich (1994) Brinkhoff's research spans geodatabase systems, spatiotemporal data processing, geosensor analytics, and location-based services. His work addresses Volunteered Geographic Information (VGI), web-based geospatial visualization, and mobile data integration, with applications in traffic management and forensic science. Recent projects include ProSaDi (Digital Provenance and Collection Research) and contributions to the Laboratory for optical 3D metrology . He has served on program committees for ACM SIGSPATIAL (2002-2019), AGILE conferences (2010-2025), and editorial boards of journals like GeoInformatica and TGIS. Notable Lectures: 2024: Forensic applications of tachograph data 2023: Geoinformatics in homicide investigations 2022: Spatiotemporal analysis for sustainability projects 2015: Open geodata standards at FOSSGIS 2014: Mobile sensor data processing
Jeff Huang is an Associate Professor and Associate Chair of Computer Science at Brown University. His research focuses on Human-Computer Interaction (HCI), with emphases on personalized systems using behavioral data, health informatics, and user interfaces. Huang holds a PhD from the University of Washington and degrees from the University of Illinois at Urbana-Champaign. His work is funded by NSF, NIH, and ARO, and he has received awards including the NSF CAREER Award and ARO Young Investigator Award. Education: PhD in Information Science, University of Washington (2013) MS in Computer Science, University of Washington (2013) BS and MS in Computer Science, University of Illinois at Urbana-Champaign (2007) Research Interests: His work spans health informatics (e.g., sleep tracking systems like Self-E and SleepCoacher), user interface design, and leveraging behavioral data for personalized systems. Notable projects include WebGazer (webcam eye tracking), filtered.ink (creative illustration tools), and Sochiatrist (mental health analytics). Teaching: He teaches courses on user interfaces, HCI, and research methods, including CSCI 0130/1300 (User Interfaces), CSCI 2300 (HCI Seminar), and CSCI 2000 (Research Methods). Awards: NSF CAREER Award Facebook Fellowship ARO Young Investigator Award Grants & Advising: Huang leads a research group advising over 50 students, many of whom have pursued academic and industry roles. His grants support projects in HCI, health tech, and collaborative systems.
Andreas Vlachos is a Professor of Natural Language Processing and Machine Learning at the Department of Computer Science and Technology, University of Cambridge, and holds the Dinesh Dhamija Fellowship at Fitzwilliam College. His research spans dialogue modeling, automated fact-checking, imitation learning, semantic parsing, biomedical text mining, and trustworthiness in AI systems. PhD in Computer Science, University of Cambridge (supervised by Ted Briscoe and Zoubin Ghahramani) Lecturer at University of Sheffield Postdoctoral roles at UCL, University of Cambridge (NLIP group, Stephen Clark), and University of Wisconsin-Madison (Mark Craven) Current research focuses on evaluating and mitigating biases in language models, advancing fact-checking methodologies, and improving model robustness through interpolation, reinforcement learning, and causal reasoning. His work integrates natural logic, knowledge graphs, and multimodal evidence for verification tasks. Recent publications address uncertainty quantification, temporal planning benchmarks, and ethical framing of NLP artifacts. Grants from ERC, EPSRC, Facebook, Google, and the Alan Turing Institute fund his research team. Collaborations include Sebastian Riedel, Stephen Clark, and Mark Craven. Key projects explore disinformation detection, long-form generation, and confidence calibration in AI systems.
Eytan Adar is a Professor of Information and Computer Science at the University of Michigan, holding dual appointments in the School of Information and the College of Engineering's Electrical Engineering and Computer Science department. His work sits at the intersection of human-computer interaction and artificial intelligence, focusing on large-scale systems analysis and novel interface design. Adar's research spans multiple domains including social media analysis (Twitter, Reddit), academic citation networks, meme propagation, information extraction, and political networks. His methodological expertise includes data mining, data visualization, and graph-based network analysis. His work often operates at internet scale, examining language, creativity, social network dynamics, and text production. His recent publications reveal a strong focus on AI-human collaboration, with particular attention to generative AI interfaces, visualization techniques for complex data, and ethical considerations in AI systems. His work shows a consistent pattern of bridging theoretical insights with practical system implementations. Best Paper Award at ProtoAI: Model-Informed Prototyping for AI-Powered Interfaces, IUI'21 Honorable Mention Award at CHI'24 for feminist interaction techniques research Best Paper Award at ICWSM'18 for Wikipedia language edition analysis Best Paper Award at ICML 2016 Workshop for neural language model visualization Best Student Paper at WSDM'09 for web dynamics research Best of CHI at CHI 2008 for web revisitation patterns analysis Adar has advised numerous PhD students who have gone on to prominent positions at organizations including Google, Apple, RAND, Northwestern University, and Stanford. His research is generously supported by the NSF, IARPA, NIH, the Education Department, and major technology companies including Adobe, Microsoft, Facebook, Google, and Yahoo. He is also a founder of the International Conference on Web and Social Media (ICWSM) and has served as Co-General Chair for WSDM and Co-Program Chair for UIST.
Deian Stefan is an Associate Professor in the Department of Computer Science and Engineering at the University of California, San Diego (UCSD). His research focuses on building secure systems through interdisciplinary approaches spanning security , programming languages , and systems . Key areas include WebAssembly security , JavaScript JITs , constant-time programming , memory safety , information flow control , and program analysis tools. He co-founded Intrinsic , a web-security startup later acquired by VMWare, and contributed to standards like W3C WebAppSec and Node.js Security Working Groups. Education: PhD in Computer Science from Stanford University (2015), advised by David Mazières, John C. Mitchell, and Alejandro Russo BE and ME in Electrical Engineering from Cooper Union (2011) Research Trends: His work targets secure systems (Web frameworks, sandboxing, runtime systems), language-based security (constant-time programming, memory safety), and verification for security . Recent publications focus on mitigating speculative execution attacks, hardware-assisted sandboxing, and WebAssembly security. Awards & Recognition: Distinguished Paper Award at POPL 2021 and IEEE MICRO Top Picks 2024 Best Paper Award at CollaborateCom 2010 2010.01 Prize by Daniel J. Bernstein (DJB) Most Influential Paper Award at ICFP 2022 Teaching: He has taught graduate and undergraduate courses including Graduate Computer Security (CSE 227) , Building Secure Systems with Rust , and Programming Languages: Principles and Paradigms at UCSD since 2018. Prior teaching experience includes Stanford and Cooper Union.
Sheila McIlraith is a Professor in the Department of Computer Science at the University of Toronto, holding the Canada CIFAR AI Chair at the Vector Institute and serving as Associate Director and Research Lead at the Schwartz Reisman Institute for Technology and Society. Her research spans AI safety, ethics, reinforcement learning, and knowledge representation, with a focus on human-compatible AI and long-term societal impacts. Her career includes six years as a Research Scientist at Stanford University and a year at Xerox PARC. McIlraith’s work has been recognized through ACM and AAAI fellowships, as well as prestigious awards like the SWSA 10-Year Award (2011) and the CAIAC Lifetime Achievement Award (2024). Research Interests: AI Safety and Alignment Human-Compatible AI Reinforcement Learning with Ethical Constraints Semantic Web Services Cognitive Robotics and Diagnostic Systems Probabilistic and Logical Reasoning Recent Contributions: Her work emphasizes ethical AI integration, such as the Embedded Ethics Education Initiative (E3I), and addresses challenges in long-term AI risks, multi-agent systems, and interpretable decision-making frameworks. Awards and Honors: ACM Fellow AAAI Fellow 2023 IJCAI-JAIR Best Paper Prize 2024 CAIAC Lifetime Achievement Award Labs and Teams: McIlraith leads initiatives at the Schwartz Reisman Institute and contributes to the Vector Institute, focusing on societal and ethical dimensions of AI technology.
Walter W. Powell is the Jacks Family Professor of Education at Stanford University, with courtesy appointments in Sociology, Organizational Behavior, Management Science and Engineering, and Communication. He serves as Faculty Co-Director of the Stanford Center on Philanthropy and Civil Society (PACS), which he co-founded in 2006, and leads the Civic Life of Cities Lab examining civil society organizations across global metropolitan areas. Powell is also an External Faculty member at the Santa Fe Institute and co-directs the CASBS summer institute on Organizations with MIT's Bob Gibbons. His educational background includes a PhD in Sociology from SUNY-Stony Brook (1978), an MA in Sociology from the same institution (1975), and a BA from Florida State University (1971). Powell's research fundamentally explores how ideas and practices transfer across organizational boundaries, with particular emphasis on network structures that enable or constrain knowledge diffusion. His work spans organizational theory, institutional analysis, university-industry relations, economic sociology, and comparative analysis of nonprofit, public, and private institutional forms. Powell's publications reveal consistent focus on network dynamics and institutional evolution, particularly examining how organizations navigate between market, hierarchy, and network governance structures. His research demonstrates how knowledge spillovers operate within biotechnology clusters, how institutional isomorphism shapes organizational fields, and how civil society organizations adapt to technological and economic pressures across different urban contexts. Dean's Award for Excellence in Graduate Teaching (2018-19) Max Weber Award for "Neither Market Nor Hierarchy" Viviana Zelizer Prize for "Network Dynamics and Field Evolution" Administrative Science Quarterly Recognition Member of Swedish Royal Academy of Science Member of The British Academy Member of American Academy of Arts and Sciences Honorary Degrees from Uppsala University, Copenhagen Business School, and Aalto University Powell has made substantial contributions to graduate education, receiving Stanford's highest teaching honor. His leadership of PACS and SCANCOR (1999-2010) demonstrates sustained commitment to advancing organizational research through collaborative initiatives. He supervises postdoctoral fellows and runs annual PhD workshops in Europe, fostering the next generation of organizational scholars. His research has been supported by grants enabling comparative studies of university-industry relations and civil society across multiple national contexts. Through the Civic Life of Cities Lab at PACS, Powell directs comparative research on civil society organizations across seven global cities, examining how these organizations navigate technological change, economic pressures, and political environments. His work bridges theoretical organizational sociology with practical applications in philanthropy, civil society development, and knowledge transfer between academic and commercial sectors.
James R. Eagan is an Associate Professor in the Computer Science and Networks Department (Infres) at Télécom Paris , part of the Institut Polytechnique de Paris. He is also a Visiting Professor at the University of Colorado, Boulder for the 2024–25 academic year. His research focuses on making computers more expressive tools for human interaction, emphasizing malleable software, collaborative dynamic media, and multi-surface environments. His work spans Human-Computer Interaction , Data Visualization , and User Interface Programming . A key theme involves adapting software for user-driven customization, exemplified by projects like Webstrates (shareable dynamic media) and SchemeLens (semantic zoom for technical diagrams). He also explores uncertainty in data analytics and gesture-based interfaces for experts. Recent publications from 2020–2024 address Explainable AI (XAI) , Financial Crime Detection , and Interactive Data Analysis . His tools Tarantula and SchemeLens have received acclaim, including the 2015 ACM SIGSOFT Impact Award and Best Paper at UIST 2015. Scientific accolades include: Prix de l’Impact 2015 d’ACM SIGSOFT Best Paper Award at UIST 2015 Honorable Mention at CHI 2017 He teaches courses in Mobile Application Development , Human-Computer Interaction , and Data Visualization . His lab, DIVA (Design, Interaction, Visualization & Applications), operates within the Information Processing and Communication Laboratory (LTCI). He actively recruits PhD candidates and postdocs for research in these domains.
Qianwen Wang is a tenure-track Assistant Professor in the Computer Science department at the University of Minnesota, Twin Cities. Her research combines interactive visualization with interpretable machine learning to foster intuitive, efficient, and reliable Human-AI collaboration. She actively seeks motivated students, research assistants, and interns to join her dynamic research team at UMN CS. Dr. Wang's research focuses on three primary themes: Human-AI Collaboration, where she designs tools to facilitate Human-AI interaction; Automatic & Intelligent Visualization, where she develops techniques to make visualizations accurately interpreted and easily used; and VIS+(X)AI in Biomed/Healthcare, where she studies how visualization and explainable AI can promote scientific discoveries in biomedicine and healthcare. Her work has made significant contributions to visualization, human-computer interaction, and bioinformatics, with particular applications in biomedical knowledge graphs and single-cell omics analysis. Her recent publications demonstrate a strong trend toward integrating visualization with large language models and graph neural networks for biomedical applications. She has published extensively at top venues including IEEE VIS, ACM CHI, and Nature Medicine, with a focus on making AI systems more interpretable and useful for domain experts, particularly in healthcare contexts. Her work often bridges theoretical advances in visualization with practical applications in genomics and healthcare. Two IEEE VIS Honorable Mention Awards (2022, 2024) Best Paper Award from IMLH@ICML 2021 Two Best Abstract Awards from BioVis ISMB (2021, 2022) HDSI Postdoctoral Research Fund Award Research covered by MIT News and Nature Technology Features Dr. Wang actively contributes to the academic community through service roles including General Chair for ISMB BioVis, VisNotes and Poster Chair for IEEE PacificVis, and Program Committee member for IEEE VIS, ACM CHI, and ACM IUI. Her research has been supported by various grants that enable her team to develop innovative visualization techniques and explore their practical applications in biomedical domains. Her research group maintains an active presence in the visualization and HCI communities, with members participating in major conferences and workshops. The lab focuses on creating tools that bridge the gap between complex AI models and human understanding, with particular emphasis on making these technologies accessible and useful for domain experts in biomedical research.
