Professor Haijiang Li is a Chair in BIM for Smart Engineering at Cardiff University's School of Engineering. His roles include leading the Computational Mechanics and Engineering AI Research Group, directing the BIM for Smart Engineering Centre, and overseeing the BIM MSc programme. He holds editorial roles for journals like Construction Innovation and Automation in Construction , and chairs the European Group of Intelligent Computing in Engineering (EG-ICE). Research focuses on smart computational engineering platforms integrating BIM, AI, and big data for sustainable infrastructure. Key areas include digital twins, disaster management, and resilient urban systems. He has secured £40M in research funding, including £9M as PI, and led over 70 research staff and students. Prof. Li is a Standards Committee Technical Executive at buildingSMART, driving international BIM standards. His work includes co-authoring a book on BIM standards across China, the US, and the UK. Awards include Fellowships from the British Computer Society (FBCS) and the Higher Education Academy (FHEA). His research outputs span over 250 publications, covering topics like AI-driven bridge maintenance, ontology-based decision-making, and energy-efficient urban systems. Collaborations with industry and global partners emphasize practical applications of BIM and smart technologies.
Mark Yatskar is an Assistant Professor in the Department of Computer and Information Science at the University of Pennsylvania. His research focuses on the intersection of natural language processing, computer vision, and fairness in machine learning. He earned his PhD from the University of Washington under advisors Luke Zettlemoyer and Ali Farhadi, and previously worked as a Young Investigator at the Allen Institute for Artificial Intelligence. Education: PhD in Computer Science, University of Washington (Advisor: Luke Zettlemoyer & Ali Farhadi) Research Interests: Yatskar's work explores how language can structure visual perception and mitigate human biases in machine learning systems. Key themes include: Natural language as a scaffold for visual intelligence Bias characterization and control in machine learning systems His lab currently investigates projects like language-guided bottlenecks, annotator cognitive heuristics, and gender bias amplification. Teaching: CIS 5300: Computational Linguistics (2021-2024) CIS 7000: Language and Vision (2020) CIS 6300: Efficient NLP (2023, 2025) Awards: Best Paper Award at EMNLP (Gender Bias Amplification Research) Advising & Grants: Yatskar advises a team of PhD/Master's students and actively seeks motivated researchers. His group has explored funding in areas like interpretable AI, multimodal reasoning, and dataset bias mitigation. Labs/Teams: Leads the Penn NLP & Vision Lab, focusing on projects like MolMo/PixMo open models, ViUniT visual unit tests, and bias mitigation frameworks.
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.
Theo Damoulas is a Professor of Machine Learning at the University of Warwick with a joint appointment in the Department of Computer Science and Statistics. He is a Turing AI Fellow (2021-2026) through UK Research and Innovation, an ELLIS member, and a Visiting Professor at New York University's Center for Urban Science and Progress (CUSP). He founded and leads the Warwick Machine Learning Group and has directed major projects at The Alan Turing Institute including Project Odysseus and the London Air Quality project. Education includes: PhD in Probabilistic Multiple Kernel Learning (University of Glasgow, 2009) MSc in Informatics (Distinction, University of Edinburgh, 2004) MEng in Mechanical Engineering (1st Class, University of Manchester, 2003) His research focuses on probabilistic machine learning and Bayesian statistics, emphasizing the integration of structural priors, spatiotemporal dependencies, physical laws, and causal relationships. Key applications include Digital Twins, urban science, and computational sustainability. His work advances robust and scalable inference methodologies for complex real-world systems. Publications demonstrate strong emphasis on Bayesian methods, spatiotemporal modeling, and uncertainty quantification, with applications spanning battery modeling, urban mobility, federated learning, and causal inference. Recent work shows increased focus on physics-informed models, federated learning frameworks, and causal abstraction techniques. Major scientific awards: Turing AI Acceleration Fellowship (2021-2026) Best Paper Awards (Wilkes 2024, AISTATS 2022, IEEE ICMLA 2010) ACM SIGMOD Most Reproducible Paper (2017) Dissertation Award (Classification Society 2012) Teaching Excellence nominations (Warwick 2015-2017) He actively advises PhD students and secured significant grants including the £multi-million Turing AI Fellowship. Current doctoral researchers investigate federated learning, causal inference, and spatiotemporal modeling. He leads the Warwick Machine Learning Group, a cross-departmental team developing foundational ML methods for scientific and societal challenges.
Holly J. Benjamin, MD is a distinguished sports medicine specialist at the University of Chicago, serving as the inaugural Director of the Primary Care Sports Medicine Program at UChicago Medicine Student Wellness Center. She holds leadership positions including Chair of the Committee on Sports Medicine & Fitness for the Illinois Chapter of the American Academy of Pediatrics, and serves as Head Medical Team Physician for the WNBA Chicago Sky and medical officer for major events including the Bank of America Chicago Marathon, US Soccer, and USA Triathlon. Her educational background includes: B.S. in Integrated Life Sciences, Kent State University (1994) Combined BS/MD, Northeastern Ohio Universities College of Medicine (1994) MD, Northeastern Ohio Universities College of Medicine (1994) Residency in Pediatrics, The University of Chicago Children's Hospital (1997) Fellowship in Primary Care Sports Medicine, Lutheran General Hospital (1998) Dr. Benjamin specializes in sports medicine with particular expertise in youth, adolescent, and collegiate athletes. Her research spans acute and overuse sports injuries, concussion management, sport biomechanics, and musculoskeletal ultrasound. She has made significant contributions to understanding injury prevention strategies, safe return-to-play protocols, and the psychological aspects of sports injuries. Her work frequently addresses female athletes and elite/professional athletes, with emphasis on evidence-based approaches to optimize performance while minimizing injury risk. Recent publications demonstrate a strong focus on concussion research, with multiple studies examining diagnostic approaches, recovery trajectories, and factors influencing outcomes. Her work spans biomechanics, mental health considerations in sports injuries, and innovative approaches to injury prevention across various athletic populations. Her scientific recognition includes: Election as Fellow of the American College of Sports Medicine (2003), becoming the second female pediatrician ever elected Election as Fellow of the American Medical Society of Sports Medicine Distinguished Alumni Award from The New Aurora High School Alumni (1999) Invitation to serve as volunteer physician for the Olympic Training Center (2008) Multiple expert contributions to national media including Runner's World and the Naperville Sun Dr. Benjamin actively mentors through her coaching roles at high school and collegiate levels, emphasizing safe return-to-play post-injury and injury prevention. She has developed educational content through video productions for the American Academy of Pediatrics and serves as head team physician for the University of Chicago Lab School and the NCAA Division III UChicago Maroons. Notably, she is one of only a few sports medicine specialists in the country offering diagnostic and procedural musculoskeletal ultrasound in her office, demonstrating her commitment to advanced diagnostic capabilities for patient care.
Jeremy K Fox is Associate Professor of Psychology at Montclair State University's College of Humanities and Social Sciences, where he directs the RECAP (Research on Emotion, Coping and Anxiety in Pediatrics) laboratory. His research examines developmental pathways to childhood anxiety and depression. Education includes: PhD in Clinical Psychology, State University of New York at Albany MA in Psychology, State University of New York at Albany BA in Psychology, Johns Hopkins University Research focuses on temperament-environment interactions, emotion regulation deficits, and school-based interventions. Current work investigates parental influences on anxiety development and peer-supported treatment models for minority youth. Articles demonstrate consistent focus on improving mental health service accessibility. Leads graduate training in clinical psychology and mentors doctoral students in anxiety research. RECAP lab investigates affective mechanisms underlying adolescent distress and develops digital interventions for school settings.
Jonathan Vance is a Lecturer in the School of Computing at the University of Georgia. He holds a Ph.D. and B.S. in Computer Science from the University of Georgia (2023 and 2009, respectively). His research focuses on applying artificial intelligence techniques to precision agriculture, particularly machine learning for biomass yield prediction and audio processing. He explores machine learning applications in agriculture, climate science, and image/audio processing. His educational background includes a strong foundation in computer science from UGA. His work emphasizes interdisciplinary approaches combining machine learning with agricultural challenges. Recent publications highlight advancements in data synthesis, domain adaptation, and feature selection for alfalfa biomass prediction. These studies contribute to sustainable agriculture through AI-driven solutions. No scientific awards or grants are explicitly listed in the provided information. He advises no listed students and maintains a professional website at jonathanvance.online .
Andrea Bajcsy serves as an Assistant Professor in the Robotics Institute and School of Computer Science at Carnegie Mellon University, leading the Interactive and Trustworthy Robotics Lab (Intent Lab). Her work focuses on enabling robots to safely interact with open-world environments through novel algorithms in control theory and machine learning. Her educational background includes a Ph.D. in Electrical Engineering & Computer Science from UC Berkeley under Anca Dragan and Claire Tomlin, followed by a postdoctoral position with Jitendra Malik and industry experience at NVIDIA's Autonomous Vehicle Research Group. Research centers on quantifying robot confidence, computing safe interaction policies for nuanced hazards (tearing, spilling, breaking), and aligning AI with human values. Key methodologies integrate optimal control, reinforcement learning, dynamic game theory, and deep learning, applied to robotic arms, quadrotors, quadrupeds, and autonomous vehicles. Core areas include safety for physical human-robot interaction, robot learning for manipulation, and world modeling. Recent publications (2024-2025) reveal a concentrated effort on uncertainty-aware safety mechanisms, out-of-distribution adaptation, and language-based safety specification. Her work increasingly bridges conformal prediction with interactive learning while leveraging vision-language models for real-time policy steering, as evidenced by multiple CoRL, RSS, ICRA, and ICLR acceptances. Scientific Awards: NSF CAREER Award (2025) Advises four active PhD students (Kensuke Nakamura, Ravi Pandya, Junwon Seo, Yilin Wu) and leads research funded by the NSF CAREER grant. Organizes community initiatives including the Northeast Systems and Control Workshop and ICRA workshops on Safely Leveraging VLMs in Robotics and Public Trust in Autonomy. Directs the Intent Robotics Lab, which develops theoretical frameworks and practical implementations for open-world robot safety. The lab maintains strong industry ties through NVIDIA collaborations and emphasizes real-world deployment across multiple robotic platforms.
Christopher Emdin is a Professor of Science Education and Maxine Greene Chair for Distinguished Contributions to Education at Teachers College, Columbia University. He directs the STEAM, DREAM & IdeaLab initiatives. Emdin holds a B.S. in Physical Anthropology/Biology/Chemistry from Lehman College (2000), an M.S. in Natural Sciences from Rensselaer Polytechnic Institute (2003), and a Ph.D. in Urban Education from the CUNY Graduate Center (2007). His research focuses on urban STEM education , culturally responsive pedagogy , and Hip-Hop Education (HipHopEd) . Emdin has received prestigious awards including the Strage Prize (2011) and PDK International Outstanding Dissertation Award (2008) . He pioneered projects like the Science Genius B.A.T.T.L.E.S. , using rap battles to engage urban students in science. His work bridges theory and practice, emphasizing reality pedagogy and cosmopolitan ethics . Key contributions include over 50 publications on urban science education, equity, and hip-hop pedagogy. He is a leader in professional organizations such as NARST and AERA. Emdin’s grants include collaborations with NSF and Fund for Teachers, focusing on science agency and leadership in urban schools. His lab, IdeaLab , advances innovative teaching strategies rooted in youth culture.
Bo Li is a Research Associate Professor in the Computer Science Department and Data Science Institute at the University of Chicago, specializing in trustworthy machine learning with emphasis on robustness, privacy, and generalization for real-world systems like autonomous vehicles and federated learning. Her academic journey includes: Ph.D. in Computer Science, Vanderbilt University, 2016 Postdoctoral Researcher, UC Berkeley (2017-2018) under Prof. Dawn Song Faculty position at UIUC (2018) prior to current role Her research bridges theoretical foundations and practical deployments in adversarial robustness, privacy-preserving techniques, and distributed learning frameworks, directly addressing reliability challenges in safety-critical AI applications. This work has established her as a leading voice in trustworthy AI development. Major recognitions include: Sloan Fellowship and MIT Technology Review TR-35 Innovator IJCAI Computers and Thought Award and NSF CAREER Award Intel Rising Star Faculty Award and Symantec Research Labs Fellowship Research funding from Amazon, Facebook, and Google Multiple best paper awards at premier ML/security conferences Her contributions extend beyond academia through media features in Nature, Wired, and New York Times, with research exhibited at London's Science Museum, demonstrating significant societal impact of her work on real-world AI safety.
Professor Winston Hsu is a distinguished faculty member in the Department of Computer Science and Information Engineering at National Taiwan University, where he has served as a full professor since 2015. He is the co-director of the Communications and Multimedia Laboratory (CMLab) and founder of the MiRA (Multimedia indexing, Retrieval, and Analysis) research group. Additionally, he serves as the Founding Director for NVIDIA AI Lab at NTU, the first such lab in Asia. Professor Hsu received his Ph.D. in Electrical Engineering from Columbia University in 2007 under the supervision of Professor Shih-Fu Chang. Prior to his academic career, he was a founding engineer and research manager at CyberLink Corp., now a public image/video software company. National Taiwan University (2007-Present): Professor (2015-Present), Assistant/Associate Professor (2007-2015) MobileDrive (2021-2024): CTO and Vice President (Joint Venture between Foxconn and Stellantis) IBM TJ Watson Research Center (2016-2017): Visiting Scientist Microsoft Research Redmond (2014): Visiting Researcher Columbia University (2007): Ph.D. in Electrical Engineering Professor Hsu's research focuses on machine learning, computer vision, large-scale image and video search and recognition, and embedded AI. His work spans from fundamental research in visual recognition to practical applications in automotive systems, medical imaging, and e-commerce. He has pioneered work in disguised face recognition, low-resolution face hallucination, 3D model search, and virtual try-on systems. His current research emphasizes Embodied AI, integrating perception, action, and learning technologies for applications in automotive and robotics domains. His research group has produced numerous influential publications, particularly in top computer vision and multimedia conferences like CVPR, where they won first place in the Disguised Face Recognition competition in 2018. Their work spans diverse application areas including security, medical diagnostics, automotive systems, and e-commerce solutions, demonstrating strong translation from academic research to real-world impact. IBM Research Pat Goldberg Memorial Best Paper Award (2018) First Place, IARPA Disguised Faces in the Wild Competition (CVPR 2018) Best Brave New Idea Paper Award, ACM Multimedia 2017 NVIDIA AI LAB Award (First in Asia, 2016) First Place, MSR-Bing Image Retrieval Challenge (2013) World's Top 2% Scientists (2023) Professor Hsu actively mentors students and researchers, with his group consistently recruiting PhD students, postdocs, and research assistants. He has successfully bridged academia and industry through multiple collaborations, including his role as CTO at MobileDrive (a joint venture between Foxconn and Stellantis) from 2021-2024. His research has been supported by significant industry partnerships with Microsoft, IBM, and NVIDIA, as well as government grants from Taiwan's Ministry of Science and Technology. His laboratory, the Communications and Multimedia Laboratory (CMLab), maintains strong industry connections and focuses on cutting-edge research in visual AI. The lab has produced numerous award-winning projects and maintains active collaborations with global technology companies, particularly in the automotive and consumer electronics sectors.
Massimo Piccardi is a Professor of Natural Language Processing (NLP), Computer Vision, and Machine Learning at the University of Technology Sydney (UTS) , where he has been since 2002. He currently serves as the Head of the School of Electrical and Data Engineering and leads the Big Data Analytics program at the Global Big Data Technologies Centre. His research focuses on advancing NLP, machine learning applications in healthcare, and cybersecurity in IoT systems. He has authored over 200 journal papers and conference proceedings, secured significant ARC and CRC grants, and holds the IEEE Computer Society Distinguished Contributor Award (2022). Education & Professional Roles: Joined UTS in 2002, progressing from Associate Professor (2002–2007) to Professor (2008–present). Serves as Associate Editor for IEEE Transactions on Big Data and Editor for Artificial Intelligence in Medicine. Active in professional societies including IEEE, ACL, and ALTA (President, 2023–2024). Research Interests: Core areas include NLP (translation, summarization, adversarial attacks), healthcare informatics (clinical NLP, health service analysis), and cybersecurity (IoT security, privacy-preserving systems). Cross-cutting themes include generative models, cross-lingual systems, and ethical AI. Grants & Projects: Principal Investigator on ARC Discovery/Linkage projects and CRC grants. Recent projects include controllable machine translation (Amazon), privacy-preserving digital agriculture, and STEM innovation (ASTRID project with NBN Co). Labs & Collaborations: Leads the UTS Global Big Data Technologies Centre, collaborating on projects like adversarial NLP attacks, medical machine translation, and secure IoT frameworks.
Yaser Sheikh is an Associate Professor at the Robotics Institute of Carnegie Mellon University (on leave) and Director of the Facebook Reality Lab, Pittsburgh . He holds appointments in the Mechanical Engineering Department and focuses on ' metric telepresence ' for AR/VR interactions. His research spans machine perception , computer vision , computer graphics , and machine learning , with applications in social behavior modeling and dynamic 3D reconstruction. University: Carnegie Mellon University Roles: Associate Professor (Robotics Institute), Director (Facebook Reality Lab) Contact: yaser@cs.cmu.edu, yasers@fb.com Research Interests include: Computer Vision: Pose estimation, 3D reconstruction, camera calibration Computer Graphics: Face/Hand animation, photorealistic rendering Machine Learning: Neural rendering, unsupervised learning for landmark detection AR/VR: Telepresence, immersive social interactions Notable Trends in Publications reveal a focus on real-time pose estimation (e.g., OpenPose), dynamic 3D reconstruction , and codec avatars for VR/AR. Recent works emphasize universal priors and neural rendering for photorealistic avatars. Scientific Awards include: Popular Science’s Best of What’s New Award Honda Initiation Award (2010) Best Paper Awards: WACV (2012), SCA (2010), ICCV THEMIS (2009) Hillman Fellowship for Excellence in Computer Science Research (2004) Advising and Grants: He has advised numerous PhD students (e.g., Hanbyul Joo, Tomas Simon) and received funding from the National Science Foundation , DARPA, and industry partners like Intel , Disney , and Honda . Labs & Teams: Leads the Facebook Reality Lab in Pittsburgh, collaborating with institutions like Carnegie Mellon University and Disney Research.
Dr. Wim Biemans is an Associate Professor at the Faculty of Economics and Business of the University of Groningen, specializing in Innovation Management and Marketing. He holds a PhD from Eindhoven University of Technology (1989) and has over 25 years of experience in research, teaching, and consulting. His work focuses on service innovation, B2B marketing, customer involvement in innovation, and the marketing-sales interface. He has authored over 100 peer-reviewed articles and books, including the best-selling Dutch B2B marketing textbook. His research has been recognized with awards like the Thomas P. Hustad Best Paper Award. He serves as Triage Editor for the Journal of Product Innovation Management and editorial board member for Industrial Marketing Management and Journal of Business & Industrial Marketing. Biemans teaches at the University of Groningen and multiple international institutions, including guest professor roles in Europe, Asia, and the U.S. He also designs executive programs for managerial training in innovation management and customer value creation. His recent research explores humor’s impact on creativity, digital tools in sales-marketing integration, and bridging technology-to-product development gaps. He emphasizes end-to-end value creation through intra-organizational collaboration and has published on the Valley of Death ecosystem challenges in innovation commercialization.
John Paisley is an Associate Professor of Electrical Engineering at Columbia University's Fu Foundation School of Engineering and Applied Science, and a member of Columbia's Data Science Institute (DSI). He holds a B.S., M.S., and Ph.D. in Electrical and Computer Engineering from Duke University (2004-2010), followed by postdoctoral research in Computer Science at Princeton University and UC Berkeley. His research focuses on Bayesian models, posterior inference techniques for Big Data, and applications in data analysis, recommendation systems, information retrieval, and compressed sensing. He has pioneered methods like Bayesian Gaussian Process ODEs and Double Normalizing Flows, with recent work emphasizing uncertainty quantification in environmental modeling and neuroimaging analysis. His collaborative workflows (e.g., bneR ) address air pollution exposure and PM2.5 concentration uncertainties, combining Bayesian nonparametric ensembles with geospatial data. He has also developed frameworks for neural network interpretability, image denoising, and compressed sensing MRI. Paisley's work bridges statistical theory and applied machine learning, with applications in healthcare, environmental science, and geophysics. His academic contributions include over 50 publications since 2016, spanning topics like deep metric learning, adversarial learning, and variational inference optimization. He maintains an active research group and serves on editorial boards for machine learning and signal processing journals.
