Xiaoming Liu is the Anil K. and Nandita Jain Endowed Professor of Engineering and MSU Foundation Professor in the Department of Computer Science and Engineering at Michigan State University . Holding a Ph.D. from Carnegie Mellon University (2004), he leads cutting-edge research in computer vision and machine learning. Research Interests : Computer Vision Pattern Recognition Image and Video Processing Machine Learning Medical Image Analysis Multimedia Retrieval Recent Research Trends : Focus on 3D object detection and depth estimation Development of robust biometric recognition systems Integration of radar-camera fusion for autonomous systems Advancements in self-supervised and multimodal learning Exploration of adversarial AI security Creation of interpretable forgery detection frameworks Teaching : Spring 2013: CSE891-006 Computer Vision Seminar Fall 2012-2015: CSE803 Computer Vision Spring 2014-2017: CSE 471 Media Processing and Multimedia Contact Information : Email: liuxm@cse.msu.edu Office: EB 3137, Michigan State University Phone: +1 (517) 355-2359
Prof. dr. Nico Van de Weghe is a full Professor of GIScience at the University of Ghent (UGent), affiliated with the CartoGIS research unit. His work bridges computer science, social science, and natural science through geospatial information studies, focusing on enabling machines to reason spatially (GeoAI). Since 2004, he has specialized in knowledge-based AI, particularly spatiotemporal reasoning and moving object analysis, with applications in animal behavior, criminology, healthcare, mobility, and sports. Van de Weghe's research emphasizes hybrid GeoAI systems combining knowledge-driven and data-driven approaches. Keywords include GeoAI, GIScience, Spatiotemporal Analysis, Moving Objects, and Data Mining. Recent publications highlight urban road network analysis, hybrid trajectory modeling, BIM semantic enrichment, and cycling safety studies using virtual reality.
Takako Fujioka is an Associate Professor of Music at Stanford University, affiliated with the Center for Computer Research in Music and Acoustics (CCRMA). Her research focuses on the neural mechanisms underlying auditory perception, auditory-motor coupling, and music-supported therapy for neurorehabilitation. She holds a Ph.D. in Physiology from the Graduate University for Advanced Studies, Japan, and M.Sc./B.Eng. degrees in Electrical Engineering from Waseda University. Her work combines neurophysiological techniques such as MEG and EEG to study brain plasticity in development, aging, and stroke recovery. Notable contributions include investigating how music influences motor and cognitive recovery in stroke patients, as well as exploring the neural basis of musical perception through rhythmic synchronization and pitch discrimination studies. Supported by awards from the Canadian Institutes of Health Research during her postdoctoral work at the Rotman Research Institute, her research bridges clinical neuroscience and music cognition. Dr. Fujioka’s expertise spans auditory neuroscience, neurorehabilitation, and technology-assisted music therapy. She has pioneered studies on tactile mapping for cochlear implant users and networked music performance systems, emphasizing cross-modal perception and human-technology interaction. Her findings contribute to both theoretical understanding of auditory processing and practical applications in medical and educational settings. Awards: Canadian Institutes of Health Research Awards (postdoctoral phase) Labs/Teams: CCRMA, Stanford Music Perception Laboratory, Rotman Research Institute collaborations Key Themes: Neuroplasticity, Music-Mediated Rehabilitation, Auditory-Motor Integration, Multisensory Processing Her recent work examines aging-related changes in binaural hearing and the role of beta/gamma oscillations in rhythmic processing. She advocates for translational research that connects neural mechanisms with real-world therapeutic interventions.
Noorbakhsh Amiri Golilarz is an Assistant Professor in the Department of Computer Science at The University of Alabama, College of Engineering. He has established himself as a prominent researcher in artificial intelligence, particularly in computer vision, deep learning, and image processing. His educational background includes: Postdoctoral Research Fellow, Computer Science, Boston College (2023) Ph.D., Electrical and Computer Engineering, Southern Illinois University Carbondale (2023) D. Eng., Computer Science and Technology, University of Electronic Science and Technology of China (2021) M.S., Electrical and Electronic Engineering, Eastern Mediterranean University (2017) B.S., Electrical Engineering, University of Guilan (2012) Dr. Golilarz's research spans multiple domains of artificial intelligence with a particular focus on computer vision, deep learning, and image processing applications. His work addresses challenges in medical imaging, satellite imagery, and cognitive neuroscience. He has made significant contributions to image denoising techniques, control chart pattern recognition, and AI applications in healthcare. His recent work has expanded into generative AI, large language models, and secure machine learning operations. His publication portfolio demonstrates consistent productivity with over 2500 citations and an h-index of 25. His most impactful work includes applications of blockchain and federated learning for COVID-19 detection, optimized support vector machines for medical diagnosis, and innovative image denoising techniques using metaheuristic optimization algorithms. Among his professional achievements: Co-founded AI Letters journal in 2024, serving as Associate Editor-in-Chief Served as Lead Guest Editor and Topic Editor for several SCI-indexed journals Held the role of Conference Program Chair Dr. Golilarz has supervised numerous graduate students and research projects, with his work spanning theoretical advancements in AI algorithms to practical applications in healthcare, energy systems, and cybersecurity. His research group has established collaborations with institutions including Boston College and Mississippi State University.
William Yang Wang serves as the Mellichamp Professor of Artificial Intelligence at the University of California, Santa Barbara (2019-present). He directs the UCSB Center for Responsible Machine Learning, the Mind and Machine Intelligence Initiative, and the UCSB NLP Group. His research focuses on theoretical foundations and practical algorithms for AI, particularly in NLP, LLMs, and neuro-symbolic reasoning. PhD in Computer Science from Carnegie Mellon University Active in AI theory and applications (2016-present) Research interests span multiple AI domains, with special emphasis on NLP and responsible machine learning. He has pioneered datasets like HybridQA, TabFact, and VaTeX, enabling advancements in multi-hop QA, fact verification, and video-language tasks. His work combines statistical relational learning with modern deep learning paradigms. Recent publications center around multimodal reasoning, knowledge graph integration, and responsible AI development. He has received numerous accolades including the IEEE SPS Pierre-Simon Laplace Award (2024) and NSF CAREER Award (2021). Karen Sparck Jones Award (2022) DARPA Young Faculty Award (2018) IBM Faculty Award Mentoring 15+ PhD and postdoc researchers who now hold positions at Microsoft Research, Amazon, Meta GenAI, and academic institutions like Arizona and Rutgers. His lab maintains active collaborations with industry partners through initiatives like ChipAgents.ai, which he founded as CEO.
Huining Li is an Assistant Professor in the Department of Computer Science at North Carolina State University . Her research focuses on Internet of Things (IoT) , cybersecurity , and mobile computing , with a specialized emphasis on mobile health (mHealth) technologies. Education: Ph.D. in Computer Science and Engineering from University at Buffalo (2024). Her work addresses privacy-preserving sensing mechanisms , biomarker measurement , and fairness in dynamic mobile environments , developing systems for chronic wound care, Parkinson’s disease management, and mental health therapy. Recent publications highlight innovations in mmWave biometrics , machine learning for health diagnostics , and non-contact monitoring . Scientific Awards: Best Paper Awards (SenSys 2019, BodyNet 2021, ICHI 2022) Best Paper Candidate (SenSys 2022) Harold O. Wolf Achievement Award (2024) EECS Rising Star (2023) NIH mHealth Training Institute Scholar (2025) She teaches courses in Mobile Health Systems and Applications and Computer Networks , and actively serves on NSF panels , TPC committees (ACM MobiSys, IEEE-EMBS BSN), and as Associate Editor for journals like Elsevier Smart Health.
David Martens is a Professor of Data Science at the University of Antwerp , where he directs the Applied Data Mining Research Group within the Faculty of Business and Economics . He also serves as Chair of the Department of Engineering Management and Director of the Antwerp Center on Responsible AI . His academic work spans data mining , interpretable machine learning , and the societal impact of AI . PhD in Applied Economic Sciences (KU Leuven, 2008) Director, Antwerp Center on Responsible AI Chair, Department of Engineering Management Martens' research focuses on responsible AI and data ethics , with applications in finance, public policy, and behavioral analysis. His recent publications emphasize counterfactual explanations , LLM interpretability , and privacy implications in AI systems. His articles reveal trends in Explainable AI (XAI) , including narrative-driven explanations , graph neural networks , and ethical challenges like monetization risks and algorithmic bias. Keywords span Computer Science , Artificial Intelligence , and Behavioral Data . Martens is a leading voice in data science ethics , authoring the book Data Science Ethics: Concepts, Techniques, and Cautionary Tales (Oxford University Press, 2022). He combines academic rigor with industry experience, having consulted for banks, telecom firms, and startups in fraud detection and digital advertising .
Raquel Fernández is Full Professor of Computational Linguistics and Dialogue Systems at the University of Amsterdam, where she leads the Dialogue Modelling Group at the Institute for Logic, Language & Computation (ILLC). As Vice-Director for Research at ILLC and a Fellow of the ELLIS Society, she bridges computational linguistics, cognitive science, and artificial intelligence through her research on language use in multimodal and conversational contexts. PhD in Computational Linguistics from King's College London Prior research positions at University of Potsdam and Stanford University's CSLI Her work explores how cognitive constraints, social interaction, and perception shape language use, with a focus on: Visually-grounded language processing Multimodal dialogue modeling Model uncertainty and calibration Language grounding in multimodal data Language learning and semantic change Dialogue reference resolution Recent publications analyze multimodal reasoning limitations, cross-lingual knowledge consistency, and uncertainty modeling in dialogue systems. She has received multiple accolades including an ERC Consolidator Grant , NWO VENI/VIDI/Aspasia fellowships , and EMNLP/GenBench awards . Outstanding Paper Award (EMNLP 2023) Best Data Award (GenBench Workshop 2023) ELLIS Society Fellow ERC Consolidator Grant #819455 recipient NWO VENI/VIDI/Aspasia awardee As a leader in academic service, she serves on the SIGDAT Executive Committee and chairs multiple conference committees. Her lab develops models for multimodal dialogue, visual storytelling, and grounded language understanding.
Jonathan Levine is a Professor of Urban and Regional Planning at the University of Michigan's Taubman College of Architecture and Urban Planning, where he has been faculty since 1991. His work focuses on transportation planning, housing and equitable development, and land use and environmental planning, with a particular emphasis on shifting from mobility-based to accessibility-based planning paradigms. Levine's educational background includes: Ph.D. in City and Regional Planning from the University of California, Berkeley M.C.P. in City and Regional Planning from the University of California, Berkeley M.S. in Engineering (Civil Engineering-Transportation focus) from the University of California, Berkeley B.S. in Political Economy of Natural Resources from the University of California, Berkeley His research centers on the potential and rationales for policy reform in transportation and land use. Levine argues that a shift in the transportation and land-use planning paradigm from a mobility to an accessibility basis is compelled by transportation theory and would be transformative to planning practices. His current work includes sponsored projects and the book "From Mobility to Accessibility: Transforming Urban Transportation and Land-Use Planning" (Cornell University Press, 2019), co-authored with Joe Grengs and Louis Merlin. He previously authored "Zoned Out: Regulation, Markets, and Choices in Transportation and Metropolitan Land Use" (Resources for the Future, 2006), which argued for policy reform based on expanding households' effective range of choices. Levine's recent publications demonstrate a clear trend toward accessibility-based evaluation of transportation systems and land-use planning. His articles explore topics such as comparing bus rapid transit and light rail, project-level accessibility analysis, overcoming the last-mile problem, and the effects of automated transit systems. His work consistently challenges traditional transportation planning paradigms and advocates for approaches that prioritize access to destinations rather than mere mobility. Levine's contributions to the field have been recognized with several prestigious awards: 2010 Chester Rapkin Award for best paper in the Journal of Planning Education and Research 2011 Residential Fellowship at the Rockefeller Foundation Center in Bellagio, Italy 2001 Excellence in Urban Policy Scholarship Award from the Association of Collegiate Schools of Planning and U.S. Department of Housing and Urban Development 1996 Best of Association of Collegiate Schools of Planning Award Levine has led significant research projects including Implementation of Accessibility-Based Evaluation for Transportation and Land-Use Planning funded by NEXTRANS, Sustainable Transportation for a 3rd Century addressing the last mile problem, and Accessibility Evaluation from Laboratory to Practice. His teaching portfolio includes Transportation and Land-Use Planning, Public Economics for Urban Planning, Research Design, and the MUP Capstone studio, where he guides students through real-world planning challenges. As a thought leader in urban planning, Levine regularly contributes to public discourse on zoning policies, housing diversity, and transportation planning. He has been featured in numerous media outlets discussing how land use policies impact sustainability, affordability, and racial justice, emphasizing that "zoning is behind all of it, and is not only the cause, but exacerbates it." His insights on the historical role of single-family zoning in perpetuating racial segregation have been particularly influential in contemporary housing policy debates.
Paulo Blikstein serves as Associate Professor of Communications, Media and Learning Technology Design at Columbia University. Previously, he was Assistant Professor of Education and (by courtesy) Computer Science at Stanford University and co-founded the Lemann Center for Brazilian Education (2008-2018). Education: Ph.D. in Learning Sciences, Northwestern University (2009) M.A. in Media Arts & Sciences, MIT Media Lab (2002) M.Eng. in Electronic Engineering, University of São Paulo (2000) B.S. in Metallurgical Engineering, University of São Paulo (1998) His research pioneers constructionist learning environments through digital fabrication, educational robotics, and tangible interfaces—focusing on equitable access for underserved communities. Inspired by Paulo Freire and Seymour Papert, he develops open-source tools like the GoGo Board robotics platform and leads the global FabLab@School initiative establishing fabrication labs in schools across four continents. Current work emphasizes multimodal learning analytics to study student interactions in maker-centered classrooms. Publications reveal strong focus on democratizing invention through maker education, with recurring themes in constructionist theory application, multimodal assessment, and context-specific technology adaptation. Brazilian education reform and low-cost computational solutions form significant threads, particularly in 2016-2017 publications. Scientific Awards: Two Google Faculty Awards National Science Foundation Early Career Award (highest U.S. government honor for early-career scientists) Blikstein directs the Transformative Learning Technologies Lab (TLTL) and co-founded Stanford's Center for Educational Entrepreneurship and Innovation in Brazil. His FabLearn conference established the first academic forum on Maker Movement applications in education. While specific grant details aren't listed, the NSF CAREER Award signifies major federal research funding. He spearheads the FabLab@School project deploying advanced fabrication labs in K-12 institutions worldwide and founded the FabLearn conference series. His work integrates teams of engineers, educators, and designers to create scalable solutions for resource-constrained learning environments.
Olga Fink is a Tenure Track Assistant Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Department of Intelligent Maintenance and Operations Systems (IMOS) within the School of Architecture, Civil and Environmental Engineering (ENAC). She also holds roles in PhD program committees for Civil and Environmental Engineering (EDCE) and Robotics, Control, and Intelligent Systems (EDRS). Her research focuses on machine learning for infrastructure monitoring, predictive maintenance, and physics-informed AI models. She teaches courses on machine learning, data science for infrastructure, and advanced deep learning topics. Fink advises multiple PhD students and is involved in interdisciplinary projects such as ThermoNeRF (multimodal 3D thermal modeling) and physics-informed neural networks for fault diagnostics. Her work bridges AI and engineering with applications in smart infrastructure, energy systems, and industrial IoT. Education: PhD in Engineering (inferred from role) Affiliations: IMOS Lab, ENAC-SGC, EPFL PhD Committees (EDCE, EDRS) Key Research Themes: Explainable AI, Digital Twins, Structural Health Monitoring, Domain Adaptation Her publications (2023–2025) emphasize robust AI for industrial systems, including fault detection in high-voltage equipment, multimodal data fusion, and physics-consistent models. She collaborates on EU and industry-funded projects, focusing on real-world applications like predictive maintenance and energy efficiency.
Dr. Jiaqi Gong serves as Associate Professor in Computer Science and Adjunct Associate Professor in Mechanical Engineering at The University of Alabama's College of Engineering, while directing the Alabama Center for the Advancement of Artificial Intelligence. His academic foundation includes: B.S. in Engineering, China University of Geoscience (2004) Ph.D. in Engineering, Huazhong University of Science and Technology (2010) Dr. Gong's research pioneers human-AI convergence through cyber-physical systems and smart health technologies, developing mobile/wearable platforms to enhance human perceptual, cognitive, and physical capabilities. His work spans artificial intelligence, machine learning, computer vision, and IoT with applications in healthcare, environmental monitoring, and education. The Sensor-Accelerated Intelligent Learning (SAIL) laboratory he founded drives innovation in behavior change interventions, human movement modeling, and educational data mining. Recent publications reveal strong interdisciplinary trends: healthcare AI dominates with medication adherence prediction and surgical classification systems, while environmental applications feature flood-risk communication and drought analysis. His work increasingly integrates generative AI and LLMs across domains, demonstrating methodological innovation in federated learning, knowledge graphs, and explainable storytelling frameworks. Notable recognitions include: Best Student Paper Award, IEEE/ACM Connected Health Conference (2022) Best Student Paper Award, Body Sensor Networks Conference (2019) Data Challenge Win, IEEE Biomedical Health Informatics (2018) Best Paper Award, Body Area Networks Conference (2014) Best Demonstration Award, IEEE Wireless Health Conference (2014) Dr. Gong leads significant funded projects including a $2M CDC/NIOSH grant for first responder safety and $3M NSF funding for hydrologic research. As SAIL laboratory director, he mentors students in developing clinically deployed technologies for multiple sclerosis, dementia, and mental health. Future work focuses on scaling AI applications in chronic disease management and climate resilience through the Alabama AI Center. The SAIL laboratory (founded 2017) operates as a multidisciplinary hub developing wearable/mobile systems for health applications, with active collaborations across medical clinics and engineering departments for real-world deployment of behavior change interventions and movement analysis tools.
Parastoo Abtahi is an Assistant Professor in the Computer Science Department at Princeton University. She leads the Situated Interactions Lab (Ψ Lab) as part of the Princeton HCI Group, focusing on AR/VR and spatial computing. Prior to Princeton, she worked at Meta Reality Labs Research. PhD in Computer Science from Stanford University BSc in Electrical and Computer Engineering from University of Toronto (Engineering Science program) Her research explores wearable interactive technologies that augment human intelligence in physical contexts through: Input on-the-go: Low-effort, privacy-preserving multimodal interfaces Intelligent output: Timely, minimal multisensory systems Situated AI: Personalizable, predictable, safe physical-digital interaction Recent publications analyze: Drone-based haptic illusions for AR/VR Microgestures for mobile AR input Interactivity in explainable AI systems Temporal dynamics in VR haptics Awards include: 2025 Google Research Scholar Award CHI 2019 & 2018 Honorable Mentions She teaches courses like: COS 436: Human-Computer Interaction COS IW: AR Meets AI
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.
Jiliang Tang is an MSU Foundation Professor in the Department of Computer Science and Engineering at Michigan State University (MSU), part of the College of Engineering. He holds a PhD from Arizona State University (2015) and previously worked as a research scientist at Yahoo Research. His research focuses on graph machine learning, trustworthy AI, and applications in education and biology. He has received numerous awards, including the 2022 AI's 10 to Watch, IAPR J.K. Aggarwal Award, and NSF CAREER Award. Education: PhD in Computer Science, Arizona State University, 2015 (Advisor: Huan Liu) Research Interests: Graph Neural Networks (GNNs) and Deep Learning on Graphs Trustworthy AI: Safety, Robustness, and Fairness AI+X Applications: Education Technology and Biological Data Analysis His work bridges theoretical advancements and practical applications, with contributions to graph representation learning, privacy in generative models, and educational AI systems. Awards & Recognition: Over 8 best paper awards (or runner-ups) Rock Star Award from Association of Chinese Scholars in Computing Extensive media coverage for innovations in AI education and biology Grants & Projects: NSF CAREER Award (2019) for research on signed networks Co-PI on a $1.7M grant for 5G research Leadership in projects like DSE Lab and Data Science initiatives Labs & Teams: Directs the Data Science and Engineering (DSE) Lab at MSU, focusing on advancing AI for real-world challenges. The lab collaborates with industry leaders and publishes widely in top conferences (e.g., KDD, SIGIR, ACL).
