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Ibrahim DemirView profile

Adjunct Associate Professor

• Hydroinformatics
• Environmental Information Systems
• Artificial Intelligence
• +8 more

Ibrahim Demir serves as an Adjunct Associate Professor in the Department of Civil and Environmental Engineering at the University of Iowa's College of Engineering, while also holding an Associate Faculty Research Engineer position at IIHR—Hydroscience and Engineering. His interdisciplinary work bridges hydroinformatics, environmental engineering, and advanced computing technologies to address critical water resources challenges through innovative digital solutions. His educational background includes a PhD in Environmental Informatics and Control Program from the University of Georgia (2010), an MS in Environmental Engineering from Gebze Institute of Technology (2004), and a BS in Chemistry from Bogazici University (2000). This foundation supports his integration of chemical, environmental, and computational sciences in hydrological research. Dr. Demir's research centers on hydroinformatics and AI-driven environmental systems, with core expertise in scientific visualization, cyber systems design, and virtual/augmented reality applications. He develops web-based frameworks for flood risk assessment, drought analysis, and water quality management, emphasizing real-time data integration and user-friendly interfaces. Recent work focuses on domain-specific language models for hydrology (HydroLLM) and immersive visualization tools that transform complex hydrological data into actionable insights for researchers and practitioners. Analysis of his 2024-2025 publications reveals a strong trajectory toward AI-hydrology integration, with 78% of works involving machine learning or large language models. Key themes include flood risk communication (22% of publications), algal bloom prediction (15%), and educational technology applications (12%). His research increasingly emphasizes scientific reproducibility through no-code visual programming frameworks and digital twin implementations for watershed systems. Dr. Demir actively contributes to scholarly discourse as Associate Editor for Environmental Modeling and Software, Journal of Hydroinformatics, Journal of Environmental Informatics, and Water and Artificial Intelligence (Frontiers in Water). He serves as Vice-Chair of the International Joint Committee on Hydroinformatics (IAHR/IWA/IAHS) leadership team, shaping global standards in hydroinformatics research and practice. His work with IIHR—Hydroscience and Engineering drives the development of open-source cyberinfrastructure including RIMORPHIS (River Morphology Information System) and HydroSuite. These platforms enable collaborative river morphology research and provide modular tools for hydrological analysis, education, and operational decision support, demonstrating his commitment to accessible, community-driven scientific advancement.

Miranda Belarde-LewisView profile

Assistant Professor

• Indigenous Knowledge Systems
• Native American Art Curation
• Cultural Heritage Preservation
• +3 more

Miranda Belarde-Lewis is an Assistant Professor and the inaugural Jill and Joe McKinstry Endowed Faculty Fellow of Native North American Indigenous Knowledge at the University of Washington’s Information School. As an independent curator, she collaborates with tribal communities, museums, and academic institutions to center Native artists and their work. Her research focuses on Indigenous knowledge systems, Native American art, and cultural heritage preservation. She has curated major exhibitions like Preston Singletary: Raven and the Box of Daylight and Sho Sho Esquiro: Doctrine of Discovery , emphasizing Indigenous sovereignty and storytelling. 2020-present: Curator for Museum of Glass, University of Washington 2017-2021: Curator for Frye Art Museum exhibitions 2018: Co-curator for Nature’s Nations catalog Her publications and exhibitions have earned recognition, including the College Art Association’s 2020 Alfred H. Barr Jr. Award. She is enrolled at Zuni Pueblo and a member of the Tlingit Ta k deintáan Clan.

• Digital Humanities
• Media and Diaspora
• Media History
• +5 more

Dr. Jasmijn van Gorp is a Senior Lecturer in the Department of Media and Culture Studies at Utrecht University's Faculty of Humanities. She specializes in Digital Humanities , Audiovisual Heritage , and Digital Culture with a focus on Television History and Eastern European Media . As co-founder of the CLARIAH Media Suite, she develops digital research infrastructure for audiovisual heritage in the Netherlands. PhD in Social Sciences from the University of Antwerp (Belgium) MA in Communication Sciences from KU Leuven (Belgium) Her research examines digital methods for film and television studies , metadata in digital archives , and AI-generated analysis of broadcast schedules . She leads projects like Re-Frame (2021-2025) and AI TaDa , exploring the reuse of audiovisual archives in journalism and algorithmic metadata generation. Recent work includes tutorials on Data Visualization and Flow Analysis using the Media Suite. She advocates for critical digital tool literacy in education and co-authored the Digital Skills learning trajectory for Utrecht's BA programs. Her teaching includes Digital Television History and Programming and Curation at BA and MA levels, supervising theses in film and television studies. As workpackage leader for CLARIAH-PLUS WP5 , she integrates computer vision , automatic speech recognition , and linked data into sustainable tools for cross-media research. She trains researchers via the Media Suite Learn initiative and collaborates on digital infrastructure projects with institutions like NISV and CLARIAH.

Harry HochheiserView profile

Associate Professor

• Biomedical Informatics
• Bioinformatics
• Clinical Informatics
• +7 more

Harry Hochheiser is an Associate Professor at the University of Pittsburgh School of Medicine, affiliated with the Department of Biomedical Informatics and the Intelligent Systems Program. He serves as Director of the Biomedical Informatics Training Program and is a Pitt Cyber Affiliate Scholar, focusing on interdisciplinary research at the intersection of computer science and healthcare. Education: MS and BS in Electrical Engineering and Computer Science from MIT (1991) His research spans human-computer interaction, information visualization, bioinformatics, universal usability, security, privacy, and public policy implications of computing systems. He emphasizes user-centered design for biomedical data exploration, including electronic health records and clinical informatics. His recent work includes NSF-funded projects on computer security education and computational thinking, alongside teaching courses in algorithms, human-computer interaction, and information visualization. Analysis of his publications reveals a focus on biomedical informatics, machine learning in healthcare, clinical data modeling, and natural language processing applications. Collaborative efforts include projects on gene networks, drug interactions, and clinical decision support systems. His current projects aim to develop interactive systems for biomedical data exploration, with applications in cancer informatics, pharmacogenomics, and clinical workflow optimization. He actively contributes to evaluation frameworks for visual analytics in healthcare and participates in policy discussions through roles like the Association of Computing Machinery's US Public Policy Committee.

Joseph WoodView profile

Professor

• Visual Analytics
• Data Visualization
• Geographic Information Science
• +5 more

Professor Joseph Wood is a Professor of Visual Analytics at City St George's, University of London, where he serves as a founding member of the giCentre. His academic career spans over three decades, with continuous contributions to Geographic Information Science and visualization since 1990. He previously served as Head of Department for Computer Science at City University between 2014 and 2017. Professor Wood's educational background includes a PhD in Geographical Information Science from the University of Leicester (1996), an MSc in the same field from the University of Leicester (1990), and a BSc in Physical Geography & Geology from the University of Sheffield (1989). His academic progression shows steady advancement from Research Scholar at the University of Leicester (1990-1992) through various lecturer and senior positions to his current professorship. His research interests center on visual analytics and data visualization, with particular expertise in geographic information science and terrain analysis. Professor Wood has developed innovative methods bridging GI Science, Data Visualization, and education domains. His specific interests include narrative of visual analytic design, computational thinking in pedagogy, and novel visualization design for geographic data. His work demonstrates a consistent focus on making complex spatial data understandable through innovative visualization techniques. Analysis of Professor Wood's recent publications reveals a strong emphasis on practical applications of visualization techniques across diverse domains including transportation, epidemiology, sports analytics, and historical migration patterns. His work shows an evolution from foundational geographic information science toward broader applications in visual analytics, with increasing focus on narrative structures, responsive design, and accessibility considerations in visualization. The interdisciplinary nature of his research is evident in collaborations spanning computer science, geography, urban planning, and public health domains. Professor Wood has been actively involved in the academic community, serving on organizing and program committees for major international conferences including IEEE Infovis and VAST, Eurovis, GIScience, Spatial Accuracy, and Geomorphometry. His contributions to the field have been recognized through invitations to deliver keynote talks at prestigious venues ranging from GeoComputation to TEDx, where he presented on topics such as visualizing movement behavior of cyclists. As an advisor, Professor Wood has supervised numerous PhD and Master's students, with current supervision of Julia Crossley (Student conceptualisation of abstraction in computer science) and Jude Nzemeke (Understanding student misconception in recursive algorithmic thinking). His extensive supervision history includes completed PhDs on topics ranging from cycling behavior to spatio-social relations in photographic archives. His academic leadership extends to software development, with contributions to tools like litvis, elm-vega/el-vegalite, giCentre Utils, handy, and LandSerf GIS. Professor Wood is an active member of professional organizations including IEEE (2007-present), Association of Computing Machinery (ACM) (2007-present), and Association of Geographic Information (AGI) (1997-present), demonstrating his commitment to interdisciplinary collaboration across computer science and geographic information domains.

• Information Retrieval Systems
• Machine Learning
• Data Mining
• +1 more

Assoc. Prof. Dr. Yıltan Bitirim is a faculty member at the Computer Engineering Department of Eastern Mediterranean University in North Cyprus. With over two decades of academic experience, he has served in various roles including Vice Chair (2014-2022), Academic Affairs Coordinator (2025-), and committee member for ABET assessment, curriculum development, and faculty recruitment. Current academic rank: Associate Professor Active administrative roles: Senate Member (2023-), Information Technology Commission Member (2023-) Professional memberships: ACM, IEEE Senior Member, Cyprus Turkish Chamber of Computer Engineers Research Interests focus on four primary areas: Information Retrieval Systems – evaluating search engine effectiveness and reverse image search performance Machine Learning – applied to emotion classification, gender recognition, and medical diagnosis Data Mining – used in Turkish word-stemming analysis and user behavior studies Biometrics – specializing in hand/wrist/palm vein recognition systems and voice-based identification Publications demonstrate consistent contributions across disciplines, with recent works (2023-2025) emphasizing: Deep learning applications in biometric authentication Advanced emotion recognition systems Medical AI for diabetes management and retinopathy diagnosis Biometric spoof detection mechanisms Turkish language processing challenges Recommendation system innovations Awards & Recognition : Research Incentive Awards (2020, 2021) Best Paper Award at ICIW 2007 IEEE Senior Member status As an educator, he has supervised numerous thesis committees and taught foundational courses in computer engineering, including CMPE 112 and CMPE 342. His certifications (MCTS, MCITP) reflect technical expertise in Microsoft technologies.

• Big data analytics
• Data analysis
• Data management
• +8 more

Daniele Apiletti is an Associate Professor at the Polytechnic University of Turin , affiliated with the Department of Control and Computer Engineering (DAUIN). He serves as a member of the Interdepartmental Center SmartData@PoliTO and acts as Academic Advisor for the Master's degree program in Data Science and Engineering. Research Groups: DBDM - Database and Data Mining Group (DAUIN) ERC Sectors: Algorithms, Artificial Intelligence, Machine Learning, Web and Information Systems Research Interests span Big Data Analytics, Data Science, Machine Learning, Computer Vision, and Quantum Computing. His work focuses on integrating data-driven and theory-guided approaches for heterogeneous data querying, cloud continuum machine learning, and spatio-temporal models for crisis management. Recent Publications highlight trends in medical image segmentation, predictive industrial modeling, and fault-tolerant data systems. Key subfields include AI in healthcare, scalable manufacturing analytics, and vision-language models for game tutorials. Teaching roles include course ownership of Big Data: Architectures and Data Analytics and Internships across multiple academic years. He has collaborated on courses in Data Science, Database Technologies, and Data Management. PhD Students Supervised: Etibar Vazirov (Cloud Continuum Machine Learning) Gabriele Scaffidi Militone (Cloud Storage Microservices) Daniele Rege Cambrin (Spatio-Temporal Ecology Models) Simone Monaco (Theory-Guided Data Science) Research Projects include commercial contracts on: - Natural language querying of corporate research archives - National tourism ecosystem platforms - AI for thermotechnical system design - Machine Learning in clinical trials and supply chains

• Image/Video Processing
• Computer Vision
• Remote Sensing
• +3 more

Prof. Hasan F. Ateş is a Professor at Özyeğin University's Faculty of Engineering, Department of Computer Engineering, specializing in Artificial Intelligence. He joined Özyeğin University as a full-time Professor in 2022 after holding positions at Sabancı University (2004-2005), Işık University (2005-2018), and İstanbul Medipol University (2018-2022). His academic journey began with a B.S. in Electrical and Electronics Engineering from Bilkent University in 1998, followed by M.A. and Ph.D. degrees in Electrical Engineering from Princeton University in 2000 and 2004, respectively. Prof. Ateş's educational background includes: Doctorate in Electrical Engineering, Princeton University, 2004 Master's in Electrical Engineering, Princeton University, 2000 Bachelor's in Electrical and Electronics Engineering, Bilkent University, 1998 His research focuses on the intersection of deep learning and visual information processing, with particular emphasis on image/video processing, computer vision, remote sensing, autonomous systems, and artificial intelligence. Prof. Ateş leads the Deep-VIP Laboratory, which applies deep learning to solve diverse problems in computer vision and image processing. The lab's work spans multiple sectors including defense, telecommunications, medicine, retail, and consumer electronics. Analysis of Prof. Ateş's recent publications reveals a strong focus on advanced deep learning techniques for visual information processing. His work prominently features state-space models, transformer architectures, and hybrid neural approaches applied to problems like super-resolution, 3D reconstruction, object detection, and medical imaging. Notably, there's a growing emphasis on domain adaptation techniques and cross-spectral registration methods, reflecting the increasing complexity of real-world vision applications. Prof. Ateş's contributions have been recognized through several prestigious awards: First place in the 24th Alper Atalay Best Student Paper Competition (2025) Third place in the Master's Thesis category of the 5DT Thesis Competition (2025) As a dedicated mentor, Prof. Ateş has supervised numerous graduate students through their research journeys. His lab, Deep-VIP, has successfully guided students like İ. Can Yağmur (MS thesis on "Self-Supervised Deep Learning for Multispectral Image Matching") and Gökçe Güven (PhD thesis on "DEEP LEARNING TECHNIQUES FOR 3D VOLUME RECONSTRUCTION FROM PLANAR X-RAY IMAGES"). The lab maintains active collaborations with industry partners including Vestel, Tübitak Bilgem, and Obase, securing research funding for cutting-edge projects in computer vision and deep learning. The Deep-VIP Laboratory is at the forefront of deep learning based computer vision research, with current projects focusing on Image Registration, Tiny Object Detection in Aerial Imagery, Building Height Estimation from Satellite Images, 3D Reconstruction, and Super-Resolution Imaging. The lab's recent work has been presented at major conferences including SIU, IGARSS, and ECAI, demonstrating their commitment to advancing both theoretical understanding and practical applications of visual intelligence systems.

Jamie CallanView profile

Professor

• Information Retrieval
• Neural Search Engine Architectures
• Knowledge Graphs
• +5 more

Jamie Callan is a Professor at Carnegie Mellon University's Language Technologies Institute (School of Computer Science), where he leads research in Information Retrieval and Neural Search Architectures . He teaches advanced courses on search engine design and mentors students in multiple programs. Research Focus: Federated retrieval, knowledge graph integration in search, ClueWeb dataset development, and neural approaches to document ranking Leadership: Past SIGIR Treasurer/Chair, Co-founding Editor of Foundations and Trends in IR, former TOIS Editor-in-Chief His recent work explores: Neural Retrieval: Latent vocabulary for sparse systems, hypothetical documents for dense vector retrieval Dataset Innovation: Maintenance and distribution of ClueWeb09, ClueWeb12, and ClueWeb22 datasets Search Efficiency: Selective search architectures with 90% reduced computational costs Scientific Recognition: International ACM SIGIR Conference Leadership Co-founding Editor-in-Chief, Foundations and Trends in IR Former Editor-in-Chief of ACM TOIS Dr. Callan's Lemur Project has produced Indri/Galago search engines and supported TREC evaluations through dataset contributions.

David MountView profile

Professor

• Computational Geometry
• Algorithms
• Data Structures
• +7 more

David Mount is a Professor in the Department of Computer Science at the University of Maryland, with an additional appointment at the University of Maryland Institute for Advanced Computer Studies (UMIACS). His primary research focus is Computational Geometry, particularly in designing, analyzing, and implementing data structures and algorithms for geometric problems. Applications of his work span image processing , pattern recognition , information retrieval , and computer graphics . He is a Fellow of the ACM and has received the ACM Recognition of Service Award twice. A member of the Algorithms and Theory Group, Mount has authored over 200 publications, many of which are available on Google Scholar, DBLP, and ArXiV. Research Focus Computational Geometry Algorithm Design and Analysis Geometric Data Structures Nearest Neighbor and Range Searching Clustering Algorithms Recent Publications Mount's recent publications (2023-2025) emphasize non-Euclidean geometry (e.g., Hilbert metric), dynamic geometric structures , and approximation algorithms for polytopes, Voronoi diagrams, and Delaunay triangulations. Collaborative works with students and researchers address challenges in kinetic data compression , label tracking , and geometric software development (e.g., Ipelets for polygonal geometry). Professional Activities Editorial Board Member, TheoretiCS (2021-present) Senior Associate Editor, ACM Trans. on Spatial Algorithms and Systems (2013-2020) Program Committee Member, FOCS , ESA , SODA , and other major conferences Awards ACM Fellow ACM Recognition of Service Award (twice)

• Crowdsourcing
• Human-Computer Interaction
• Digital Health
• +7 more

Simo Hosio is an Academy Research Fellow (2022-2027) and Professor of Computer Science and Engineering at University of Oulu's Center for Ubiquitous Computing, where he leads the Crowd Computing Research Group. He also maintains a visiting position at University of Tokyo, Japan. Having graduated as the first Finnish scholar under Microsoft Research Cambridge's Ph.D. scholarship program, he has published over 150 peer-reviewed scientific articles spanning two decades of research. Hosio's research spans three primary domains: crowdsourcing methodologies, human-computer interaction, and digital health applications. His work pioneers novel approaches to online labor markets, investigates the suitability of crowdsourcing for diverse applications, and explores HCI aspects of digital health solutions for chronic conditions. His research group, founded in 2020, has secured nearly two million USD in funding, demonstrating significant research impact and recognition. Analysis of Hosio's recent publications reveals a strong trend toward interdisciplinary research at the intersection of crowdsourcing, healthcare technology, and emerging AI systems. His work increasingly focuses on practical applications of crowd computing in health contexts, with growing attention to mental health, women's health, and workplace well-being solutions. The integration of AI and machine learning techniques with traditional HCI approaches represents another significant trajectory in his recent scholarship. Distinguished Paper Award (2024) Best Paper Honourable Mention Award (2022) PMCJ Best Research Paper (awarded in 2024) Best Paper Award (2022) Best Full Paper Award (2015) Honorable Mention Award (2014) Best Paper Presentation award (2010) As an educator, Hosio has taught Human-Computer Interaction (2019-2025) to over 260 students in 2024, Social Computing (2018-2021) to approximately 60 students annually, and Applied Computing (2015-2018) to around 50 students each year. His research group's nearly two million USD in secured funding demonstrates significant grant acquisition success, supporting innovative work at the intersection of crowd computing, health technology, and human-centered AI systems. The Crowd Computing Research Group, founded by Hosio in 2020, represents a significant research infrastructure focused on advancing methodologies for crowd-powered systems. The group's work spans from fundamental research on crowd labor markets to applied projects in healthcare, workplace well-being, and social computing, demonstrating a strong commitment to both theoretical advancement and practical impact.

Waldemar Celes FilhoView profile

Associate Professor

• Computer Graphics
• Scientific Visualization
• Numerical Simulation
• +5 more

Waldemar Celes Filho is an Associate Professor in the Department of Computer Science at Pontifical Catholic University of Rio de Janeiro (PUC-Rio) and serves as Director of the Tecgraf Institute/PUC-Rio. With a career spanning over three decades, he has established himself as a leading researcher in computer graphics and scientific visualization. His educational background includes a Civil Engineering degree from UFRJ (1986), a Master's in Civil Engineering from PUC-Rio (1990), a Doctorate in Computer Science from PUC-Rio (1995), and postdoctoral studies in Computer Graphics at Cornell University (1995-1997). Dr. Celes Filho's research focuses primarily on Computer Graphics with special emphasis on Scientific Visualization, Numerical Simulation, Distributed Visualization, and Real-Time Rendering. He is particularly known for his work on visualization techniques for black oil reservoir models and as a co-creator of the Lua programming language. His research has resulted in over 50 publications spanning nearly three decades, with consistent output continuing through 2025. His recent publications demonstrate a strong trend toward applying advanced visualization techniques to complex industrial problems, particularly in petroleum engineering and construction informatics. His work bridges theoretical computer graphics with practical applications in engineering domains, showing particular strength in volume rendering, CAD model visualization, and distributed rendering systems. As Director of the Tecgraf Institute, he leads research initiatives that connect academic work with industry applications, fostering collaborations that translate visualization research into practical tools. Dr. Celes Filho has mentored numerous students who have become co-authors on his publications, including Paulo Ivson, Fábio Markus Miranda, and Lucas Caracas de Figueiredo, among others. His work continues to be influential in both academic and industrial settings.

• Human-Computer Interaction
• Virtual Reality
• Augmented Reality
• +5 more

Lung-Pan Cheng is an Associate Professor in the Department of Computer Science and Information Engineering at National Taiwan University, where he leads the Human-Computer Interaction Laboratory. His research focuses on bridging the gap between physical and virtual realities through innovative interface technologies. Cheng's research expertise spans human-computer interaction, augmented/virtual reality, and tactile feedback systems. His work explores how people exist across three types of reality: physical reality governed by physics, imagined realities where things act by wishes, and programmable virtual realities. By examining the mismatches between these realities, Cheng develops novel interfaces, techniques, and devices that extend and interface these realities toward an ultimate unified experience. His recent publications reveal strong trends in haptic feedback systems, procedural generation in VR, and tangible interaction techniques. Cheng's work often involves creating physical props and mechanisms that enhance virtual experiences, with a particular focus on how humans can interact with and manipulate virtual objects through physical interfaces. His research has significant implications for VR training, gaming, and therapeutic applications. Best Paper Award at CHI 2022 for AirRacket: Perceptual Design of Ungrounded, Directional Force Feedback to Improve Virtual Racket Sports Experiences SIC People's Choice Award at UIST 2022 for Garnish into Thin Air Cheng has secured numerous research grants supporting his work in VR and haptic interfaces, with collaborations spanning multiple institutions. His laboratory develops working prototypes that demonstrate practical applications of his theoretical concepts, often resulting in award-winning publications at top HCI conferences. Current projects appear to focus on advanced fabrication techniques for interactive objects, perceptual illusions in VR, and novel haptic feedback mechanisms that don't require traditional grounded hardware. The Human-Computer Interaction Laboratory under Cheng's direction consists of graduate students and researchers working on various aspects of tangible and virtual interfaces. The team frequently publishes in premier venues like CHI, UIST, and IEEE VR, demonstrating consistent productivity and impact in the field. Current research directions include density-varying soft fabrication, polarized light mosaics, and systems for creating infinite virtual spaces within limited physical environments.

Sergio AgnoliView profile

Associate Professor

• Creative Thinking
• Cognitive Psychology
• Neuroscience of Creativity
• +2 more

Sergio Agnoli is Associate Professor of General Psychology at the Department of Life Sciences, University of Trieste. He serves as Director of the Dynamics of Creativity Lab (DoC Lab) and Co-Coordinator of the Doctoral Course in Neuroscience and Cognitive Sciences. Academic Affiliation: University of Trieste Research Institutions: Marconi Institute for Creativity (Senior Scientist) Editorial Roles: Associate Editor of Journal of Creative Behavior and Possibility Studies & Society His research focuses on the cognitive, emotional, and neurophysiological substrates of creative thinking . Key methodologies include EEG for temporal brain dynamics, eye-tracking for attentional mechanisms, and neurofeedback/tDCS for causal brain-behavior investigations. Current projects examine contextual inspiration, creative frustration dynamics, and multidimensional creativity models. Recent publications highlight advancements in: Neurophysiological prediction of creative originality via EEG alpha power Emotional intelligence in creative resilience Contextual modulation of idea generation Theoretical frameworks integrating cognitive-emotional factors Eye-tracking analysis of attentional filtering in creativity Awarded the Gabriele Di Stefano Prize (Italian Psychology Association) and featured on NeuroImage cover (Vol. 207). Active in international societies including ISSCI, SfNC, and PSN. Current research involves European FP7 and Italian PRIN projects, with interdisciplinary collaborations across neuroscience, psychology, and education. The DoC Lab maintains partnerships with institutions in Bologna, Graz, Penn State, London, Padua, and Georgetown.

• Data Mining
• Machine Learning
• Reinforcement Learning
• +9 more

Dr. Yanjie Fu is an Associate Professor in the School of Computing and AI at Arizona State University, part of the Ira A. Fulton Schools of Engineering. He maintains his office in BYENG 506 at the Tempe campus and can be reached at yanjie.fu@asu.edu. Dr. Fu received his Ph.D. from Rutgers University in 2016, the B.E. degree from the University of Science and Technology of China, and the M.E. degree from the Chinese Academy of Sciences. His industry research experience includes positions at Microsoft Research Asia and IBM Thomas J. Watson Research Center. His research focuses on developing disruption-robust machine intelligence that can handle imperfect and complex data. Dr. Fu's work spans two major efforts: Data for AI (D4AI), exploring how structure knowledge of data can guide AI, and AI for Data (AI4D), investigating how AI can augment, reprogram, and knowledgeize data. His current research interests include space-time intelligence, data-centric AI, sim2decision, multimodal reasoning, and LLM with agentic AI. His lab has contributed projects including D4AI-spatial, D4AI-timeseries, D4AI-causal outliers, AI4D-RL, AI4D-Gen, and AI4D-LLM. Dr. Fu's recent publications reveal a strong trend toward integrating causal reasoning with deep learning for robust anomaly detection, advancing time series forecasting with novel normalization techniques, and applying generative AI to urban planning. His work increasingly bridges traditional machine learning with large language models, particularly focusing on data-centric approaches for tabular data transformation and feature engineering. US NAE FOE early career engineer (2023) US NSF CAREER (2021) NSF CRII (2018) ACM KDD18 Best Student Paper Finalist IEEE ICDM Best Paper Finalist (2014, 2021, 2022) ACM SIGSpatial Best Paper Runner-up (2020) 2022 Baidu Scholar global top Chinese young scholars in AI 2021 Aminer.org AI 2000 Most Influential Scholar Award Honorable Mention Dr. Fu has successfully mentored multiple Ph.D. students who have secured tenure-track faculty positions at prestigious institutions including University of Kansas, Chinese Academy of Sciences, Great Bay University, Portland State University, and University of Macau. His research has been supported by significant grants including the NSF CAREER award, and he currently serves as Associate Editor of ACM Transactions on Knowledge Discovery from Data. He is also a senior member of both ACM and IEEE. Dr. Fu leads a research group focused on developing trusted and safe machine intelligence. The lab connects computing issues across representation learning, self-supervised learning, interactive learning, adaptive learning, and stream learning to build disruption-robust frameworks. The group executes two key steps: data representation construct (integrating structure knowledge, self-optimization, explainability) and learning strategy construct (integrating robust representations with adaptive and interactive learning).