Dr A. I. Shihab is a Senior Lecturer at Kingston University's Faculty of Engineering, Computing and the Environment, Department of Networks and Digital Media. He teaches programming languages (C++/Java), data structures, web development, and AI/machine learning. His research focuses on affective computing and machine learning applications including: Acoustic event detection in sports environments Audio signal analysis for tennis match modeling Multi-camera visual surveillance systems Medical imaging analysis using fuzzy clustering techniques Publications demonstrate expertise in combining audio/video modalities for sports analytics (tennis rallies, court-shots) and developing Markov models for sound event sequence analysis. Contact: a.shihab@kingston.ac.uk
Mani Golparvar Fard is a Professor at the University of Illinois at Urbana-Champaign, holding joint appointments in the Siebel School of Computing and Data Science and the Department of Civil and Environmental Engineering. He also contributes to the Technology Entrepreneur Center. His research focuses on integrating artificial intelligence, computer vision, and data analytics to advance construction management, infrastructure monitoring, and automation. Key areas include BIM integration, reality capture systems, and deep learning-based progress tracking. His work emphasizes automated construction progress monitoring through semantic segmentation, vision-language models, and UAV-based data collection. He has pioneered methods like Scan2BIM-NET for converting point clouds into BIM models and developed frameworks for worker safety analysis using machine learning. Awards: Walter L. Huber Civil Engineering Research Prize (2018) Daniel W. Halpin Award for Scholarship (2016) Advising & Grants: While no specific grant details are provided, his research is supported by collaborations with industry and government initiatives, such as the Japanese national bridge inspection project. He advises a team focused on AI-driven construction solutions and maintains active partnerships with engineering firms. Labs & Teams: Leads research groups in vision-based construction analytics, automated scheduling systems, and BIM integration. His work is disseminated through platforms like the VisualSiteDiary system and the InstaDam open-source platform for structural damage analysis.
Yang Liu is a tenured Associate Professor at the Department of Management and Engineering , Linköping University, Sweden, and an Adjunct Professor at the University of Oulu, Finland. His expertise spans smart manufacturing, clean energy transition, and Industry 4.0 applications. He holds an M.Sc. and D.Sc. from the University of Vaasa, Finland. Research & Awards: Liu's work focuses on sustainable systems, decision support systems, and AI-driven energy efficiency. He has authored over 140 Web of Science publications, including top 0.1% ESI Hot Papers. He is ranked among the world's top 2% scientists (Stanford-Elsevier) and leads globally in 'big data analytics in manufacturing' and 'Industry 4.0-driven circular economy' research. Leadership & Projects: He leads projects like FlexSUS (EU Horizon 2020) and PERSEUS, developing tools for smart urban energy planning and 15-minute city models. He serves as Editor-in-Chief of Cleaner Engineering and Technology and Guest Editor for multiple journals. His research emphasizes bridging data science with sustainability challenges in manufacturing and energy systems. Key Achievements: Top-ranked in global citations, ESI Highly Cited Papers, and industry-driven sustainability frameworks. Grants: Leads EU-funded projects and collaborates with Siemens Energy on energy transition solutions. Labs & Teams: Part of the Environmental Technology and Management (MILJÖ) division and Unit for Product Service Innovation (MILJOPSI) at Linköping.
Rebecca Willett is a Professor of Statistics and Computer Science at the University of Chicago and Faculty Director of AI at the Data Science Institute. She holds the Worah Family Professorship and is a member of the Wallman Society of Fellows. Her research focuses on machine learning, signal processing, and scientific computing, with applications in astronomy, climate science, and biochemistry. She has held visiting roles at institutions including UCLA and INRIA. Key roles include Deputy Directorships at the NSF-Simons Institute for Theory and Mathematics in Biology and the SkAI Institute. Education: PhD in Electrical and Computer Engineering from Rice University (2005), followed by faculty roles at Duke University (2005–2013) and the University of Wisconsin-Madison (2013–2018). Awards include the 2024 SIAM Data Science Career Award, NSF CAREER Award (2007), and AFOSR Young Investigator Award (2010). Research interests span inverse problems, optimization theory, and interdisciplinary applications. Her work bridges high-dimensional statistics and imaging science. Recent articles emphasize neural network theory, climate data assimilation, and biophysical modeling. Awards include SIAM Fellowship, IEEE Fellowship, and teaching excellence awards. She leads initiatives in AI ethics, broadening participation in STEM, and serves on key committees like the National Academies' CATS. Labs/Groups: Machine Learning Group at UChicago, CERES Center for Unstoppable Computing. Grants include NSF, DOE, and collaborations with Argonne National Laboratory.
Univ-Prof. Dr. med. Malek Bajbouj serves as Director of the Institute for Affective Neuroscience and Emotion Modulation at Charité – University Medicine Berlin's Campus Benjamin Franklin (CBF), operating within the Department of Neurology, Neurosurgery and Psychiatry (CC 15). His position integrates clinical leadership with translational neuroscience research focused on severe mental illnesses. Dr. Bajbouj's research program centers on affective neuroscience and emotion dysregulation mechanisms in psychiatric disorders, particularly schizophrenia spectrum conditions and depression. He pioneers multimodal intervention approaches combining neuromodulation (tDCS), oxytocin augmentation, mindfulness therapies, and digital health tools. His work emphasizes translational biomarker development using neuroimaging, machine learning, and physiological stress parameter analysis to personalize treatment for treatment-resistant populations. Analysis of his 2023-2025 publications reveals three dominant research trajectories: (1) novel treatment combinations for negative symptoms in schizophrenia (oxytocin + mindfulness, yoga therapy); (2) real-world implementation of neuromodulation (at-home tDCS protocols, technical efficacy monitoring); and (3) global mental health responses to crises (pandemic impacts on vulnerable groups, culturally adapted refugee interventions). His methodology consistently employs rigorous randomized controlled trials with embedded biomarker studies. As director of his eponymous institute, Dr. Bajbouj leads a multidisciplinary team conducting neuroimaging studies, clinical trials, and international collaborations focused on emotion modulation pathways. The institute coordinates research across CC 15's clinical infrastructure at CBF Building V, with particular emphasis on bridging laboratory neuroscience with clinical psychiatry through the DepressionDC and OXYMIND trial frameworks.
Christophe Ancey is an Associate Professor at the École Polytechnique Fédérale de Lausanne (EPFL), where he serves in the School of Architecture, Civil and Environmental Engineering (ENAC), specifically in the Institute of Civil Engineering (IIC) and the Environmental Hydraulics Laboratory (LHE). He also holds teaching appointments in SGC-Teaching and EDME-Teaching departments at EPFL. His office is located at GC A1 401, Station 18, 1015 Lausanne, Switzerland. Dr. Ancey holds both a PhD and engineering degree from Ecole Centrale de Paris and Grenoble National Polytechnic Institute. After completing his doctoral work (1994-1997) on rheology of granular flows under Pierre Evesque, he worked as a researcher at Cemagref before joining EPFL in 2004. He directs the Environmental Hydraulics Laboratory and serves as associate editor for Water Resources Research, a leading journal in hydrology. Dr. Ancey's research spans fluid dynamics, rheology, and hydraulics with emphasis on geophysical flows and natural hazards. His work focuses on three interconnected themes: rheology of concentrated suspensions (particularly granular flows in avalanches and mudflows), inverse problems in rheology (determining microscopic behavior from macroscopic measurements), and particle entrainment in turbulent suspensions (erosion and sediment transport processes). His recent publications (2022-2025) demonstrate continued innovation in sediment transport modeling, experimental techniques like PIV for complex flows, and integration of machine learning approaches. The research shows progression from fundamental fluid mechanics to practical applications in natural hazard assessment and river engineering, with particular attention to granular segregation, avalanche-obstacle interactions, and river morphodynamics. Associate Editor, Water Resources Research Co-founder of Toraval, an engineering consulting firm specializing in avalanche risk management Dr. Ancey teaches courses on Fluid Mechanics, Flood and Dam Break Waves, Hydrological Risks and Structures, and Similarity and Transport Phenomena in Fluids. He has supervised numerous PhD students whose work spans environmental hydraulics, granular flows, and sediment transport, with current students including Chen Yanan, Farazande Sofi, and Giboulot Axel Loïc among others.
Prof. Christian Heipke is a distinguished academic serving as Dean of the Faculty of Civil Engineering and Geodetic Science at Leibniz University Hannover, Germany. He also holds the position of Executive Director at the Institute of Photogrammetry and GeoInformation (IPI), one of the leading research institutions in geospatial sciences within the faculty. His leadership extends across multiple committees including the Curriculum and Teaching Committee, Admissions and Examination Boards for Geodetic Science and Geoinformatics, and Navigation and Environmental Robotics. As a Professor at IPI, he maintains active research while overseeing significant academic and administrative responsibilities at the university. Professor Heipke's research spans multiple domains within geospatial sciences, with particular emphasis on: Advanced photogrammetric techniques and algorithms Remote sensing applications for environmental monitoring Computer vision approaches for geospatial data analysis Urban development monitoring using satellite imagery Machine learning applications in geoinformatics Disaster prediction and management systems His recent scholarly output reveals a strong focus on integrating cutting-edge computer vision and deep learning techniques with traditional photogrammetric methods. Analysis of his 15 most recent publications shows a clear trajectory toward more sophisticated AI-driven approaches for processing geospatial data, with particular attention to time-series analysis, uncertainty quantification, and multi-view systems. His work bridges theoretical advancements with practical applications in flood forecasting, deforestation monitoring, urban planning, and construction materials analysis. The geographic scope of his research has expanded significantly, with recent projects focusing on international case studies in the Philippines and tropical regions. Professor Heipke leads the Institute of Photogrammetry and GeoInformation, a major research hub that has celebrated 75 years of contributions to the field. His leadership extends to the Graduiertenkolleg 2159: "Integrity and Collaboration in Dynamic Sensor Networks," where he serves as a professor overseeing doctoral research. The institute maintains state-of-the-art facilities for processing satellite imagery, aerial photography, and developing novel algorithms for geospatial data analysis. Under his direction, the institute has strengthened its international collaborations and interdisciplinary research approaches, particularly in addressing Sustainable Development Goals through geospatial technologies.
Devis Tuia serves as Associate Professor at the Swiss Federal Institute of Technology Lausanne (EPFL), holding appointments in the Institute of Environmental Engineering (IIE) within the School of Architecture, Civil and Environmental Engineering (ENAC). He leads the Environmental Computational Science and Earth Observation Laboratory (ECEO) since 2020 and contributes to EPFL's Doctoral Program in Civil and Environmental Engineering. His academic journey began in Lausanne with studies at UNIL and EPFL, culminating in a PhD in remote sensing from UNIL. Postdoctoral research followed at institutions in Valencia, Boulder, and EPFL, focusing on machine learning model adaptation. He progressed from Research Assistant Professor at University of Zurich to Associate and Full Professor at Wageningen University before joining EPFL. Tuia's research bridges Earth observation with artificial intelligence, specializing in interpretable deep learning for environmental applications. His lab develops algorithms for making remote sensing accessible, with particular emphasis on digital wildlife conservation through automated censuses using drone and satellite imagery. Current projects tackle the 'black box' problem in environmental modeling while advancing spatial intelligence for sustainable urban development. His 2023-2025 publication portfolio reveals three dominant trends: (1) species distribution modeling using incomplete observations, (2) multimodal fusion of satellite/drone data with textual descriptions, and (3) interpretable AI frameworks for environmental decision-making. This work consistently addresses real-world challenges like wildfire forecasting and biodiversity monitoring. As an educator, Tuia supervises 12 current PhD students and has graduated 4 former EPFL doctoral candidates. His teaching portfolio includes Frontiers of Deep Learning for Engineers , Sensing and Spatial Modeling for Earth Observation , and Image Processing for Earth Observation courses. The ECEO laboratory maintains active collaborations with ESA-NASA initiatives and conservation organizations globally.
Zaklina Spalevic is a distinguished Professor at Singidunum University, specializing in the intersection of law, technology, and business. With a strong academic background in criminal law and extensive research in cyber law, artificial intelligence applications in legal systems, and tourism law, she has established herself as a leading scholar in digital legal frameworks. Her work spans multiple disciplines, connecting traditional legal principles with emerging technological challenges. Dr. Spalevic earned her doctoral degree in Criminal Law from the University of the Academy of Economics in Novi Sad (2008-2012), following basic studies in General Law at the University of Pristina (1996-2001). Her educational foundation in Science and Mathematics from First Pristina High School (1992-1996) provided the analytical basis for her interdisciplinary approach to legal scholarship. Her research interests focus on the evolving landscape of cyber law, particularly the legal implications of artificial intelligence in judicial systems, electronic governance, and tourism industry regulation. Dr. Spalevic has pioneered work on digital evidence processing, algorithmic justice, and the integration of green law principles into sustainable business practices. Her publications reveal a consistent pattern of addressing emerging legal challenges in the digital age, with increasing emphasis on AI applications across various legal domains. Analysis of her recent publications shows a clear trend toward interdisciplinary research that bridges law, technology, and business. Her work increasingly focuses on practical applications of legal frameworks for emerging technologies, particularly in tourism, agriculture, and judicial systems. The integration of technical solutions with legal regulation represents a distinctive feature of her scholarly contributions. Dr. Spalevic has authored numerous publications including three books: 'Fundamentals of Law with Special Reference to Business and Tourism Law' (2021, 2024 editions) and 'Legal Aspects of Cyberspace' (2018). Her extensive publication record includes over 100 journal articles and conference papers spanning cyber security, electronic governance, intellectual property in digital environments, and legal frameworks for emerging technologies. She actively contributes to academic discourse through conference presentations and collaborative research projects, particularly focusing on the legal implications of artificial intelligence across various sectors. Her work demonstrates a commitment to developing practical legal frameworks that address real-world technological challenges while maintaining foundational legal principles.
Dr. Yang Liu is the Chair and Gangarosa Distinguished Professor in the Gangarosa Department of Environmental Health at Emory University's Rollins School of Public Health. His research focuses on satellite remote sensing applications for public health, climate change impacts, and air pollution exposure modeling. He leads federally funded projects integrating satellite data into health studies and serves as a PI for the NIH-funded Climate & Health Actionable Research and Translation (CHART) Center. Education: Ph.D. (Harvard University), M.S. (University of California), B.S. (Tsinghua University). Professional Affiliations include NASA mission science teams and the ORISE Faculty Fellowship at CDC. Research interests span satellite aerosol retrieval, climate-health linkages, and machine learning in environmental statistics. Notable work includes advancing wildfire smoke health impacts, PM2.5 modeling, and climate dashboard development for Georgia. Awards include Gangarosa Distinguished Professorship, ORISE CDC Fellowship, and leadership in NIH/NASA initiatives. His lab develops innovative tools for environmental health monitoring and policy translation.
Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .
Beverley J. McKeon is a Professor of Mechanical Engineering at Stanford University, previously holding the Theodore von Kármán Professorship in Aeronautics at Caltech. Her research focuses on fluid mechanics, particularly turbulence, flow control, and boundary layer dynamics. She earned her B.A. and M.Eng. from the University of Cambridge, and her Ph.D. from Princeton University. McKeon's work integrates experimental and theoretical approaches to manipulate wall-bounded flows for drag reduction and performance enhancement. Her research interests include resolvent analysis, high Reynolds number turbulence, and the application of machine learning to fluid dynamics. She has led interdisciplinary projects on morphing surfaces and viscoelastic turbulence. Awarded the Vannevar Bush Faculty Fellowship and PECASE, McKeon has been recognized for her teaching and mentoring. Her honors include Fellowships from the APS and AIAA. She chairs editorial boards for journals like Physical Review Fluids and has served on national committees for theoretical and applied mechanics. Her academic leadership includes roles as Deputy Chair of Caltech’s Division of Engineering and Applied Science and Associate Director of GALCIT. She advises numerous students and collaborates globally on initiatives like the Stories of Women in Fluids.
Dr. Carolyn Conner Seepersad is a Woodruff Professor in the George W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology. She leads the Digital Design and Manufacturing research group and previously founded the Center for Additive Manufacturing and Design Innovation at The University of Texas at Austin. Her research focuses on additive manufacturing, materials design, and process innovation. She holds editorial roles, including Editor-in-Chief of the ASME Journal of Mechanical Design, and has received numerous awards for research and teaching. Education: PhD, Mechanical Engineering, Georgia Tech, 2004 MS, Mechanical Engineering, Georgia Tech, 2001 BA, Philosophy, Politics, and Economics, Oxford University, 1998 BS, Mechanical Engineering, West Virginia University, 1996 Her research interests span design for additive manufacturing, simulation-based materials and structures, and metamaterials. She emphasizes manufacturing-aware design and sustainability. Key contributions include lattice structure optimization, negative stiffness composites, and process-aware manufacturing techniques. Her publications reflect advancements in additive manufacturing processes, materials characterization, and design methodologies. Awards include the ASME Design Automation Award and recognition as a University of Texas System Academy of Distinguished Teachers. Seepersad has advised on grants such as the LEAP-HI GOALI project and contributed to initiatives like the Solid Freeform Fabrication Symposium. Her work bridges academia and industry, emphasizing practical applications and innovation. Labs/Teams: Leads the Digital Design and Manufacturing group at Georgia Tech, previously directed the UT Austin Additive Manufacturing Center.
Padhraic Smyth is a Distinguished Professor and Hasso Plattner Endowed Chair in Artificial Intelligence at the University of California, Irvine (UCI), holding joint appointments in the Department of Computer Science and Department of Statistics. He leads the DataLab research group, focusing on machine learning, AI, and their applications in climate science, healthcare, and education. His research spans probabilistic modeling, deep learning, and human-AI collaboration. Education: PhD in Electrical Engineering from the California Institute of Technology (1988), MSEE (1985), and BEng (1984). Prior to UCI, he worked at NASA's Jet Propulsion Laboratory (1988–1996). Research Interests: Machine learning, AI, pattern recognition, Bayesian methods, climate science applications, algorithmic fairness, and human-AI interaction. He has published over 200 papers and co-authored textbooks like Modeling the Internet and the Web . Awards: ACM Fellow, IEEE Fellow, AAAI Fellow, AAAS Fellow, and ACM SIGKDD Innovation Award recipient. He has held leadership roles in UCI's Center for Machine Learning and Data Science. Key Projects: Human-AI collaboration frameworks, robustness in deep learning, climate modeling using spatio-temporal data, and AI fairness with missing attributes. Collaborates with institutions like NASA and industry partners (e.g., Google, eBay). Labs/Teams: Director of UCI’s Data Science Initiative and HPI Research Center in Machine Learning. Supervises a vibrant PhD program with over 30 alumni in academia and industry.
Matthew B. Blaschko is a Professor in the Department of Electrical Engineering at KU Leuven, Belgium. He serves as director of the KU Leuven ELLIS unit and is a fellow in the ELLIS Health program. He is a Core PI in the Flanders AI Research Program, working as a workpackage lead for Decision Support Systems and Medical Imaging. Blaschko is also a member of the KU Leuven Institute for Artificial Intelligence and one of the leaders of the working group on Machine Learning and Data Science. Professor Blaschko received his B.S. from Columbia University, M.S. from the University of Massachusetts Amherst, and Dr. rer. nat. from Technische Universität Berlin (awarded for work at Max Planck Institutes Tübingen). He was a Newton International Fellow at the University of Oxford and received his Habilitation (HDR) from École Normale Supérieure de Cachan. Prior to joining KU Leuven, he was a Permanent Research Scientist in the INRIA Saclay Research Center and a Faculty Member at Ecole Centrale Paris. His research focuses on machine learning techniques applied to visual data, with particular emphasis on calibration in deep learning, medical image analysis, and federated learning. Blaschko's work bridges theoretical foundations with practical applications, as evidenced by technology developed in his research being incorporated into MONA, software for ophthalmic image analysis. His research group has made significant contributions to the fields of model calibration, uncertainty estimation, and medical imaging analysis, with recent publications showing strong trends toward improving reliability of AI systems in medical contexts and advancing theoretical understanding of calibration metrics. Professor Blaschko has been recognized with several awards including the Université Paris-Saclay STIC Doctoral School Best Scientific Contribution Award, Best Paper Award at CVPR 2008, Main Award at DAGM 2008, and Best Student Paper Award at ECCV 2008. Professor Blaschko has supervised numerous PhD and Master's students, with current and former students including Deniz Soysal, Claire Marchal, Dongli Xu, Sebastian Gruber, Jiameng Li, Marco Mezzina, and many others working on diverse topics from Alzheimer's disease analysis to surgical phase recognition. His research has been supported by various funding sources including the Flanders AI Research Program. He has co-organized several influential workshops including the "Another Brick in the AI Wall: Building Practical Solutions from Theoretical Foundations" at CVPR 2025, Commands 4 Autonomous Vehicles workshop at ECCV 2020, and the Learning from Limited Labeled Data workshop series at NIPS 2017 and ICLR 2019. His laboratory focuses on machine learning for medical image analysis, with applications in ophthalmology, neurology, and surgical robotics. The group maintains active collaborations with medical institutions and participates in international challenges such as the KNee OsteoArthritis Prediction (KNOAP2020) challenge.
