Dr. Vahid Hosseini is an Associate Professor and Graduate Program Chair in the School of Sustainable Energy Engineering at Simon Fraser University (SFU). His research focuses on sustainable energy systems, urban air pollution, and clean mobility solutions. He holds a Ph.D. in Mechanical Engineering from the University of Alberta (2008), and M.A.Sc. and B.Eng. degrees from Sharif University of Technology (Iran). His academic roles include leadership in graduate academic programs and engineering education. Key research areas include thermo-fluid systems analysis, vehicle emissions reduction, and urban air quality modeling. He is actively involved in projects addressing real-world driving emissions, emission inventory development, and the impact of cold climates on transportation energy consumption and pollution. Notable contributions include studies on retrofit emission control devices for motorcycles, high-emitter vehicle identification, and policy recommendations for emission reduction. His work integrates experimental methods, computational fluid dynamics (CFD), and machine learning to tackle complex environmental challenges. Teaching interests span thermodynamics, fluid mechanics, and air pollution control engineering. Current courses include SEE 325 D100 Mechanical Design and Finite Element Analysis . Research highlights include collaborations on Tehran’s air quality management, particulate matter (PM2.5) source apportionment, and the development of high-resolution emission inventories. He contributes to international conferences and journals, with a focus on practical solutions for sustainable urban transportation systems.
Dae-Jin Lee is an Assistant Professor at IE University’s School of Science and Technology, specializing in statistical modeling and data science. Previously, he served as a Research Line Leader at the Basque Centre for Applied Mathematics (BCAM) and coordinated the Knowledge Transfer Unit in Data Science/AI. His academic background includes a Ph.D. in Mathematical Engineering (2010) from Universidad Carlos III de Madrid and postdoctoral research at CSIRO (Australia). His research focuses on statistical methods for complex data, including penalized splines, tensor product smooths, and applications in biomedicine, epidemiology, environmental science, and sports analytics. He has led multidisciplinary projects funded by public and industry grants, collaborating globally with experts across fields like engineering, medicine, and biology. Key research themes include predictive modeling for health outcomes (e.g., SARS-CoV-2 pneumonia severity), sports injury prevention, and AI in healthcare. His work integrates machine learning with traditional statistical techniques, addressing real-world challenges like pedestrian dynamics simulations and automated medical diagnostics. He is actively involved in scientific organizations, including the Spanish Biostatistics Society and the Statistical Modelling Society. His recent publications highlight innovations in growth curve modeling, AI ethics, and spatiotemporal data analysis, reflecting his commitment to advancing both theoretical and applied statistics.
Dr. Sheng Yang is an Assistant Professor in the School of Engineering at the University of Guelph. He leads the Design Innovation and Intelligent Manufacturing (DIIM) lab, focusing on advancing additive manufacturing, generative design, and smart manufacturing technologies. His research integrates IoT, big data analytics, and bio-inspired design to address challenges in aerospace, green energy, and healthcare. Key areas include computational design for additive manufacturing, data-driven mass customization, and digital twin-based optimization. Education: Ph.D. in Mechanical Engineering from McGill University (2019), followed by a Postdoctoral Fellowship at McGill (2019–2020). Joined University of Guelph in 2020. Research interests span energy efficiency, complex system optimization, and personalized healthcare products. Recent work emphasizes digital twin synchronization in robotics, machine learning for quality prediction, and sustainable additive manufacturing processes. Notable awards include the 2019 Association of Commonwealth Universities Blue Charter Fellowship and 2018 ASME Best Paper Award. His lab actively seeks partnerships in personalized healthcare, product design, and smart manufacturing. Grants and collaborations focus on advancing manufacturing technologies and sustainability. No formal advisees listed, but active in graduate training through lab projects. The DIIM lab explores cutting-edge solutions for industrial and societal challenges through interdisciplinary approaches.
Meeyoung Cha is a Professor at KAIST and Scientific Director of the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany. Her research focuses on Data Science for Humanity, encompassing computational social science, misinformation dynamics, and human-machine interaction. She holds a PhD in Computer Science from KAIST (2008) and previously served as Chief Investigator at the Institute for Basic Science and Visiting Professor at Facebook. Her work addresses societal challenges such as poverty mapping, fraud detection, and AI ethics. Key achievements include best paper awards and recognition like the Hong Jin-Ki Creator Award (2024) and Test-of-Time Awards (ACM IMC 2022, AAAI ICWSM 2020). Research interests span AI ethics, social media analysis, and interdisciplinary applications of machine learning. Notable projects include modeling climate risks via satellite imagery and analyzing chatbot interactions' societal impacts. She leads the MPI-SP's Data Science for Humanity Group, mentoring over 20 students across PhD and postdoc programs. Education: PhD in Computer Science (KAIST, 2008) Affiliations: MPI-SP (Germany), KAIST Key Awards: Hong Jin-Ki Creator Award, Korean Young Information Scientist Award, Test-of-Time Awards Her publications bridge computational methods with societal issues, including climate modeling, protein engineering, and algorithmic fairness. Current projects explore geospatial AI for economic development and ethical AI design frameworks.
Dr. Jing Wang is a Professor in the Department of Bioinformatics at Southern Medical University's School of Medicine, with extensive research at the intersection of artificial intelligence and biomedical applications. Her work demonstrates strong cross-disciplinary collaboration across medical institutions, engineering departments, and computer science research groups. Her primary research interests include Artificial Intelligence in Healthcare , Biomedical Engineering , and Traditional Chinese Medicine Informatics , with recent publications showing particular expertise in medical imaging analysis, diagnostic assistance systems, and clinical decision support. Her work spans both theoretical algorithm development and practical clinical implementations. Analysis of her 15 most recent publications (2025-2026) reveals a strong trend toward clinically applicable AI systems, with approximately 60% of publications focused on medical diagnostics and treatment support systems. The remaining publications demonstrate expertise in industrial applications of computer vision and fundamental AI research. Her work shows consistent collaboration with both domestic Chinese institutions and international research groups. Notable scientific contributions include: Development of 'Tianyi', a traditional Chinese medicine language model for clinical practice Innovations in bionic soft robotics for rehabilitation assistance Novel approaches to medical image analysis for cancer diagnostics Her research program appears well-funded with consistent publication output across high-impact journals in biomedical engineering, AI, and medical informatics. Current work suggests strong emphasis on translating AI research into clinical practice, particularly in diagnostic support systems and rehabilitation technology.
Ruoqing Zhu is an Associate Professor in the Department of Statistics at the University of Illinois at Urbana-Champaign, with a primary appointment in the College of Liberal Arts & Sciences. He also serves as an inaugural member of the Carle Illinois College of Medicine, a Faculty Fellow at the National Center for Supercomputing Applications, and an affiliated researcher with the Carl R. Woese Institute for Genomic Biology and the Center for Genomic Diagnostics. His roles include PhD Program Director and Advisory Board member of Prenosis Inc. Dr. Zhu holds a Ph.D. in Biostatistics from the University of North Carolina at Chapel Hill (2013), an MA in Statistics from Bowling Green State University (2008), and dual B.S. degrees in Mathematics and Financial Engineering from Nanjing University (2006, 2005). His postdoctoral training was at Yale University’s Department of Biostatistics (2013–2015). His research focuses on developing statistical methods for decision-making in personalized medicine and reinforcement learning, addressing challenges such as model interpretability, high-dimensional data, and distributional shifts. Key areas include uncertainty quantification, causal inference, and applications in bioinformatics, nutrition, and infectious diseases. He co-teaches courses at Carle Illinois, including Data Science Project and Foundations: Molecules to Populations , and contributes to interdisciplinary initiatives like the Personalized Nutrition Initiative. His recent work emphasizes trustworthy AI in healthcare, including sepsis prediction tools, metabolomic analysis, and biomarker discovery. He is actively involved in translational research, bridging computational methods with clinical and public health applications.
Professor Elspeth Guild holds a PhD and is a Professor of Law at the University of Liverpool, affiliated with both the School of Law and Social Justice and the Faculty of Humanities and Social Sciences. Her research focuses on EU migration law, human rights frameworks, and the legal implications of Brexit. She has published extensively on topics such as Frontex operations, post-pandemic governance dynamics, and transnational legal challenges. Recent work includes analyses of Ireland’s opt-in clauses in EU asylum policies and the Marrakesh Compact’s institutional mechanisms. Her academic contributions span journals like European Journal of Migration and Law and European Law Journal, with notable chapters in edited volumes addressing pandemic governance and academic freedom debates. Guild’s expertise bridges legal scholarship with policy analysis, particularly in areas of border control, sovereignty disputes, and international human rights.
Matthew Gandy is a Professor of Cultural and Historical Geography at the University of Cambridge and a Fellow of King’s College. He holds affiliations with the Infrastructural Geographies and Vital Geographies research groups. His work spans urban metabolism, epidemiology, critical landscape studies, and cinematic methodologies. Gandy earned a BA (First Class) from St. Catharine’s College, Cambridge, and a PhD in Geography from the London School of Economics. His research explores intersections of nature, culture, and infrastructure in cities like Mumbai, Berlin, and Lagos. Notable contributions include The Fabric of Space (2014) and Natura Urbana (2022), award-winning works analyzing urban water systems, biodiversity, and ecological constellations. He has held visiting roles at institutions including Columbia University and UCLA, and founded the UCL Urban Laboratory (2005–2011). Key honors include the AAG Meridian Award (2014), British Academy Fellowship (2016), and John Brinckerhoff Jackson Prize (2023). Gandy’s films, such as Liquid City (2007) and Natura Urbana (2017), blend documentary techniques with academic research. He is actively involved in urban biodiversity initiatives, including Hackney Biodiversity Partnership. Teaching focuses on global urbanism, cultural landscapes, and the Anthropocene. Editorial roles include co-editing The International Journal of Urban and Regional Research (2012–2018). His work addresses urban political ecologies, multispecies urbanism, and the socio-cultural dimensions of urban environments.
Tridas Mukhopadhyay is the Deloitte Consulting Professor of e-Business at Carnegie Mellon University's Tepper School of Business, where he has served on the faculty since 1986. His academic journey at CMU progressed from Instructor of Information Systems (1986-1987) to Assistant Professor (1987-1993), Associate Professor (1993-1997), Professor (1998-present), and Deloitte Consulting Professor of e-Business (2000-present). He also served as Director of the MS in Electronic Commerce program from 1999-2004. Ph.D. in Computer and Information Systems, University of Michigan–Ann Arbor, 1987 M.B.A. in Computer and Information Systems, Indian Institute of Management Calcutta, 1981 B. Tech. in Electrical Engineering, Indian Institute of Technology Kharagpur, 1978 Professor Mukhopadhyay's research spans multiple critical areas in information systems and technology management. His work on strategic IT use examines how organizations derive business value from information technology investments. He has conducted extensive research on business-to-business commerce, particularly focusing on e-procurement systems, web-based marketplaces, and electronic intermediation models. His cybersecurity research investigates the economic aspects of cyber security, including liability mechanisms and patch release strategies. In software engineering, he has studied productivity, quality metrics, and offshore software development contracts. His most recent publications reveal several key trends in his research trajectory. There's a growing focus on digital platform economics, examining advertising models, virtual currency systems in gaming, and sharing economy dynamics. His work increasingly incorporates behavioral aspects, studying how users respond to personalized content and how backers exert control in crowdfunded projects. Methodologically, his research employs sophisticated analytical approaches including hierarchical Bayesian models, structural equation modeling, and natural experiment designs. CART Research Frontier Award, Carnegie Mellon, 2005 Distinguished Ph.D. Alum, Michigan Business School, 2004 Best Paper, International Conference on Information Systems, 2001 Best Paper, MIS Quarterly, 1995 Xerox Research Chair, Tepper School of Business, 1988-1989 Information Systems Society Distinguished Fellow, 2012 Professor Mukhopadhyay has served on numerous editorial boards including Information Systems Research (1994-2003), Management Science (1999-2003), and MIS Quarterly (1997-1999), demonstrating his significant contributions to the field. His consulting work with major organizations including Alcoa, Chrysler, Ford, General Motors, IBM, and governmental agencies like the United States Post Office and Pennsylvania Turnpike has provided practical insights that inform his academic research. He has been actively involved in university governance through committee service including the Business Technology Faculty Search Committee and the CMU Faculty Senate. His research has been supported through various industry partnerships and academic grants, though specific grant details aren't provided in the source material. His teaching focuses on Business Computing and Strategic IT courses, reflecting his expertise in both foundational information systems concepts and strategic applications of technology in business contexts.
Dr. Jingyun Wang is an Assistant Professor in the Department of Computer Science at Durham University. Previously, she held an Assistant Professor position at Kyushu University, Japan. Her primary affiliations include the Centre for Neurodiversity & Development and the Artificial Intelligence and Human Systems Group (AIHS), as well as the Pedagogical Innovation in Computer Science Group (PICS). She is a Fellow of the Higher Education Academy and has led or contributed to research projects funded by JSPS, JST, NICT, Innovate UK, and industry partners. Her research focuses on AI-driven educational technologies, including AI-based feedback systems, computational thinking education, game-based learning, and ontology techniques. She actively contributes to editorial boards (e.g., Computers & Education: Artificial Intelligence ) and serves as a conference chair for AIED, ICCE, and LTLE. Current research includes adaptive learning systems for mathematics education, serious games for cybersecurity training, and visualization tools for e-learning. Her scientific contributions span over 50 peer-reviewed publications, with recent work emphasizing learning analytics, multimodal systems, and digital health interventions. She advises multiple PhD students and mentors in professional recognition pathways. Key projects include developing the BETTER speech training system, the MEMORABLE cybersecurity game framework, and ontology-based language learning platforms.
Elias G. Carayannis is a Full Professor of Science, Technology, Innovation, and Entrepreneurship at the George Washington University School of Business (GWSB), Washington, D.C. He serves as Director of Research on Science, Technology, Innovation, and Entrepreneurship at the European Union Research Center (EURC) and co-founder/co-director of the Global and Entrepreneurial Finance Research Institute (GEFRI). His academic journey includes a B.S.C.E.E. from the National Technical University of Athens (1985), M.B.A. and Ph.D. in Management of Technology from Rensselaer Polytechnic Institute (1990, 1994). His research focuses on strategic Government-University-Industry partnerships, technology transfer/commercialization, international science policy, and regional economic development. He pioneered the Quintuple Innovation Helix framework, integrating Government, University, Industry, Civil Society, and Environment into innovation ecosystems. Carayannis has authored over 100 academic papers and 16 books with Springer, Edward Elgar, and others, and serves as editor-in-chief for multiple journals and book series. Professionally, he advises global entities like the World Bank, European Commission, and U.S. agencies on innovation and technology strategies. He is fluent in English, French, German, and Greek, with Spanish proficiency. His work emphasizes AI-driven innovation, Industry 5.0/Society 5.0 transitions, and fusion energy policy. He delivers keynote speeches globally on innovation frameworks and sustainable development. Key institutions affiliated include GWSB, EURC, GEFRI, and visiting roles at Jönköping School of Entrepreneurship (Sweden) and Grenoble Ecole de Management (France). His contributions span policy design, entrepreneurship ecosystems, and interdisciplinary approaches to global challenges like climate change and digital transformation.
Dr. Chelsea Fisher is Assistant Professor of Anthropology at the University of South Carolina, where she researches environmental justice, traditional ecological knowledge, and sustainable food systems. She directs the Yaxunah Haciendas and Lands Archaeological Project (PAHTY) in Yucatán, Mexico, collaborating with Maya community members to study colonial ranching sites and contemporary land relations. Education: PhD Anthropology - University of Michigan (2019) MA Anthropology - University of Michigan (2014) BA Archaeology - The College of Wooster (2011) Her interdisciplinary research bridges archaeology, environmental studies, and food systems. Fisher's 2023 book Rooting in a Useless Land examines colonial narratives, celebrity chef interventions, and Maya environmental justice. She has received numerous awards including the Louise Ann Williams Dissertation Award and multiple teaching honors. Research funding includes grants from Wenner-Gren Foundation, National Science Foundation, and U.S. Department of Education.
Vassilios Tzerpos is an Associate Professor at the Lassonde School of Engineering, York University, where he has been since 2001. He holds a Ph.D. in Computer Science from the University of Toronto (2001). His research focuses on audio processing for musical applications, deep learning, digital signal processing, machine listening, and software engineering education. He directs the APTLY lab exploring music-technology intersections and leads the LaSSoftE lab developing socially-oriented software solutions. Education: Ph.D. in Computer Science, University of Toronto, 2001 Research Highlights: Dr. Tzerpos' work spans music information retrieval (e.g., automatic music classification), synthetic speech detection using neural networks, and software engineering pedagogy. His recent projects include Music-STAR for audio re-instrumentation and OER-based learning path creation systems. He has pioneered methods in design pattern detection and software clustering evaluation. Grants & Labs: Leads two research groups: APTLY (music-tech) and LaSSoftE (social impact software). Active in developing adaptive cybersecurity solutions against DoS attacks and refining software architecture recovery techniques. Key Themes in Publications: Recent work emphasizes machine learning applications in music technology and cybersecurity, with foundational contributions to software clustering methodologies and design pattern detection algorithms. His work bridges theoretical computer science with practical applications in education and creative industries.
FANG Yuan is a tenured Associate Professor at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU). He holds the prestigious Lee Kong Chian Fellowship and leads research in artificial intelligence and data science. His institutional affiliation includes: School of Computing and Information Systems, Singapore Management University Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (2014) Bachelor of Computing (First Class Honors), National University of Singapore (2009) - Top student in Computer Science Research Focus: Dr. FANG specializes in data mining, machine learning, and AI with emphasis on graph learning, information networks, recommendation systems, and knowledge graph applications. His work bridges theoretical foundations with practical applications in social analytics, biomedical informatics, and digital transformation, often employing advanced neural network architectures. Publication Trends: Recent works (2024-2025) demonstrate strong focus on graph machine learning innovations, including graph foundation models, prompt-based learning for dynamic graphs, and LLM-graph integrations. Key themes include few-shot/zero-shot learning, non-homophilic graph processing, and applications in recommendation systems, bioinformatics, and NLP. Methodological advancements frequently involve contrastive learning, transformer architectures, and explainable AI techniques. Awards & Honors: Lee Kong Chian Fellow World's Top 2% Scientist (2024) by Stanford/Elsevier #1 Most Influential Paper at WWW'23 (GraphPrompt) - Paper Digest (2024-09) Top 5 Most Influential Papers at WWW'23 (GraphPrompt) - Paper Digest (2024-05) Top Computer Science Graduate, NUS (2009) Student Advising: Currently advises doctoral candidates including DONG Viet Hoang, LIU Ran, and NIU Yudong. Recently supervised Dr. Zhongzhou Liu's successful PhD defense (2024) on trustworthy recommendation systems. Professional Engagement: Regularly organizes tutorials at premier venues (WWW, KDD) and delivers invited talks internationally on graph learning advancements. Leads multiple research projects in collaboration with industry partners.
Bo Wu is an Associate Professor in the Department of Computer Science at Colorado School of Mines. His research focuses on compilers and programming systems, particularly program optimizations for heterogeneous computing and emerging architectures, with applications in machine learning and graph processing. He joined Mines in 2014 after earning a Ph.D. from The College of William and Mary and earlier degrees from Central South University in China. Education : B.S. in Computational Science and Technology (Central South University, 2005) M.S. in Computer Science (Central South University, 2008) Ph.D. in Computer Science (The College of William and Mary, 2014) Research Interests : Wu's work emphasizes enhancing data locality in heterogeneous systems, GPU scheduling, and optimizing applications for emerging architectures. His contributions include frameworks like GraphZero for efficient graph mining and FLEP for GPU preemption. Awards & Grants : NSF SPX Award (2018) NSF CAREER Award (2018) Supercomputing Best Paper Award (2015) Multiple NSF grants for GPU-related research Advising & Grants : Wu has led several NSF-funded projects and actively participates in conference program committees (e.g., PPoPP, SC, ICS). His research spans compiler optimizations, parallel computing, and high-performance systems. Labs & Teams : While specific labs aren’t named, his work involves collaborations on GPU-based systems, graph processing frameworks, and compiler toolchains.
