Professor Maia Angelova is a leading academic in data science and mathematical physics at Aston University's Aston Digital Futures Institute (ADFI) and College of Engineering and Physical Sciences. Her research focuses on interdisciplinary AI applications in healthcare, including precision medicine, chronic disease modeling, and athlete performance analytics. She previously held roles as Professor of Data Analytics at Deakin University (2017–2023) and Professor of Mathematical Physics at Northumbria University (1997–2016), with early experience as a College Lecturer at Oxford University (1991–1996). Education: PhD, MSc, and BSc in Physics from Sofia University 'St. Kliment Ohridski'. Research interests span AI-driven healthcare solutions, dynamical systems modeling, and sports performance analysis. Her work addresses sleep disorders, diabetes management, chronic pain, and athlete performance using advanced machine learning and data analytics. She has secured over £5M in research funding and supervised over 30 PhD students and postdoctoral researchers. Awards include Fellowship of The Institute of Physics. Professional memberships include The London Mathematical Society, Australian Mathematical Society, and Complex Systems Society. Key achievements include founding the Data to Intelligence research centre (2018–2020) and leading large-scale interdisciplinary projects. Current initiatives focus on precision healthcare through AI integration in clinical decision-making systems.
Smita Ghosh is an Assistant Professor in the Department of Mathematics and Computer Science at Santa Clara University, part of the College of Arts and Sciences. Her research focuses on social network analysis, algorithms for information diffusion, and applications in cybersecurity, disaster management, and machine learning. She holds a B.Tech. from the West Bengal University of Technology, India, and an M.S. and Ph.D. from the University of Texas, Dallas. Her work addresses challenges in rumor containment, clickbait detection, and optimizing network models for social media content analysis. Recent publications include studies on hypergraph-based solutions for rumor blocking and stochastic models for emergency response in social networks. She also explores cross-modal topic modeling for enhancing content detection algorithms. Notable contributions include developing data-driven strategies for identifying hate speech spreaders and improving wildfire severity predictions using environmental features. Her research bridges theoretical computer science with real-world applications in public health, education, and disaster management. Her academic contributions include organizing conference proceedings like the 18th International Conference on Algorithmic Aspects in Information and Management (AAIM 2024). She actively contributes to educational initiatives such as the Classroute project, creating multilingual educational content for Punjabi and Urdu speakers.
Samantha King is a Professor in the Department of Kinesiology and Health Studies at Queen's University. She holds a PhD from the University of Illinois at Urbana-Champaign. Her interdisciplinary research integrates feminist, queer, and critical race theories to examine the intersections of health, sport, and the body. She leads the King Research Group, based in KINE 206J & 301V offices. King's research interests span topics such as drug cultures, food politics, and the sociology of sport. Her influential work includes the book Pink Ribbons, Inc: Breast Cancer and the Politics of Philanthropy , adapted into an NFB documentary. She explores themes like pharmaceutical use in professional sports, the ecological dimensions of embodiment, and multispecies approaches to sport studies. Her recent publications analyze racial capitalism in NFL pain management, pandemic-era human-animal relations, and the sociological implications of whey protein consumption. She has co-edited volumes on food ethics and bodily embodiment, reflecting her commitment to interdisciplinary dialogue. Her work bridges cultural studies, sociology, and health studies, addressing contemporary issues in sport, medicine, and everyday life. King’s academic contributions include over 30 peer-reviewed articles and edited collections. She has received international attention for critiquing corporate philanthropy in health activism and challenging normative frameworks in sport studies. Her current projects explore multispecies methodologies and the political economies of meat production.
John R Anderson is the Richard King Mellon University Professor of Psychology and Computer Science at Carnegie Mellon University (CMU), affiliated with the Department of Psychology within the Dietrich College of Humanities and Social Sciences. His research focuses on understanding higher-level cognition, particularly mathematical problem-solving, through the development of the ACT-R cognitive architecture—a computational framework simulating human cognitive processes. This architecture integrates behavioral, neural, and educational data to model learning and decision-making. Anderson’s work bridges cognitive science, neuroscience, and educational technology. He investigates how brain imaging (e.g., fMRI, EEG) can reveal the temporal dynamics of cognitive processes and improve instructional methods. His research emphasizes analyzing brain activity time courses to uncover underlying mechanisms of problem-solving and skill acquisition. Key Research Themes: Cognitive architectures, neural correlates of learning, computational models of memory, and intelligent tutoring systems. Notable Contributions: Development of the ACT-R architecture, integration of neuroimaging with cognitive modeling, and studies on skill transfer and learning strategies. Anderson’s publications include seminal books like Cognitive Psychology and Its Implications and How Can the Human Mind Occur in the Physical Universe? His work has advanced understanding of associative memory, strategic decision-making, and the application of cognitive models in educational technology. His lab, the ACT-R Research Group, collaborates across disciplines to model complex cognitive tasks and their neural foundations. Current projects analyze real-time brain activity to refine educational interventions and improve human-machine interaction.
Xiaolei Fang is Associate Professor in the Edward P. Fitts Department of Industrial and Systems Engineering at North Carolina State University. His research develops advanced statistical learning, deep learning, and optimization methods for industrial applications involving high-dimensional data, with particular focus on condition monitoring, failure prognostics, and system performance optimization. He holds a PhD in Industrial Engineering and MS in Statistics from Georgia Tech. Professor Fang's research integrates machine learning with industrial engineering to solve complex problems in predictive maintenance, quality control, and energy systems. His methodological innovations include federated learning approaches for privacy-preserving prognostics, distributionally robust machine learning models, and tensor-based statistical methods for manufacturing quality diagnostics. He has received multiple prestigious awards including the ISE Outstanding Research Award (2024), Sigma Xi Best PhD Thesis Award (2019), and SAS Data Mining Best Paper Award (2016). His research has been funded by NSF, Cisco Systems, and the US Department of Energy. Professor Fang teaches courses in Quality Design & Control, Statistical Models for Systems Analytics, High-Dimensional Data Analytics, and Optimization Models. He has supervised 9 PhD students to completion and currently advises 7 graduate students working on projects spanning federated learning for prognostics, tensor-based quality control, and machine learning applications in manufacturing and energy systems.
Terese Løvås serves as Vice Dean of Research and Innovation at the Faculty of Engineering, Norwegian University of Science and Technology (NTNU), where she leads strategic development of research and innovation activities. She concurrently holds the position of Professor of Combustion and Thermodynamics within the Department of Energy and Process Engineering. Her leadership responsibilities include oversight of Centers of Excellence, Horizon Europe projects, and PhD researcher training. Her research focuses on combustion engineering and alternative fuel technologies , particularly investigating ammonia and hydrogen combustion for zero-emission engines, biomass gasification processes, and reactive multiphase flow modeling. She heads the Engine Lab at NTNU and teaches Thermodynamics, Heat, and Combustion courses. Her work bridges theoretical modeling with experimental validation in sustainable energy systems. Løvås actively contributes to major research initiatives including LowEmission (SFI center), ACTIVATE (ammonia-powered agricultural vehicles), AMAZE (ammonia zero-emission), and CAHEMA (marine ammonia/hydrogen engines). Her publications reveal strong trends in ammonia combustion chemistry , emissions reduction , and advanced computational modeling for sustainable fuel systems, with increasing focus on nitrogen oxide formation mechanisms and dual-fuel strategies. Member of the Board of Directors, Combustion Institute (2022–present) Joint Editor, Proceedings of the Combustion Institute (2019–present) Alumni Fellow in Engineering, Churchill College, Cambridge University As Vice Dean, she manages NTNU's Research and Innovation Committee and represents the faculty in NTNU's Research and Innovation Committee. She supervises multiple PhD candidates and leads international collaborations through projects funded by the Norwegian Research Council, Nordic Energy Research, and EU programs. Her laboratory work focuses on optical engine diagnostics and advanced combustion testing. Løvås maintains active industry engagement through her leadership in the ComKin Research Group and membership in the Institute of Physics and Scandinavian-Nordic Section of the Combustion Institute. Her current work emphasizes practical implementation of ammonia-fueled engine technologies for marine and agricultural applications.
Adam Jatowt is a Professor and Head of the Data Science group at the Department of Computer Science, University of Innsbruck. He also serves as Deputy Head of the Digital Science Center and Research Center for Digital Humanities. His academic career spans roles at Kyoto University (2010-2020), National Institute of Advanced Industrial Science and Technology (AIST), and visiting positions at Karlsruhe Institute of Technology, University of La Rochelle, and University of California Berkeley. Research interests focus on temporal aspects of NLP/IR, computational history, large language models, and future forecasting. He leads projects combining digital humanities with advanced AI techniques, including temporal validity assessment and hint generation systems. Recent publications (2025) emphasize LLM applications in temporal analysis, QA systems, and energy sector digitalization. His work has been recognized through awards like the Friedrich Wilhelm Bessel Research Award (2024) and top-cited paper distinction in Information Sciences. He actively organizes conferences like ECIR 2026 and Text2Story workshops. Key contributions include developing WikiHint dataset, PlausibleQA framework, and tools like Rankify. His research also addresses societal challenges through ESG rating prediction and medical LLM applications.
Professor Reynold Cheng is a faculty member at the University of Hong Kong (HKU), specifically within the Department of Computer Science in the School of Computing and Data Science (CDS). He currently serves as the Division Head of the AI & Data Science Division at CDS and is part of the Steering Committee of the Musketers Foundation Institute of Data Science. His academic journey includes a BEng and MPhil from HKU (1998–2000) and an MSc and PhD from Purdue University (2003–2005). Prior to HKU, he was an Assistant Professor at the Hong Kong Polytechnic University (HKPU) from 2005 to 2008. Cheng’s research focuses on data science, big graph analytics, and uncertain data management. He has received numerous awards, including the SIGMOD Research Highlights Reward 2020, HKICT Awards 2021, and HKU Knowledge Exchange Award (Engineering) 2021. His work has been recognized through grants such as the HKU-TCL Joint Research Centre for AI-funded project (HKD 1M, 2020–2022) and a CRF-funded project for real-time monitoring of infectious diseases (HKD 6.5M, 2021–2022). Cheng actively contributes to academic service, including serving as PC co-chair for IEEE ICDE 2021 and editorial roles in journals like IS and DAPD. His publications span top venues like SIGMOD, VLDB, and KDD, emphasizing algorithm design for large graphs and probabilistic data systems.
Norman Sadeh is a Professor in the School of Computer Science at Carnegie Mellon University (CMU), where he has made significant contributions to cybersecurity, privacy, and AI research. He has co-founded and co-directed several groundbreaking graduate programs at CMU, including the Privacy Engineering Program (2012-present), the Ph.D. Program in Societal Computing (2003-2013), and the MBA track in Technology Strategy and Product Management (2005-2017). Carnegie Mellon University, School of Computer Science Software and Societal Systems Department CyLab Security and Privacy Institute Manufacturing Futures Institute Dr. Sadeh received his Ph.D. in Computer Science at CMU with a major in Artificial Intelligence and a minor in Operations Research. He holds an M.Sc. in computer science from the University of Southern California and a BS/MS degree in electrical engineering and applied physics from the Free University of Brussels (Belgium) as 'Ingénieur Civil Physicien.' Professor Sadeh's research spans cybersecurity, online privacy, Human-AI Interaction, AI governance, mobile computing, the Internet of Things, user-oriented machine learning, and language technologies. He is particularly known for his pioneering work on AI-based privacy enhancing technologies, including privacy assistants, automated privacy compliance tools, and NLP-based privacy solutions. His work has influenced the design of privacy features at major technology companies including Apple, Google, and Facebook/Meta, as well as privacy policies at regulatory agencies like the Federal Trade Commission and the California Office of the Attorney General. Analysis of his recent publications shows a strong focus on practical privacy solutions, particularly in mobile and IoT contexts, with an emphasis on making privacy more usable and understandable for end users. His work bridges technical innovation with policy implications, addressing both the technological and human aspects of privacy protection. 2018 Outstanding Entrepreneur of the Year award from the Pittsburgh Venture Capital Association Test of time award by the AAAI Conference on Web and Social Media (ICWSM) Gartner Group's Magic Quadrant leader in Security Awareness Computer-Based Training for 4 consecutive years Deloitte's Technology Fast 500 recognition for 3 consecutive years Professor Sadeh has advised numerous students, including PhD candidates like Aerin (Shikhun) Zhang, whose dissertation focused on understanding diverse privacy attitudes. His research has been funded through various grants, including NSF SaTC projects, and has resulted in technologies that protect tens of millions of users worldwide. He also founded Wombat Security Technologies, which was acquired by Proofpoint in 2018 and whose technologies are used by over 75% of Fortune 100 companies. Professor Sadeh leads several research initiatives including the Privacy Engineering Program, the Usable Privacy Policy Project, the Personalized Privacy Assistant Project, and CMU's Privacy Infrastructure for the Internet of Things. His Mobile Commerce Lab and E-Supply Chain Management Lab have produced influential research that has been commercialized by major organizations including IBM, Raytheon, Boeing, and the U.S. Army.
Olga Kokshagina serves as an Associate Professor in Innovation & Entrepreneurship at The University of Sydney, with adjunct research appointments at Monash University's Emerging Technology Lab and the UNU Hub - Learning Planet Institute. She is also an active member of the French Digital Council. Her research program investigates technology-mediated collaboration in complex innovation systems, focusing on healthcare transformation, deep tech commercialization, and co-design methodologies. Kokshagina has led high-impact projects with global institutions including the World Health Organization, OECD, STMicroelectronics, Vall d’Hebron Hospital, and Roche, demonstrating strong translational research capabilities. Her scholarly work centers on value-based healthcare innovation, digital platform governance, and AI-enhanced collaborative systems. She examines how organizational capabilities evolve during technological transitions, particularly in healthcare ecosystems, and investigates regulatory frameworks for algorithmic control in digital markets. Kokshagina's research bridges theoretical innovation management with practical applications, evidenced by her co-founding of Ninti—an initiative advancing women's health in workplace environments—and her Open Covid-19 crowdsourcing campaign that mobilized global expertise during the pandemic. Analysis of her 2021-2025 publications reveals a cohesive trajectory examining innovation in socio-technical systems. Key themes include value digitalization in healthcare, mission-oriented interdisciplinary collaboration, and the impact of big data on technology management. Her work consistently addresses grand challenges through mixed-methods approaches, spanning conceptual frameworks in journals like Research Policy to applied studies in Technovation and R&D Management, with increasing focus on quantum readiness and AI-augmented learning systems. No scientific awards are documented in the provided materials. Kokshagina currently holds a 2025 research grant for "Co-designing societal readiness and scenario building for quantum" through the University of Sydney Nano Institute/Catalyst program. While no student supervision activities are mentioned, her collaborative projects involve multi-institutional teams across industry, government, and academic sectors. Kokshagina maintains active roles within the University of Sydney Nano Institute and contributes to international policy discourse through the French Digital Council. Her Ninti initiative exemplifies her commitment to human-centered innovation, while ongoing collaborations with healthcare providers like Roche and Vall d’Hebron Hospital demonstrate sustained engagement with real-world implementation challenges in value-based care systems.
Dr. Peter Fokker is a Researcher at Utrecht University's Faculty of Geosciences, specifically within the Department of Earth Sciences and the Experimental Rock Deformation/HPT group. He is affiliated with the Research Programme in Earth Sciences Utrecht (DES/IVAU) and has been actively publishing in geomechanics, subsidence modeling, and induced seismicity for over three decades. His work primarily focuses on the application of geomechanical principles to understand and model subsurface processes related to resource extraction and geothermal energy. Dr. Fokker's research interests span several interconnected domains in geomechanics and subsurface engineering. His primary focus is on experimental rock deformation , studying how rocks behave under various stress conditions. He has made significant contributions to subsidence modeling , particularly in the context of gas field depletion in the Netherlands. His work on induced seismicity has helped understand the relationship between subsurface operations and seismic events. Additional interests include geothermal energy systems , reservoir engineering , and the application of data assimilation techniques to improve subsurface characterization. His research often bridges theoretical models with practical applications in energy resource management. An analysis of Dr. Fokker's recent publications (2020-2025) reveals a strong focus on practical applications of geomechanics to real-world challenges. His work increasingly integrates InSAR technology and data assimilation methods to monitor and model subsidence processes. There's a clear emphasis on geothermal energy applications , reflecting growing interest in sustainable energy solutions. His research also demonstrates a sophisticated approach to modeling complex reservoir behaviors across multiple scales, from laboratory experiments to field-scale operations. The interdisciplinary nature of his work is evident in collaborations spanning geology, engineering, and environmental science. Dr. Fokker has supervised multiple research projects and students throughout his career, as indicated by the "Supervised Work (4)" reference in his profile. His research has been supported by various grants focused on subsidence modeling, geomechanics of energy resources, and induced seismicity. He has been involved in significant collaborative efforts, including the Dutch National Scientific Research Program on Land Subsidence. Dr. Fokker is part of the Experimental Rock Deformation/HPT group at Utrecht University, which conducts laboratory experiments and develops theoretical models to understand rock behavior under various conditions. His work contributes to the broader research ecosystem focused on sustainable resource management and understanding subsurface processes, with particular relevance to the Dutch context of gas extraction and land subsidence.
Marta Molinas is a Professor at the Department of Engineering Cybernetics within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). Her research spans multiple interdisciplinary domains with a focus on EEG technology and brain-computer interfaces. She actively supervises numerous Master's projects and maintains extensive international collaborations with institutions including Kavli Institute for Systems Neuroscience, RIKEN Center for Brain Science, University of Tsukuba, Juntendo University, and several European universities. Professor Molinas' research interests center on developing innovative EEG technologies, particularly her FlexEEG concept for reduced-channel EEG systems with brain imaging capabilities. Her work integrates signal processing, artificial intelligence, and neuroscience to create practical applications in mental health, sleep research, neurorehabilitation, and human-computer interaction. She specializes in EEG source imaging, machine learning for brain signal analysis, and the development of brain-computer interfaces for various applications including locked-in syndrome communication, ADHD treatment, and driver monitoring systems. Her publication portfolio demonstrates strong trends in interdisciplinary research combining neuroscience with electrical engineering and artificial intelligence. The work shows particular emphasis on developing practical EEG-based systems that minimize invasiveness while maintaining analytical power, with applications spanning healthcare, rehabilitation, and human augmentation. Her research bridges theoretical signal processing with real-world implementations through numerous student projects and international collaborations. Professor Molinas actively supervises a large team of Master's and PhD students across multiple projects, with each project typically requiring two students working collaboratively. Her research is supported through numerous international collaborations with institutions in Japan, India, and Europe, indicating substantial research funding and project leadership. She has developed a pipeline of student projects that build upon previous work, creating a cumulative knowledge base within her research group. She leads the EEG ITK research team at NTNU, which focuses on developing the FlexEEG headset prototype featuring flexible, wireless, dry electrodes designed to move across the scalp. This team works at the intersection of neuroscience, electrical engineering, and computer science, developing applications for sleep research, mental health monitoring, neurorehabilitation, and brain-computer interfaces. The team collaborates extensively with international partners including the Kavli Institute for Systems Neuroscience, the International Institute of Integrative Sleep Medicine at University of Tsukuba, and several engineering departments across Europe and Asia.
Ion Stoica is a Professor in the Electrical Engineering and Computer Sciences Department at the University of California, Berkeley, where he holds the Xu Bao Chancellor Chair. He serves as Director of the Sky Computing Lab and is Executive Chairman of both Databricks and Anyscale. His research spans distributed systems, cloud computing, and AI systems, with significant contributions to large-scale data processing frameworks. Stoica's research interests focus on the intersection of AI and systems, with emphasis on developing practical implementations that bridge theoretical foundations with real-world deployability. His work addresses fundamental challenges in distributed computing, resource management, and large-scale machine learning systems. Current projects include Ray (a distributed execution framework), vLLM (a high-throughput inference engine for LLMs), Chatbot Arena (an open platform for human preference evaluations), and SkyPilot (a framework for running AI workloads across clouds). His research output demonstrates a consistent trajectory toward more efficient, scalable systems for modern AI workloads, particularly focusing on optimizing inference performance, resource utilization, and cross-cloud deployment. Recent publications reflect growing interest in large language model serving, video generation optimization, and agent-based systems. ACM Fellow SIGOPS Hall of Fame Award (2015) SIGCOMM Test of Time Award (2011) ACM Doctoral Dissertation Award (2001) Member of National Academy of Engineering Honorary Member of the Romanian Academy Stoica has advised an extensive number of doctoral students who have gone on to prominent positions in academia and industry, including assistant professorships at Stanford, MIT, Carnegie Mellon, and other top institutions. He has received significant research funding through his lab activities and startup ventures. His research group has been particularly successful in translating academic research into widely adopted open-source technologies and commercial products. Stoica leads the Sky Computing Lab at UC Berkeley, which focuses on developing systems for AI workloads across multiple clouds. His research group has produced numerous influential open-source projects including Apache Spark, Apache Mesos, and Alluxio, which have become industry standards for large-scale data processing. The lab maintains strong industry partnerships while pursuing fundamental research in distributed systems and AI infrastructure.
Thomas Grenier is an Associate Professor in the Department of Electrical Engineering at INSA Lyon and a member of the CREATIS laboratory (CNRS UMR 5220, INSERM U1294). He obtained his HDR (Habilitation à Diriger des Recherches) in 2023 and his Ph.D. in Image Processing from INSA Lyon in 2005. His research focuses on medical image segmentation, clustering, and filtering using feature space, scale-space, and deep learning approaches. Doctoral School: EEA (Electronics, Energy, and Automatics) Research Affiliation: CREATIS Lab (CNRS/INSERM/INSA Lyon/Université Lyon 1/Université Jean Monnet Saint-Etienne) He has contributed to 20 papers and co-supervised 5 PhD students, including Léo Dumortier and Florent Guépin. Grenier leads the annual Deep Learning for Medical Imaging (DLMI) school, which he co-founded, and has organized five editions across Lyon and Montreal since 2019. The school emphasizes practical deep learning applications in medical imaging for participants of all expertise levels. His work spans interdisciplinary domains such as medical imaging , deep learning , and image processing , with recent publications on generative AI for MRI synthesis, explainable networks, and segmentation of neurological pathologies in preclinical models. He manages pedagogical platforms, coordinates LabEx PRIMES project activities, and oversees lab room infrastructure for 200 hours/year across 10 training programs. Grenier also leads the MUSIC transversal project on Multiple Sclerosis since 2019.
Dr. Qing Jin is an Assistant Professor of Food Science at the School of Food and Agriculture, College of Natural Sciences, Forestry and Agriculture, University of Maine. Her research focuses on economically-viable and environmentally-friendly food system innovation, with emphasis on value-added utilization of food waste and by-products through advanced processing technologies. Education: Ph.D. (2020) from Virginia Tech She teaches courses on food engineering principles (FSN 485), food engineering lab (FSN 486), food preservation (FSN 502), and co-teaches an advanced graduate seminar (AVS 633/FSN 671). Supported by the Maine Agricultural and Forest Experiment Station, her current Hatch project (ME022423) explores sustainable food industry solutions through biorefinery approaches. Her work spans food waste valorization, biofuels, bioproducts, and techno-economic analysis. Recent publications highlight her expertise in dietary fiber modification, fermentation optimization, biochar development, and polyphenol extraction from agricultural waste streams. She actively seeks undergraduate and graduate research assistants for her lab. Contact: qing.jin@maine.edu | Office: 109 Hitchner Hall | Phone: 207-581-1687
