Martin Riegler is a researcher at the Institute of Physics and Materials Science , part of the University of Natural Resources and Life Sciences, Vienna (BOKU). His work focuses on advanced material analysis and sustainable construction within the wood technology sector. Specializes in electrical resistivity measurements of wood Applies machine learning to wood machining acoustics Studies adhesive bondlines modified with carbon fillers Investigates moisture dynamics in wood Recent publications highlight his contributions to smart wood composites , non-invasive testing , and machine learning applications in wood processing. He has presented research at international conferences and collaborated with institutions like the Northern European Network for Wood Science and Engineering. Riegler's work intersects with Materials Science , Wood Technology , and Sustainable Engineering , particularly in optimizing particleboard production and wood moisture prediction . His research spans technical innovation and environmental stewardship in forestry applications.
Geir Andre Ringstad is an Associate Professor at the Department of Radiology and Nuclear Medicine , University of Oslo, specializing in neuroimaging and cerebrospinal fluid (CSF) dynamics . His research focuses on the glymphatic system , CSF tracer studies , and MRI-based quantification of brain clearance mechanisms. Research Themes Neurofluid dynamics and glymphatic dysfunction in normal pressure hydrocephalus , Chiari malformation , and pineal cysts Impact of sleep deprivation on CSF tracer clearance Novel MRI protocols for assessing CSF disorders and neuroimmune interfaces Publications cover diverse topics including glymphatic-lymphatic coupling , CSF flow modeling , contrast agent safety , and computational analysis of neurofluid pathways. Collaborative work spans neurosurgery , endocrinology , and biophysics .
Professor Robin Purshouse is a leading academic at the University of Sheffield , currently serving as Professor of Decision Sciences in the Department of Automatic Control and Systems Engineering within the School of Electrical and Electronic Engineering . With a career spanning academia and industry, his work bridges computational modelling , optimization , and systems science to address complex challenges in public health and engineering. His research has been pivotal in developing mechanisms for agent-based modelling and evolutionary multi-objective optimization . Education: PhD in Control Systems (2004), MEng in Control Systems Engineering (1999) from the University of Sheffield Professor Purshouse's research focuses on computational modelling of complex social systems , decision analytics for population health policy , and Bayesian optimization . He has pioneered the integration of machine learning and uncertainty quantification in social science simulations, with notable projects like the Sheffield Alcohol Policy Model and CASCADE initiative. His work spans interdisciplinary domains, including health economics , policy evaluation , and engineering design . Recent publications highlight his expertise in agent-based modelling for smoking/vaping dynamics , intersectional disparities in alcohol consumption , and inclusive economy frameworks . He has secured substantial funding (exceeding £16 million) through grants from NIH , CRUK , UKPRP , and MRC , including his role as co-PI in the HealthMod cluster. His contributions to multi-objective optimization and evolutionary algorithms have advanced methodologies in both engineering and public health domains. Scientific Awards: ESRC Future Research Leaders Award (2012-2015) As a co-developer of the Liger optimization environment , Purshouse has fostered open-source tools for complex decision-making. He leads the SIPHER consortium for systems science in public health and serves on editorial boards for journals like Environmental Modelling & Software . His teaching includes Agent-Based Modelling (ACS6132), and he maintains professional memberships in the Association for Computing Machinery and Research Society on Alcohol .
David Minh is an Associate Professor of Chemistry and the Robert E. Frey, Jr. Endowed Chair in Chemistry at Illinois Institute of Technology (IIT), affiliated with the Lewis College of Science and Letters. He serves as Associate Director of the Center for Interdisciplinary Scientific Computation (CISC). His research focuses on computational chemical biology, developing methods to predict protein dynamics and molecular interactions for structure-based drug design. Education: Ph.D. in Chemistry, University of California, San Diego M.S. in Chemistry, University of California, San Diego B.A. in Chemistry, University of California, Berkeley Research Interests: Dr. Minh's group specializes in computational methods to study small molecule-biological interactions, including: Structural mechanisms of G protein-coupled receptors (GPCRs) and signaling proteins Advanced binding free energy calculations incorporating entropy Enhanced sampling in molecular simulations Bayesian statistical integration of experimental data Modeling bacterial metabolic enzymes and inhibitor development Articles Trends: Recent work emphasizes antiviral drug discovery (e.g., SARS-CoV-2 protease inhibitors), Bayesian analysis of binding data, and computational tools like AlGDock for free energy predictions. Collaborations with biologists (e.g., Oscar Juárez) drive antibiotic discovery targeting pathogenic bacteria. Advising & Grants: Leads interdisciplinary projects funded by NIH and industry partnerships. Mentors students in computational modeling and experimental validation. Active in open science initiatives like the D3R Grand Challenge in drug design. Labs & Teams: Directs the Minh Computational Chemistry Lab at IIT, focusing on molecular simulations, machine learning, and interdisciplinary collaborations to address biomedical challenges.
Peter Mandl is an Associate Professor affiliated with the Institute for Computer Science Education at the University of Klagenfurt. His research focuses on Geoinformatics, Artificial Intelligence, Environmental Research, Remote Sensing, and Computer Simulation. He explores topics such as socio-ecological systems, spatial agent-based modeling, land use change, and urban and regional analysis. His work integrates interdisciplinary approaches, combining GIS technology with environmental policy, social sciences, and engineering. Key research interests include the application of geospatial tools for sustainable land use planning, renewable energy assessment, and modeling complex geographic systems. He has contributed to studies on forest fuel markets, satellite image interpretation, and fog phenomena in climate research. His publications emphasize spatial decision support systems, agent-based simulation frameworks, and the development of open geoinformatics platforms like RadWeb Mittelkärnten. His articles demonstrate a consistent focus on bridging theoretical modeling with practical applications, often using agent-based methods to analyze environmental, economic, and social systems. While no scientific awards are explicitly mentioned, his contributions to geoinformatics education and research are evident through his extensive publication record and teaching roles. Collaborative projects and grants are not detailed here, but his work suggests involvement in interdisciplinary teams addressing environmental and urban challenges. Labs and teams associated with his research are not explicitly listed, though his affiliations imply participation in geoinformatics and environmental science groups at the University of Klagenfurt.
Professor Phillip Morgan is a leading academic in Human Factors and Cognitive Science at Cardiff University's School of Psychology, holding a Personal Chair since 2020. He directs the Human Factors Excellence (HuFEx) Research Group and serves as Director of Research for the Centre for Artificial Intelligence, Robotics & Human-Machine Systems (IROHMS) . Since March 2019, he has been seconded part-time to Airbus as Director of their Centre of Excellence in Human-Centric Cyber Security . BSc (Hons) Psychology, Cardiff University (2001) PGDip Research Methods, Cardiff University (2002, Distinction) PhD in Cognitive Psychology, Cardiff University (2005) PGCHE, University of Wales (2012, Distinction) His research merges Human Factors with Cognitive Science to address real-world challenges in: Human-machine interaction in autonomous systems Cyberpsychology and security behavior Transport human factors (connected/autonomous vehicles) Interruption/distraction effects on cognition Trust and blame dynamics in AI systems Industry 5.0 human-centric manufacturing Recent publications show AI and cybersecurity as dominant themes, with specific focus on autonomous vehicle interfaces, human fatigue analysis, and trust calibration in human-machine systems. His work integrates behavioral experiments, driving simulators, and human-state monitoring. Scientific Recognition: Associate Fellow of the British Psychological Society Best Paper Award at AHFE 2021 Member of Experimental Psychology Society Keynote speaker at multiple international conferences As supervisor, he leads projects on cybersecurity frameworks, fatigue detection, and human-AI interaction. His grants portfolio exceeds £37m from sources including EPSRC, ESRC, Airbus, and Wellcome Trust. Current supervisees include Victoria Marcinkiewicz, George Raywood-Burke, and Nicola Turner.
James Flory is an Associate Professor of Population Health Sciences at Weill Cornell Medical College , Cornell University. With an MD and MSCE from the University of Pennsylvania (2009) and a BA from Columbia University (2002), he specializes in pharmacoepidemiology and clinical research related to diabetes management, drug safety, and healthcare policy. Education: M.D., University of Pennsylvania School of Medicine, 2009 M.S.C.E., University of Pennsylvania School of Medicine, 2009 B.A., Columbia University, 2002 His research focuses on the intersection of diabetes pharmacology , cardiovascular risk , and health services research . He has contributed extensively to understanding drug safety (e.g., sulfonylureas, SGLT2 inhibitors, TRK inhibitors), medication adherence , and clinical decision-making in diabetes and oncology settings. His methodological work includes instrumental variable analysis , pragmatic trial design , and real-world data validation . James Flory's recent publications examine temperature-related diabetes complications , AI in clinical decision support , and policy impacts on medication utilization . His work combines epidemiological methods with health informatics to address critical questions in diabetic care , drug safety surveillance , and health equity .
Ruwen Qin is an Associate Professor in the Department of Civil Engineering at Stony Brook University. Her research focuses on integrating data analytics, machine learning, and systems engineering into civil infrastructure systems to develop cyber-physical systems and intelligent automation. She applies these technologies to enhance human-AI collaboration, improve transportation safety, and advance smart infrastructure monitoring. Developing AI models for structural health monitoring Applications in worker safety and transportation systems Specializes in computer vision and sensor fusion Her recent work includes deep learning frameworks for drone-assisted inspections, structural component segmentation using weak annotations, and attention-based networks for traffic risk prediction. She also explores explainable AI for crash anticipation and interactive systems for bridge inspectors. Ruwen Qin's research spans interdisciplinary domains, combining civil engineering with AI-driven analytics to address challenges in infrastructure resilience, transportation safety, and human-centric automation systems.
Professor Jang Yoon is a faculty member in the Department of Computer Engineering at Sejong University, South Korea. He currently holds the position of Daeyang Distinguished Professor and leads the Data Visualization Lab. His academic journey includes postdoctoral research at the Swiss National Supercomputing Center (2007-2009), ETH Zurich (2009-2011), and Purdue University (2011-2012). His educational background includes a Bachelor's degree from Seoul National University in Electrical Engineering (2000), and Master's and Doctoral degrees from Purdue University in Electrical and Computer Engineering (2002 and 2007). His academic progression at Sejong University shows his appointment as Assistant Professor (2012-2016), Associate Professor (2016-2022), and Professor (2022-present). Professor Jang's research spans multiple domains within data science and visualization, with primary focus on data visualization, visual analytics, and their applications in various domains. His work bridges theoretical computer science with practical applications in traffic analysis, healthcare, and smart city infrastructure. He has developed innovative techniques for spatiotemporal data visualization, volume rendering, and causal analysis in complex datasets. His recent publications (2023-2025) demonstrate a strong focus on integrating deep learning with visualization techniques, particularly in traffic analysis, volume rendering, and large language model interpretability. His work shows a clear trajectory toward combining causal inference with visual analytics, applying these methods to urban traffic systems, structural health monitoring, and public relations analysis. Professor Jang has served in numerous leadership roles in major visualization conferences including IEEE VIS, IEEE PacificVis (as General Chair in 2023), EuroVis, and HCI Korea conferences. His service contributions include program committee memberships and chair positions across multiple prestigious conferences in the visualization field. His laboratory work focuses on practical applications of visualization techniques with numerous patents registered in Korea. His research has resulted in multiple practical systems for traffic analysis, VR sickness detection, data quality improvement, and eye-tracking applications. The lab maintains strong industry connections through applied research projects addressing real-world problems.
Marco Schutten is an Associate Professor at the Digital Society Institute of the University of Twente, affiliated with the Industrial Engineering & Business Information Systems department. His work bridges Artificial Intelligence , Transportation , and Operations Research , focusing on optimizing complex systems. Expert in Urban Logistics and Freight Transport Specializes in Mathematical Programming and Optimization Research interests include Vehicle Routing , Machine Scheduling , and Agent-Based Simulation . Key trends in his 15 most recent articles (2015–2025) involve: Dynamic scheduling under time constraints Urban logistics and smart city applications Heuristics for combinatorial optimization Integration of MILP and Simulation models No scientific awards, formal supervisory roles, or part-time appointments are explicitly mentioned.
Gerhard de Haan is a Professor of Futures and Educational Research at the Free University of Berlin since 2011, where he has been a faculty member since 1991. He serves as Director of the Institute for Educational Futures Research and as Scientific Director of the Master's program in Futures Studies. His academic work spans educational science, psychology, and sociology with a strong focus on sustainability and future-oriented approaches to education. De Haan's research centers on futures studies, knowledge society development, innovation research, and sustainable development education. He has dedicated significant work to understanding how societies can position themselves to become sustainable knowledge societies and how futures research can be effectively conducted. His expertise has made him a leading figure in Education for Sustainable Development (ESD) in Germany and internationally. His publications and projects reveal a strong emphasis on educational landscapes, school development, and the implementation of sustainability principles throughout educational systems. The National Monitoring of Education for Sustainable Development in Germany represents one of his major contributions to tracking progress in this field. Chairman of the German National Committee of the UN Decade 'Education for Sustainable Development' (2005-2014) Scientific Advisor in the World Action Program for Education for Sustainable Development Chairman of the German Society for Environmental Education Member of the Ernst-Reuter-Prize Committee since 2007 Reviewer for the Fonds National de la Recherche Luxembourg De Haan has supervised numerous research projects including ESD for 2030 (2023-2026), Learning for Sustainability in Non-formal and Informal Settings, and the RuhrFutur real-world laboratory. His work consistently bridges theoretical futures research with practical applications in educational settings, emphasizing the need for whole-institution approaches to sustainability education.
Delphine Joseph is a prominent researcher in pharmacochemistry at the University of Paris-Saclay's Faculty of Pharmacy, serving as director of the Graduate School of Health & Drug Sciences and deputy director of the BioCIS laboratory (Biomolecules: Design, Isolation, Synthesis). Her leadership spans academic administration, research direction, and teaching innovation within the University Paris-Saclay ecosystem. Her research focuses on two major areas: tobacco addiction mechanisms through nicotinic receptor studies funded by ANR, and drug allergy research particularly regarding neuromuscular blocking agents funded by Anses. Joseph employs a multidisciplinary approach integrating chemistry, biology, and bioinformatics to develop therapeutic solutions and diagnostic strategies. Her work on tobacco addiction targets molecular-level understanding of receptor movements to design targeted molecules, while her allergy research investigates molecular mechanisms of reactions to curare-based medications. Joseph's leadership extends to directing the Graduate School of Health & Drug Sciences, which unites academic and research teams from multiple universities and research organizations (CNRS, Inserm, CEA, INRAE) around drug development and therapeutic innovation. She champions interdisciplinary collaboration through initiatives like mixed cohort events and research days designed to foster innovative projects. PEPS (Passion for Teaching and Pedagogy in Higher Education) Prize recipient ANR-funded research on nicotinic receptors for tobacco addiction treatment Anses-funded project on drug allergy mechanisms to neuromuscular blocking agents Leadership in reforming organic chemistry teaching curricula Her research demonstrates consistent thematic focus on molecular design for therapeutic applications, with increasing emphasis on computational approaches and interdisciplinary collaboration. Recent publications reflect growing integration of bioinformatics with experimental validation, particularly in fragment-based drug design and molecular dynamics studies targeting specific receptor interfaces. Joseph maintains strong industry connections through past collaborations with Novartis and Menarini, and current partnerships with UCB Pharma.
Zhi Jin is a Professor in the Department of Computer Science and Technology at Peking University, where he has been employed since 2009. Previously, he served as a professor at the Academy of Mathematics and System Sciences, Chinese Academy of Sciences from 1994-2009. He received his BS from Zhejiang University in 1984 and MS/PhD from National University of Defense Technology in 1984 and 1992 respectively. He progressed from assistant professor (1992) to associate professor (1995) to full professor (2001). His research focuses on knowledge engineering and software engineering, with special interests in knowledge graphs, self-adaptive systems, and deep learning applications. Current research directions include Self-Adaptive Software in Human-Cyber-Physical Systems, Crowd-based Requirements Engineering, and Learning from both Natural Language and Programming Language. His work bridges theoretical knowledge engineering with practical software development challenges. His recent publications demonstrate a strong trend toward applying large language models and AI techniques to traditional software engineering problems, particularly in requirements engineering, code generation, and vulnerability detection. The articles span multiple high-impact venues including ASE, ICSE, and RE, with significant focus on aerospace applications and multi-agent collaboration approaches. Scientific honors include: Winner of National Science Fund for Distinguished Young Scholars (2006) Project 973 project lead scientist (2014) Member of Discipline Appraisal Group of the Academic Degree Commission (2015) Multiple ACM Distinguished Paper Awards He serves in numerous editorial roles including Associate Editor for IEEE Transactions on Software Engineering (2018-present) and IEEE Transactions on Reliability (2019-present). He is also an Editorial Board Member for Empirical Software Engineering and Requirements Engineering Journal, and holds leadership positions in the China Computer Federation. His extensive conference service includes PC membership for ICSE, FSE, RE, and other major software engineering venues.
Lisa Lee is a Research Scientist at Google DeepMind, focusing on creating AI agents that emulate biological learning and adaptability. She previously taught at Princeton University and received TA awards for Deep Reinforcement Learning and Probabilistic Graphical Models. Education: PhD in Machine Learning from Carnegie Mellon University (advised by Ruslan Salakhutdinov and Eric Xing); A.B. in Mathematics from Princeton University (advised by Sanjeev Arora). Her research centers on AI embodiment, intrinsic motivation, and hierarchical planning. She explores how evolutionary-inspired inductive biases and memory mechanisms can enable agents to generalize across physical and conceptual domains, as demonstrated in her work on robotic agility benchmarks and multimodal transformers. Notable scientific contributions include the Barkour quadruped robot benchmark, Gemini multimodal models, and theoretical work on causal language models. She co-organized key AI workshops at NeurIPS and ICML, and her awards include Princeton's TA of the Year for technical courses. Leadership: ICML Workflow Chair (2019), NeurIPS workshop co-organizer (2019, 2021), peer reviewer for top AI conferences.
Fernando Anjos is an Associate Professor of Finance at the Nova School of Business and Economics (NOVA SBE), part of NOVA University of Lisbon. He holds a Ph.D. in Financial Economics from Carnegie Mellon University (2008), a Master's in Finance from ISCTE Business School (2004), and a B.A. in Economics from the Catholic University in Lisbon (1998). Prior to his current role, he served as an Assistant Professor at the University of Texas at Austin's McCombs School of Business (2009–2015) and ISCTE Business School (2008–2009). His non-academic experience includes strategy and business development roles at the Boston Consulting Group and Portugal Telecom. His research focuses on corporate finance , corporate diversification , mergers and acquisitions , and social and economic networks . He explores topics such as resource allocation, inter-firm networks, and organizational performance through interdisciplinary lenses, blending economics, sociology, and mathematical modeling. Anjos' publications span journals like the Journal of Financial and Quantitative Analysis and Management Science , analyzing themes such as technological specialization in firms, managerial decision-making biases, and the role of networks in economic development. His work often employs agent-based models to simulate complex organizational and market dynamics. No scientific awards are explicitly mentioned in the provided text. His advising and grant activities are not detailed, though his academic roles suggest active involvement in doctoral supervision and research projects. He is affiliated with the Finance Knowledge Center at NOVA SBE, contributing to research initiatives in corporate finance and strategic management.
