Insa Feinkohl is a Professor at the Chair of Medical Biometry and Epidemiology within the Faculty of Health at the University of Witten/Herdecke . Her research focuses on risk factors for cognitive dysfunction and mental health in older adults, particularly post-surgery, with emphasis on metabolic and cognitive risk factors. Bachelor of Science (BSc) in Psychology (1 st class honors) – University of Dundee (2006-2009) Master of Science (MSc) in Psychology of Individual Differences (with distinction) – University of Edinburgh (2009-2010) PhD in Community Health Sciences – University of Edinburgh (2010-2014) Post Doc in Knowledge Construction Group – Leibniz Institute for Knowledge Media, Tübingen (2014-2015) Postdoc in Molecular Epidemiology Group – Max Delbrück Center, Berlin (2015-2022) Habilitation in Molecular Epidemiology – Charité Universitätsmedizin Berlin (2021) Her research integrates medical biometry and epidemiology to study postoperative cognitive dysfunction (POCD), delirium, and aging-related cognitive decline. Key areas include biomarker validation (e.g., leptin, interleukins), brain connectivity (dopaminergic networks, thalamus), and metabolic risk factors (diabetes, obesity). She contributed to the BioCog project , an EU-funded initiative for personalized risk prediction of postoperative cognitive impairment. Her recent publications highlight trends in perioperative neuroscience, including brain mineralization, cytokine associations with neurocognitive disorders, and structural/functional imaging in delirium. Articles also explore metabolic syndrome, cognitive reserve, and delirium prediction models using machine learning. Insa Feinkohl is affiliated with major academic societies, including the German Society for Epidemiology , German Society for Medical Informatics, Biometry and Epidemiology , and the German University Association .
Hugo Paquet is a Researcher at INRIA Paris and a member of the ANTIQUE team at École Normale Supérieure , PSL University. He completed a PhD in Computer Science (2015–2019) at the University of Cambridge under Glynn Winskel , focusing on concurrent game semantics for probabilistic programming. His postdoctoral work includes positions at LIPN, Paris (2022–2024, funded by a Marie Skłodowska-Curie Award) and University of Oxford (2020–2022). He has contributed to conferences including LICS , ESOP , FSCD , and POPL . Education : PhD in Computer Science (University of Cambridge, 2019) Research Interests : Probabilistic programming (semantics, inference algorithms, nonparametric models), categorical semantics (game semantics, concurrency models, adjunctions), combinatorial species, and 2-dimensional categories. Teaching : Category Theory (2023–2024), Bayesian Statistical Probabilistic Programming (2021–2022), Lambda-calculus and Types (2020–2021), and small-group teaching at Cambridge (Logic, Discrete Mathematics, Semantics). Awards : Marie Skłodowska-Curie Award under the Paris Region Fellowship Programme Labs : INRIA Paris, ANTIQUE team (2024–present)
Prof. Dr. Markus Strohmaier holds the Chair of Data Science in Economics and Social Sciences at the University of Mannheim's Business School. His research focuses on applying machine learning and data science to understand socio-economic systems and human behavior, leveraging text, relational, and emerging data types. He teaches graduate courses and supervises theses in these areas. Research interests include algorithmic fairness, behavioral analysis via computational methods, and the societal implications of AI. Notable work addresses demographic representativeness in large language models, bias mitigation in rankings, and gender gaps in blockchain adoption. His team is located at L15, 3rd floor – Room 307 in Mannheim, adjacent to the central station. Collaborations span interdisciplinary topics like social media analysis, network dynamics, and computational social science. Key contributions include developing benchmarks for neural network similarity (Resi) and frameworks for measuring algorithmic fairness perceptions (FairCeptron). Research often bridges technical innovation with societal impact, addressing ethical challenges in AI and data-driven decision-making.
Professor Tomasz Kapitaniak is a distinguished academic in the field of nonlinear dynamics and theoretical mechanics. He serves as a Professor of Theoretical and Applied Mechanics and Head of the Division of Dynamics at the Faculty of Mechanical Engineering, Technical University of Lodz, Poland. His career spans over three decades at the university, where he has made significant contributions to the understanding of nonlinear systems, chaos theory, and mechanical oscillations. Professor Kapitaniak holds advanced degrees in both mechanics and applied mathematics from the Technical University of Lodz and the University of Lodz. His educational background includes: M.Sc. in mechanics, Faculty of Mechanical Engineering, Technical University of Lodz (1982) M.Sc. in applied mathematics, Faculty of Mathematics, Physics and Chemistry, University of Lodz (1985) Ph.D. in mechanics, Faculty of Mechanical Engineering, Technical University of Lodz (1985) D.Sc. in mechanics, Faculty of Mechanical Engineering, Technical University of Lodz (1988) Professor of technical science, title given by the President of Poland (1995) His research focuses on nonlinear dynamics, with particular emphasis on mechanical oscillations, stability, bifurcations and chaos, stochastic dynamics, and applications of nonlinear dynamics in mechanical engineering. Professor Kapitaniak is renowned for his work on the development of methods for controlling chaos without feedback, identification of new types of bifurcations, synchronization mechanisms in coupled mechanical oscillators, and explaining the origin of randomness in mechanical systems. His research has evolved from fundamental theoretical work to increasingly applied studies involving complex networks, biological systems, and engineering applications. Professor Kapitaniak has published over 300 scientific papers in renowned journals, cited over 8,000 times. His work exhibits a consistent focus on understanding complex nonlinear phenomena across various physical systems. The trend in his recent publications shows continued exploration of synchronization phenomena, extreme events in dynamical systems, and applications of nonlinear dynamics to biological, mechanical, and physical systems. His most recent work demonstrates a growing interest in multistability, chimera states, and the prediction of tipping phenomena in complex systems. Among his notable scientific achievements and distinctions are: Election as a member of the Polish Academy of Sciences (corresponding member in 2013, ordinary member in 2019) Election to Academia Europaea in 2021 Honorary doctorates from Saratov State University (Russia, 2001) and Lublin University of Technology (Poland, 2014) Multiple prestigious fellowships including the British Council Fellowship (1989), King Abdul Aziz Award Fellowship (1990), and Fulbright Fellowship (1997) Editorial roles including Associate editor of Chaos, Solitons and Fractals since 1990 and member of editorial boards of several other prestigious journals Throughout his career, Professor Kapitaniak has been actively involved in mentoring the next generation of researchers, having supervised numerous PhD students including Jerzy Wojewoda, Anton van Wyk, Barbara Błażejczyk-Okolewska, Andrzej Stefański, Andrzej Kozłowski, and Przemysław Szumiński. He has secured significant research funding from various national and international sources including the Ministry of Science and Higher Education (Poland), Deutscher Akademischer Austauschdienst, The Royal Society of London, and others. His research team has maintained strong international collaborations with institutions worldwide, including universities in the United States, United Kingdom, Germany, Brazil, Russia, and Ukraine. He leads the Division of Dynamics at the Technical University of Lodz, which serves as a hub for research in nonlinear dynamics, mechanical oscillations, and related fields. The division maintains strong international collaborations with institutions worldwide and continues to produce cutting-edge research in the field of nonlinear dynamics and its applications.
Professor Susanne Braun is a distinguished faculty member in the Department of Management and Marketing at Durham University's Faculty of Business. Her academic work focuses on leadership identity dynamics, narcissism in leadership contexts, and authentic leadership development. Braun maintains an active research program with numerous publications spanning leadership theory, organizational psychology, and identity studies. Her research interests center on leader and follower identity dynamics , examining how individuals' perceptions of themselves as leaders evolve and influence their behavior. She extensively investigates narcissism in leadership , differentiating between grandiose and vulnerable forms and exploring their contextual manifestations. Her work on authentic leadership examines how leaders can develop genuine self-expression in organizational contexts. Braun's research employs diverse methodologies including multi-study investigations, quasi-experimental designs, and dynamical systems approaches to capture the fluid nature of leadership identities. Braun's publication record reveals consistent scholarly output with a notable concentration in 2023-2025, demonstrating her active research trajectory. Her work spans multiple publication types including journal articles, book chapters, and authored books. Thematically, her research shows progression from foundational work on narcissism and motivation to lead toward more complex investigations of identity dynamics in response to organizational events and contexts. She frequently collaborates with international scholars, indicating strong global research networks. Braun has supervised nine doctoral students, including both PhD and Doctor of Business Administration candidates. Her supervision portfolio shows consistent activity from 2015 to present, with five current students and four completed degrees. She frequently serves as Director of Studies and collaborates with other faculty members as second supervisors, demonstrating integration within her academic community.
Roman Matuszewski is a retired Associate Professor at the University of Bialystok, affiliated with the Faculty of Philology's Department of Applied Linguistics. His research focuses on automated reasoning, formalized mathematics, and the Mizar Project, which he has been involved with since its inception in 1973. He holds a PhD in Computer Science from Shinshu University (2000) and has held academic positions at multiple institutions, including part-time roles at Bogdan Janski University. Education: PhD in Computer Science (2000), Master of Science in Mechanics (1975), Engineer (1973), all from Polish institutions. His work emphasizes formal proof systems, mathematical knowledge management, and education integration of automated reasoning tools. Research interests include automated deduction, formal proof verification, and the application of these methods to mathematics education. His contributions to the Mizar Mathematical Library and its 50-year history (celebrated in 2023) are foundational for interactive theorem proving. Key awards include the Silver Cross of Merit (2004) and multiple Rector’s prizes. He has organized major conferences like MKM2004 and served on program committees for events such as IJCAR and Tableaux. Grants include leadership roles in EU-funded projects like TYPES and CALCULEMUS. His work bridges computer science and mathematics through formalized systems, impacting both research and education.
Chris Matzner is a Professor and Associate Graduate Chair at the University of Toronto's Department of Astronomy and Astrophysics, affiliated with the Dunlap Institute for Astronomy & Astrophysics. He earned his Ph.D. from UC Berkeley in 1999. His research focuses on astrophysical fluid dynamics, particularly star formation processes (protostellar disks, molecular clouds, energy feedback) and stellar explosions (supernovae, gamma-ray bursts), employing analytical, numerical, and observational approaches. His research encompasses: Dynamics of protostellar outflows and molecular cloud interactions Models for supernova shocks and gamma-ray burst mechanisms Fragmentation in star and planet formation Massive black hole accretion processes Evolution of giant molecular clouds Stellar feedback in galactic environments Analysis of his 15 most recent publications reveals strong emphasis on supernova dynamics (particularly Type Ia explosions), star formation mechanisms in clusters and molecular clouds, shock wave physics in astrophysical contexts, and the development of astronomical instrumentation. The works demonstrate consistent focus on explosive transients, fluid dynamics in cosmic environments, and observational constraints on theoretical models. As Associate Graduate Chair, he oversees academic programs and student development. His laboratory affiliations include the Dunlap Institute's computational astrophysics and instrumentation groups. Current work involves modeling star cluster-galaxy interactions, tidal disruption events, and developing next-generation UV/IR detectors.
Dr. Tom Aben is a Researcher at Tilburg University's Tilburg School of Economics and Management (TiSEM), specifically within the Department of Information Systems and Operations Management. His work focuses on the intersection of digital transformation, supply chain management, and data governance in complex organizational settings, with particular emphasis on collaborative networks and critical infrastructure management. Dr. Aben's research interests center around purchasing and supply management (PSM) in multi-party networks, data sharing solutions, and collaborative governance approaches. He investigates how organizations can effectively manage digital transformation in critical infrastructure contexts, with particular attention to contract design, trust-building mechanisms, and network governance structures. His work often addresses societal challenges through collaborative approaches across public and private sectors, contributing to United Nations Sustainable Development Goals related to sustainable infrastructure and innovation. His recent publications reveal a consistent focus on how digitalization transforms traditional buyer-supplier relationships and necessitates new governance approaches. Aben's research shows particular expertise in data sharing within critical infrastructure networks, examining how organizations jointly develop data clauses and navigate the challenges of information asymmetry in digital environments. His work has significant implications for both academic theory and practical implementation of collaborative governance models. Dr. Aben has received recognition for his contributions to the field, including: Nomination for the 2021 Tilburg University Impact Award for the NWO/NGinfra LONGA VIA project Nominee for the Best PhD Dissertation Award in 2023 Currently, Dr. Aben is actively involved in the VIA AUGUSTA research project (2022-2026), which focuses on leveraging System-of-Systems approaches to infrastructure management. Previously, he contributed to the Longavia project (2018-2022) examining legal and organizational aspects of data-driven innovations in infrastructure management. He has also organized academic events such as the workshop 'Moving Towards Cross-Sectoral Collaboration: Challenges and Opportunities of 'Joint' Action' in December 2024, demonstrating his leadership in advancing knowledge in his field. His research spans multiple sectors including critical infrastructure management, healthcare, and public administration, with a particular focus on Dutch contexts but with implications that extend internationally. Dr. Aben frequently collaborates with researchers including Wendy van der Valk, Luc van de Sande, and David Wodak, indicating strong research networks both within Tilburg University and with external partners.
Gerard J. van den Berg is a Professor at the University of Groningen, holding dual affiliations in the Faculty of Economics and Business (Department of Economics, Econometrics and Finance) and the Faculty of Medical Sciences (Department of Epidemiology). His research focuses on labor economics, public policy, health economics, and econometrics. He has contributed to studies on unemployment dynamics, labor market policies, and transgenerational health effects. Notably, he has led randomized controlled trials evaluating integration agreements for the unemployed and examined the impact of economic conditions on health outcomes. He is a Fellow of the Econometric Society and a member of the Royal Netherlands Academy of Arts and Sciences. His work spans interdisciplinary areas including epidemiology, development economics, and behavioral economics. Education details are not explicitly provided in the text, but his roles suggest advanced academic training in economics and epidemiology. He has been involved in numerous collaborative projects across institutions such as the Institute for Employment Research (IAB), IZA, and J-PAL. His recent publications explore topics like minimum wage effects, biomarker-driven health studies, and the long-term impacts of early-life environments. Awards: Royal Netherlands Academy membership, Fellow of Econometric Society. Grants/Projects: Evaluation of labor market programs (IZA), research on economic determinants of diabetes (Health Economics). Labs/Teams: Affiliated with multiple institutions including CEPR, ZEW, and IFAU.
Jilles Vreeken is a Professor of Computer Science at Saarland University and tenured faculty at the CISPA Helmholtz Center for Information Security, where he leads the Exploratory Data Analysis research group. He is also an ELLIS Fellow and Faculty of the Saarbrücken Unit on AI and ML. His work bridges theoretical foundations with practical applications in causal inference, unsupervised learning, and exploratory data analysis. Dr. Vreeken's research focuses on developing theory and algorithms for answering fundamentally exploratory questions about data: "what is going on in my data?", "what causes what and how?", and "what can we learn from this model?" without making unnecessary or unjustified assumptions. He takes a principled approach based on information theory to identify what is worth knowing, then develops efficient algorithms for extracting useful interpretable results. His work spans causal inference under realistic conditions (including hidden confounding, selection bias, and non-i.i.d. data), summarizing complex data and models in understandable terms, and combining these threads to create more robust and useful models across diverse data types. His recent publications demonstrate a strong trend toward causal discovery in increasingly realistic settings, including non-stationary time series, event sequences, and scenarios with hidden confounders. He has made significant contributions to federated learning, interpretable machine learning, and pattern mining. His work consistently applies information-theoretic principles to develop methods that are both theoretically sound and practically useful for extracting insights from complex data. Dr. Vreeken has received numerous prestigious awards including: IEEE ICDM'18 Tao Li Award for Excellence in Research IEEE ICDM'18 Best Paper Award UdS-CS'15 Busy Beaver Teaching Award ACM SIGKDD'11 Best Student Paper Award ACM SIGKDD'10 Doctoral Dissertation Runner-Up Award ECML PKDD'09 Best Student Paper Award As an advisor, Dr. Vreeken has mentored numerous doctoral researchers to completion, including Dr. Osman Ali Mian, Dr. David Kaltenpoth, Dr. Boris Wiegand, Dr. Sebastian Dalleiger, Dr. Janis Kalofolias, Dr. Jonas Fischer, Dr. Alexander Marx, Dr. Panagiotis Mandros, Dr. Kailash Budhathoki, Dr. Roel Bertens, Dr. Koen Smets, and Dr. Michael Mampaey. He has secured significant research funding as PI for multiple projects including "AI for Prediction and Therapy Guidance in Acute Stroke" (HAICU, 2025-2028), "Neuro-Explicit Models of Language, Vision and Action" (RTG, DFG, 2023-2028), and "Crushing Antimicrobial Resistance using Explainable AI" (HAICU, 2021-2024). Dr. Vreeken leads the Exploratory Data Analysis (EDA) research group at CISPA, which focuses on developing theory and algorithms for discovering novel insights from data, learning inherently interpretable models, and drawing reliable causal conclusions. The group has produced numerous influential algorithms and frameworks in causal inference, pattern mining, and exploratory data analysis, with applications spanning healthcare, materials science, and cybersecurity.
Prof. Mike Barth is a Professor for Networked Secure Automation Technology at the Karlsruhe Institute of Technology (KIT), affiliated with the Department of Electrical Engineering and Information Technology (ETIT) and the Institute for Control Systems (IRS). His academic background includes a doctorate from Helmut Schmidt University (2011) and a master's degree from Pforzheim University (2008). He previously held roles as a researcher at ABB and as a professor at Pforzheim University, focusing on blended learning and Industry 4.0 integration. Education: PhD in Automation Technology, Helmut Schmidt University (2011) M.Sc. in Product Development, Pforzheim University (2008) Diploma in Mechanical Engineering, Pforzheim University (2006) Research Interests: Automation technology, control systems, Industry 4.0, cyber-physical systems, digital twin engineering, cybersecurity, and IoT protocols. Teaching: Courses include System Modeling, Cyber Physical Production Systems, and Digital Twin Engineering. His research emphasizes secure automation architectures, decentralized systems, and model-based engineering. He chairs multiple committees including IFAC TC3.1 and the VDI/VDE Society for Measurement and Automation. Over 50+ publications span topics like simulation models, industrial security, and robotic integration. Labs/Teams: Leads the IRS Automation Technology team, focusing on innovation in control systems and digital twin applications.
Jure Leskovec is a Professor of Computer Science at Stanford University, affiliated with the Stanford AI Lab, Machine Learning Group, and the Center for Research on Foundation Models. He holds academic appointments in the Department of Computer Science and is a member of Bio-X, the Institute for Human-Centered Artificial Intelligence (HAI), and the Wu Tsai Neurosciences Institute. Leskovec earned his BSc from the University of Ljubljana (2004), PhD from Carnegie Mellon University (2008), and postdoctoral training at Cornell University. His research focuses on social networks, data mining, machine learning, and computational biomedicine, with contributions to graph neural networks, drug discovery, and AI applications in healthcare. His work has been applied to combat the COVID-19 pandemic and integrated into products at major tech companies. Leskovec’s publications reflect his expertise in network analysis, medical AI, and biological systems. His recent work includes foundational contributions to graph neural networks (e.g., PyG) and medical AI frameworks. His research has garnered numerous awards, including the Microsoft Research Faculty Fellowship and ICDM Research Contributions Award. Leskovec advises numerous doctoral and postdoctoral researchers, contributing to over 200 publications. His interdisciplinary collaborations span computational biology, healthcare analytics, and social systems, with a focus on leveraging AI to address real-world challenges.
Brandon Lucia is a Full Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University's College of Engineering. He leads the abstract research group focusing on the intersection of computer architecture, systems, and programming languages. His work bridges theoretical foundations with practical implementations in energy-constrained environments. Lucia's research centers on intermittent computing systems and edge computing in extreme environments. His work on energy-harvesting systems has established fundamental principles for batteryless computing, while his orbital edge computing research pioneers computational intelligence for nanosatellite constellations. These research thrusts address critical challenges in reliability, efficiency, and programmability for systems operating under severe power constraints. His publication record shows a clear evolution from foundational work on intermittent computing models to sophisticated applications in space computing and edge intelligence. Recent publications demonstrate increasing integration of dataflow architectures with energy-harvesting constraints, particularly in satellite constellations where computational resources must be managed across distributed, power-constrained platforms operating in extreme environments. NSF CAREER Award (2017) IEEE TCCA Young Computer Architect Award (2019) Sloan Foundation Fellowship (2021) ASPLOS Best Paper Awards (2018, 2020) OOPSLA Distinguished Paper and Artifact Awards (2015) Lucia actively mentors numerous PhD students including Brad Denby, Zhuo Cheng, and Emily Ruppel, many of whom contribute significantly to his research program. His abstract research group maintains strong industry connections while pursuing fundamental advances in computing systems. The group has developed multiple open-source tools including Legerdemain for program analysis and MultiCacheSim for cache coherence simulation. His laboratory work spans from theoretical foundations of intermittent computing to practical implementations in space systems. Current projects include computational nanosatellite constellations, energy-minimal dataflow architectures, and secure edge computing systems that operate reliably despite frequent power failures.
Professor Ivan Haigh is an Associate Professor in Ocean and Earth Science at the University of Southampton, UK. His research focuses on sea level variations, coastal flooding, and climate change impacts. He leads multiple international research projects, including the CHANCE project (NERC/NSF-funded) and Vietnam flood risk studies (NERC/NAFOSTED-funded), totaling £4.8M across 8 active grants. He is a Principal Investigator (PI) and Co-Investigator (Co-I) on projects addressing compound flooding, storm surge barriers, and coastal resilience. His work bridges academic research with practical coastal management, collaborating with agencies like the UK Environment Agency and Dutch Ministry of Infrastructure. Educated at the University of Southampton (BSc Oceanography/Maths, 1998-2001) and with a PhD (2005-2009) on sea level changes in the English Channel, Haigh held a postdoctoral fellowship at the University of Western Australia before returning to Southampton as a lecturer. Promoted to Associate Professor in 2016, he also leads the NERC-funded Knowledge Exchange Fellowship (2021), focusing on climate-resilient infrastructure. Teaching includes coordinating coastal processes modules at undergraduate and MSc levels, with a focus on stakeholder engagement. His external roles include keynote speaking at global conferences (e.g., 'All at Seas Conference' 2013) and membership in the Institute of Marine Engineering. Current PhD supervision spans 10 students across interdisciplinary topics like compound flooding and storm surge modeling. Research groups include the Physical Oceanography Institute and Southampton Marine and Maritime Institute. Notable projects include ACROSS (colonisation of Sahul via seafaring) and RISeR (sea-level change projections for northwest Europe). His work emphasizes translating scientific insights into actionable coastal management strategies, addressing both academic and societal challenges.
Jeff Huang is an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University, affiliated with the College of Engineering. His research focuses on software engineering, programming languages, concurrency, and runtime verification, with notable contributions to static analysis, race detection, and vulnerability mitigation in concurrent systems. Education: Postdoc, Computer Science, University of Illinois at Urbana-Champaign (2013-2014) Ph.D., Computer Science, Hong Kong University of Science and Technology (2012) B.E., Electrical Engineering, National University of Defense Technology, China (2008) Research Interests: Huang's work bridges theoretical foundations and practical applications in concurrency debugging, static analysis tools, and cybersecurity for smart contracts. He emphasizes scalable solutions for pointer analysis, race detection, and vulnerability detection in distributed systems and blockchain technologies. Recent Trends in Publications: His recent work explores AI-driven program execution (e.g., SGLang), blockchain security (e.g., Smart Contract analysis), and dynamic/static analysis techniques for memory safety. These studies underscore advancements in automated tools for securing concurrent and distributed systems. Awards: 2023 ACM SIGSOFT Distinguished Paper Award 2019 DARPA Young Faculty Award 2016 NSF CAREER Award 2013 ACM SIGSOFT Outstanding Doctoral Dissertation Award Advising & Grants: Huang has advised PhD students including Bozhen Liu and Peiming Liu, who have contributed to OpenMP race detection and pointer analysis tools. His grants include NSF awards for pointer analysis as a service and DARPA funding for young faculty research. Labs & Teams: He leads the O2 Lab, focused on concurrency verification and cybersecurity, collaborating with industry partners like Coderrect Inc. and DOE on scalable static analysis frameworks.
