Prof. Dick den Hertog is a Full Professor at Tilburg University's Department of Econometrics and Operations Research, part of the Tilburg School of Economics and Management (TiSEM). His research focuses on operations research methodologies with applications in humanitarian logistics, supply chain optimization, and robust decision-making under uncertainty. He collaborates with organizations like the UN World Food Programme to enhance operational efficiency in complex environments. Key research areas include robust optimization techniques, supply chain management in developing regions, and the integration of satellite data with machine learning for infrastructure analysis. His work addresses challenges such as food aid distribution, disaster response logistics, and predictive modeling for transportation systems in data-scarce areas. Notable projects include developing analytical tools for the WFP's supply chain planning and creating algorithms for weather-informed road speed prediction. He is affiliated with the Tilburg Sustainability Center and the Operations Research research group, contributing to both academic advancements and real-world impact through optimization solutions.
Michael Halassa is an Associate Professor in Neuroscience at the Tufts University School of Medicine in Boston, MA. His work focuses on understanding thalamocortical circuits and their role in cognitive functions like attention, decision-making, and psychiatric disorders. He holds dual degrees: a Doctor of Medicine (MD) from the University of Jordan (2004) and a PhD in Neuroscience from the University of Pennsylvania (2009). Research Interests: Dr. Halassa investigates how the thalamus regulates executive control, cognitive flexibility, and uncertainty processing. His lab explores neural mechanisms underlying schizophrenia, ADHD, and neurodevelopmental disorders using computational models, optogenetics, and electrophysiological techniques. Key themes include thalamic regulation of prefrontal cortex interactions, circuit dysfunction in mental illnesses, and translational approaches to restore neural connectivity. Recent Work Trends: His publications emphasize thalamic contributions to attentional control, decision-making under uncertainty, and circuit-level interventions for psychiatric conditions. Computational models link hyperactive D2 receptors to impaired belief updating in schizophrenia, while genetic studies (e.g., Shank3 mutants) reveal thalamic circuit修复 strategies. Articles highlight cross-species comparisons and clinical applications of thalamic neuroscience. Awards: No specific prizes/fellowships are listed in the provided texts. Advising/Grants: No student names or grant details were explicitly mentioned, though his research involves collaborative projects with labs studying neural circuits and psychiatric mechanisms. His work is likely funded through NIH grants and institutional support. Labs/Teams: His lab at Tufts focuses on thalamocortical systems and psychiatric disorders, employing interdisciplinary methods including electrophysiology, optogenetics, and computational modeling to dissect neural circuits underlying cognition.
Dr. Sasha Rubin is a Senior Lecturer and leader of the Computational Logic for AI (LOGIC-AI) group at the School of Computer Science, The University of Sydney. He holds a PhD in Mathematics and Computer Science from the University of Auckland and previously worked at the University of Naples Federico II. His research focuses on logic foundations of AI, including synthesis, planning, formal methods, and multi-agent systems. He teaches courses like Models of Computation and supervises students in topics like probabilistic systems and reinforcement learning. Research Interests: Mathematical Logic, Formal Verification, Temporal Logic Synthesis, Automated Reasoning, and Multi-Agent Systems. He has published extensively in top venues like IJCAI, AAAI, and ACM Transactions. His work includes verification of agent navigation, strategy logic, and planning under uncertain environments. Awards: Recognized as an Australian Research Field Leader in Theoretical Computer Science (2020). He serves on editorial boards for JAIR and conferences like KR, and organizes events such as the Australasian Association for Logic Conference (2024). Supervision and Grants: Current students include Ethan HIRSCHOWITZ and Kunal OSTWAL. Past supervision spans MPhil/PhD projects on probabilistic systems, ML classifier fairness, and symbolic automata. His grants include studies on logic and robots in anonymous graphs. Professional Activities: Member of EATCS, ACM, and mentor for the Sydney Summer Innovation Programme. He leads the LOGIC-AI lab and collaborates internationally, notably with Giuseppe De Giacomo at Sapienza University of Rome.
Samory Kpotufe is an Associate Professor of Statistics at Columbia University's Faculty of Arts and Sciences, affiliated with the Data Science Institute (DSI) as a Foundations of Data Science Co-Chair. He holds additional affiliations in Cybersecurity, Health Analytics, and Smart Cities. His academic journey includes a PhD in Computer Science from UC San Diego (2010), followed by research roles at the Max Planck Institute, Toyota Technological Institute at Chicago, and Princeton University's ORFE department. His research focuses on nonparametric methods and high-dimensional statistics, emphasizing adaptive procedures that self-tune to unknown data structures (e.g., manifolds, sparsity) while addressing modern application constraints like computational efficiency and labeling costs. Key themes include transfer learning, active learning, and online algorithms. Notable contributions span theoretical guarantees for nearest-neighbor methods, covariate shift adaptation, and contextual bandits. His work often bridges statistical theory and practical machine learning challenges, with applications in IoT, cybersecurity, and anomaly detection. He has led collaborative grants, such as the NSF CPS project on data augmentation for IoT systems. As a DSI member and Foundations Co-Chair, he contributes to advancing data science foundations through interdisciplinary collaboration. His lab's research frequently explores the interplay between algorithmic performance and intrinsic data properties.
Niklas Linde is a full professor at the University of Lausanne's Faculty of Geosciences and Environment, leading the Department of Earth Sciences. He holds a PhD in Geophysics from Uppsala University (2005) and has held roles including Assistant Professor (2008), Associate Professor (2013), and Full Professor (2019). His research focuses on transforming geophysical signals into realistic hydrogeological models with rigorous uncertainty quantification. Key areas include probabilistic inversion, Bayesian methods, and geostatistical modeling applied to environmental and subsurface processes. Education: PhD in Geophysics (Uppsala University, 2005), postdoctoral positions at Lawrence Berkeley National Lab (USA), CNRS-CEREGE (France), and ETH Zurich (Switzerland). He joined UNIL in 2008 as an Assistant Professor in Environmental Geophysics. Research interests span geophysical inversion techniques, subsurface heterogeneity characterization, and the integration of geophysical and hydrological data. Current projects emphasize Bayesian approaches for model selection and rare event estimation, supported by grants from the European Commission and Swiss National Science Foundation. Collaborations involve international teams addressing challenges in hydrogeology, rock fracture dynamics, and 4D hydrogeology. Publications reflect advancements in inverse problem solving, stochastic simulation, and machine learning applications. His work bridges theory and practice, with field studies in alpine environments, fractured media, and environmental monitoring. Students under his supervision have explored topics like deep generative networks and Bayesian hydrogeological inversion. Advising: Supervised over a dozen PhD students, including recent works on variational Bayesian methods and geophysical data fusion. Grants include projects on uncertainty quantification and experimental design. Active in scientific societies and editorial roles, contributing to methodological advancements in Earth sciences.
Lokukaluge Prasad Perera is a Professor in Maritime Technology at UiT The Arctic University of Norway and a Senior Research Scientist in Smart Data at SINTEF Digital . He holds a BSc in Mechanical Engineering from Oklahoma State University (1999), MSc in Systems & Controls from the same institution (2001), and a PhD in Naval Architecture and Marine Engineering from Technical University of Lisbon (2012). His research focuses on Maritime and Offshore Systems , Advanced Data Analytics , Autonomous Navigation , Energy Efficiency , and Digital Twin Applications . He has published over 100 peer-reviewed papers and was recognized in the World's Top 2% Scientists (2021-2022) by Stanford University. Key professional experiences include roles at SINTEF Ocean (2014–2017), Center for Marine Technology and Engineering in Portugal (2008–2012), and Wärtsilä Finland (2012–2014). He has also held academic positions at Naval & Maritime Academy and Ocean University of Sri Lanka . His work addresses challenges in emission reduction , renewable energy integration , and safety-critical systems for maritime operations. Current projects emphasize trustworthiness of autonomous ships and data-driven decision frameworks for energy efficiency.
Usman Ali is an Assistant Professor and Adjunct Lecturer at the School of Mechanical and Materials Engineering, University College Dublin. He holds a Ph.D. in 'A data-driven GIS-based approach for multi-scale residential building energy modeling' (2020) from UCD and an M.Sc. in Computer Science from Lahore University of Management Science (2013). His research focuses on machine learning, GIS modeling, urban building energy systems, and energy performance certification. He has contributed to projects like the U.S.-Ireland R&D initiative on building stock classification and energy prediction, and collaborated with the Sustainable Energy Authority of Ireland (SEAI) on energy policy research. His work emphasizes data-driven solutions for energy efficiency, urban sustainability, and policy decision-making. Education: Ph.D., University College Dublin (2020) M.Sc., Lahore University of Management Science (2013) B.Sc., International Islamic University Islamabad (2008) Research emphasizes machine learning applications in energy modeling, GIS integration for urban planning, and energy policy frameworks. His recent work includes synthetic building datasets, occupancy-based energy analysis, and uncertainty quantification in energy systems.
Ljiljana Trajkovic is a Professor in the Department of Engineering Science at Simon Fraser University's Faculty of Applied Sciences. She holds a Ph.D. from the University of California, Los Angeles (1986), M.Sc. from Syracuse University (1979), and Dipl.Ing. from the University of Pristina (1974). Her research focuses on communication networks, nonlinear circuits, and machine learning applications for network security. She actively contributes to IEEE initiatives, including roles as conference committee chair and editorial board member. Education highlights include a strong foundation in electrical engineering and advanced studies in circuit theory and systems science. Her work bridges theoretical analysis with practical applications, such as anomaly detection in communication networks using machine learning. She teaches courses like ENSC 220 D100 Electric Circuits I, integrating research insights into education. Research interests emphasize network security, traffic analysis, and distributed systems. Recent articles explore BGP anomaly classification, ransomware detection, and virtual network embedding. She collaborates on tools like VNE-Sim and Anonym for network analysis. Awards and recognitions are highlighted through her leadership roles in IEEE and academic contributions. Advising and grants involve mentoring graduate students in cybersecurity and networking projects. She leads research teams exploring complex networks and their applications in autonomous systems. Her lab focuses on interdisciplinary projects merging electronics engineering with AI-driven network solutions.
Marc Plantevit is a Full Professor at EPITA, member of the Laboratoire LRDE (LRDE). Previously, he served as an Associate Professor at University Claude Bernard Lyon 1 (2010–2021), leading the Data Mining & Machine Learning group at LIRIS lab. He holds a PhD in Computer Science from the University of Montpellier (2008), supervised by Maguelonne Teisseire and Anne Laurent at LIRMM Lab. His research focuses on foundational data mining, graph mining, subgroup discovery, and explainable AI. He is an editorial board member of Data Mining and Knowledge Discovery Journal and has held roles such as CAPES NSI jury member and former head of the Data Mining & Machine Learning group at LIRIS. Research Interests : Data Mining, Machine Learning, Explainable AI, Graph Mining, Subgroup Discovery, Exceptional Model Mining, Constraint-based Pattern Mining, and applications in neuroscience and energy systems. His work explores interpretable AI, GNN explainability, and interdisciplinary applications like odor perception modeling and electricity price forecasting. Key Contributions : Best Paper Award at EGC'22 for work on GNN representations. Active in program committees for ECMLPKDD, IJCAI, and IEEE ICDM . Supervised PhD students working on GNN explainability, electricity forecasting, and machine learning in exposome studies. Labs & Teams : LRDE (EPITA), previously involved with LIRIS (UMR CNRS 5205) and collaborative projects with institutions like INSA Lyon and ISGlobal (Barcelona).
Prof. Dr. Ben Wagner is a Professor of Media, Technology & Society at Inholland University, Director of TU Delft's AI Futures Lab on Rights and Justice, and Professor of Human Rights & Technology at IT:U. His work bridges social sciences, technology, and human rights, focusing on digital governance, AI ethics, and societal impacts of technological change. He holds a PhD from the European University Institute (2013) and has led institutions like the Center for Internet & Human Rights (Viadrina) and the Sustainable Computing Lab (WU Wien). Key initiatives include Inholland's Digital Rights Research Team (DRRT), Sustainable Media Lab (SML), and contributions to the European Cloud for Heritage OpEn Science (ECHOES). His research emphasizes designing accountable tech systems, digital rights frameworks, and sustainable digital infrastructures. Recent work addresses gaps between legal/ethical guidelines and public sector data practices, AI governance across nations, and audit mechanisms for platform transparency. Awards include the 2023 Best Paper Award at HICSS for AI governance research and a 2013 Best Student Paper at Internet Science. Collaborations span academia, governments, and industries to shape equitable tech policies. Active in advisory roles for ENISA, Patterns Journal, and the UKRI Trustworthy Systems Hub.
Susan Howick is a Professor of Management Science and Vice-Dean (Academic) at Strathclyde Business School, University of Strathclyde. Her academic leadership and research are centered on system dynamics and decision support modelling, with applications in healthcare, energy, and public policy. Research Interests: System dynamics and hybrid modelling (SD & ABM) Integration of multiple modelling methods for decision support Disruption and delay analysis in complex projects Systemic risk evaluation in engineered and social systems Client-oriented modelling processes The recent publications show a strong trend towards interdisciplinary applications, particularly in health systems and Arctic search and rescue, often involving community-driven and culturally sensitive models. Her work increasingly combines Bayesian networks with system dynamics to support decision-making in uncertain, high-stakes environments. Scientific Awards: Operational Research Society Goodeve Medal (2001) EURO Prize for OR for the Common Good Finalist (2022) European Journal of Operational Research Editor's Choice Award (2021) Best Paper Award at 17th International Conference on Group Decision and Negotiation (2017) Elected to General Council of the OR Society (2018) Susan has supervised PhD students and served as co-investigator and principal investigator on multiple research projects, including those funded by EPSRC and GCRF. She has also delivered executive training for organizations such as Bombardier, PwC, and the Scottish Government, demonstrating strong industry engagement. Labs and Research Teams: She is actively involved in research groups focused on system dynamics, risk modelling, and decision support, collaborating with teams in healthcare (e.g., SSM - Health Systems), Arctic resilience (Nunavut Search and Rescue Project), and hybrid simulation. She contributes to the academic community through editorial roles in European Journal of Operational Research and System Dynamics Review .
Kyle Hanquist is an Assistant Professor in the Department of Aerospace and Mechanical Engineering at the University of Arizona, where he is also a member of the Graduate Faculty. He directs the Computational Hypersonics and Nonequilibrium Laboratory (CHANL), focusing on advanced simulation techniques for high-speed flows. His academic journey includes a PhD and MSE in Aerospace Engineering from the University of Michigan and a BSE in Mechanical Engineering from the University of Nebraska. PhD, Aerospace Engineering, University of Michigan, Ann Arbor MSE, Aerospace Engineering, University of Michigan, Ann Arbor BSE, Mechanical Engineering, University of Nebraska, Lincoln Dr. Hanquist's research centers on hypersonics, aerothermodynamics, and nonequilibrium flows , with strong emphasis on computational fluid dynamics , low-temperature plasmas , and thermal management systems . His work involves modeling complex physical phenomena such as electron transpiration cooling, plasma-assisted flow control, and high-temperature gas effects in reentry environments. He also investigates molecular gas dynamics and finite-rate chemistry in extreme conditions. His recent publications reveal a strong trend in computational modeling of hypersonic boundary layers , plasma sheaths , and shock-tube validation of thermochemical models . The interdisciplinary nature of his work spans aerospace engineering, plasma physics, and materials response under extreme thermal loads. Much of his research integrates multi-physics simulations to address fluid-thermal-structural interactions critical for next-generation hypersonic vehicles. Dr. Hanquist has received several scientific honors, including: 2020 AIAA Plasmadynamics and Lasers Best Paper Award Editor's Choice, AIP Publishing - Physics of Fluids (Summer I 2020) Featured Article, AIP Publishing - Physics of Fluids (Summer I 2021) Frontiers in Physics – Plasma Physics (Spring 2020) As an advisor and lab director, he mentors graduate students in computational hypersonics and collaborates with institutions like NASA and the University of Michigan. His research is supported by grants from aerospace and defense agencies, though specific funding sources are not listed. He teaches courses in fluid mechanics, numerical methods, and nonequilibrium flows, contributing to both undergraduate and graduate education. He leads the Computational Hypersonics and Nonequilibrium Laboratory (CHANL) , which develops and applies high-fidelity simulation tools for hypersonic applications. The lab focuses on kinetic modeling, plasma interactions, and optimization of thermal protection systems, often using massively parallel CFD codes and multi-fidelity surrogate models.
Prof. Dr. Katja Rösler is a Professor of Automotive Engineering at the Institute of Mechanical Engineering, Ruhr West University of Applied Sciences since March 2012. Her career spans academic research and industrial development with key positions at TU Braunschweig, Volkswagen AG, and Fraunhofer Institute. Education: Industrial Mathematics degree completed under standard period Doctorate: Engineering (Driver Modeling) from TU Braunschweig, 2008 Her research focuses on automotive engineering with special emphasis on modeling/simulation, vehicle dynamics, driver assistance systems, accident research, alternative drives, and mobility concepts. She actively combines simulation with experimental verification and has significant involvement in Formula Student projects. Recent publications highlight her work in intelligent mobility systems (2018-2020), with particular attention to electromobility, accessibility solutions for elderly/disabled populations, and micromobility analysis. Earlier works established her expertise in driver modeling, vehicle measurement technology, and simulation-experiment correlation. Labs: Automotive Engineering Lab Teaching: Mechanics (Statics, Strength of Materials, Dynamics), Vehicle Dynamics, Driver Assistance Systems
George Haller is a Professor at the Department of Mechanical and Process Engineering at ETH Zurich . He leads the Institute of Mechanical Systems and holds the Chair in Nonlinear Dynamics . His research focuses on: Nonlinear dynamical systems theory Data-driven model reduction Spectral submanifolds (SSMs) Coherent structure identification in fluids and solids Control of complex nonlinear systems His recent work emphasizes equation- and data-driven modeling across solids, fluids, and control systems . Key contributions include: SSMTool - a MATLAB package for nonlinear model reduction SSMLearn - open-source software for data-driven modeling Transport barrier detection algorithms with oceanographic applications Scientific accolades include: 2025 Lyapunov Award (ASME) 2023 Stanley Corrsin Award (APS) Fellowships: ASME, APS, SIAM External Member, Hungarian Academy of Sciences His group has trained notable alumni: Thomas Breunung (Assistant Professor, University of Wisconsin-Madison) Shobhit Jain (Assistant Professor, Delft University of Technology) Mattia Serra (Assistant Professor, UCSD) Publications span Nonlinear Dynamics, Nature Communications , and Physical Review Fluids , with a 2025 book Modeling Nonlinear Dynamics for Equations and Data (SIAM Press). Current projects include: Reduced-order modeling of fluid-structure interactions Control of soft robots via nonlinear dynamics Identifying material barriers in turbulence
James R. Lee is a Professor in the Department of Computer Science at the University of Washington. His research spans theoretical computer science, probability, and geometry. He has held visiting scientist roles at Microsoft Research (2023, 2018, 2017) and participated in programs at the Simons Institute (2023, 2020, 2018, 2017, 2014). Research Interests: Algorithms, complexity theory, convex optimization, metric embeddings, spectral graph theory, probability, stochastic processes, and the interplay between discrete and continuous analysis. Teaching: Courses on modern algorithms, quantum computing, optimization theory, and spectral methods in theoretical computer science. Scientific Contributions: Developed sparsification algorithms for generalized linear models and norms with near-linear size guarantees (STOC'24, FOCS'23). Extended Cheeger-type inequalities to higher eigenvalues (STOC'12, STOC'18). Proved super-polynomial lower bounds for LP/SDP relaxations in constraint satisfaction (STOC'15, FOCS'13). Disproved Benjamini-Papasoglou conjectures on annular separators (Discrete Comp. Geom. 2024). Advanced understanding of random walks in geometric and unimodular graphs (Israel J. Math. 2023, GAFA 2023). Scientific Awards: Best Paper Award, STOC 2015
