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Nico BeckerView profile

Researcher

• Impact modeling
• Generalized Linear Models
• Extreme Value Statistics
• +2 more

Dr. Nico Becker is a Researcher at the Institute of Meteorology , Free University of Berlin, specializing in weather-related impact modeling and climate diagnostics. His work bridges meteorological data with practical applications in transportation safety and catastrophe risk assessment. Key Projects : WEXICOM, RAIN, ClimXtreme Research Focus : Impact modeling, extreme value statistics, and regional climate modeling Collaborations : Active in European Meteorological Society conferences and Geo.X network His publications emphasize weather-traffic interactions (2020-2022), storm damage mechanisms (2023-2025), and climate model dynamics (2015-2018). Recent work develops predictive models for storm impacts on infrastructure. Scientific Activities include: Operational storm surge forecasting systems (2016-2017) Development of generalized additive models for accident prediction Investigation of Mediterranean wind events and medicane dynamics

Marcos Escobar-AnelView profile

Professor

• Stochastic Processes
• Stochastic Covariance
• Financial Mathematics
• +5 more

Marcos Escobar-Anel is a Professor in the Department of Statistical and Actuarial Sciences at Western University. His research focuses on stochastic processes, financial mathematics, and statistical modeling, particularly in areas such as multivariate stochastic covariance, dynamic portfolio optimization, and GARCH models. He has supervised numerous graduate students pursuing PhD and Master’s degrees, including Wei Li Fan, Yijao Jiao, and Shiying Li. Ph.D. in Mathematics from the University of Toronto His recent work explores applications of affine GARCH models in option pricing, robust portfolio strategies under stochastic volatility, and the impact of derivatives in portfolio optimization. He has received significant research funding through NSERC Discovery Grants and Mitacs Postdoctoral Accelerate programs. Publications highlight advancements in multivariate stochastic covariance modeling, constrained portfolio strategies, and utility-based optimization frameworks. Collaborations with researchers like Rudi Zagst and Lars Stentoft demonstrate interdisciplinary approaches to financial risk management and sustainable investing.

Samuel G. B. JohnsonView profile

Assistant Professor

• Cognitive Science
• Behavioral Economics
• Moral Psychology
• +3 more

Samuel G. B. Johnson is an Assistant Professor of Psychology at the University of Waterloo , where he directs the Decision, Inference, and Cognitive Economics (DICE) Lab . His research focuses on how humans use mental models and moral intuitions to navigate complex systems, often revealing systematic errors in domains like voting, investing, and charitable giving. PhD in Cognitive Psychology from Yale University Postdoctoral research at University College London Former Lecturer at University of Bath School of Management Current affiliation with University of Waterloo's Psychology Department since 2017 His work spans Cognitive Science and Behavioral Economics , examining how people make sense of uncertainty through narrative construction, probabilistic reasoning, and explanatory biases. Key findings include the optimality bias in moral judgment and latent scope bias in evaluating explanations. Though his research primarily investigates basic cognitive mechanisms, he emphasizes real-world applications in economics, politics, and education. The DICE Lab employs interdisciplinary methods from cognitive psychology, computational modeling, and cross-cultural studies, reflecting Johnson's commitment to letting research questions dictate methodologies. The lab actively recruits graduate students interested in collaborative projects exploring cognitive economics, social reasoning, and decision-making under uncertainty.

• Earthquake Engineering
• Seismic Design
• Steel Structures
• +2 more

Lydell Andree Wiebe is a Professor in the Department of Civil Engineering at McMaster University's Faculty of Engineering. He specializes in earthquake engineering, seismic design of steel structures, and resilient infrastructure systems. His academic credentials include a BASc (2005) and PhD (2013) in Civil Engineering from the University of Toronto, and an MSc in Earthquake Engineering (2008) from the ROSE School in Pavia, Italy. His research focuses on developing innovative structural systems such as controlled rocking braced frames and masonry walls to enhance seismic resilience. Key areas include performance-based design, structural dynamics, and risk assessment of infrastructure under seismic loads. He actively contributes to professional committees, including the CSA S16 Technical Committee (Steel Structures) and the Canadian Association for Earthquake Engineering. Dr. Wiebe's recent publications (2020–2025) emphasize seismic loss mitigation, optimization of controlled rocking systems, and machine learning applications in structural engineering. His work integrates experimental testing with advanced computational models to improve design methodologies for earthquake-resistant structures. Awards & Honors: GJ Jackson Fellowship Award (2005) HA Krentz Research Award (2015) McMaster Faculty of Engineering Teaching Excellence Award (2017) McMaster Students Union Teaching Award (2014, 2017) He teaches courses including Steel Structures , Seismic Design and Analysis , and Introduction to Civil Engineering , reflecting his commitment to education and mentorship.

• Electric Power Systems
• Smart Grids
• Renewable Energy Integration
• +4 more

Olof Samuelsson is a Professor at the School of Engineering (Lund University), serving as Director of Third Cycle Studies and Coordinator for the LTH Profile Area: The Energy Transition. His work focuses on modernizing power systems to support renewable energy integration and electromobility infrastructure. Division for Industrial Electrical Engineering and Automation UN SDG: Clean Energy & Climate Action Research Themes: Grid Capacity, Reliable Power Supply, Blackstart Operations His research interests span power systems analysis , electrical machines , power electronics , and smart grid automation , with a focus on enabling renewable energy integration and electric transport networks. Key subtopics include: Wind/solar grid integration Electric vehicle charging impacts System integrity protection schemes Probabilistic grid modeling Space weather resilience Recent Scientific Trends His research group analyzes 15+ publications since 2023, emphasizing: Electrified transport grid impacts (2025) Renewable energy congestion management (2023) Probabilistic modeling of sub-transmission systems System integrity schemes across Nordic grids Awards : IEEE PES Chapter Outstanding Engineer (2017) Projects : Leads SPS4SE (2023–2026) and co-leads VINST (2020–2023) with the Swedish Energy Agency.

• Marine Technology
• Arctic Engineering
• Risk Management
• +3 more

Liangliang Lu is a Postdoctoral Researcher at the Department of Energy and Mechanical Engineering , Aalto University. Their research focuses on Marine and Arctic Technology , with significant contributions to understanding ice mechanics, ship performance in polar conditions, and maritime risk assessment. Specializes in Arctic and Antarctic navigation Develops Bayesian networks for oil spill risk modeling Engages in collaborative polar research projects Research interests span ice-induced ship hull loads , winter navigation systems , and oil spill response effectiveness . Their work combines computational modeling , field measurements , and data analytics to improve maritime safety in extreme environments. Recent publications emphasize CFD/FSI/AI integration , real-time conflict probability ranking , and flexural strength analysis of freshwater ice. Collaborations with experts in naval architecture and polar logistics appear frequently. Active in polar research groups, Lu contributes to developing decision-support tools for ice-covered operations and participates in international conferences like POAC and IAHR Symposiums.

• Volcanology
• Geochemistry
• Geophysics
• +3 more

Dr. Elisabeth Gallant is an Assistant Professor in the Department of Geology at the University of Hawaii at Hilo, College of Natural and Health Sciences. Her research focuses on volcanic processes, including eruption response dynamics, edifice growth mechanisms, and lava flow behavior quantification. She collaborates extensively with the Hawaiian Volcano Observatory, employing ground-based radar interferometry and geochemical analysis for real-time hazard assessment. Research interests center on: Eruption Response : Real-time geochemical analysis of eruptive products from Kīlauea and Mauna Loa Edifice Growth : Field observations and ground-penetrating radar studies of cinder cone formation Lava Flow Dynamics : Novel instrumentation development to improve flow modeling and hazard prediction Her publications predominantly address volcanic hazard assessment, lava flow mechanics, and monitoring technologies, with recent work emphasizing operational applications during the 2022 Mauna Loa eruption. Teaching responsibilities include undergraduate courses in mineralogy, petrology, volcanology, and geologic remote sensing.

Hyunsun LeeView profile

Senior Lecturer

• Statistical data analysis
• Stochastic branching process in wireless ad-hoc network
• Sound generation mechanism for aircraft engine nozzle
• +2 more

Hyunsun Lee is a Senior Lecturer at the School of Design , University of Massachusetts Amherst , affiliated with the Department of Mathematics and Statistics . She contributes to the M.S. Option in Statistics and holds a PhD in Mathematics from Stony Brook University, alongside M.S. and B.S. degrees in Mathematics from Yonsei University. Education: PhD in Mathematics, Stony Brook University M.S. in Mathematics, Yonsei University B.S. in Mathematics, Yonsei University Research Interests span statistical data analysis, stochastic branching processes in wireless networks, aircraft engine sound generation mechanisms, homogenization for heterogeneous composites, and Rayleigh-Taylor instabilities. These areas intersect applied mathematics, fluid dynamics, and computational modeling. Scientific Awards include the 2017 Golden Apple Award for Excellence in Mentoring at Hawaii Pacific University and a 2021-22 Distinguished Teaching Award nomination at UMass Amherst.

Dhireesha KudithipudiView profile

Professor

• Artificial Intelligence
• Neuromorphic Computing
• Energy-Efficient Machine Learning
• +5 more

Dhireesha Kudithipudi is a Professor in the Department of Computer Science at the University of Texas at San Antonio (UTSA) and Director of the MATRIX AI Consortium. Her work focuses on advancing neuromorphic computing, energy-efficient machine learning architectures, and lifelong learning systems. She holds a Ph.D. in Electrical and Computer Engineering from UTSA and an M.S. in Computer Engineering from Wright State University. Her research interests include AI algorithms, neuromorphic hardware design, spiking neural networks, and memristor-based systems. She leads initiatives in neuromorphic benchmarking (NeuroBench), energy-efficient computing roadmaps (EES2), and collaborative frameworks like the Neuromorphic Commons (THOR). Her lab develops neuromorphic chips with on-device learning capabilities, such as the Genesis chip, and explores applications in edge computing. Dr. Kudithipudi has pioneered techniques for continual learning in spiking networks, probabilistic metaplasticity, and low-precision numerical formats (e.g., PositCL). Her work bridges theoretical neuroscience principles with practical hardware implementations, emphasizing sustainability and scalability. She also contributes to NSF-funded projects like EFRI BRAID and NAIAD, advancing interdisciplinary AI research. Her lab’s collaborations include developing the NeuroBench framework for fair benchmarking and exploring neuromorphic systems for tasks like video/activity recognition and time-series forecasting. She advises on hardware-software co-design strategies for efficient neural network deployment on constrained devices.

Babak JamhiriView profile

Researcher

• Structural Health Monitoring
• Geotechnical Engineering
• Machine Learning Applications in Civil Engineering
• +3 more

Babak Jamhiri holds a Doctoral Researcher position with expertise in structural health monitoring and reliability analysis. He is affiliated with the 3DCP and Construction Automation Research Group, focusing on AI-driven simulations, infrastructural risk assessment, and probabilistic modeling. His research spans geoenvironmental reliability, machine learning algorithms for signal processing, and risk assessment in construction materials. Education: BSc, MSc, and PhD in relevant disciplines (institutions unspecified). Current projects include structural health monitoring of 3DCP components, machine learning for signal processing, and probabilistic crack propagation in problematic soils. Recent work involves performance assessment of post-tensioned concrete beams and developing compressed earth bricks with marl sediment. Research interests emphasize geotechnical challenges such as expansive soils, desiccation cracking, and soil stabilization using industrial byproducts. He explores Bayesian inference, Markov decision chains, and fractal approaches in clay-based materials for nuclear waste disposal applications. Contributions include YouTube tutorials on information value theory, signal processing for anomaly detection, and probabilistic modeling with R. His work bridges theoretical advancements with practical engineering solutions, emphasizing uncertainty quantification and decision-making under uncertainty.

Ranga PitchumaniView profile

Professor

• Energy Systems
• Renewable Energy
• Materials Processing
• +7 more

Ranga Pitchumani is the George R. Goodson Professor in the Department of Mechanical Engineering at Virginia Tech since 2009, following prior roles including the John R. Jones III Professor at Virginia Tech (2009–2014) and Distinguished Professor at the University of Connecticut (2003–2008). He directs the Advanced Materials and Technologies Laboratory (AMTL), focusing on energy systems, materials processing, and uncertainty quantification. Education: Ph.D., Mechanical Engineering, Carnegie Mellon University (1992) M.S., Mechanical Engineering, Carnegie Mellon University (1988) B.S., Mechanical Engineering, Indian Institute of Technology (1986) Research Interests span energy conversion and storage , micro-/nanotechnologies , grid integration of renewables , and transport phenomena . His lab develops fractal-textured surfaces for solar absorbers, corrosion-resistant coatings for CSP systems, and machine learning tools for grid optimization. Sponsors include AFOSR, DOE, NSF, and ONR. Scientific Awards include the 2022 ASME Frank Kreith Energy Award, 2022 ASES Charles Abbot Award (for R&D in solar energy), and being named a Fellow of ASME in 2004. Earlier recognitions include the 1996 ONR Young Investigator Award and multiple University of Connecticut honors. AMTL research emphasizes dual-use innovations like solid-infused surfaces for condensation enhancement (5× improvement) and corrosion-resistant coatings that match nickel-based alloys' performance at lower costs. Key projects involve thermal control of battery packs using probabilistic forecasting and fractal-textured surfaces delaying freezing seven-fold.

Tim MarjoribanksView profile

Senior Lecturer

• Hydrodynamics
• Water Engineering
• Eco-hydrodynamics
• +4 more

Dr. Tim Marjoribanks is a Senior Lecturer in Water Engineering with a focus on numerical modeling and eco-hydrodynamic processes. His research integrates computational fluid dynamics, flume experiments, and fieldwork to study flow-vegetation interactions in rivers and coastal environments, emphasizing fluvial geomorphology and natural flood management. Current projects include saltmarsh vegetation dynamics, swimming pool circulation efficiency, and the representation of vegetation in hydraulic models. Recent work explores climate extremes and blue-green infrastructure through large ensemble simulations. Education: Holds an MSci, PhD, and PGCAP, indicating expertise in academic teaching and advanced water engineering research. His work has been supported by NERC through a PDRA role in reducing uncertainties in flood prediction. Research Interests: Key areas include vegetation hydraulics, sediment-ecology relationships, and public health implications of water systems. His methodologies span high-resolution modeling, analytical frameworks, and community engagement strategies for sustainable infrastructure projects. Grants & Projects: Led NERC-funded research on vegetation representation in hydraulic models. Collaborates with interdisciplinary teams on ecohydraulic processes and climate adaptation. Labs/Teams: Affiliated with research groups studying hydraulics, coastal systems, and climate extremes, though specific lab names are not disclosed.

• Artificial Intelligence
• Explainable AI (XAI)
• Computational Argumentation
• +8 more

Antonio Rago is a Researcher at Imperial College London's Department of Computing (Faculty of Engineering). His work focuses on explainable AI (XAI), computational argumentation, and their applications in engineering design, Formula One strategy, and healthcare. He is affiliated with the ADIX, XAI, and Computational Logic & Argumentation research groups. Education: PhD (Computational Logic & Argumentation) from Imperial College London (2019), MEng in Automotive Engineering from Loughborough University (2012) Professional Experience: Former Race Strategy Engineer at Mercedes AMG Petronas F1 Team (2012-2014), Quantitative Development roles in industry Research interests span argumentation frameworks, counterfactual explanations, and ethical AI systems. He organizes international workshops like ArgXAI and Arg&App, and co-leads tutorials on Argumentative Explanations in AI at conferences including KR and ECAI. His publications emphasize robust AI systems, human-centric explanations, and applications in high-stakes domains like motorsport strategy and clinical decision-making. He collaborates with institutions such as Sapienza University of Rome under the UK-Italy Trustworthy AI Programme.

Laurence RozéView profile

Lecturer

• Explainable Artificial Intelligence (XAI)
• Machine Learning Interpretability
• Temporal Automata Learning
• +3 more

Laurence Rozé is a Lecturer at INSA Rennes's Computer Science Department since 1998 and a researcher at IRISA (UMR 6074), affiliated with the LACODAM team. Her work focuses on explainable AI (XAI), temporal automata learning, and time series analysis, particularly in stabilizing explanations and generating counterfactuals. She has co-supervised over 10 PhD students, including recent theses on counterfactual explanations and individual behavior modeling. Key projects include collaborations with Stelantis (2019–2022), Orange Labs (2016–2019), and academic initiatives like the WS-DIAMOND European project (1998–2001). Her research interests combine theoretical advances (e.g., timed automata learning from time series) with industrial applications such as car-sharing fleet optimization and precision livestock farming. Teaching responsibilities include mobile programming, probability, and introductory courses for international students. She has held leadership roles at INSA Rennes, including heading the second-year program since 2024 and leading the third-year IT program from 2004–2015.

• Decision science
• Data science
• Causal machine learning
• +6 more

Wouter Verbeke is Professor of Decision Science at KU Leuven's Faculty of Economics and Business (FEB), where he directs the POC Doctoraatsprogramma Economie en Bedrijfswetenschappen and chairs the Doctoral Committee. He is a core member of Leuven.AI and leads the Information Systems Engineering Research Group (LIRIS), focusing on data-driven business decision-making. His research spans causal machine learning, uplift modeling, and cost-sensitive learning with applications in fraud detection, credit risk, operations management, and pricing. Verbeke pioneers decision-centric frameworks that optimize business outcomes rather than purely predictive accuracy, emphasizing causal inference for resource allocation and fairness in algorithmic decision systems. Analysis of his recent publications reveals a strong trend toward graph-based anti-fraud systems (particularly for AML/smurfing detection), interpretable causal machine learning in finance, and HR analytics using process mining. His work increasingly integrates fairness considerations into operational decision systems while advancing theoretical foundations of treatment effect estimation under network interference. Scientific awards: EURO award for best article in European Journal of Operational Research (Innovative Applications of O.R.) (2014) Verbeke actively supervises doctoral candidates including S. De Vos and M. Reusens, with research funded through major grants like ANUBIS (Aligned oNline and multilevel User and entity Behavior) and prescriptive analytics projects for intelligent organizations. He serves on editorial boards of Decision Support Systems, Business Information Systems Engineering, and INFORMS Journal on Data Science. As leader of the LIRIS research group within FEB, he drives interdisciplinary collaborations between data science and business domains, maintaining strong industry partnerships for real-world validation of fraud analytics, credit risk, and operational decision systems.