Steven O. Kimbrough is a Professor of Operations, Information and Decisions at the Wharton School, University of Pennsylvania. His research spans artificial intelligence, computational rationality, and strategic optimization with applications to political science, economics, and service innovation. He teaches courses like Agents, Games, and Evolution and Thinking With Models , focusing on experimental approaches to bounded rationality and uncertainty in decision-making. Primary Email: kimbrough@wharton.upenn.edu Office: 3730 Walnut Street, 565 Jon M. Huntsman Hall, Philadelphia, PA 19104 His research interests include: Artificial intelligence and metaheuristics for constrained optimization Evolutionary computation in electoral redistricting Agent-based modeling of market dynamics Logic modeling for normative reasoning Text mining applications in event analysis Publications demonstrate expertise in computational economics, political modeling, and service analytics. Recent work focuses on: Empirical validation of electoral compactness Strategic learning in oligopolies Multi-objective matching algorithms Feasible-infeasible solution spaces Service network optimization Teaching emphasizes: Game-theoretic approaches to strategic behavior Modeling life-cycle for energy sustainability Computational experiments in social science
PD Dr. habil. Thomas Wöhling serves as a Senior Research Scientist and Team Leader for Stochastic Modelling of Hydrosystems at the Chair of Hydrology, Dresden University of Technology's Faculty of Environmental Sciences. His research spans integrated environmental systems modeling with particular expertise in surface water-groundwater interactions, braided river systems, and vadose zone processes. Previously, he held research positions at Water and Earth System Sciences Competence Cluster in Tübingen (2010-2015) and Lincoln Environmental Research in New Zealand (2006-2010). Dr. Wöhling completed his Dipl.-Hydrol. (1999) and PhD in Hydrology (2005) at Dresden University of Technology, followed by habilitation in Stochastic Hydrology (2021). His educational background includes extensive research at the Institute of Hydrology and Meteorology at TU Dresden (1999-2005) where he developed foundational expertise in hydrological modeling. Wöhling's research focuses on integrated modeling of coupled environmental systems , particularly flow and contaminant transport in surface water-groundwater systems, nutrient and energy fluxes in soil-plant-atmosphere systems, and distributed hydrological modeling. His work emphasizes stochastic modeling and uncertainty analysis , with significant contributions to inverse modeling, model calibration, multiobjective optimization, and Bayesian model averaging techniques. He has pioneered methods for evaluating monitoring network worth and data utility for environmental models. His publication record demonstrates consistent contributions to hydrological science, with recent work (2023-2025) focusing on machine learning applications in hydrology, advanced statistical inversion techniques, and complex karst system modeling. Key trends include integration of physics-based and data-driven approaches, improved uncertainty quantification methods, and applications to climate change impacts on water resources. His work bridges theoretical advances with practical applications in New Zealand's braided rivers and European hydrological systems. STAHY Best Paper Award (2018) ASCE Journal of Irrigation and Drainage Engineering Best Reviewer Awards (2008, 2010, 2011, 2015, 2018) ASCE Journal of Irrigation and Drainage Engineering Best Paper Awards (2008, 2009) Dr. Wöhling leads the Stochastic Modelling of Hydrosystems team and has secured funding for numerous projects including Klimakonform, ISOSIM, VAMOS II, and the International Research Training Group 'Integrated Hydrosystem Modelling.' His work combines novel monitoring techniques with modeling and optimal sensor placement to improve prediction reliability for river-groundwater exchange fluxes. He collaborates extensively with international partners, particularly in New Zealand through the Lincoln Agritech's Braided Rivers program. His laboratory work focuses on combining traditional hydrological measurements with advanced computational techniques, including deep learning applications for soil surface hydrology and time-windowed Bayesian analysis for predictive modeling. The team maintains strong connections with field sites in Germany's Saxon region and New Zealand's Canterbury Plains, facilitating integrated theoretical and empirical research approaches.
Dr. P.M. Mohite is a Professor at the Department of Aerospace Engineering , Indian Institute of Technology Kanpur , with a career spanning over two decades in composite materials research. He holds a PhD in Aerospace Engineering from IIT Kanpur (2007) and has been a Visiting Assistant Professor (2008-2009) and later Assistant , Associate , and Professor (2019-present) at IIT Kanpur. Research Interests include: Advanced composite structures analysis Micromechanics and damage modeling Finite element adaptive methods Metal plasticity and polymer composites Aerostructural optimization Key Publications focus on damage mechanics, structural optimization, and micromechanical characterization, with recent works on evolutionary algorithm-based composite design and boundary layer effects in laminates. Scientific Awards received: Best Student Award (B.E. Mechanical, 1998) AIAA Students Paper Contest Finalist (2006) Teaching Contributions include core courses like Composite Materials and Finite Element Methods , alongside advanced postgraduate topics in aerospace structural analysis. Advising has impacted 30+ M.Tech/Ph.D. students, including international collaborations with Université de Biskra (Algeria) and Ecole Centrale de Nantes (France).
Dr. Kiril Kuzmin is a Lecturer in the Department of Computer Science at Georgia State University, where he teaches Data Structures, Algorithms, Data Science, and Machine Learning. He holds a Ph.D. in Computer Science (2024) with a concentration in Bioinformatics from Georgia State University and a Ph.D. in Mathematics (2009) from the National Academy of Sciences of Belarus. His academic journey includes roles as Assistant and Associate Professor at Belarusian State University and a postdoctoral fellowship at the University of Turku, Finland. Dr. Kuzmin’s research focuses on Bioinformatics, Machine Learning, Discrete Optimization, and Graph Theory. He has published over 50 papers, with notable contributions in stability analysis of combinatorial optimization problems and applications of machine learning in genomics. His work includes predicting host specificity of coronaviruses and developing algorithms for heterogeneous genomic population analysis. Dr. Kuzmin has received the Scopus Award in Mathematics (2013) and served as PI/co-PI on three international projects. His teaching spans Java programming, Data Structures, and advanced mathematical courses like Calculus and Algebra. He is affiliated with Georgia State’s bioinformatics research group and has actively contributed to academic and political advocacy in Belarus.
Zhongguo Li is a Lecturer in Robotics, Control, Communication & AI at the University of Manchester. He holds a B.Eng. (2017) and Ph.D. (2021) in Electrical and Electronic Engineering from the University of Manchester. Prior to his current role, he was a Lecturer at University College London (2022-2023) and a Research Associate at Loughborough University (2020-2022). His research focuses on distributed control, optimization, and reinforcement learning, particularly in robotics and autonomous systems. Key areas include multi-agent coordination, networked systems, and applications in autonomous vehicles. He has authored over 40 papers in top journals/conferences and co-authored a book on Distributed Optimization and Learning (2024). Teaching responsibilities include courses such as Control Systems II, Nonlinear and Adaptive Control, and Embedded Systems Project. He serves as an Associate Editor for Drones and Autonomous Vehicles and Guest Editor for Machines and Frontiers in Control Engineering. Dr. Li actively mentors PhD students, offering guidance on funding opportunities and research projects in distributed algorithms, robotics, and control systems. His work aligns with UN Sustainable Development Goals related to innovation and infrastructure.
Abdullah Karaman is a Professor at the Department of Geophysical Engineering, Istanbul Technical University (ITU). His research focuses on geophysical modeling, subsidence due to longwall mining, hydrothermal systems, and seismic data analysis. He leads projects on geothermal applications using particle swarm optimization and collaborates internationally. Dr. Karaman has supervised 8 ongoing theses and has authored/co-authored over 17 publications since 1997. His work spans geophysical exploration, environmental geohazards, and subsurface imaging techniques. Education: Master of Science in Geophysics (1989) Research Interests: Hydrothermal system characterization using magnetotelluric methods Seismic inversion and diffraction imaging Geophysical monitoring of coal mine subsidence Optimization algorithms in geophysical data analysis Awards: None explicitly mentioned. Grants & Projects: Geothermal Applications of Particle Swarm Optimization Modeling Technique (2016–2021) Kordil Engineering Company: Geophysical consulting for international projects (2021) His lab focuses on integrating geophysical techniques with machine learning for high-resolution subsurface imaging.
Ram Mohapatra is a Professor in the Department of Mathematics at the University of Central Florida (UCF), part of the College of Sciences. His research interests span mathematical analysis, operator theory, variational inequalities, approximation theory, cybersecurity, and fluid dynamics. He has published extensively on topics including inverse scattering problems, generalized inverses, and optimal control theory. His work often intersects with applications in engineering and data science. Recent research focuses on operator theory applications, tensor decompositions, and mathematical modeling of physical systems. He has contributed to advancements in frame theory, numerical radius studies, and cybersecurity methodologies. His academic activities include teaching undergraduate and graduate courses in mathematics, such as MAC 1105C and MAC 2311C, and maintaining active collaborations in interdisciplinary research areas. Professional contributions include over 150 peer-reviewed articles and editorial roles in mathematics journals. While no explicit awards are listed in the provided texts, his prolific publication record underscores his scholarly impact in applied and theoretical mathematics.
Daniel Tauritz is a Professor in the Department of Computer Science and Software Engineering at Auburn University and serves as Director for National Laboratory Relationships. He holds a Ph.D. and M.S. in Computer Science from Leiden University, along with propaedeutic studies in Computer Science and Mathematics. His research focuses on AI-driven cybersecurity, automated algorithm design using hyper-heuristics, computational game theory, and evolutionary computation. Tauritz leads initiatives such as the Auburn Cyber Research Center and collaborates with institutions like Los Alamos National Laboratory on critical infrastructure protection and satellite network security. His work bridges academia and national security through projects like the Cyber Fire Puzzles competition and the Satellite Tycoon economic simulation game. His educational background includes advanced studies at Leiden University, with a strong foundation in computer science and mathematics. Research contributions span evolutionary algorithms for molecular evolution, coevolutionary defense strategies, and generative hyper-heuristics. Tauritz has secured grants including an NSF award for AI-cybersecurity education and contributed to Auburn’s partnerships with national laboratories. He is actively involved in fostering student engagement through ethical hacking clubs and experiential learning programs. Notable achievements include moderating panels on AI in cybersecurity and AI workforce development, as well as developing frameworks like Galaxy for network emulation and DCAFE for automated cyber experiments. His publications emphasize applying evolutionary computation to real-world challenges, including satellite constellation economics and adversarial network defense strategies.
Juliana Felkner is an academic affiliated with ETH Zurich, primarily associated with the Department of Architecture within the School of Architecture, Civil and Environmental Engineering. Her research focuses on integrating structural efficiency with energy performance in tall buildings, emphasizing multi-objective optimization and sustainable design principles. Key contributions include studies on embodied vs. operational energy in buildings, structural design frameworks balancing sustainability and efficiency, and interactive optimization techniques using NURBS curves and Particle Swarm Optimization (PSO). Her doctoral thesis (2016) explored multi-objective design strategies addressing structural, environmental, and architectural considerations. Felkner has published extensively on topics such as energy lifecycle analysis, natural ventilation potentials, and computational design methods. Her work highlights the importance of reducing operational energy in existing building stocks to mitigate greenhouse gas emissions. Awards and grants are not explicitly mentioned in the provided texts, but her peer-reviewed publications indicate active engagement in academic research and collaboration across disciplines.
Dr. Pouyan Nejadhashemi is an MSU Research Foundation Professor in the Departments of Plant, Soil and Microbial Sciences and Biosystems & Agricultural Engineering at Michigan State University. He serves as Director of the MSU Institute of Water Research and the Center for Intelligent Water Resources Engineering. His expertise spans ecohydrology, climate change adaptation, environmental impact assessment, and decision support systems for ecosystem sustainability. He holds a Ph.D. in Biological Resources Engineering from the University of Maryland. Dr. Nejadhashemi's research focuses on water quality modeling, non-point source pollution prevention, and integrating machine learning with hydrological systems. Key areas include irrigation optimization, PFAS contamination monitoring, and watershed management. He teaches courses on water resources systems analysis and ecohydrology. Awards: MSU Research Foundation Professor Title Grants: FAA grant for PFAS remediation, grants for developing agricultural innovations in Senegal and Bangladesh Labs/Teams: MSU Institute of Water Research, Center for Intelligent Water Resources Engineering His work bridges computational methods with environmental challenges, addressing global water security, food systems, and sustainable resource management. Recent projects include modeling water quality in the Chesapeake Bay and developing AI tools for agricultural extension platforms.
Martin Servin is an Associate Professor at the Department of Physics, Umeå University, and leads the Digital Physics research group within the UMIT Research Lab. His work focuses on computational modeling and simulation of granular materials, robots, and vehicles, with applications in AI-based control and perception. He holds a doctoral degree from Umeå University (2003) and has pioneered research in real-time physics simulation, particularly in the context of autonomous machinery and off-road robotics. Research Interests : Digital physics, granular materials simulation, autonomous systems, reinforcement learning, and simulation-to-reality transfer. His group develops advanced simulation tools for industries like forestry, mining, and construction. Key Projects : Mistra Digital Forest (2019–2026) AILUR (Digital Twin for AI-controlled Lunar Robotics) XSCAVE (Explainable, Safe Control for Heavy Machinery) Publications emphasize simulation methodologies, AI integration, and real-world validation across robotics, vehicle dynamics, and granular mechanics. Notable contributions include work on wheel loader dynamics, deep reinforcement learning for control systems, and terrain modeling. Awards include the Spin-off award for industry-grade physics in Unreal Engine (2018) , recognizing his role in Algoryx Simulations, a spin-off company commercializing his research. Labs/Teams : UMIT Research Lab, Digital Physics Group, and collaborations with Algoryx Simulations.
Marta Zaniolo serves as an Assistant Professor in Civil and Environmental Engineering at Duke University since January 2024, addressing critical water sustainability challenges through integrated hydrology, machine learning, and systems modeling approaches. Her work focuses on ensuring equitable water access amid climate change, scarcity, and competing demands across diverse spatial scales. Her academic foundation includes: B.S. in Environmental Engineering from Politecnico di Milano (2014) M.S. in Environmental Engineering from Politecnico di Milano (2017) Ph.D. in Information Technology (Environmental Intelligence Lab) from Politecnico di Milano (2020) Postdoctoral Research at Stanford University (2020-2023) Dr. Zaniolo's research integrates water resources planning , drought modeling , and climate adaptation with cutting-edge machine learning techniques including reinforcement learning and feature extraction. The ZEDD Lab develops computational models that synthesize climate, hydrological, and socio-economic data to enhance decision-making for water security. Her work emphasizes real-world applicability through stakeholder engagement and participatory design, particularly in vulnerable regions like the Omo-Turkana basin where mismanaged infrastructure operations can trigger socio-ecological conflicts. Analysis of her 2021-2025 publications reveals three dominant research trajectories: (1) development of synthetic weather generators (e.g., FIND model) for precise drought characterization, (2) neuro-evolutionary algorithms for multi-objective reservoir control under climate uncertainty, and (3) quantification of flexibility value in water infrastructure planning. These studies consistently bridge technical innovation with equity considerations, particularly in transboundary water systems. She leads the Zaniolo Lab for Environmental Data and Decisions (ZEDD Lab), which operates at the intersection of hydrology, systems modeling, and machine learning. The lab's framework combines computational water system dynamics with stakeholder-inclusive approaches to develop decision support tools for extreme event preparedness. Current projects focus on urban drought resilience, dam reoperation strategies for climate extremes, and heat-substance use risk intersections in rural communities, all grounded in case studies spanning California, Ethiopia, and the American South.
Lakhmi C. Jain is a distinguished academic affiliated with the University of South Australia. As a Professor, she has made significant contributions to the fields of Artificial Intelligence, Computational Intelligence, and Fuzzy Systems. Her research spans neural networks, decision support systems, robotics, and data analysis, with a focus on interdisciplinary applications. Her career includes over 445 publications, including books like Complex Networks in Software, Knowledge, and Social Systems (2019) and E-Learning Systems - Intelligent Techniques for Personalization (2017). She has held editorial roles in journals such as the International Journal of Intelligent Decision Technologies (IDT) and the Journal of Intelligent & Fuzzy Systems. Jain's work emphasizes practical applications of computational intelligence, including efforts in software development, biomedical signal processing, and multi-agent systems. She has collaborated extensively with researchers globally, contributing to advancements in AI-driven technologies and decision-making frameworks.
Melvin Wong is an Assistant Professor in the Department of Urban Planning and Transportation within the Built Environment school at Eindhoven University of Technology. His research focuses on transportation engineering, machine learning applications in urban mobility, reinforcement learning for traffic systems, and sustainable transportation solutions. He utilizes advanced computational methods including graph neural networks, generative AI, and physics-informed models to address challenges in traffic prediction, electric vehicle infrastructure, and urban design. His research interests encompass transportation optimization, spatiotemporal modeling, generative design methods, and behavioral analysis in urban systems. Recent publications demonstrate a strong focus on AI-driven solutions for traffic management, battery-swapping systems, and multimodal design optimization. Dr. Wong has received recognition including the Best Research Paper Award (2024) and Swiss Government Excellence Scholarship (2020). He contributes to academic activities through conference presentations, peer reviews, and course development in urban mobility and big data analytics.
Messaoud Chibane is an Assistant Professor in Finance at NEOMA Business School and Director of the MSc Finance & Big Data program. His research focuses on asset valuation, macro-finance linkages, sustainable finance, and cryptocurrencies. He holds a PhD in Finance from EDHEC Business School, a DEA in Applied Economics from Université Paris-La Sorbonne, and engineering degree from École Centrale Paris. Prior to academia, he spent 18 years as a quantitative analyst at international investment banks. His research spans topics such as cryptocurrency behavior during geopolitical events, housing market disasters' impact on asset prices, and ESG investment strategies under rating uncertainty. Notable publications include Finance Research Letters , Economic Modelling , and Risk Magazine . He actively engages in academic conferences, presenting at venues like the Southern Economic Association and the International Finance and Banking Society. Chibane's work bridges theoretical finance with practical applications, leveraging both academic and industry expertise. His recent articles highlight trends in quantifying Bitcoin's safe-haven properties and modeling rare disaster effects on financial markets.
