Xiaofeng Shao is a Professor of Statistics & Data Science at Washington University in St. Louis, with a joint appointment in the Department of Economics. He holds a PhD from the University of Chicago and previously served at the University of Illinois at Urbana-Champaign for 18 years. He is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association. His research focuses on econometrics, time series analysis, change-point detection, high-dimensional statistics, nonparametric methods, and functional data analysis. Recent work emphasizes object-valued time series modeling and machine learning applications in high-dimensional and imaging data. Notable contributions include the dependent wild bootstrap method and self-normalization techniques for time series inference. Key awards include Fellowships from leading statistical societies. His publications span over 20 years, addressing topics like change-point detection in climate projections, statistical methods for COVID-19 infection trends, and high-dimensional dependence testing.
Dr. Godfred Afrifa is a Senior Lecturer in Accounting and Senior Tutor (Canterbury) at Kent Business School , University of Kent. He is also a Visiting Lecturer at IESEG School of Management (France) since 2014 and at ISCID-CO International Business School (France) since 2016. PhD in Finance and Risk (Bournemouth University, 2013) MSc in Finance (University of Leicester, 2008) BSc in Applied Accounting (Oxford Brookes University) HND in Accounting (Kumasi Polytechnic) Chartered Accountant and Certificate in Insurance (Chartered Insurance Institute) His research explores the intersection of corporate governance , working capital management , and microfinance institutions . He investigates how CSR governance affects business practices, the dynamics of trade credit in SMEs, and the role of regional development in service-sector performance. His recent work also covers linguistic influences on financial reporting and pedagogical innovations in accounting education. Dr. Afrifa's 15 most recent publications (2020–2025) span topics like CSR decoupling , firm identity changes , credit policies , and microfinance integrity , with methodologies including stochastic frontier analysis and cross-country comparisons . His work frequently addresses financial strategies in SMEs and sustainability in supply chains and development. Scientific Awards : Fellow of The Higher Education Academy (2015), Affiliate Member of ACCA (2004). Dr. Afrifa supervises Mohammad Abwen (PhD thesis: Credibility of CSR Disclosure ) and welcomes PhD candidates in SME management , working capital , and microfinance . His Visiting Lecturer roles at French institutions highlight international collaboration. The 2023 Evaluation Report on Re:Generation 2031 underscores his work in sustainable development beyond traditional finance.
Professor Shaomin Wu is a faculty member at the University of Kent's Kent Business School, where he holds the academic rank of Professor of Business/Applied Statistics. He earned an MSc and PhD in applied statistics and has extensive industry experience, including a five-and-a-half-year stint at a global manufacturer in Shanghai before moving to the UK in 2001. He has held roles as a postdoctoral researcher and lecturer before joining Cranfield University and later the University of Kent. His research focuses on recurrent event data analysis, machine learning, and reliability mathematics, with funding from the EPSRC and ESRC. His research projects include managing risk in warranty servicing policies, smart data analytics for local government, and sustainable supply chain demand forecasting. He teaches modules such as risk analysis, reliability engineering, and machine learning. Currently supervising PhD students in time series forecasting, explainable AI, and recurrent event data analysis, he also serves as a co-chair of international conferences, editorial board member, and external examiner for doctoral degrees. Notably, he ranks among the top 2% of global scientists by Stanford University. His work integrates machine learning with business analytics, resilience engineering, and environmental sustainability. Key contributions include IoT-driven resilience methodologies for smart grids and unmanned systems, as well as frameworks for corporate carbon disclosure and maintenance optimization under uncertainty.
Amin Hammad is a Professor at the Concordia Institute for Information Systems Engineering, with an additional appointment as Affiliate Professor in Building, Civil, and Environmental Engineering at Concordia University. His research focuses on advancing construction technology through digital transformation, automation, and AI integration. He leads work in BIM applications, 4D simulation, robotic systems, and sustainable infrastructure management. His interdisciplinary approach bridges civil engineering with computer science and data analytics. Key research areas include: Automation and robotics in construction (Construction 4.0) BIM and digital twin lifecycle management AI-driven defect detection and inspection systems Occupational safety through exoskeleton performance evaluation Multi-purpose utility tunnel optimization Energy-efficient building systems Recent work emphasizes applying machine learning to construction equipment activity recognition, UAV path optimization for infrastructure inspection, and ontology development for integrated systems. His research addresses industry challenges in productivity, safety, and sustainability through data-driven solutions.
Esteban G. Tabak is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He holds a Ph.D. in Mathematics from MIT (1992) and a Hydraulic Engineer degree from the University of Buenos Aires (1988). His research spans fluid dynamics, data science, and optimization, with notable contributions to optimal transport theory, atmospheric and ocean modeling, and machine learning methodologies. He leads the Research and Training Group in Mathematical Modeling and Simulation at NYU. Research Interests include Data Analysis, Optimal Transport, Applied Mathematics, and Physics, particularly in fluid dynamics and geophysical flows. His work bridges theoretical advancements with practical applications, such as sea ice dynamics, internal waves, and turbulence modeling. Publications highlight innovations in density estimation, constrained optimization, and energy spectrum analysis of oceanic internal waves. Collaborations span disciplines, including biomedical applications (e.g., heart transplant diagnostics) and climate science. His methodologies, such as dual ascent algorithms and prototypal analysis, emphasize data-driven solutions to complex systems. Teaching includes courses on partial differential equations, fluid dynamics, and mathematical modeling. His work has been supported by grants addressing stratified flows, internal wave energy spectra, and turbulent mixing.
Cathy Wu is an Associate Professor at MIT, with affiliations in the Laboratory for Information and Decision Systems (LIDS), Department of Civil and Environmental Engineering (CEE), and Institute for Data, Systems, and Society (IDSS). Her research group focuses on integrating machine learning with model-based optimization to solve complex problems in transportation systems and cyber-physical systems. Academic Leadership: Class of 1954 Career Development Associate Professor (MIT) Research Grants: NSF CAREER Award, Amazon Robotics, Mathworks, MIT Mobility Initiative, US DOT, Microsoft Research, Cintra, Symbotic Research Interests : Wu's work bridges AI and engineering challenges in transportation. Key areas include: Hybrid ML/Model-based Optimization (large neighborhood search, branch-and-cut) Sustainable Mobility (Project Greenwave, eco-driving) Multi-Agent Coordination (warehouse automation, cooperative driving) Cyber-Professional Systems (generalization in RL, transfer learning) Recent work demonstrates significant advances in eco-driving (11-22% emissions reduction), large-scale multi-agent path finding (1000+ agents), and foundational RL methods for traffic control. Her group has produced 15+ major publications since 2015, with notable media coverage in Science, Wired, and NewScientist. Selected Scientific Awards NSF CAREER Award (2023) Ole Madsen Mentoring Award (2025) IEEE ITSS WiE/YP Fellowship (2024) Harold L. Hazen Teaching Award (2022) Her lab has advised 12+ graduate students and postdocs, including: Vindula Jayawardana (PhD '24, now at Anthropic) Sirui Li (PhD '25, now at Microsoft Research) Yining Ma (Postdoc, active researcher) Zhongxia Yan (PhD '24, now at Anthropic)
Christopher O'Donnell is a Professor of Econometrics at the University of Queensland's School of Economics. He holds a PhD from the University of Sydney and has held academic leadership roles including Director of the Centre for Efficiency and Productivity Analysis. His research focuses on productivity and efficiency analysis, econometric methods, and their applications in agriculture, public policy, and environmental economics. Education: PhD (University of Sydney), MCom (University of New South Wales), BAgEc (Hons) (University of New England). Research interests include economic and statistical methods for measuring productivity changes, stochastic frontier analysis, and metafrontier frameworks. He has authored/co-authored three books and over 80 journal articles, with notable contributions in the American Journal of Agricultural Economics , Journal of Econometrics , and European Journal of Operational Research . Scientific awards include being a Distinguished Fellow of the Australian Agricultural and Resource Economics Society. His work has been applied in sectors like healthcare, fisheries, and public utilities through collaborations with organizations such as the World Bank and Asian Productivity Organization. Key projects include measuring agricultural productivity in China, analyzing hospital efficiency, and evaluating climate impacts on farming. Grants include studies on productivity measurement in Australian universities and Northern Grains Region farms. Labs/Teams: Former Director of the Centre for Efficiency and Productivity Analysis, collaborating with global institutions on productivity benchmarking and policy analysis.
Dr. Divya Jayakumar Nair serves as Senior Lecturer in the School of Civil and Environmental Engineering at the University of New South Wales (UNSW), where she also holds the position of Associate Dean International – South Asia for UNSW Engineering. Her academic work is centered at the Research Centre for Integrated Transport Innovation (rCITI), focusing on transportation systems, network optimization, and disaster management applications. Dr. Nair's research interests span transportation engineering, disaster management, and urban mobility systems. Her work investigates complex network design problems, particularly in pre-disaster evacuation planning, transportation resilience, and food rescue logistics. She employs advanced analytical methods including traffic equilibrium modeling, network optimization techniques, and data-driven approaches to solve real-world transportation challenges with significant societal impact. Her recent publications demonstrate a strong focus on transportation resilience during disasters, with particular attention to equity considerations in pre-event planning. Dr. Nair's research shows how transportation networks can be optimized to improve emergency response while ensuring equitable access to safety for all population segments. Her work bridges theoretical network optimization with practical applications in urban environments facing climate-related challenges. Dr. Nair collaborates extensively with leading researchers in transportation including S. Travis Waller, Vinay V. Dixit, David Rey, and Taha Rashidi. Her collaborative approach spans multiple institutions and addresses pressing challenges in urban transportation systems worldwide.
Patrick Willems is a full Professor at KU Leuven's Faculty of Engineering Sciences, Department of Civil Engineering. He serves as the head of the Hydraulics Subdivision within the Hydraulics and Geotechnics unit. Professor Willems holds multiple significant roles including chairman of the ADS Bureau, member of the Faculty Council of Engineering Sciences, and participant in the Leuven One Health Institute and KU Leuven Institute for Urban Studies (LUSI). His work addresses critical global water management challenges through advanced hydrological modeling and climate change adaptation strategies. Professor Willems' research spans several critical areas in water resources engineering. His primary expertise includes urban hydrology and river engineering, statistical hydrology with focus on flood prediction and risk analysis, integrated river basin management, precipitation analysis, and climate change impacts on hydrological extremes. He employs both traditional hydrological modeling approaches and innovative machine learning techniques to develop practical solutions for flood warning systems, urban water management, and climate adaptation planning. His research integrates statistical methods, numerical modeling, and data science to address complex water management problems across multiple geographical contexts. Professor Willems leads multiple major international research projects examining hydrological extremes in transboundary river basins, natural climate adaptation measures, hydrological modeling of peatland areas, and deep learning-based prediction of hydrological extremes. His work spans various geographical contexts including Belgium, Vietnam, Bolivia, Tanzania, and the Congo Basin, demonstrating both local relevance and global applicability of his research. Within KU Leuven, Professor Willems teaches diverse courses including Environmental Problems and Techniques, Statistics and Data Science, Stochastic Hydrology, Urban and River Hydrology and Hydraulics, River Modeling, and Probability and Statistics. He also leads the Hydraulic Engineering Project course and an Artificial Intelligence Project course, reflecting his commitment to integrating traditional engineering knowledge with modern computational approaches. As head of the Hydraulics Subdivision, he oversees research activities focused on developing advanced water engineering tools and methodologies that bridge theoretical advancements with practical applications for water management authorities.
Axel Gandy is a Professor of Statistics at the Department of Mathematics, Imperial College London. He serves as Director of the EPSRC CDT in Modern Statistics and Statistical Machine Learning , overseeing PhD supervision and advanced statistical training.
Prof. Jack van der Vorst is the Personal Professor of AgriFood Supply Chain Logistics at Wageningen University's Operations Research and Logistics Group. Previously serving as a member of the Board of Directors of Wageningen University & Research (until 2024) and General Director of the Social Sciences Group, he leads over 1000 personnel across three institutes. His advisory roles include the Topteam AgriFood (Science Captain), Top consortium for Knowledge and Innovation (TKI), The Sustainability Consortium (TSC), and Florensis BV's Supervisory Board. With 20+ PhD supervisions and 200+ publications, his research focuses on innovative logistics concepts in AgriFood systems, including supply chain resilience, sustainability, and system innovation. His work integrates modeling frameworks with practical industry applications, emphasizing perishable products, horizontal collaboration, and environmental efficiency. Research interests span AgriFood System Design, Supply Chain Strategy, and Performance Management. His recent studies address challenges like postharvest loss reduction in developing countries, CO2 emission minimization in cold chains, and circular economy implementation in mushroom supply chains. Methodologically, he employs multi-criteria decision models, optimization techniques, and simulation to address complex logistics problems. Publications highlight themes such as horizontal collaboration success factors, vulnerability assessment frameworks, and green supply chain design. His work bridges academic rigor with industry needs, often collaborating with policymakers and international organizations to promote sustainable agri-food systems.
Richard DUNCAN is a Professor in Conservation Biology at the University of Canberra's Science School and Department of Centre for Conservation and Ecology Genetics. His research focuses on the ecology of invasive species, particularly understanding how introduced species arrive, establish, and spread in new environments. Key areas include invasive weeds and pests, ecological community structure, and applying quantitative methods to address both fundamental and applied ecological questions. Education: PhD in Forest Ecology, University of Canterbury (1989, award date noted as 2024 in text) Research Interests: His work spans invasive species ecology, population dynamics, and restoration strategies. He employs diverse methods including field experiments, modeling, and genomics, with recent focus on grasslands and aquatic systems. Current projects address amphibian chytrid fungus, carp invasions, and rodent control. Articles Trends: Recent publications emphasize invasive species impacts on ecosystems, pathogen-driven niche contractions, and conservation tools like geospatial modeling and genomic simulations. Themes include species distribution modeling, pest management, and restoration ecology. Awards & Grants: Lead investigator on 35+ projects, including ARC Plant Biosecurity Training Centre and chytrid fungus mitigation efforts Associate Editor for Proceedings of the Royal Society B and Diversity and Distributions Advising: Accepting PhD students in invasive species ecology, with focus on using biological invasions to explore ecological theory and management applications. Labs/Teams: Collaborates widely on conservation genetics, aquatic invasions, and biosecurity, including work with the Biological Heritage Science Challenge and Indigenous Grasslands Trust.
Saurabh Amin is a Professor in the Department of Civil and Environmental Engineering at the Massachusetts Institute of Technology (MIT), where he also serves as the Edmund K. Turner Professor and Undergraduate Officer. He is a Principal Investigator at the Laboratory of Information and Decision Systems and holds affiliations with the Operations Research Center and the Center for Computational Science and Engineering. His educational background includes: B.Tech. 2002, Indian Institute of Technology (IIT) Roorkee M.S. 2004, University of Texas (UT) Austin Ph.D. 2011, University of California (UC) Berkeley Saurabh Amin's research focuses on the design and control of infrastructure systems using game theory and optimization in networks. His work spans three main areas: resilient network control, information systems and incentive design, and optimal resource allocation in large-scale infrastructure systems. By concentrating on critical infrastructure domains including highway transportation, electric power distribution, and urban water networks, his research develops innovative theory and tools to enhance system performance against both stochastic and adversarial disruptions. His approach involves modeling cyber-physical interactions in infrastructures to assess vulnerabilities, developing detection and response tools for failures at various scales, and designing economic incentive schemes that improve aggregate public good while accounting for dependencies and private information among strategic entities. Amin's work bridges mathematical systems theory with practical civil engineering applications, creating a rigorous theoretical foundation for infrastructure resilience that addresses diverse failure mechanisms from natural disasters to deliberate malicious actions. His recent publications demonstrate a strong focus on decarbonization of energy systems, resilient infrastructure planning under climate uncertainty, optimization methods for complex networked systems, and game-theoretic approaches to sustainable infrastructure management. His work increasingly integrates artificial intelligence and machine learning techniques with traditional control theory to address contemporary challenges in infrastructure resilience and sustainability. The research shows a clear trajectory toward addressing climate change impacts on infrastructure systems while maintaining economic efficiency and operational reliability. Professor Amin has received numerous prestigious awards and honors: Common Ground Excellence in Teaching Award, 2025 HSCC Test-of-Time Award, 2024 MIT CEE, Distinguished Service and Leadership Award, 2023 Samuel M. Seegal Prize (SoE) – inspiring students in pursuing and achieving excellence, 2022 Earll M. Murman for Excellence in Undergraduate Advising, 2022 C3.ai Digital Transformation Institute Research Award, 2020 MIT, Ole Madsen Mentoring Award, 2020 MIT, Energy Initiative Research Award, 2020 National Academy of Engineering, China-America Frontiers of Engineering Symposium speaker, 2019 MIT, Robert N. Noyce Career Development Professor, 2015-2018 Google Faculty Research Award, 2015 National Science Foundation CAREER Award, 2015 Siebel Energy Institute Research Award, 2015 MIT, Solomon Buchsbaum AT&T Research Fund Award, 2012 Professor Amin has been actively involved in significant research projects including the C3.ai DTI project on Causal Reasoning for Real-Time Attack Identification in Cyber-Physical Systems and another on Learning in Routing Games for Sustainable Electromobility. He serves as the chief scientist on multi-institutional NSF grants, including the $9 million Foundations of Resilient Cyber-Physical Systems (CPS) project. His teaching portfolio includes courses such as 1.008 Engineering for a Sustainable World, 1.104 Sensing and Intelligent Systems, 1.020 Engineering Sustainability: Analysis and Design, and 1.208 Resilient Networks. As Undergraduate Officer, he plays a key role in shaping the educational experience for civil and environmental engineering students at MIT. Professor Amin leads the Resilient Infrastructure Networks Lab at MIT, where his team develops theoretical foundations and practical tools for infrastructure resilience. The lab focuses on the intersection of control theory, game theory, and optimization applied to cyber-physical infrastructure systems. Current research directions include pandemic-resilient urban mobility and hurricane-resilient smart grid operations, reflecting the lab's commitment to addressing pressing societal challenges through rigorous systems engineering approaches.
Feng Fu is an Associate Professor of Mathematics at Dartmouth College, with an adjunct appointment in Biomedical Data Science. He leads the Fu Lab, focusing on interdisciplinary research at the intersection of evolutionary game theory, computational social science, and biomedical data science. His academic roles include teaching courses such as Evolutionary Game Theory, Stochastic Processes, and Game Theory and Artificial Intelligence. Education: Senior Postdoc, ETH Zurich (2012-2015); Postdoc, Harvard University (2010-2012); PhD, Peking University (2010); B.S., Fudan University (2004). Research interests span evolutionary dynamics of cooperation, computational models of human behavior and social networks, cancer evolution, and behavioral epidemiology. Notable work includes studies on vaccine hesitancy, misinformation dynamics, and the hysteresis effect in vaccination uptake. His lab has received prestigious funding, including a Bill & Melinda Gates Foundation Grant (2019). Teaching and mentoring: Advised numerous graduate and undergraduate researchers, many of whom have received awards and advanced to academic or industry roles. Courses taught include QSS/MATH 30.04 (Evolutionary Game Theory) and MATH 146 (Game Theory and AI). Labs/Teams: Fu Lab at Dartmouth collaborates across disciplines, with projects in cancer immunotherapy modeling, network-based interventions, and computational social science. Recent lab highlights include advancements in understanding polarization and the development of targeted public health strategies.
Rachid Cherkaoui is a Senior Scientist at École polytechnique fédérale de Lausanne (EPFL), affiliated with the School of Engineering, specifically within the Department of Electrical Engineering. He is actively associated with research units SEL-ENS, EDEY-ENS, and DESL, contributing to the Distributed Electrical Systems Laboratory (DESL). His work focuses on advanced power system optimization, smart grids, and energy market modeling. Ph.D. in Electrical Engineering, EPFL, 1992 M.S. in Electrical Engineering, EPFL, 1983 Dr. Cherkaoui's research interests include electrical power and distribution systems, distributed generation, energy storage, electricity market deregulation, and power system vulnerability mitigation. His work bridges theoretical modeling and real-world applications, particularly in flexibility provision, grid resilience, and market integration of renewable energy. His recent publications (2020–2025) reflect a strong focus on smart grid technologies, energy storage integration, and market mechanisms. Key themes include optimal dispatch of hybrid systems, TSO-DSO coordination, frequency control, and stochastic optimization under uncertainty. His work is frequently published in top-tier journals such as IEEE Transactions on Power Systems and IEEE Transactions on Smart Grid. ABB Swiss Award '83 Senior Member, IEEE Member, CIGRE Task Forces C5-2 IEEE Swiss Chapter Officer since 2005 Dr. Cherkaoui actively supervises doctoral students and collaborates extensively with researchers like Mario Paolone. He has contributed to numerous projects funded by industry, CTI/Innosuisse, and Horizon 2020. His research includes experimental validation and real-time control systems, particularly in hydropower and battery storage applications. He is also involved in national and international energy strategy discussions, including Switzerland's path to carbon neutrality.
