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Susan M. SanchezView profile

Professor

• Design of Experiments
• Data-Intensive Statistics
• Robust Selection
• +4 more

Susan M. Sanchez is a Distinguished Professor at the Naval Postgraduate School (NPS) in Monterey, California, and Co-Director of the SEED Center for Data Farming. She holds a Ph.D. in Operations Research from Cornell University (1986). Her academic career includes roles at the University of Arizona and the University of Missouri-St. Louis, as well as a visiting scholar position at INSEAD in France. Her research focuses on design of experiments, data-intensive statistics, and robust selection, with applications in military operations, healthcare, and manufacturing. She has authored over 80 publications and secured grants from the National Science Foundation and U.S. Department of Defense agencies. Key achievements include the 2013 INFORMS Military Application Society’s Koopman Prize, INFORMS Fellow designation (2017), and Titan of Simulation (2016). She co-founded the SEED Center, advancing simulation experiments and data farming for decision-making. She has advised over 50 students and served on thesis committees, with notable contributions to funded projects like 'Resources to Readiness' (U.S. Marine Corps) and 'Enhancing STORM Analytic Utility' (U.S. Navy). Her work spans logistics, energy systems, and unmanned vehicle analysis, with a focus on operational efficiency and resilience. Professional affiliations include INFORMS Simulation Society, Military Operations Research Society, and NATO Science & Technology Organization. The SEED Center and MOVES Institute collaborations underscore her commitment to interdisciplinary research.

Seokyon HwangView profile

Associate Professor

• Construction Project Management
• Civil Engineering
• Data Analytics
• +5 more

Seokyon Hwang, Ph.D., is an Associate Professor at Lamar University, specializing in Construction Project Management. He holds a Ph.D. in Civil Engineering from the University of Illinois at Urbana-Champaign and has over six years of construction field experience as a project engineer/superintendent. His research focuses on probabilistic modeling, BIM integration, sensor technologies, and organizational sustainability, with over 20 publications and a book in these areas. Education: Ph.D. in Civil Engineering, University of Illinois at Urbana-Champaign Research Interests: Statistical modeling of longitudinal data Computer simulation for process dynamics BIM and sensor technologies in construction Resilience engineering for infrastructure systems His work bridges engineering and technology, emphasizing practical applications in construction safety, cost analysis, and sustainable practices. Teaching Contributions: Teaches courses like Construction Planning and Scheduling and Construction Project Management in Lamar’s MBA program. Emphasizes career development and collaborative learning, fostering student resilience through real-world projects. Advising & Grants: While no advisees are listed, his research has received attention for innovations in infrastructure resilience and automated inspection techniques. Active in grant proposals for BIM integration and drone-based monitoring systems. Labs/Teams: Engaged in interdisciplinary projects involving UAV technology, AI, and smart infrastructure systems, collaborating with industry partners to advance practical solutions.

Paolo BocchiniView profile

Professor

• Catastrophe Modeling
• Infrastructure Resilience
• Computational Mechanics
• +7 more

Paolo Bocchini is a Professor and Director of the Center for Catastrophe Modeling and Resilience at Lehigh University, also serving as Director of Graduate Programs in Civil and Environmental Engineering. He specializes in probabilistic methods for civil infrastructure resilience, catastrophe modeling, and computational mechanics. His research bridges engineering and policy, emphasizing interdependent infrastructure systems' recovery under natural disasters. Education: Ph.D. in Structural Mechanics from the University of Bologna (2008), Laurea (B.Sc. + M.Sc.) in Civil Engineering (2004), and Degree from Collegio Superiore (2004), all at the University of Bologna. Research focuses on: Catastrophe modeling for hurricanes, earthquakes, and wildfires Resilience metrics for transportation and energy systems AI applications in disaster management Climate change impacts on infrastructure 3D concrete printing for thermal energy storage Key projects include the NSF-funded PRAISys initiative (2017-2022), involving 58 researchers to model infrastructure resilience, and leadership roles in ASCE's Objective Resilience Manual. His work informs U.S. Congress guidelines for transportation resilience funding and contributes to global climate adaptation strategies. Awards: Fellow of the Structural Engineering Institute of ASCE Grants and collaborations include a $12M DOE grant for marine energy resilience (2024) and a $1M DOE Climate Resilience Center (2024). He advises on policy through National Academies committees and co-leads ASCE's climate impact publications. His lab's website: www.paolobocchini.com .

• Machine Learning
• Energy Systems Optimization
• Predictive Analytics
• +2 more

Dr. Ebrahim Mortaz is an Associate Professor at Pace University's Lubin School of Business in the Department of Management and Management Science. He holds a PhD in Industrial and Systems Engineering from Auburn University and an ME in Industrial Engineering from the same institution. His research focuses on machine learning applications in energy systems, optimization models for renewable energy integration, and predictive analytics for healthcare and infrastructure planning. Key research areas include microgrid energy scheduling using electric vehicle storage, renewable portfolio optimization, and transmission expansion planning. Dr. Mortaz's publications demonstrate a consistent focus on applying operations research and machine learning techniques to sustainability challenges. Recent work explores predictive modeling for prescriptive applications, opioid prescription forecasting, and imbalance classification metrics, reflecting interdisciplinary approaches combining engineering, data science, and policy analysis. He maintains active engagement in the academic community through peer-reviewed publications in journals including Electric Power Systems Research and Knowledge-Based Systems .

Daniele DuranteView profile

Associate Professor

• Network Science
• Bayesian Methods and Computation
• Categorical Data
• +5 more

Roles and Affiliations: Associate Professor of Statistics at Bocconi University's Department of Decision Sciences. Research Affiliate at Bocconi Institute for Data Science and Analytics (BIDSA), DONDENA Centre, and Laboratory for Coronavirus Crisis Research. Former Post-Doctoral Fellow at University of Padova and Visiting Scholar at Duke University. Education: Ph.D. (2016) and M.Sc. (2012) in Statistics from University of Padova. Research Interests: Bayesian methodology, network science, categorical data, latent variable models, criminology, and demography. Focus on statistical models for complex data, including criminal networks and cause-of-death dependencies. Grants and Awards: ERC Starting Grant (NEMESIS, 2024–2029), PRIN-MUR Grant (CARONTE), COPSS Emerging Leader Award, Leonardo da Vinci Medal, Mitchell Prize (twice), Laplace Prize. Editor for Biometrika , Journal of Computational and Graphical Statistics , and Journal of Multivariate Analysis . Teaching and Service: Courses on Machine Learning, Data Science, and Statistical Methods. Organized Stats under the Stars hackathons, Bocconi Data Science Challenge platform. Served on committees for ISBA, ASA, and others. Labs/Teams: Leads NEMESIS and CARONTE projects. Collaborates with interdisciplinary teams in quantitative criminology and demography.

• Supply Chain Management
• Circular Economy
• Humanitarian Operations
• +3 more

Felix Papier is a Professor of Supply Chain Management at ESSEC Business School since 2011 and holds the ESSEC Global Circular Economy Chair (2023–present). He specializes in industrial strategy, circular economy, humanitarian logistics, and supply chain information systems. His career includes roles as Academic Director of the ESSEC & Mannheim Executive MBA (2015–2017), Deputy Director General overseeing pre-experience programs (2017–2022), and visiting professorships at UCLA Anderson School of Management (2022–2023) and HKUST (2006). Prior to academia, he was a senior strategy consultant at McKinsey & Company (2008–2011). Education: Ph.D. in Management Sciences (University of Cologne, 2008), M.Sc. in Information Systems (Universität Münster, 2003), and graduate studies at the University of Illinois at Chicago (2001). Awards include the POMS Best Working Paper Award (2017) and top teaching awards from ESSEC (2012–2013). His research focuses on optimizing supply chain operations through collaborative strategies, dynamic modeling, and sustainability. Key contributions include studies on humanitarian logistics during the pandemic, online food delivery platforms, and remanufacturing systems. He has co-advised doctoral theses on behavioral supply chain decisions, sustainable operations, and data-driven service optimization. Professional activities include memberships in INFORMS, POMS, and EURO StochMod. He has delivered keynote speeches on supply chain complexity and circular economy, authored chapters for Springer publications, and published in leading journals such as Manufacturing & Service Operations Management and European Journal of Operational Research .

• Environmental Statistics
• Climate Variability
• Wildfire Modeling
• +2 more

Bryson Bates is an Adjunct Professor at the University of Western Australia's School of Agriculture and Environment, specializing in environmental statistics and climate science. His research employs Bayesian methods to analyze climate variability, streamflow dynamics, and wildfire ignition patterns. Recent work focuses on disentangling climatic drivers of hydrologic systems and probabilistic wildfire risk modeling. Bates has developed frameworks for lightning prediction and evaluated stochastic rainfall models in collaboration with international teams.

Michael BertolacciView profile

Senior Lecturer

• Spatio-temporal Statistics
• Carbon Cycle Modeling
• Bayesian Inference
• +2 more

Michael Bertolacci is a Senior Lecturer in Mathematics and Statistics at the University of Western Australia. His research tackles large-scale spatio-temporal problems, particularly in environmental statistics and carbon cycle modeling. He co-developed the WOMBAT framework for global carbon flux inversion and contributed to the UNFCCC Global Stocktake. Recent projects include GeoWarp for subsea sediment analysis and probabilistic forecasting for maritime engineering. Bertolacci collaborates internationally on climate data initiatives, with work featured in high-impact journals like Earth System Science Data and the Journal of the American Statistical Association .

François RoueffView profile

Professor

• Statistics
• Signal Processing
• Time Series Analysis
• +3 more

François Roueff is a Professor at TELECOM Paris, part of the Institut Polytechnique de Paris. He holds a position within the Laboratoire Traitement et Communication de l'Information (LTCI). His research focuses on statistical signal processing, time series analysis, stochastic processes, and mathematical statistics. He has been actively involved in supervising 14 PhD students, contributing to advancements in statistical methodologies and their applications in various domains. Key research interests include wavelet analysis, long-memory processes, and nonparametric estimation. His work spans theoretical contributions to applied problems such as energy spectrum reconstruction and network traffic analysis. Recent publications emphasize methodological advancements in machine learning, variational inference, and tensor factorization for multi-site energy management. Roueff’s articles demonstrate expertise in time series modeling, stochastic processes, and statistical theory. Notably, his work on fractional calculus and operator theory has expanded the understanding of Hilbert space-valued processes. He has collaborated on projects involving signal processing for nuclear spectroscopy and infrasound detection, showcasing interdisciplinary applications. His advising contributions include co-supervising prominent students like Tepmony Sim (2016) and Amaury Durand (2022). Research grants and collaborations have enabled advancements in statistical methodologies for modern data analysis challenges.

Öncü HAZIRView profile

Professor

• Project Management
• Inventory Management
• Machine Scheduling
• +4 more

Öncü HAZIR is a Full Professor of Supply Chain Management at Rennes School of Business (since December 2024), previously holding the rank of Associate Professor there from 2017–2024. He also served as Programme Manager for the MSc in Supply Chain Management (2017–2021) and held academic roles at TED University (2012–2017) and Cankaya University (1999–2010). His academic journey includes a PhD in Operations Management from Bilkent University (2008), an MBA (2001), and a B.S. in Industrial Engineering (1999) from Middle East Technical University. He completed post-doctoral research in France at École Nationale Supérieure des Mines de Saint-Étienne (2010–2012) and Université Pierre et Marie Curie (2008–2010). Research Focus: His work spans project management, inventory control, machine scheduling, and assembly line balancing with a focus on robust optimization under uncertainty. Key contributions include risk-averse project scheduling models, machine learning-enhanced cost estimation, and workload smoothing algorithms for assembly lines. Publications: Over 30 peer-reviewed articles in top journals like European Journal of Operational Research , International Journal of Production Research , and IEEE Transactions on Engineering Management . Notable works address payment delay risks, perishable inventory policies, and big data adoption in project management. Education Contributions: Co-authored the textbook An Introduction to Project Modeling and Planning (Springer, 2021) and developed pedagogical materials on project management methodologies. Professional Engagement: Presented at major conferences (EUROMA, MISTA, PMS) and contributed to research centers like the Center for Unframed Thinking (CUT) and the AI-Driven Business research group at Rennes SB.

Maximo Cosme Camacho AlonsoView profile

Professor

• Econometrics
• Time Series Analysis
• Business Cycles
• +3 more

Maximo Cosme Camacho Alonso is a Professor at the University of Murcia's Faculty of Economics and Business, specializing in Quantitative Methods for Economics and Business. He leads the Time Series and Econometrics research group. He earned his PhD from Universitat Autònoma de Barcelona in 2002 with a thesis on non-linear macroeconometrics. His research encompasses econometric modeling, time series analysis, and forecasting techniques applied to business cycles, tourism economics, energy markets, and socioeconomic phenomena. Recent publications develop novel methodologies for recession prediction post-COVID, entrepreneurial ecosystem analysis, and business cycle dating, with applications spanning macroeconomics, environmental economics, and criminology. His work advances dynamic modeling frameworks including Markov-switching and factor models.

Marco TurcoView profile

Researcher

• Climate Science
• Environmental Science
• Meteorology
• +6 more

Marco Turco is a Ramón y Cajal Researcher at the University of Murcia's Faculty of Biology, Department of Physics. He holds a PhD from the Universitat de Barcelona (2012), focusing on climate change impacts on forest fires in Catalonia. His research integrates atmospheric modeling, wildfire risk analysis, and climate projections. Key interests include fire weather trends, drought prediction, and interdisciplinary wildfire management strategies. His work emphasizes Mediterranean environments, addressing topics like human exposure to wildfires, climate-driven fire synchronicity, and model validation for seasonal forecasts. He leads the Regional Atmospheric Modelling Group, developing hybrid models for fire prediction and collaborating on drought assessments. Publications span climate change impacts, flood damage, and the interplay between meteorology and vegetation. Recent studies highlight non-stationary climate systems, probabilistic drought forecasting, and the role of anthropogenic factors in extreme weather. His research bridges academic and operational applications, aiming to inform policy and improve disaster resilience in vulnerable regions.

Malcolm WhitworthView profile

Associate Professor

• Natural Hazards
• Geomorphology
• Geographical Information Systems (GIS)
• +9 more

Malcolm Whitworth is an Associate Professor in Engineering Geomorphology and Natural Hazards at the University of Portsmouth , within the School of the Environment and Life Sciences . His career bridges academic research with extensive commercial applications in natural hazard assessment and terrain evaluation. Specializes in GIS and remote sensing Develops VR applications for geoscience Focuses on infrastructure risk management Research Interests include global natural hazard modeling, landslide susceptibility analysis in mountainous regions, and innovative use of Unmanned Aerial Vehicles and Virtual Reality for hazard visualization. His recent work has concentrated on Niger Delta environmental risks and immersive educational technologies. Article Trends show a strong focus on subsurface hazard modeling , VR applications , and environmental risk assessment . Key subfields include oil spill impact analysis , coastal contamination , and 3D geological education . Teaching integrates real-world case studies, particularly using immersive technologies to enhance fieldwork training. He supervises MSc and PhD students in geomorphology and remote sensing projects across multiple continents. Labs & Collaborations include the Institute of the Earth and Environment and international partnerships in Tajikistan, China, and Greece. His projects span disaster risk reduction along the Pamir Mountain roads and Niger Delta environmental monitoring.

Sarah BentleyView profile

Assistant Professor

• Space Weather
• Machine Learning
• Space Physics
• +3 more

Dr. Sarah Bentley is an Assistant Professor at Northumbria University, part of the Faculty of Engineering and Environment. She holds a PhD in Mathematics from the University of Reading (2019) and a MMath from Durham University (2013). Her research focuses on space physics, space weather forecasting, and the application of machine learning to understand magnetospheric dynamics. She investigates ultra-low frequency (ULF) waves and their role in energizing Earth’s radiation belts, with a particular interest in developing predictive models for space weather impacts. Joined Northumbria as a Vice-Chancellor's Fellow in 2020. Current projects include STFC-funded research on solar and space physics, emphasizing radial diffusion and wave-particle interactions. Her work bridges computational methods and physical phenomena, leveraging AI to analyze large datasets from spacecraft observations. She supervises PhD students in topics like graph neural networks for magnetic field characterization and machine learning-driven space weather forecasting. Key contributions include probabilistic models for ULF wave prediction, radial diffusion benchmarking, and causal network analysis for space weather variables. She actively engages in EDI initiatives, advocating for neurodivergent inclusivity in academic environments.

Sinan GonulView profile

Senior Lecturer

• Judgmental Forecasting
• Forecasting/Decision Support Systems
• Judgment & Decision Making
• +2 more

Dr. Sinan Gonul is a Senior Lecturer in the Department of Business Administration at Northumbria University. Previously, he served as an Associate Professor at Middle East Technical University (METU) in Ankara, Turkey. His academic journey includes a PhD in Decision Science & Operations Management (2007), an MBA in Business Administration (2001), and a BSc in Electrical and Electronics Engineering (1999). He also conducted postdoctoral research at Glasgow Caledonian University (2009–2010). PhD: Business Administration (Decision Science & Operations Management), 2007 MBA: Business Administration, 2001 BSc: Electrical and Electronics Engineering, 1999 Fellow of The Higher Education Academy (FHEA), 2019 Dr. Gonul’s research focuses on judgmental forecasting, decision support systems, behavioral operations management, and cybersecurity resilience. He explores how individuals and organizations make decisions under uncertainty, particularly in financial and operational contexts. His work bridges cognitive psychology with quantitative modeling, emphasizing the interplay between human judgment and algorithmic predictions. His recent publications analyze consumer behavior in digital environments, cybersecurity tool adoption in mobile banking, and behavioral responses to forecasting scenarios. He has supervised PhD candidates such as Uzonyibo Okoye (Consumer Behavior Analytics, 2024), Syeda Batool (Cyberbullying Mitigation, 2022), and Irfan Riasat (Cybersecurity Resilience, 2022). His methodological expertise spans experimental design, multicriteria decision-making, and behavioral experiments on information uncertainty. Fellow of The Higher Education Academy (FHEA), 2019 Dr. Gonul teaches undergraduate and graduate courses in Management Science, Quantitative Decision-Making, Judgment & Decision-Making, and Business Forecasting. His research has appeared in journals like Journal of Behavioral Decision Making , European Journal of Operational Research , and Production and Operations Management .