جست‌وجوی استاد و پژوهشگر

Steven Crimpمشاهده پروفایل

پژوهشگر ارشد

• Climate Risk Management
• Climate Change Adaptation
• Food Security
• +۱ مورد دیگر

Dr. Steven Crimp is Senior Research Fellow and Deputy Head of Climate with the ANU Institute for Climate Energy and Disaster Solutions (ICEDS). With expertise in climate risk management and adaptation, he examines climate impacts on agriculture and food systems in Australia and the Indo-Pacific region. His research focuses on translating climate scenarios into decision-relevant information for agriculture, developing quantitative models for seasonal forecasting, and designing practical adaptation pathways. Current projects include evaluating carbon markets for climate-resilient coffee agroforestry in PNG and probabilistic seasonal forecasting in Queensland. Crimp's publications demonstrate strong focus on fire danger prediction, drought adaptation strategies, food system resilience, and climate service design. Recent work addresses compound risks from climate extremes and pandemics, with emphasis on vulnerable Pacific Island communities and agricultural systems.

Gareth Harrisonمشاهده پروفایل

استاد

• Renewable Energy Integration
• Energy Systems Optimization
• Climate Change Impacts on Energy
• +۳ مورد دیگر

Professor Gareth Harrison, the Bert Whittington Chair of Electrical Power Engineering at the University of Edinburgh, serves as Head of the School of Engineering. His research focuses on renewable energy integration, meteorological modeling, and optimization of energy systems. BEng (1997) and PhD (2001) in Electrical Power Engineering from the University of Edinburgh Chartered Engineer and Fellow of the Institution of Engineering and Technology His work spans energy systems integration, climate change adaptation, and multi-vector energy networks. Current projects include EPSRC and Ofgem-funded initiatives on energy storage and grid resilience. Recent publications highlight machine learning for hybrid energy systems, life cycle assessments of floating wind farms, and power-to-gas optimization. Key subfields include solid oxide fuel cells, tidal turbines, and weather-sensitive demand modeling. Scientific Recognition: Senior Member of IEEE Affiliate of Association of Chartered Certified Accountants Emeritus Member of RSE Young Academy of Scotland He supervises PhD students on high-resolution weather modeling for renewables and climate impacts on energy systems, while participating in UK energy policy discussions.

• Applied Machine Learning
• Large Language Models
• Generative ML
• +۴ مورد دیگر

Dr. Andrei Kucharavy is an Assistant Professor at HES-SO Valais-Wallis in the Faculty of Economics and Services, where he teaches Business Economics courses including Fundamentals of Algorithms and Computational Thinking. He co-founded the GenLearning Center, an applied research entity focused on safety and deployment challenges of Generative AI technologies. His research centers on machine learning safety, privacy, and distribution from cybersecurity and human factors perspectives. PhD from Paris Sorbonne University (research at Johns Hopkins University and Stowers Institute for Medical Research) Engineer degree from Ecole Polytechnique Former postdoctoral researcher at EPFL's Distributed Computing Lab Dr. Kucharavy's research spans Applied Machine Learning, Large Language Models, and Cybersecurity with particular expertise in generative ML since 2018 and its cybersecurity applications since 2020. His work bridges theoretical machine learning with practical security implementations in Swiss defense contexts. His publications reveal strong focus on LLM vulnerabilities, privacy leakage detection, and developing bibliometric methods resilient to AI-generated content. His recent publications demonstrate expertise in LLM security vulnerabilities, code generation risks, and developing frameworks for technology forecasting using iterative self-prompting. The research shows consistent progression from foundational LLM understanding to specialized security applications, particularly in code vulnerability injection through dataset poisoning and malicious fine-tuning. Distinguished Cyber-Defence (CYD) Postdoctoral Fellowship recipient Dr. Kucharavy leads multiple Cyber-Defence Campus-funded projects totaling over CHF 430,000, including work on characterizing LLM attacks in code generation, developing vulnerability characterization frameworks, and secure privacy leakage detection protocols. His research team includes Percia David Dimitri and Vallez Cyril, with collaborations extending to Cyber-Defence Campus experts like Dr. Ljiljana Dolamic. He actively develops applied solutions through the GenLearning Center, focusing on on-premises deployment of generative models for healthcare applications and knowledge transfer tools that maintain data privacy while enhancing educational outcomes. His work directly informs Swiss defense technology standards for LLM-based solutions in cyber-physical systems.

Roni Rosenfeldمشاهده پروفایل

استاد

• Epidemic Forecasting
• Computational Biology
• Machine Learning
• +۴ مورد دیگر

Roni Rosenfeld is a Professor in the School of Computer Science at Carnegie Mellon University, holding joint appointments in Machine Learning, Language Technologies, Computer Science, and Computational Biology. He has served as Head of the Machine Learning Department from 2018 to 2024. His education includes a BSc in Mathematics and Physics from Tel Aviv University (1985) and a PhD in Computer Science from Carnegie Mellon University (1994). His research focuses on epidemic forecasting via the Delphi Group, named a National Center for Epidemic Forecasting by the U.S. CDC. Past work includes speech recognition, SLT4D (Spoken Language Technologies for Development), and ML4SG (Machine Learning for Social Good). He has advised over a dozen PhD students and mentored postdocs in epidemiology, computational biology, and speech technologies. Rosenfeld has published 150+ peer-reviewed articles and received the Spira Teaching Excellence Award (2017) and Allen Newell Medal for Research Excellence (1992, 2022). His current projects emphasize real-time epidemic tracking, public health data quality, and collaborative forecasting frameworks. Key collaborations include the CDC-funded Delphi Group’s work on influenza, dengue, and pandemic surveillance systems. He also pioneered viral spread analysis in low-literacy regions through speech-based technologies, addressing global development challenges.

Ali Aouadمشاهده پروفایل

دانشیار

• Operations Research
• Algorithms
• Market Design
• +۵ مورد دیگر

Ali Aouad is an Associate Professor at MIT Sloan School of Management, joining in 2024 as part of its new faculty. Previously, he held an Associate Professor position at London Business School (LBS). His research bridges operations, computer science, and economics, focusing on algorithms and decision processes applied to supply chain management, market design, digital platforms, and public sector operations. Education: PhD in Operations Research from MIT, MS and BS in Applied Mathematics from École Polytechnique (Paris). Professional experience includes roles as an applied scientist at Uber’s Marketplace (2017–2018) and strategic consulting at Boston Consulting Group (Paris and Casablanca). Research interests emphasize algorithms for dynamic matching, choice modeling, and public policy applications. Notable awards include the 2023 Poets & Quants Best 40-Under-40 Professors recognition, a 2023 ERC Starting Grant (£1.1M), and multiple INFORMS awards. He currently serves as an associate editor for Operations Research and Management Science . Recent work includes studies on food subsidy impacts, cultural institution layout optimization, and stochastic matching frameworks. His research often integrates machine learning techniques with operational decision-making challenges in both private and public sectors. Grants and collaborations highlight interdisciplinary efforts, such as the ERC-funded project on pathway operations in museums. Teaching accolades include multiple best teacher awards at LBS.

Chen Yingمشاهده پروفایل

دانشیار

• Forecasting
• Non-stationary Time Series Analysis
• High Frequency Data
• +۱۵ مورد دیگر

Dr. Chen Ying is an Associate Professor in the Department of Mathematics at the National University of Singapore (NUS), with joint appointments in the Risk Management Institute, Department of Statistics and Applied Probability, and courtesy appointments in the Department of Economics. She serves as Academic Director of the Digital FinTech PhD program at the Asian Institute of Digital Finance and is a Faculty member in NUS Graduate School for Integrative Sciences and Engineering. Her research spans financial statistics, data science, and applied mathematics, focusing on non-stationary time series, high-frequency data, functional data analysis, and quantum computing applications in finance. Education: B.Sc. Economics (Renmin University of China, 1998), M.A. Economics and Management Science (Humboldt University, 2002), M.Sc. Statistics (Humboldt & Freie Universität Berlin, 2005), and Ph.D. in Statistics (Humboldt University, 2007, Summa Cum Laude). Research interests include AI forecasting, quantum computing for finance, non-stationary time series analysis, energy data analytics, and citation metrics. She has published extensively on topics like Bitcoin blockchain dynamics, natural gas demand modeling, and credit rating fairness. Dr. Chen serves on editorial boards of Statistica Sinica , Statistics and Its Interface , and Digital Finance , and holds leadership roles in the International Statistical Institute and International Association for Statistical Computing. Notable achievements include ISI Elected Membership (2016), Council Member of the International Statistical Institute (2023–2027), and roles in global initiatives like the EU FIN-TECH project. Her work bridges statistical theory with practical applications in finance, energy, and healthcare.

• Computer Vision
• 3D Reconstruction
• Neural Rendering
• +۵ مورد دیگر

Dr. Miaomiao Liu is a Research Fellow at the School of Computing, The Australian National University. Her research focuses on computer vision, 3D reconstruction, and neural rendering, with applications in robotics, autonomous systems, and renewable energy forecasting. She leads multiple projects including Next-Generation Aviation Safety Net Air Traffic Management, Machine Vision Techniques for Solar Power Forecasting, and 3D Vision Geometric Optimization in Deep Learning. Her work integrates cutting-edge techniques such as neural radiance fields, depth estimation, and self-supervised learning to address challenges in dynamic scene reconstruction, motion forecasting, and image deblurring. She has pioneered methods for mining supervision signals in dynamic regions and developing language-driven deblurring networks. Key Projects: Aviation safety systems, solar irradiance prediction, and geometric optimization in deep learning Research Themes: 3D scene understanding, human motion prediction, and neural rendering Technical Expertise: Neural networks, multi-view stereo, and physics-based modeling Dr. Liu's research has been published in top-tier venues like CVPR and IEEE conferences, with over 2,450 citations. While not explicitly listed as part of a lab, her work demonstrates strong collaboration with industry partners such as CSIRO and aerospace stakeholders. She actively supervises research students in areas like 3D vision and energy systems.

Keith Jacks Gambleمشاهده پروفایل

استاد

• Financial Decision Making
• Behavioral Finance
• Aging
• +۴ مورد دیگر

Dr. Keith Jacks Gamble is a Professor of Finance and Director of the Data Science Institute at Middle Tennessee State University (MTSU). He holds a Ph.D. from the University of California, Berkeley (2008) and a B.A. from Harvard University (2003). His research focuses on Financial Decision Making , Behavioral Finance , and Aging , particularly examining how cognitive aging impacts financial choices and the role of uncertainty in decision-making processes. His work bridges finance, economics, and gerontology, addressing topics like short selling, risk-taking behavior, and technology's role in post-pandemic HR strategies. Publications highlight his contributions to understanding informed trading, the behavioral aspects of investor decisions, and the implications of cognitive decline on elderly financial behavior. Dr. Gamble’s research has been published in top-tier journals such as Management Science , Journal of Corporate Finance , and PLOS ONE . He collaborates widely, with co-authors exploring intersections of finance, technology, and aging. As Director of the Data Science Institute, he drives interdisciplinary research and education initiatives in data-driven decision-making. His work emphasizes translating academic insights into practical applications, such as improving financial literacy and leveraging technology for organizational adaptation.

Susan M. Sanchezمشاهده پروفایل

استاد

• Design of Experiments
• Data-Intensive Statistics
• Robust Selection
• +۴ مورد دیگر

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 Hwangمشاهده پروفایل

دانشیار

• Construction Project Management
• Civil Engineering
• Data Analytics
• +۵ مورد دیگر

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 Bocchiniمشاهده پروفایل

استاد

• Catastrophe Modeling
• Infrastructure Resilience
• Computational Mechanics
• +۷ مورد دیگر

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
• +۲ مورد دیگر

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 Duranteمشاهده پروفایل

دانشیار

• Network Science
• Bayesian Methods and Computation
• Categorical Data
• +۵ مورد دیگر

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
• +۳ مورد دیگر

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
• +۲ مورد دیگر

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.