Find Academic Supervisors

• Data Science
• Scenario Planning
• Decision Making
• +7 more

Megan Crawford is a Lecturer at Edinburgh Napier University's Business School, specializing in Data Science and Scenario Planning. She serves as Principal Investigator of the Futures and Analytics Research (FAR) Hub and Deputy Lead of Enterprise Data Intelligence & Analytics within the Centre for Business Innovations and Sustainable Solutions (CBISS). In 2023, she became Turing Academic Liaison for the Business School following Edinburgh Napier's membership in the prestigious Turing University Network. Dr. Crawford's educational background includes a PhD in Management Science from the University of Strathclyde, an MSc in Behavioural & Economic Sciences from Warwick University, and joint BAs in Cognitive Psychology and Philosophy from Texas A&M University-Corpus Christi. Her research focuses on how individuals and organizations frame decisions about the future, structured group techniques for optimization, and strategy development with AI integration, all approached through behavioral and complexity science lenses to advance UN Sustainable Development Goals. Her research output reveals a consistent focus on scenario planning methodology, cognitive biases in future thinking, and the application of structured group techniques. Recent publications show increasing emphasis on AI integration with scenario planning, pandemic response scenarios, and developing frameworks for evaluating scenario quality. Her work spans theoretical development of scenario typologies alongside practical applications in organizational settings. As Turing Academic Liaison, Dr. Crawford bridges Edinburgh Napier's research with the UK's National Institute for AI and Data Science. She has established the FAR Hub as an inclusive online learning center focused on advancing digital literacy, particularly addressing digital poverty concerns across the UK through partnerships with organizations representing underrepresented populations. Dr. Crawford actively supervises doctoral students, including external PhD candidates at Mary Immaculate College, Northumbria University, and collaborative projects with Universidad Maimónides and Harvard University. Her supervision topics span AI in education, banking compliance, and cross-cultural memory studies, reflecting her interdisciplinary approach.

Enrique SentanaView profile

Professor

• Econometrics
• Asset Pricing
• Financial Economics

Enrique Sentana is a Professor of Economics at CEMFI (Centro de Estudios Monetarios y Financieros) in Madrid, Spain. He is also a Research Fellow at the CEPR Financial Economics Programme and a Senior Research Associate at the LSE Financial Markets Group. His academic career spans prestigious institutions including the London School of Economics and the University of Alicante. Degrees: PhD in Economics (LSE, 1991), MSc in Econometrics and Mathematical Economics (LSE, 1987), Licenciado en Ciencias Económicas y Empresariales (University of Alicante, 1985) Dr. Sentana specializes in Econometrics , with a focus on Asset Pricing , Financial Economics , and VIX Derivatives . His methodological contributions include work on ARCH models, indirect estimation, and identification issues in econometrics, advancing volatility modeling and financial risk assessment. His research trends highlight innovations in empirical asset pricing , nonlinear time series , and financial market linkages . Notable achievements include the Rey Jaime I Prize in Economics (2014) , Fellowships at the Econometric Society and Journal of Econometrics , and prestigious prizes from the University of London and LSE. Scientific Awards: Rey Jaime I Prize in Economics (2014) Fellow of the Econometric Society (2012) Fellow of the Journal of Econometrics (2010) Sayers Prize, University of London (1992) Ely Devons Prize, London School of Economics (1987) Dr. Sentana has advised 10 PhD students at CEMFI and held editorial roles including Managing Editor of the Review of Economic Studies and Co-Editor of the Journal of Financial Econometrics . He has also served as Executive Vice-President of the Econometric Society and Treasurer of its European Standing Committee.

Xingpeng LiView profile

Associate Professor

• Power Systems
• Machine Learning
• Renewable Energy Integration
• +5 more

Dr. Xingpeng Li is an Associate Professor in the Department of Electrical & Computer Engineering at the University of Houston (UH), where he has held this position since 2024, following his tenure as Assistant Professor (2018–2024). His research focuses on smart grid technologies, renewable energy integration, and optimization of power systems. He holds a Ph.D. in Electrical Engineering from Arizona State University (2017) and multiple master’s degrees in Computer Science (Georgia Tech, 2023), Industrial Engineering (ASU, 2016), and Electrical Engineering (Zhejiang University, 2013). His work emphasizes machine learning applications in power systems, including battery degradation modeling, microgrid design, and climate-resilient grid planning. He leads the RPG Lab (https://rpglab.github.io), advancing solutions for offshore renewable energy systems, hydrogen transmission, and grid cybersecurity. His research has been funded by the NSF CAREER Award (2024) and the NAS Gulf Research Program (2023). Education : Ph.D., Electrical Engineering, Arizona State University (2017) M.S., Computer Science, Georgia Institute of Technology (2023) M.S., Industrial Engineering, Arizona State University (2016) M.S., Electrical Engineering, Zhejiang University (2013) B.S., Electrical Engineering, Shandong University (2010) Dr. Li’s recent publications highlight advancements in AI-driven grid optimization, battery lifecycle modeling, and offshore microgrid planning. He has received awards such as the Early-Career Research Follow (NAS Gulf, 2023) and the IEEE Phoenix Section Student Scholarship (2016). Awards : NSF CAREER Award (2024) Early-Career Research Follow, NAS Gulf Research Program (2023) Emerging Leader, Offshore Technology Conference (2023) His teaching includes courses like ECE 6327 (Smart Grid Systems) and ECE 6379 (Power System Operations), bridging theoretical research with practical grid challenges. His lab collaborates on projects like 100% renewable offshore platforms and hydrogen energy transmission systems, addressing climate resilience and decarbonization goals.

Saumen MandalView profile

Professor

• Optimal experimental design
• Biostatistics
• Data science
• +7 more

Dr. Saumen Mandal is a Professor in the Department of Statistics at the University of Manitoba, Faculty of Science. He holds a PhD from the University of Glasgow, UK, and MSc/BSc (Gold Medal) from the University of Calcutta, India. His research focuses on optimal experimental design, biostatistics, data science, shrinkage estimation, and constrained optimization. He has received numerous teaching awards including the Dr. and Mrs. H.H. Saunderson Award for Excellence in Teaching, Students Choice Best Professor Award, and multiple Merit Awards. He is also a P.Stat. designee from the Statistical Society of Canada. Education: PhD (Statistics), University of Glasgow, UK MSc (Statistics), University of Calcutta, India (First Class First, Gold Medal) BSc Honours (Statistics), University of Calcutta, India Research Interests: Optimal design theory and applications Biostatistical methods for clinical trials and healthcare data Data science and machine learning techniques Shrinkage estimation and model selection Linear models and goodness-of-fit testing Publications span topics like optimal regression designs, response-adaptive clinical trial methods, and statistical models for healthcare data. His work emphasizes practical applications in medicine and data-driven decision making. Awards include: Teaching Excellence Awards (2005-2007) Merit Awards for Teaching and Research (2010-2019) Faculty of Science Innovation in Teaching Award (2020) He advises graduate students in statistics and contributes to research teams in biostatistics and data science. His office is temporarily located at 256 Parker Building during construction.

• Computer Science
• Machine Learning
• Data Science
• +4 more

Yong Zhang is affiliated with Tsinghua University's Research Institute of Information Technology in Beijing, China. His research focuses on machine learning, optimization algorithms, edge computing, and their applications in areas like time series analysis, federated learning, and sensor networks. He has collaborated on projects involving neural networks, scheduling problems, and privacy-preserving techniques. Education: Yong Zhang earned a PhD in Computer Science and Engineering from Fudan University in 2007. His academic career includes roles at institutions like the Chinese Academy of Sciences and the University of Hong Kong, reflecting a strong interdisciplinary background. Research Contributions: His work spans theoretical computer science, algorithm design, and applied machine learning. Notable areas include developing efficient scheduling algorithms for energy systems, creating robust federated learning frameworks for industrial demand forecasting, and advancing methods for sentiment analysis using multimodal data. He has also contributed to biomedical engineering through smartphone-based health monitoring systems. Collaborations: He frequently collaborates with researchers at institutions like the University of Electronic Science and Technology of China, Nanyang Technological University, and The Hong Kong Polytechnic University. Key projects involve data caching optimization in edge computing, distributed algorithms for dynamic networks, and combinatorial optimization problems. Labs & Future Work: His team explores cutting-edge topics in AI-driven systems, including trust-aware machine learning, distributed resource allocation, and real-time data processing for IoT applications. Current research emphasizes scalable solutions for complex optimization challenges in both academic and industrial settings.

Rickard SandbergView profile

Associate Professor

• Machine Learning
• Deep Learning
• Data Analytics
• +9 more

Rickard Sandberg is an **Associate Professor** and **Center Director** at the **Department of Entrepreneurship, Innovation and Technology** at the **Stockholm School of Economics (SSE)**. His work bridges econometrics, statistics, and business analytics with a focus on time series analysis, machine learning applications, and sustainability measurement. **Research Interests**: Machine Learning, Deep Learning, Data Analytics, Predictive Analytics, Forecasting, Nonlinear Time Series Modelling, Structural Economic Modelling, Econometrics, and Measuring Sustainability. His research emphasizes theoretical advancements in statistical methods and their practical application in economic and business contexts. **Key Contributions**: His publications explore unit root testing in nonlinear models, ESG rating challenges, and the impact of energy policies. Notable works include analyzing Scandinavian unemployment trends, cartel damage calculations, and Nordic companies' data-driven transformations. His 2023 paper on ESG ratings proposes solutions for consistency in ambiguous evaluation systems. **Teaching & Outreach**: Teaches advanced econometric time series courses (e.g., MSc 5314) and actively engages in international academic collaborations through presentations in Japan and Brazil. His work on AI for sustainability highlights interdisciplinary outreach efforts. **Labs/Teams**: Leads research initiatives within SSE’s Department, focusing on entrepreneurship and innovation through data and economic modeling frameworks.

Giuliano BianchiView profile

Associate Professor

• Law and Economics
• Corporate Governance
• Forecasting
• +3 more

Giuliano Bianchi is an Associate Professor of Economics at EHL Hospitality Business School, specializing in law and economics, corporate governance, and forecasting. He holds a PhD in Economics from the University of Bologna, a Master's in Economics from the University of Edinburgh, and a Master in Law from the University of Fribourg. His research focuses on hospitality economics, asset-light business models, and macroeconomic forecasting in the Swiss hotel sector. Education: PhD in Economics, University of Bologna (Italy) Master's Degree in Economics, University of Edinburgh (UK) Bachelor's in Economics, University of Lugano (Switzerland) Bachelor in Law (BLaw), UniDistance Master in Law (MLaw), University of Fribourg His research interests include empirical analysis of hotel industry dynamics, regulatory frameworks affecting hospitality operations, and corporate governance mechanisms. He pioneered the Swiss Hospitality Macroeconomics Forecasting Index (2018-2020), a project funded by HES-SO to develop demand forecasting tools for Swiss hotels. Recent work explores legal aspects of hotel rate parity, brand affiliation impacts on asset values, and the socio-economic implications of hospitality employment. Awards: Wertheim Fellowship, Harvard Law School Bologna University Economics Department Scholarship Marco Polo Fellowship Program Dr. Bianchi serves on EHL's academic board and teaches Macroeconomics and Microeconomics in the BSc International Hospitality Management program. His research has been published in Tourism Economics, Journal of Property Research, and Applied Economics.

Ayman El-HagView profile

Associate Professor

• Electrical Engineering
• High Voltage Engineering
• Power Systems
• +5 more

Ayman El-Hag is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. He is affiliated with the Outdoor Insulation and Condition Monitoring Research Group, focusing on advancing technologies for high-voltage insulation systems and smart grid infrastructure. His work integrates machine learning, signal processing, and materials science to improve condition monitoring and diagnostic methods for power equipment. Research interests include partial discharge detection using UHF and acoustic sensors, machine learning applications for defect classification in outdoor insulators, and the development of non-invasive sensing techniques for real-time monitoring. He also explores energy management systems leveraging fuzzy logic and smart meter data analysis for residential and grid-level applications. Recent publications highlight advancements in capsule networks for insulator discharge prediction, deep learning-based hydrophobicity classification, and novel antenna designs for partial discharge localization. His work emphasizes practical solutions for power system reliability and environmental resilience of insulation materials under extreme conditions. El-Hag is a Full-time faculty member and holds Adjunct faculty status, contributing to interdisciplinary research projects. He actively engages in promoting condition monitoring methodologies through educational initiatives and industry partnerships.

Andrea ScottView profile

Associate Professor

• Fluid Dynamics
• Machine Learning
• Remote Sensing
• +8 more

Andrea Scott is an Associate Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo, Faculty of Engineering. Her research focuses on fluid dynamics, remote sensing, and machine learning applications in environmental systems. She holds a Doctorate in Mechanical Engineering from the University of Waterloo (2008) and has taught courses such as ME 351 (Fluid Mechanics) and SYDE 621 (Numerical Methods). Notable awards include the 2022 Outstanding Performance Award and 2021 Distinguished Performance Award from the University of Waterloo. Research interests span turbulence modeling, data-driven approaches, and physically inspired neural networks. Her work includes developing algorithms for sea ice concentration estimation, SAR imagery analysis, and fluid flow simulations. She collaborates with groups like the Vision and Image Processing Lab and the Remote Sensing of Environmental Change group. Current projects involve small object detection in remote sensing and graph neural networks for unstructured grid problems. Education: PhD, Mechanical Engineering, University of Waterloo, Canada (2008) MASc, Mechanical Engineering, McMaster University, Canada (2001) BASc, Mechanical Engineering, University of Waterloo, Canada (1999) Teaching responsibilities include undergraduate and graduate courses in fluid mechanics and systems engineering. She actively mentors students through the IEEE GRSS Women-to-Women Mentorship program and serves as an Associate Editor for the AGU Journal of Machine Learning and Computation. Her lab oversees over 20 current and past graduate students, focusing on topics like space debris tracking, AI-driven environmental modeling, and sea ice dynamics. Research outputs include over 40 publications in journals such as Physical Review Fluids and IEEE Transactions on Geoscience and Remote Sensing .

Salem S. GharbiaView profile

Professor

• Climate Resilience
• Environmental Monitoring
• Environmental Modeling
• +3 more

Dr. Salem S. Gharbia is the Head of the Department of Environmental Science and a Principal Investigator at Atlantic Technological University (ATU). He leads the Centre for Environmental Research Innovation and Sustainability (CERIS) and co-founded the Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE). His research focuses on climate resilience, environmental monitoring, and modeling, with expertise in Geographic Information Systems (GIS), wireless sensor networks, and climate change impacts. Dr. Gharbia coordinates major EU projects like Horizon 2020 SCORE and has secured over €14M in research funding. He holds a PhD in Environmental Engineering from Trinity College Dublin, where he received the prestigious Ussher Award. His work spans collaborations with the Irish EPA, Horizon Europe, and the Marine Institute, addressing coastal flooding, microplastic detection, and sustainable agriculture. Education: PhD in Environmental Engineering, Trinity College Dublin. Research interests include climate adaptation strategies, urban resilience, and innovative sensor technologies for environmental monitoring. His projects emphasize interdisciplinary approaches to address global challenges such as rising sea levels, extreme rainfall, and coastal erosion. Awards: Trinity College Ussher Award. Grants: Over €14M secured over five years for projects like EmpowerUS, Pro-climate, and WaterFutures. Advising: Accepting PhD students in environmental science and engineering. Labs: CERIS and MISHE, focusing on climate modeling and environmental systems.

• Neurosymbolic AI
• Answer Set Programming
• Visual Question Answering
• +3 more

Nelson Nicolas Higuera Ruiz is a PreDoc Researcher at the Vienna University of Technology, affiliated with the Faculty of Informatics' Knowledge-Based Systems research group. His work bridges logic programming and deep learning for explainable AI. Research Focus: Neurosymbolic AI, Visual Question Answering (VQA), Answer Set Programming (ASP), and hybrid reasoning systems Projects: Leads optimization research in the LCS (2017–2025) project, developing neurosymbolic approaches for intelligent systems Key Contributions: Pioneering adaptive large-neighbourhood search algorithms for ASP optimization, modular neurosymbolic architectures, and contrastive explainability frameworks for VQA Collaborations: Active in international workshops and conferences including IJCAI, AAAI, and CLeaR, frequently collaborating with researchers like Thomas Eiter and Johannes Oetsch Publications: Focus on neurosymbolic integration, optimization algorithms, and explainability across AI, logic programming, and computer vision domains

Pedro GaleanoView profile

Associate Professor

• Outlier Detection
• Time Series Analysis
• Bayesian Inference
• +3 more

Pedro Galeano is an Associate Professor in the Department of Statistics at Universidad Carlos III de Madrid (UC3M) since 2009. He holds a PhD in Statistics (2004) under Prof. Daniel Peña, focusing on multiple time series. Previously, he served as Visiting Assistant Professor of Statistics and Econometrics at the University of Chicago’s Graduate School of Business and as a Postdoctoral Fellow at the Department of Statistics and Operations Research at Universidade de Santiago de Compostela. His research focuses on time series analysis, outlier detection, Bayesian inference in financial models, and functional data analysis with applications to missing data. He is an Associate Editor of the Journal of Time Series Analysis and advises the Heliyon journal. Key contributions include developing methodologies for detecting structural breaks, modeling systemic risk via copula approaches, and advancing robust statistical techniques for high-dimensional data. Active in academic leadership, Galeano co-organized the NICDA Workshop 2025 and has published extensively on topics like dynamic factor models, sequential parameter change detection, and functional data applications in energy markets. His work bridges theoretical statistics with practical applications in finance, economics, and environmental science.

Mohammad DehghaniView profile

Teaching Professor

• Reinforcement Learning
• Simulation Optimization
• Healthcare Operations
• +5 more

Mohammad Dehghani is an Associate Teaching Professor in the Department of Mechanical and Industrial Engineering at Northeastern University, where he also serves as Program Director of the Galante Engineering Business Program. He holds a Ph.D. in Engineering Management from Western New England University (2016), an M.S. in Industrial Engineering from Tarbiat Modares University (2011), and a B.S. in Industrial Engineering from Yazd University (2008). His research focuses on Reinforcement Learning (RL), Simulation Optimization, and Healthcare Operations, with applications in manufacturing, digital twin systems, and UAV routing. He has developed multiple courses in Industrial Engineering and Data Analytics, receiving the 2020 Fostering Engineering Innovation in Education Award and the 2025 DAIS Data Analytics Teaching Award. Education: Ph.D. in Engineering Management, Western New England University, 2016 M.S. in Industrial Engineering, Tarbiat Modares University, 2011 B.S. in Industrial Engineering, Yazd University, 2008 Dehghani’s research bridges AI and operations research, emphasizing practical applications. His work includes developing RL frameworks for manufacturing scheduling and UAV routing, as well as simulation-optimization models for healthcare and pandemic preparedness. He has collaborated on projects addressing supply chain resilience during the COVID-19 pandemic and multi-objective supplier selection processes. His publications span journals like Simulation and conferences such as Winter Simulation Conference (WSC). His honors include the 2015 Best Ph.D. Paper Award at WSC and recognition from the Institute of Industrial and Systems Engineers (IISE). He actively contributes to professional societies, including the American Society of Engineering Management and Institute of Industrial Engineers. His teaching focuses on integrating data analytics and simulation tools into engineering curricula, with courses emphasizing Python integration, simheuristics, and digital twin technology. Dehghani leads initiatives in the Galante Program to enhance engineering-business synergies, preparing students for industry roles through interdisciplinary training. His work emphasizes practical problem-solving, with grants supporting projects in healthcare logistics and sustainable construction in cold climates.

Ihtisham MalikView profile

Senior Lecturer

• Sustainable Finance
• Asset Pricing
• Corporate Finance
• +6 more

Ihtisham Malik is an Honorary Senior Lecturer in Finance at the University of Queensland (UQ), where he earned his PhD in 2020. With over a decade of teaching experience, he specializes in finance courses such as Portfolio Management and Public Financial Management, delivered in both traditional and online formats. He has served as Academic Director for UQ's joint public sector finance program with the Queensland Treasury Corporation (2021–2024), bridging academic theory with practical industry insights. His research focuses on Sustainable Finance, Asset Pricing, and Climate Policy Uncertainty, with publications in top-tier journals like International Review of Financial Analysis and Corporate Governance: An International Review . Recognized for his teaching excellence, he received the Student Engagement Award for fostering dynamic learning environments. His research explores the intersection of natural disasters, corporate governance, and financial markets, including studies on insider trading during extreme weather events and board diversity's impact on eco-innovation. Recent work addresses climate policy uncertainty's effects on stock returns and corporate default risk. He actively engages in capacity-building initiatives for public sector professionals through Executive Education programs. Key awards: Student Engagement Award (UQ) Grants/Advising: His academic leadership in the UQ-Queensland Treasury program exemplifies his commitment to applied research. He collaborates with global peers on topics like board gender reforms and CSR's role in disaster resilience. Labs/Teams: Engaged with UQ Business School's research hubs focusing on Sustainable Finance and Financial Markets Analysis.

Eric SimsView profile

Professor

• Macroeconomics
• Monetary Policy
• Business Cycles
• +3 more

Eric Sims is a Professor and Department Chair of Economics at the University of Notre Dame, affiliated with the College of Arts and Letters. He specializes in macroeconomics, focusing on business cycles, monetary policy, and fiscal policies. Sims has been at Notre Dame since 2009, teaching graduate and undergraduate courses in macroeconomics and monetary theory. He is also a Research Associate at the National Bureau of Economic Research (NBER). In 2023, he received the Sheedy Excellence in Teaching Award, the highest teaching honor in the College of Arts and Letters. Education: Ph.D., University of Michigan, 2009 M.A., University of Michigan, 2006 B.A., Trinity University, 2003 Research Interests: Sims' work examines monetary policy implementation, the effects of quantitative easing, business cycle dynamics, and the role of central bank credibility. His research integrates theoretical models with empirical analysis to address policy challenges in post-crisis economies. Recent Articles: Recent publications include studies on state-level economic tracking, optimal quantitative easing strategies, and the effectiveness of forward guidance in inflation targeting. His work often bridges theoretical frameworks (e.g., New Keynesian models) with real-world policy applications. Awards: Recipient of the prestigious Sheedy Excellence in Teaching Award (2023), recognizing his impactful pedagogy in macroeconomics. Advising & Grants: While explicit student advisees are not listed, Sims is actively involved in mentoring graduate students through coursework and research collaborations. His research has been supported by grants tied to his NBER affiliation and institutional funding at Notre Dame. Labs/Teams: Collaborates with NBER on projects analyzing macroeconomic trends and policy impacts. Engages with interdisciplinary teams at Notre Dame’s Wilson Sheehan Lab for Economic Opportunities (LEO).