Find Academic Supervisors

• Time Series Econometrics
• Financial Econometrics
• Volatility Modeling
• +3 more

Paolo Santucci de Magistris is a Professor of Econometrics at the Department of Economics and Finance of Luiss University (Rome) since February 2018 and previously served as Head of the Department from 2021 to 2024. He held roles as Associate Professor and Assistant Professor at Aarhus University (Denmark) from 2013 to 2018, with prior postdoctoral research at the University of Padova and CREATES. His education includes a PhD from the University of Pavia and visiting research at Northwestern University’s Kellogg School of Management. His research focuses on time series econometrics, financial econometrics, and energy economics, with emphasis on volatility modeling, liquidity risk, and climate impacts. Notable contributions include work on liquidity coverage in financial markets, cointegration models, and the interplay between wind energy and CO2 emissions. His publications span top journals like the Journal of Financial Economics and Journal of Econometrics , exploring topics ranging from stochastic volatility to climate policy analysis. Ongoing projects include research on option price dynamics, energy market connectedness, and risk-neutral density fitting via the RNDfittool MATLAB application. Prof. Santucci de Magistris collaborates internationally with institutions like CREATES and has contributed to policy-relevant studies on energy transition and financial stability. His methodological innovations include Bayesian econometric techniques and tools for analyzing financial risks embedded in derivatives markets.

Dora ErdosView profile

Senior Lecturer

• Algorithm Design
• Data Mining
• Combinatorial Optimization
• +3 more

Dora Erdos is a Senior Lecturer and Director of Undergraduate Studies in the Department of Computer Science at Boston University. She specializes in algorithmic challenges, data mining, and combinatorial optimization with a focus on network-based problems. Her work bridges theoretical computer science and practical applications in education technology and network analysis. Education: PhD in Computer Science, Boston University (2015) MSc in Pure Mathematics, Eotvos University (Advisor: Andras Frank) Postdoctoral Research at Brown University's Raphael Lab (Advisor: Ben Raphael) Research Interests: Erdos investigates algorithms for network analysis, including centrality measures, graph reconstruction, and optimization problems in educational systems. She develops scalable methods for tensor factorization and content placement in navigational networks. Her work often integrates combinatorial approaches with real-world applications. Professional Roles: As Director of Undergraduate Studies, Erdos oversees academic advising and curriculum development. She emphasizes student accessibility, maintaining office hours and encouraging direct communication via email (edori@bu.edu). Recent Research Trends: Her publications (2011–2017) focus on network-centric problems such as centrality evaluation frameworks, boolean tensor decomposition, and team formation algorithms for educational scheduling. These contributions highlight her dual expertise in theoretical algorithm design and applied educational technology.

Arno SiebesView profile

Professor

• Data Mining
• Pattern Mining
• Minimum Description Length Principle
• +2 more

Arno Siebes is Professor of Algorithmic Data Analysis in the Department of Information and Computing Sciences at Utrecht University's Faculty of Science. His research focuses on data mining methodologies, particularly pattern mining and Minimum Description Length (MDL) principles. Key research areas include: Developing efficient algorithms for pattern discovery Applying MDL to data characterization Creating interpretable models for complex datasets Addressing challenges in data science education Recent publications demonstrate applications in diverse domains including mobility analysis, genomic screening, and pandemic response. His work combines theoretical foundations with practical implementations for knowledge discovery.

Tony LindebergView profile

Professor

• Computer Vision
• Computational Neuroscience
• Medical Image Analysis
• +4 more

Tony Lindeberg is a Professor of Computer Science—Computational Vision at KTH Royal Institute of Technology, affiliated with the Division of Computational Science and Technology. He teaches the course Image Analysis and Computer Vision (DD2423). His research focuses on scale-space theory, early vision, and computational modeling of biological and auditory vision systems. Key contributions include theories on receptive fields, time-causal spatio-temporal models, and feature detection algorithms. Research interests span computational neuroscience, medical image analysis, and spatio-temporal recognition. Lindeberg has pioneered work on scale-invariant image features, affine transformations, and Galilean diagonalization for motion analysis. He is the author of the foundational book Scale-Space Theory in Computer Vision (1993). His work bridges computer vision and biological vision systems, with applications in gesture recognition, dynamic texture analysis, and neural networks. He leads the Vision Lab and Computational Brain Science Lab at KTH, emphasizing theoretical rigor and practical algorithms for visual perception tasks.

Brandon KarchewskiView profile

Teaching Professor

• Computational Geoscience
• Engineering Geology
• Natural Hazards
• +2 more

Brandon Karchewski is an Associate Head (Undergraduate) and Teaching Professor in the Department of Earth, Energy and Environment at the University of Calgary. He earned his PhD in Civil Engineering from McMaster University and teaches courses including Engineering Geology, Computational Methods, and Natural Disasters. His research program focuses on: Computational methods in geophysics and geomechanics Geoscience education innovation Climate change impacts on frozen soils Inverse modeling applications Karchewski has pioneered virtual field experiences and developed open-source tools for modeling climate impacts on permafrost. His educational research examines metaphor use in geoscience communication and field pedagogy. Recent computational work includes Python-based permafrost modeling and stochastic inversion methods for biogeochemical transport. He leads the geophysics field school program emphasizing team-based learning. Award recognition includes: Geoscience Teaching Award (2019) Best Poster Award for teaching innovation research (2018) Team Teaching Excellence award (2016) Multiple teaching assistant awards

• Control Systems
• Model Predictive Control
• Power Systems Engineering
• +3 more

Morten Hovd is a Professor in the Department of Engineering Cybernetics at the Norwegian University of Science and Technology (NTNU). His research focuses on advanced control systems, model predictive control (MPC), power electronics, and optimization algorithms. He has contributed significantly to the development of robust control strategies for uncertain systems and has published extensively in leading journals and conferences in the field of control engineering. His research interests span several key areas in control systems engineering, including model predictive control, nonlinear control systems, optimization under uncertainty, and applications in power systems and energy efficiency. He is particularly known for his work on discrete-time bilinear systems, modular multilevel converters (MMCs), and the integration of machine learning techniques with control theory. His contributions address both theoretical advancements and practical implementations in industries such as energy and petroleum engineering. Hovd's recent publications highlight advancements in energy-efficient control systems, stochastic surrogate modeling for subsurface flows, and optimization algorithms tailored for complex engineering problems. His work often combines rigorous mathematical frameworks with real-world applications, such as improving the reliability of power systems and enhancing reservoir management through data-driven methods. He is actively involved in teaching courses such as TTK4210 (Advanced Control of Industrial Processes) and TK8118 (Mini-seminar in Cybernetics). His research has led to innovations in fault detection for power systems, energy-efficient building climate control, and robust MPC strategies for uncertain systems.

Simon DevereauxView profile

Professor

• Early Modern British History
• Criminal Justice History
• Political History
• +3 more

Simon Devereaux is Professor and Undergraduate Advisor in History at the University of Victoria. He specializes in early modern British history, particularly criminal justice, political institutions, and penal reform. He holds a PhD from University of Toronto and was Killam Postdoctoral Fellow at UBC. His research examines capital punishment, pardon systems, and the transformation of judicial practices in 18th-19th century England. Recent work includes creating the digital resource 'Capital Punishment and Pardon at the Old Bailey, 1730-1837' and authoring 'Execution, State and Society in England, 1660–1900'. Research Focus: Evolution of execution practices and abolition movements Royal pardons and judicial discretion in Georgian England Historiography of the English state during the long 18th century

Bojan PopovView profile

Professor

• Numerical Analysis
• Partial Differential Equations
• Approximation Theory
• +3 more

Bojan Popov is a Professor in the Department of Mathematics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on numerical analysis, nonlinear partial differential equations, and approximation theory, with a particular emphasis on invariant domain preserving schemes and hyperbolic conservation laws. He holds a Ph.D. from the University of South Carolina (1999) and an M.S. from the University of Sofia (1992). Popov has led or co-led numerous grants from agencies like NSF, DOD, and DOE, totaling over $30 million. He has advised four Ph.D. students and organized major conferences, including the 2007 'Approximation and Learning in High Dimensions' and the 2008 'Nonlinear Approximation Techniques Using L1'. His work bridges numerical methods with applications in fluid dynamics, materials science, and high-performance computing. Recent research includes invariant domain preserving techniques for hyperbolic systems, entropy viscosity methods, and robust finite element approximations. He teaches advanced courses such as Hyperbolic Conservation Laws (Math 638) and Linear Algebra (Math 304).

Quan ZhouView profile

Assistant Professor

• Markov chain Monte Carlo sampling
• Bayesian methodology
• Variable selection
• +7 more

Quan Zhou is an Assistant Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on developing advanced sampling methods, particularly Markov chain Monte Carlo (MCMC) algorithms, with applications in Bayesian methodology, variable selection, stochastic optimization, and statistical genetics. He holds a BS from Fudan University and a PhD from Baylor College of Medicine, followed by a postdoctoral fellowship at Rice University. He teaches courses such as Mathematical Probability, Multivariate Analysis, and Advanced Stochastic Processes. Notable contributions include work on informed MCMC samplers, Schrödinger bridge theory, and high-dimensional structure learning. He advises PhD students like Hyunwoong Chang (now at UT Dallas) and Guanxun Li (Beijing Normal University). Active in academic service, he served as President of the Southeastern Texas Chapter of the American Statistical Association (SETCASA).

Seth SullivantView profile

Professor

• Algebraic Statistics
• Computational Algebra
• Combinatorics
• +3 more

Seth Sullivant is a Distinguished Professor in the Department of Mathematics at North Carolina State University (NC State), within the College of Sciences. His research focuses on algebraic statistics, computational and combinatorial algebra, and mathematical phylogenetics. He holds a Ph.D. in Mathematics from the University of California, Berkeley (2005). His expertise spans interdisciplinary areas, including algebraic approaches to statistical problems, combinatorial methods in phylogenetics, and the development of algebraic tools for graphical models. He is affiliated with research groups in Algebra and Combinatorics, Mathematical Biology, and Symbolic Computation at NC State. Recent research trends in his publications emphasize the application of algebraic geometry and combinatorics to statistical models, phylogenetic network analysis, and identifiability problems in systems biology. His work bridges abstract mathematical concepts with practical statistical methodologies, addressing challenges in data analysis and model interpretation. No specific scientific awards are listed in the provided texts. His advising and grant activities are not detailed here, though his extensive publication record suggests active research collaborations. He is part of research teams focused on advancing algebraic methods in statistics and computational biology.

Xin GaoView profile

Professor

• Artificial Intelligence
• Machine Learning
• Statistical Genetics
• +5 more

Xin Gao is a Professor in the Department of Mathematics and Statistics at York University, Toronto. His research focuses on Artificial Intelligence , Machine Learning , and Statistical Genetics , with applications in biomedical data analysis and planetary science. He leads the Artificial Intelligence and Machine Learning Lab , which has developed impactful tools like an online Type 2 Diabetes risk predictor using logistic regression and Mars rock composition analyzers for NASA. His methodological work includes penalized composite likelihood and multi-task feature learning , implemented in R packages FusionLearn and lassoGEE . Scientific Awards NSERC Discovery Acceleration Award ($120,000, 2018-2020) Key Software Contributions FusionLearn : Correlated multi-task feature learning lassoGEE : High-dimensional clustered/longitudinal data analysis Notable Collaborations Vector Institute (AI scholarship mentoring) Fields Institute (committee roles) International genomic data integration projects

Mouhamad Shaker Ali AghaView profile

Associate Professor

• Supply Chain Management
• Industry 4.0
• Supply Chain Risk and Resilience Analysis
• +4 more

Dr. Mouhamad Shaker Ali Agha is a Senior Teaching Fellow (Associate Professor, Scholarship Track) in the Department of Management Science at the University of Strathclyde, where he earned his PhD and MSc. He specializes in operations management, enterprise resource planning (ERP), and technical risk management, serving as Programme Director for the MSc Business Analysis and Consulting and the International Master in Project Management (IMPM). A Senior Fellow of the Higher Education Academy (UK) and a Certified Management & Business Educator, he focuses on supply chain risk and resilience, digital supply chains, Industry 4.0, and renewable energy sector challenges. His research has been recognized through awards like the Strathclyde Business School Excellence Teaching Award (2022) and the 2024 Supply Chain and Logistics Competition Second Place Award. He actively supervises six PhD students, emphasizing real-world applications of research. His work includes collaborative projects on Industry 4.0 adoption barriers for SMEs and virtual reality in education, supported by grants such as the EPSRC-funded DTP 2224 project (2025–2028). He also contributes to professional activities like peer reviewing for journals and conferences. Education: PhD and MSc in Management Science from the University of Strathclyde. Research Interests: Supply chain resilience, digital transformation in supply chains, renewable energy sector logistics, and operational innovation. His work intersects with UN Sustainable Development Goals related to industry, innovation, and infrastructure (SDG 9) and climate action (SDG 13). Grants & Projects: Co-investigator in the EPSRC DTP 2224 project (2025–2028) and principal investigator in the Virtual and Physical Reality in Operations Management project (2023–2024). Collaborates with institutions like the FUTO Centre of Excellence in Sustainable Procurement. Awards: Highlighted awards include the 2024 Certified Management & Business Educator, 2022 Teaching Excellence Award, and recognition as a Senior Fellow of the Higher Education Academy (2021).

• Health Care Management
• Operations Research
• Patient Scheduling
• +5 more

Professor Patrick Jonathan is a Full Professor at the Telfer School of Management, University of Ottawa. His research focuses on applying operations research methods to healthcare management challenges, particularly in patient scheduling, capacity planning, and disaster response logistics. He holds a B.A.Sc. from McMaster University, M.Sc. and Ph.D. from the University of British Columbia (UBC). Key research areas include healthcare system efficiency, stochastic optimization, approximate dynamic programming, and resource allocation in emergency and long-term care settings. He has contributed to projects like optimizing inpatient unit staffing during pandemics and developing robust logistics models for disaster relief operations. Education: B.A.Sc. (McMaster), M.Sc. (UBC), Ph.D. (UBC) Affiliations: INFORMS, CORS, Centre for Health Care Management (UBC) Recent work emphasizes pandemic-related healthcare operations, including staff scheduling under crisis conditions and elective patient admission strategies during the COVID-19 pandemic. His research also addresses dynamic home care routing and multi-priority patient scheduling policies. He has secured over $2.3 million in research funding through grants from NSERC, MITACS, and the National Research Council. Notable projects include the 'Resilience and Adaptation to Climatic Extreme (RACE) Wildfires' initiative and 'Stochasticity in Approximate Dynamic Programming.' Labs/Teams: Collaborates with interdisciplinary teams on healthcare logistics and emergency department optimization projects.

Nicolas ZuffereyView profile

Professor

• Operations Management
• Supply Chain Management
• Optimization
• +4 more

Nicolas Zufferey is a Full Professor of Operations Management at the University of Geneva, Switzerland, where he has served since 2008. He leads research in optimization methods for complex systems, focusing on applications in supply chain management, production planning, inventory control, and transportation logistics. His affiliations include the Research Institute of Management and collaborations with CIRRELT (Transportation & Logistics) and GERAD (Decision Analysis). Education: PhD in Operations Research (EPFL, 2002), MSc/BSc in Mathematics (EPFL) Prior Experience: Postdoc at University of Calgary (2003–2004), Assistant Professor at Université Laval (2004–2007) Research Interests: His work emphasizes developing advanced metaheuristics (e.g., VNS, Tabu Search, PSO) for challenging optimization problems. Key domains include: Multi-objective scheduling with resource constraints Inventory deployment under uncertainty Network design for supply chains and transportation systems Publications: Over 150 peer-reviewed articles across journals like European Journal of Operational Research , Transportation Research , and INFORMS Journal on Computing . Recent work addresses electric vehicle routing, drone integration in delivery systems, and robust decision-making under uncertainty. Collaborations: Engaged with 35+ universities and 27 private companies globally. Active in applying operations research to industrial problems (e.g., Swiss railways, luxury watch production, pharmaceutical networks).

Adam SzymkiewiczView profile

Professor

• Groundwater flow
• Hydraulic properties of porous media
• Numerical methods for flow and transport in porous media
• +4 more

Summary Adam Szymkiewicz is a full Professor and Vice-Dean for Scientific Research at the Faculty of Civil and Environmental Engineering, Gdańsk University of Technology. He leads the Department of Geotechnics and Water Engineering and holds academic positions since 2021. His research focuses on groundwater flow, contaminant transport, and numerical modeling in porous media. Key roles include Head of Department (2022–present) and Vice-Dean (2024–present). Education includes a PhD from Joseph Fourier University (2004) and habilitation from Gdańsk University of Technology (2013). Research Interests: Hydraulic properties of porous media, multiphase fluid flow, climate change impacts, and integrated hydrological modeling. Notable projects include AQUIGROW (HORIZON EUROPE) and SOILPROM. Publications: Over 50 peer-reviewed articles on topics like PFAS migration, groundwater recharge, and MODFLOW coupling. Recent work emphasizes climate change effects on aquifers and coastal hydrogeology. Awards: Multiple Rector awards (2012–2022) and Polish Academy of Sciences Award (2013). Membership: AGU, EGU, InterPore, and Polish Hydrogeologists Society. Editorial roles include Associate Editor of Acta Geophysica .