Find Academic Supervisors

• Energy Economics
• Energy Policy
• Technology Diffusion Economics
• +5 more

Reinhard Madlener serves as University Professor of Economics, specializing in Energy Economics, at RWTH Aachen University's Faculty of Business and Economics. He directs the Institute for Future Energy Consumer Needs and Behavior (FCN) within the E.ON Energy Research Center and holds the position of Vice-Director of JARA Energy since May 2014. His academic foundation includes studies in Business and Economics at Vienna University of Economics and Business (1987-1994) complemented by Economics training at Vienna's Institute for Advanced Studies (1992-1994), culminating in a PhD in Social and Economic Sciences from WU Vienna in 1996. Professor Madlener's research integrates energy economics with behavioral and ecological dimensions, focusing on technology diffusion dynamics, policy evaluation, and sustainable energy systems. His work bridges theoretical economic frameworks with practical energy transition challenges, emphasizing interdisciplinary approaches to market design and consumer behavior in decarbonizing economies. Analysis of his 2012-2016 publications reveals consistent methodological sophistication through agent-based modeling, spatial econometrics, and stochastic analysis. His research portfolio demonstrates evolving emphasis from foundational energy rebound theory toward applied investigations of renewable integration economics, storage solutions, and policy mechanisms for accelerating clean energy adoption across residential, industrial, and agricultural sectors. As FCN Director, Madlener leads interdisciplinary research on energy consumer behavior within the E.ON ERC framework. His leadership extends to JARA Energy, the strategic collaboration between RWTH Aachen and Forschungszentrum Jülich, where he shapes large-scale energy research initiatives including participation in the upcoming IAEE International Conference 2025 in Paris.

• Financial Economics
• Econometrics
• Forecasting
• +3 more

YU MIN YEN is a Professor at the Department of International Business, College of Commerce, National Chengchi University (NCCU), Taiwan. He has held academic positions at NCCU since 2014, progressing from Assistant Professor to Associate Professor, and since February 2025, to full Professor. His research and teaching focus on Financial Economics and Econometrics. Education: Ph.D. in Finance, London School of Economics and Political Science (2006–2012) M.Sc. in Finance, London School of Economics and Political Science (2005–2006) B.A. in Economics, National Taiwan University (1993–1998) Research Interests: His research spans a wide range of topics in financial econometrics, including forecasting macroeconomic variables, financial risk modeling, portfolio optimization, and the application of machine learning techniques in economic data analysis. He is particularly known for his work on quantile and expectile forecasting, robust estimation under model uncertainty, and the use of derivative-implied information for policy and investment decisions. His recent projects emphasize integrating big data analytics into inflation forecasting and developing robust methods for estimating treatment effects in the presence of endogeneity and heavy-tailed distributions. Scientific Awards: 資深優良教師 (10年) – National Chengchi University (2024) 學術研究優良獎 – National Chengchi University (2024, 2017) 國科會研究獎勵 – Ministry of Science and Technology (2021, 2019, 2018) Research Funding & Leadership: He has served as Principal Investigator on multiple three-year grants from Taiwan’s National Science and Technology Council (MOST), including: 「使用機器學習之分布中介處裡效果的穩健估計」(2023–2026) 「條件尾端平均處裡效果在有內生性之下估計方法之探究」(2021–2023) 「FZ損失函數之應用: 預測風險衡量指標及其他用途」(2019–2021) These projects reflect his commitment to advancing statistical methodologies for real-world economic and financial problems.

• Applied Mathematics
• Numerical Analysis
• Computational Finance
• +5 more

Carlos Vázquez Cendón is a Professor in the Department of Applied Mathematics at the School of Computer Science, University of A Coruña. With over two decades of academic experience, he has established himself as a leading researcher in applied mathematics with specializations in numerical methods, computational finance, and quantum computing applications. His teaching portfolio includes advanced courses in numerical methods, mathematical finance, and quantum computing across multiple master's programs. Professor Vázquez Cendón's research focuses on the development and application of numerical methods for solving complex problems in mathematical finance and industrial mathematics. His work spans stochastic numerical methods, quantum computing applications in finance, mathematical models for financial markets, and computational techniques for energy markets. He has made significant contributions to the field of numerical analysis for partial differential equations arising in financial modeling. His publication record demonstrates consistent output in high-impact journals spanning computational mathematics, financial mathematics, and quantum computing applications. The research trends show a progression from classical numerical methods in finance toward innovative applications of quantum computing in financial modeling. His work bridges theoretical mathematics with practical financial applications, particularly in derivatives pricing, risk management, and commodity markets. Professor Vázquez Cendón has secured substantial research funding from diverse sources including the Ministry of Science and Innovation, Consellería de Educación, BBVA foundation, and European Union programs. His research group has been active in multiple collaborative projects spanning computational finance, quantum information technology, and industrial mathematics applications. He has supervised numerous master's theses since 2013, with topics ranging from stochastic local volatility models in commodity markets to numerical methods in quantum computing. His current research activities include exploring quantum algorithms for financial applications and developing advanced numerical techniques for complex financial instruments.

Kirill GolubnichiyView profile

Research Fellow

• Mathematical Finance
• Partial Differential Equations
• Inverse Problems
• +3 more

Kirill Golubnichiy serves as a Post Doctoral Fellow in the Department of Mathematics & Statistics at Texas Tech University, specializing in mathematical finance and computational methods for financial modeling. Education: Ph.D. in Mathematics, University of Washington, Seattle (2022) His research centers on developing mathematical tools for analyzing partial differential equations governing financial asset pricing, solving inverse problems with physics applications, and creating machine learning algorithms for forecasting financial markets. This interdisciplinary work bridges theoretical mathematics, computational finance, and artificial intelligence to address complex problems in quantitative finance. Analysis of his 15 most recent publications (2021-2025) reveals a dominant focus on solving the Black-Scholes equation for option pricing through innovative combinations of numerical methods and machine learning. His research shows increasing integration of deep learning techniques with traditional mathematical finance models, particularly in handling ill-posed problems and volatility forecasting. Secondary research streams include theoretical physics applications involving Einstein equations and academic textbook development in economic analysis. He participates in Texas Tech's Mathematical Finance Program, collaborating with faculty leads Dr. Zari Rachev and Dr. Brent Lindquist on quantitative finance research and graduate training initiatives.

Ulya R. KarpuzcuView profile

Associate Professor

• Physics of Computing
• Energy-Efficient Computing
• Application-Domain Specialized Architectures
• +6 more

Ulya R. Karpuzcu is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Minnesota, Twin Cities. Her research spans physics of computing, energy-efficient computing, application-domain specialized architectures, hardware security and reliability, and approximate computing. Her research interests focus on understanding how physical principles shape computing paradigms to address emerging energy problems. Key areas include: Physics of computing and physical limits of computation Energy-efficient computing through novel architectures Hardware security and reliability Approximate computing techniques Spintronics and non-volatile memory computing Quantum computing architectures Dr. Karpuzcu's recent publications show a strong focus on in-memory computing, Ising machines for combinatorial optimization, spintronics-based architectures, and quantum computing. Her work bridges theoretical foundations with practical hardware implementations, particularly exploring energy-efficient solutions through novel computing paradigms that address physical constraints. Her notable awards include: NSF CAREER Award (2016) on 'Trading Communication and Storage for Computation to Enhance Energy Efficiency' NSF SPX grant (2017) on scalable in-memory processing using spintronics NSF CCF grant (2014) on soft (approximate) near-threshold voltage computing NSF XPS grant (2014) on approximate communication to enhance energy efficiency Dr. Karpuzcu actively mentors PhD students and undergraduate researchers, with her lab 'wide open to motivated Ph.D. students and undergraduate researchers.' She has served on technical program committees for major conferences including ISCA, HPCA, MICRO, and ICCAD, and has co-organized workshops on Near-threshold Voltage Computing. She teaches courses on computer architecture, physics of computation, and quantum computing, including developing innovative courses like 'Physics of Computation' inspired by Richard Feynman's lectures.

• Theoretical Statistics
• Probability Theory
• Asymptotic Expansion
• +14 more

Nakahiro Yoshida is a Professor at the Graduate School of Mathematical Sciences, University of Tokyo, specializing in Theoretical Statistics and Probability Theory. He leads the YUIMA Project, developing computational frameworks for stochastic differential equations. His affiliations include memberships in the Mathematical Society of Japan, Japan Statistical Society, International Statistical Institute, and Bernoulli Society, with editorial roles for several academic journals. Research Interests: His work spans asymptotic expansion theory, limit theorems, and statistical inference for stochastic processes, with applications in finance and insurance mathematics. Key areas include: Martingale expansions and Malliavin calculus Non-synchronous covariance estimation for high-frequency data Quasi-likelihood analysis for jump diffusions Stochastic numerical methods and computational statistics Statistical learning theory for econometrics and biostatistics Publication analysis reveals consistent focus on asymptotic expansion techniques for Wiener functionals and martingales (2023), adaptive estimation methods (2021), financial applications of stochastic processes (2017), and foundational work on quasi-likelihood analysis (2011-2013). Awards & Honors: Mathematical Society of Japan Autumn Prize (2024) Hiroshi Fujiwara Prize for Mathematical Sciences (2019) Japan Statistical Society Award (2009) Research Achievement Award, Japan Statistical Society (2007) Analysis Prize, Mathematical Society of Japan (2006) He leads the YUIMA Project Team, developing open-source software for stochastic differential equation analysis, with applications in quantitative finance and computational statistics.

• Decision Making under Uncertainty
• Robust Optimization
• Operations Research
• +5 more

Dr. Gargoei Loke serves as an Associate Professor in the Department of Management and Marketing at Durham University Business School since November 2023, specializing in data-driven decision frameworks for complex business environments. His academic foundation includes: Bachelor's degree earned in the UK Master's degree completed in the UK Dr. Loke's research pioneers the integration of robust optimization with machine learning to solve decision-making challenges under uncertainty. His work bridges theoretical models with practical applications across service operations, healthcare systems, and resource-constrained sectors like energy and water management. Key contributions include developing queueing network frameworks for healthcare operations and novel workforce planning methodologies under volatile conditions. His recent publications (2022-2024) in premier journals like Operations Research and Manufacturing & Service Operations Management demonstrate consistent focus on optimization under uncertainty, with growing emphasis on healthcare analytics and supply chain resilience. The research portfolio reveals a clear trajectory from foundational queueing theory toward integrated AI-optimization paradigms for real-world business problems. Dr. Loke actively contributes to doctoral education having graduated two PhD students while currently co-supervising two additional candidates. His teaching portfolio spans the Online MBA Project Management course and development of specialized Data Science for Business curriculum for Executive Masters programs, building on extensive prior experience teaching business analytics across undergraduate, graduate, and executive levels.

Stefan KarpitschkaView profile

Research Professor

• Complex Fluids
• Interfacial Phenomena
• Wetting Dynamics
• +4 more

Dr. Stefan Karpitschka serves as a Senior Researcher and Research Group Leader at the Max Planck Institute for Dynamics and Self-Organization (MPI-DS) in Göttingen, Germany, within the Dynamics of Complex Fluids department. His group investigates fluid behavior at solid-gas interfaces, with applications spanning everyday phenomena (ink on paper), biology (cyanobacteria motility), and semiconductor manufacturing. Affiliated with the Max Planck-University of Twente Center for Complex Fluid Dynamics, he also participates in the Priority Program SPP 2171 on dynamic wetting of adaptive surfaces. His research focuses on interfacial phenomena in complex fluids , particularly wetting dynamics, Marangoni effects, and fluid-structure interactions. Key discoveries include the 'boomerang effect' where droplets spread and self-contract without residue (enabling chip cleaning), phase separation's role in wetting dynamics (PNAS 2022), and the 'memory of folds' in soft materials where unfolding leaves scar-based nucleation points. His work bridges fundamental physics with practical applications in microfluidics, printing technology, and biological systems. Analysis of his 15 most recent publications reveals dominant themes in soft matter deformation (60%), active matter systems (25%), and biological fluid dynamics (15%). Key methodological approaches include high-precision force measurements, theoretical modeling of viscoelastic substrates, and experimental quantification of microbial locomotion forces. Scientific recognition includes: Robert-Wichard-Pohl-Medal for 'Physics of Sports' teaching (2022) Alexander von Humboldt Foundation research fellowship (2022) Dr. Karpitschka advises doctoral and master's students, with recent theses on wetting dynamics (Jeon H., 2022) and biofilm analysis (Schwirz T., 2018). His group collaborates extensively through the Max Planck-University of Twente Center and participates in the SPP 2171 coordination board. Current projects explore liquid-liquid phase separation effects, cyanobacteria gliding mechanics, and programmable folding in soft materials. The group maintains active laboratory facilities for microfluidics experimentation, force measurement, and biological interface studies, with recent public engagement including a Science Night exhibition on cyanobacteria at the MPI for Solar System Research.

• Forest industry analytics
• ESG and financial markets
• Green investment and finance
• +5 more

Andreas Stephan serves as the Acting Dean (Prodean) of the School of Business and Economics at Linnaeus University, a position he has held since May 2023. He is affiliated with the Department of Forestry and Wood Technology and actively participates in multiple research centers including the Linnaeus University Centre for Data Intensive Sciences and Applications (DISA), the Linnaeus University Centre for Equality of Opportunity and Big Data Policy Analysis, and The Bridge—a unique partnership between Södra, IKEA, and Linnaeus University focused on innovation and sustainability in forestry and wood. Stephan holds a master's degree in industrial engineering and management from Technical University in Berlin and a PhD in Economics from Humboldt University Berlin. His research spans forest industry analytics, ESG and financial markets, green investment and finance, with particular focus on how innovation facilitates the green transition of industries. He has published extensively across economics, finance, and sustainability journals, with recent work examining sustainable finance, green bonds, forest economics, and the impact of environmental policies on business performance. His research portfolio includes significant projects such as TallBoard (exploring tall oil application in industrial fiberboards) and investigating the green transformation of the Swedish pulp and paper industry. Stephan serves as an associate editor for the journals Eurasian Economic Review and Business Strategy and the Environment, demonstrating his scholarly leadership in sustainable economics and finance. His recent publications show a strong trend toward integrating environmental considerations with financial decision-making, particularly in portfolio optimization and sustainable investment frameworks. Stephan is deeply embedded in Linnaeus University's research ecosystem, leading and participating in multiple research groups including Deterministic and Stochastic Modelling, Forest Economics, Forest Management, and Small-scale Forestry. His work bridges theoretical economic modeling with practical applications in sustainable industry transformation, reflecting Linnaeus University's commitment to addressing global sustainability challenges through interdisciplinary research.

• Bayesian time series analysis
• forecasting
• finance
• +4 more

Dr Kostas Triantafyllopoulos serves as Senior Lecturer in Statistics and MSc Statistics Course Director at the University of Sheffield's School of Mathematical and Physical Sciences. He joined Sheffield in February 2005 after academic positions at Newcastle University (2002-2004) and a post-doctoral appointment at Bristol University (2001-2002). His educational background includes: BSc in Mathematics from Aristotle University of Thessaloniki (1996) MSc in Quality Management from Napier University of Edinburgh (1998) PhD in Statistics from the University of Warwick (2002) Triantafyllopoulos specializes in Bayesian time series analysis and forecasting , with significant applications across finance, bioinformatics, medicine, signal processing, and quality control. His methodological innovations include multivariate stochastic volatility modeling using Wishart processes, real-time covariance estimation for state-space models, and particle filter applications for count time series. Recent work (2019-2022) demonstrates strong focus on medical decision making through flexible survival models and pandemic impact analysis using hybrid Bayesian-neural network approaches for shipping economics. Publication trends reveal a strategic evolution from foundational Bayesian state-space work (2006-2012) toward high-impact interdisciplinary applications. His 30+ journal articles show consistent emphasis on solving autocorrelation challenges in control charts, developing non-parametric monitoring systems for environmental data, and creating flexible parametric frameworks for survival data extrapolation in healthcare contexts. Research funding includes Nuffield Foundation support as Principal Investigator for "The multivariate general steady model" and EPSRC collaboration as Co-investigator on "Simulation Tools for Automated and Robust Manufacturing". He actively contributes to the Mathematical and Statistical Modelling research group, with current work bridging theoretical statistics and real-world industry/healthcare challenges.

• Bayesian Statistics
• Bayesian Robustness
• Cost-Effectiveness Analysis
• +6 more

Francisco José Vázquez Polo is a Full Professor at the Department of Quantitative Methods in Economics and Management, University of Las Palmas de Gran Canaria. He leads the TIDES research group focusing on Bayesian and Decision-Making Statistical Techniques in Economics and Business. His work bridges Bayesian statistics with applications in health economics, actuarial science, and environmental economics. His research interests include Bayesian robustness and meta-analysis Cost-effectiveness tools for health technology assessment Actuarial modeling and risk analysis Statistical methods for sparse data Decision theory in healthcare and economics Recent publications highlight his expertise in Bayesian modeling for tourism data, medical treatment evaluation, and statistical distributions. Key collaborations include M.A. Negrín, E. Moreno, M. Martel-Escobar, and the Spanish Sleep Network. He has directed numerous research projects funded by entities like the Spanish Ministry of Science and Innovation, with a focus on Bayesian decision-making in healthcare, economic policy, and insurance risk management. Notable grants include: PID2021-127989OB-I00 (4 years, 2022) ECO2017-85577-P (3 years, 2017) ECO2009-14152 (3 years, 2014) His academic activities include organizing seminars and workshops on Bayesian econometrics, stochastic volatility models, and health economics. He maintains active roles in editorial boards and research networks related to risk management and Bayesian statistics.

Marcos Escobar-AnelView profile

Professor

• Stochastic Processes
• Stochastic Covariance
• Financial Mathematics
• +5 more

Marcos Escobar-Anel is a Professor in the Department of Statistical and Actuarial Sciences at Western University. His research focuses on stochastic processes, financial mathematics, and statistical modeling, particularly in areas such as multivariate stochastic covariance, dynamic portfolio optimization, and GARCH models. He has supervised numerous graduate students pursuing PhD and Master’s degrees, including Wei Li Fan, Yijao Jiao, and Shiying Li. Ph.D. in Mathematics from the University of Toronto His recent work explores applications of affine GARCH models in option pricing, robust portfolio strategies under stochastic volatility, and the impact of derivatives in portfolio optimization. He has received significant research funding through NSERC Discovery Grants and Mitacs Postdoctoral Accelerate programs. Publications highlight advancements in multivariate stochastic covariance modeling, constrained portfolio strategies, and utility-based optimization frameworks. Collaborations with researchers like Rudi Zagst and Lars Stentoft demonstrate interdisciplinary approaches to financial risk management and sustainable investing.

• Statistics for Stochastic Processes
• Financial Market Econometrics
• Stochastic Differential Equ
• +3 more

Prof. Dr. Markus Bibinger holds the Chair of Mathematics VIII (Applied Stochastics) at the University of Würzburg since October 2020. Previously, he was a W2 Professor of Stochastics at Philipps University of Marburg (2016–2020) and a Junior Professor for Theoretical Econometrics at the University of Mannheim (2015–2016). Education: Dr. rer. nat. (2011, Humboldt University Berlin), Diplom in Mathematics (2007, University of Heidelberg) Research Interests: Statistics for stochastic processes, financial market econometrics, high-frequency financial data analysis, asymptotic statistics, and stochastic differential equations Article Trends: His recent work focuses on volatility estimation, jump detection in high-frequency data, and statistical inference for stochastic partial differential equations, with applications in financial econometrics and market microstructure. Collaborative research spans institutions like the University of Chicago and Humboldt University Berlin. Scientific Awards: Förderpreis der DMV-Fachgruppe Stochastik (2012) Students: Dr. Mehmet Madensoy, Dr. Patrick Bossert Current roles include organizing international conferences (e.g., 16th Workshop on Stochastic Models, Statistics and Their Applications , 2026) and editorial contributions to journals such as Annals of Statistics and Journal of Econometrics . Teaching includes advanced courses in time series analysis and stochastics.

• Mathematical Finance
• Stochastic Processes
• Financial Engineering
• +3 more

Dr. Andrey Pogudin is a mathematical finance researcher with expertise in stochastic processes, volatility modeling, and cryptocurrency dynamics. His work appears in journals such as Advances in Applied Probability , Quantitative Finance , and Journal of Network Theory in Finance . Focuses on financial mathematics and applied probability Collaborates with researchers like Mike Forde and Tiziana Di Matteo Studies memory effects in diffusion processes and volatility patterns His research spans both classical financial modeling (SABR/CEV-Heston models) and emerging cryptocurrency markets, revealing universal patterns in price stability and scaling relationships.

• High-dimensional time-series analysis
• Statistical modeling under non-stationarity
• Sparse dynamic factor models
• +3 more

Dr Alex Gibberd is a Senior Lecturer (equivalent to Associate Professor) in Statistics within the School of Mathematical Sciences at Lancaster University. He has been a faculty member since 2018, following postdoctoral research at Imperial College London and a PhD in Statistics from University College London (UCL) in 2017. He also holds an MPhys in Astrophysics from the University of St Andrews (2012). Education: PhD in Statistics, University College London (2017) MPhys in Astrophysics, University of St Andrews (2012) Research Interests: Dr Gibberd's research focuses on high-dimensional time-series analysis, with methodological contributions in statistical modeling under non-stationarity and high-dimensionality. His work spans both theoretical developments and practical applications, particularly in neuroscience and finance. Key areas include: Sparse dynamic factor models and regularized estimation techniques Spectral analysis and locally-stationary wavelet models Optimization algorithms for model selection in high-dimensional settings Applications in brain connectivity analysis and economic forecasting Research Themes: His recent publications demonstrate a strong focus on developing interpretable statistical methods for complex systems. These include advances in principal component analysis with joint rank and covariance estimation, sparse dynamic factor models, and regularized spectral estimation for high-dimensional point processes. The applications range from neural connectivity modeling to energy efficiency policy analysis. Grants & Funding: Research in Paris Grant (2024) Support of Collaborative Research with Dr S. Roy at University of Bath (2021) Model Selection for High-Dimensional Temporal Disaggregation in Official Statistics (2021-2023) Reducing End Use Energy Demand in Commercial Settings Through Digital Innovation (2021-2025) Wavelet Methods for Dependency Analysis in Multivariate Time Series (2020) STOR-i: Information Fusion for Non-homogeneous Panel and Time-series Data (2019-2025) PhD Supervision & Students: Dr Gibberd supervises PhD students at the intersection of high-dimensional statistics and time-series analysis. Current students include Carla Pinkney (STOR-i CDT), Ziyan Zhao, and Kai Zheng (Centre for Marketing Analytics & Forecasting). Research Affiliations: STOR-i Centre for Doctoral Training Centre for Marketing Analytics & Forecasting Changepoints and Time Series Research Group Data Science Institute - Foundations Social and Economic Statistics Group