Find Academic Supervisors

• Monetary Economics
• Macroeconomics
• Labor Economics
• +3 more

Guillaume Rocheteau is a Professor of Economics at the University of California, Irvine. His research focuses on monetary economics, financial frictions, liquidity dynamics, and unemployment. He explores topics such as hyperinflation, market power, information asymmetry, and the long-run Phillips curve. Rocheteau has contributed to understanding the interplay between monetary policy, banking systems, and economic stability. His recent work examines zombie firms, optimal credit policies, and liquidity distribution effects. Education: Details not explicitly provided in the text. Research Interests: Rocheteau investigates how market frictions and liquidity constraints shape macroeconomic outcomes. His work addresses questions such as: What drives speculative hyperinflations? How do decentralized markets function under information asymmetry? What are the long-run trade-offs between inflation and unemployment? Key themes include monetary policy design, banking crises, and the role of liquidity in economic fluctuations. Publications Trends: Recent articles emphasize liquidity dynamics, monetary policy channels, and the impact of negative interest rates. He also explores consumer search behavior and its implications for inflation and labor markets. His research often combines theoretical models with empirical insights, particularly in the context of heterogeneous-agent frameworks. Grants & Advising: No specific grants or advisee names are listed in the provided texts. Labs/Teams: Affiliation with the Department of Economics at UCI, though no specific lab or collaborative team is mentioned.

Michael WolfView profile

Professor

• Nonparametric Inference Methods
• Multiple Testing Procedures
• Financial Econometrics
• +4 more

Michael Wolf is a Professor of Econometrics and Applied Statistics at the University of Zurich's Department of Economics, where he has been since 2005. He holds a Vordiplom in Mathematics from the University of Augsburg (1991), an MSc in Statistics from Stanford University (1995), and a PhD in Statistics from Stanford (1996). His research focuses on nonparametric inference methods, multiple testing procedures, financial econometrics, and large-dimensional covariance matrices. He has contributed to influential work on covariance matrix estimation, portfolio optimization, and statistical methodology. Wolf has served as an Associate Editor for journals including the Annals of Statistics (2004–2007), Statistics and Probability Letters (2014–2016), and the Journal of Financial Econometrics (2019–2022). His academic career includes roles at institutions like UCLA, Universidad Carlos III, and Universitat Pompeu Fabra, where he advanced from Assistant to Associate Professor before joining UZH. His research explores resampling techniques, financial markets analysis, and statistical methods for handling high-dimensional data. Notable contributions include the development of shrinkage-based covariance matrix estimators and their applications in portfolio management. He collaborates with researchers like Olivier Ledoit on nonlinear shrinkage methods and maintains software packages like covShrinkage for covariance matrix estimation. Wolf advises students on thesis topics in econometrics and maintains active roles in professional associations such as the American Statistical Association and the Institute of Mathematical Statistics. His work bridges theoretical statistics and applied finance, addressing challenges in modern portfolio theory, risk management, and statistical inference.

Birgit RudloffView profile

Professor

• Optimization under Uncertainty
• Dynamic Optimization
• Multicriteria Optimization
• +6 more

Prof. Birgit Rudloff is a Full Professor at the Institute for Statistics and Mathematics, Vienna University of Economics and Business (WU). She holds the title of Deputy Institute Chair and leads a research group focused on optimization and financial mathematics. Her work bridges dynamic programming, set-valued risk measures, and systemic risk analysis, with applications in finance and economics. Affiliations: Vienna Graduate School on Computational Optimization (VGSCO), FWF-funded projects. Education: Habilitation (2016), Ph.D. (2006) in Financial Mathematics, M.Sc. (2002). Research Interests: Multivariate dynamic programming, set-valued Bellman principle, time consistency in optimization, systemic risk measurement, and algorithm development for vector optimization. Her group explores financial risk modeling under transaction costs and dynamic game equilibria. Publications: Over 47 peer-reviewed articles in top journals (e.g., Operations Research, Mathematical Programming ). Recent work includes set-valued systemic risk measures and Nash equilibrium approximations in convex games. Grants: FWF (€1.8M), OeNB (€170k), NSF-funded research communities. Labs/Teams: Leads a team of 3 PostDocs and 2 PhD students, fostering cross-disciplinary projects with experts in optimization, dynamic systems, and computational finance.

Michel F. C. HaddadView profile

Associate Professor

• Machine Learning
• Statistical Methodology
• Econometrics
• +5 more

Dr. Michel F. C. Haddad is an Associate Professor in Business Statistics at Queen Mary University of London, serving as Director of the Computational and Quantitative Methods (CQM) Research Cluster and a member of the Department of Business Analytics and Applied Economics. He holds a PhD from the University of Cambridge and has conducted postdoctoral research at The Alan Turing Institute and the University of Cambridge, focusing on AI explainability and economic systems modeling. His research spans two core areas: methodological innovations in statistical and machine learning (e.g., time series, unsupervised learning) and applied econometric modeling for real-world data (e.g., financial markets, pandemic analysis). Notable achievements include an Honorable Mention for Best Research at the Early Career Researchers Conference (2017) and full-time PhD scholarships from CAPES and Cambridge Trusts (2015–2019). Teaching contributions include awards for supervision and lecturing at Cambridge (2017–2019), and courses such as BUS135 and BUS164. He actively supervises PhD students José Medina-Reyes, Cagri Yuksel, and Yuanzhe Li. His work explores intersections between quantitative methods and societal challenges, with a focus on climate policy, financial systems, and health economics.

Tri-Dung NguyenView profile

Professor

• Business Analytics
• Operational Research
• Game Theory
• +2 more

Professor Tri-Dung Nguyen is Professor of Business Analytics and Departmental Research & Innovation Lead at Kent Business School, University of Kent. He holds a PhD in Operational Research from MIT and previously served as Associate Professor at University of Southampton. His research applies game theory, optimization, and stochastic modeling to business analytics problems. Current projects examine cooperative game frameworks, inventory control systems, and robust optimization methods for electricity markets. He has supervised doctoral candidates in operational research games and stochastic business models. Professional collaborations include projects with Unilever, FICO, Expedia, and Virgin Atlantic Airways.

Fabio CaraffiniView profile

Associate Professor

• Evolutionary Computing
• Heuristic & Hyperheuristic Optimization
• Algorithm Behaviour
• +5 more

Dr. Fabio Caraffini is an Associate Professor in Computer Science at Swansea University's School of Mathematics and Computer Science. He holds dual PhDs in Mathematical Information Technology (University of Jyväskylä, 2016) and Computer Science (De Montfort University, 2014), along with BSc and MSc degrees in Engineering from the University of Perugia. His research focuses on computational intelligence, particularly heuristic optimization methods like evolutionary algorithms and differential evolution. He also holds an honorary position as Senior Research Fellow at De Montfort University (2022–2024). Education History: BSc in Electronics Engineering (University of Perugia, 2008) MSc in Telecommunications Engineering (University of Perugia, 2011) PhD in Mathematical Information Technology (University of Jyväskylä, 2016) PhD in Computer Science (De Montfort University, 2014) Research Interests: Dr. Caraffini's work bridges theoretical optimization and practical applications. Key areas include evolutionary computing, structural bias analysis in algorithms, and interdisciplinary AI applications such as medical imaging, climate risk modeling, and robotics. His SOS Platform and BIAS toolbox are notable contributions to algorithm benchmarking and bias detection. Recent projects include AI-driven solutions for crop mapping, medical record analysis, and rail scheduling optimization. Publications & Trends: His 150+ publications span journals like Information Sciences , IEEE Transactions , and Applied Soft Computing . Themes include algorithmic robustness, constraint handling, and real-world optimization challenges. Notable works address differential evolution improvements, climate transition risk prediction, and medical decision support systems. Awards & Grants: Fellow of the Higher Education Academy (FHEA) Recipient of multiple research grants for projects in optimization and AI applications Advising & Collaboration: Actively supervises PhD students in AI-driven optimization and interdisciplinary applications. Collaborates with institutions globally on topics like microgrid energy management and pandemic prediction through self-organizing maps. Labs & Teams: Engaged in Swansea's Computational Foundry and the Morgan Advanced Studies Institute (MASI), contributing to cross-disciplinary research initiatives in AI and computational science.

Yuehua WuView profile

Professor

• Statistics
• M-estimation
• Model selection
• +8 more

Professor Yuehua (Amy) Wu is a leading statistician in the Department of Mathematics and Statistics at York University’s Faculty of Science. Her work bridges theoretical statistics and practical applications, focusing on developing analytical tools for data modeling. Research Interests Change-point analysis M-estimation Model selection Multivariate analysis Spatio-temporal modeling High-dimensional statistics Financial econometrics Data mining Missing data Computational algorithms During the COVID-19 pandemic, Wu collaborated virtually to advance research in applying change-point analysis to medical imaging, notably for detecting gastrointestinal bleeds and lung injuries. Her team’s methods identify pixel-level abnormalities in images, aiming to automate alerts for doctors. These tools also have potential applications in quality control, pollution detection, and more. Her research emphasizes practical software development, enabling non-experts to utilize statistical methods without deep theoretical knowledge. Contact: Office Location DB 2036 | Phone: (416) 736-5250 or (416) 736-2100 ext. 30109 | Email: wuyh@yorku.ca

Juliana Malagón PenenView profile

Associate Professor

• Asset Pricing
• Idiosyncratic Risk Anomalies
• Corporate Finance
• +9 more

Juliana Malagón Penen is an Associate Professor in the Faculty of Management at Universidad de los Andes (Uniandes), Colombia, where she teaches courses in corporate finance, investment decisions, financial mathematics, and financial strategy at both undergraduate and graduate levels. She holds a PhD and Master’s in Business Economics and Quantitative Methods from Carlos III University of Madrid, and a Bachelor’s in Economics from the Colombian School of Engineering Julio Garavito. PhD in Business Economics and Quantitative Methods (2013), Carlos III University of Madrid, Spain Master’s in Business Economics and Quantitative Methods (2013), Carlos III University of Madrid, Spain Professional in Economics (2007), Colombian School of Engineering Julio Garavito, Colombia Her research centers on asset pricing, with a focus on idiosyncratic risk anomalies, portfolio performance, equity returns, and market dynamics in high-frequency trading environments such as eurodollar futures. She explores the impact of climate change news on financial markets and investigates the intersection of corporate finance with ESG practices and cryptoasset markets. She has also contributed to interdisciplinary research on employee silence across 33 countries, reflecting her engagement with behavioral and organizational aspects of finance. Her recent publications span journals like Sustainability , Finance Research Letters , Journal of Futures Markets , Journal of Banking and Finance , and Journal of Organizational Behavior . These works collectively highlight trends in market anomalies, ESG integration, high-frequency trading risks, and the growing role of textual and climate-related data in financial decision-making. Her research increasingly bridges traditional finance with sustainability and technological innovation. Juliana actively contributes to the academic community as a doctoral dissertation supervisor and as an anonymous referee for journals including the European Journal of Finance and the Spanish Journal of Finance and Accounting . She is a member of the Finance and Financial Economics research group at Uniandes. Supervised doctoral student: Manuel Andrés Martínez Patiño (Thesis on DeFi pricing techniques) Referee for: European Journal of Finance, Spanish Journal of Finance and Accounting She leads ongoing research in cryptoasset markets and ESG integration, advising on decentralized finance (DeFi) and sustainable investment strategies. While no grants are explicitly mentioned, her active publication and supervision record indicate sustained research funding and institutional support. Her work is conducted within the Finance and Financial Economics research group, fostering collaboration across quantitative, behavioral, and sustainable finance domains.

B BuchananView profile

Professor

• Cryptography
• Cybersecurity
• Blockchain Technology
• +5 more

Professor B Buchanan OBE FRSE is a Professor of Cryptography at the University of Edinburgh, based in Edinburgh. His research focuses on cryptography, cybersecurity, blockchain technology, quantum computing, and IoT security. He leads initiatives in post-quantum cryptography, secure IoT architectures, and privacy-preserving machine learning. His work spans theoretical developments and practical implementations, including frameworks for threat intelligence sharing and ethical AI integration in security systems. Research Interests: His core areas include cryptographic algorithm design, quantum-resistant protocols, machine learning in cybersecurity, and secure distributed systems. Recent projects explore homomorphic encryption for financial applications, GANs for anomaly detection, and blockchain frameworks for trust management. He is a vocal advocate for ethical cybersecurity practices and interdisciplinary collaboration. Scientific Awards: He has been awarded the OBE (Order of the British Empire) and is a Fellow of the Royal Society of Edinburgh (FRSE). These honors recognize his contributions to advancing cybersecurity and cryptography in both academic and industry contexts. Labs/Teams: His work is supported by the asecuritysite.com , a platform for cybersecurity research and analysis. Collaborations include EU initiatives on digital governance and industry partnerships in secure manufacturing.

Euan McGonigleView profile

Lecturer

• Nonstationary time series analysis
• Change point detection
• Multiscale methods in statistics
• +3 more

Euan McGonigle is a Lecturer in Statistics at the University of Southampton, affiliated with the Department of Mathematical Sciences and the Statistical Sciences Research Institute (S3RI). He holds a PhD in Statistics and Operational Research from Lancaster University (2020), an MRes in the same field (2017), and an MSci in Mathematics from the University of Glasgow (2016). His research focuses on nonstationary time series analysis, change point detection, and multiscale statistical methods, particularly involving wavelet-based techniques for analyzing complex temporal data. His work emphasizes developing robust methodologies for segmenting time series data, analyzing locally stationary processes, and addressing nonstationarity in statistical models. He has contributed to software packages like TrendLSW and CptNonPar, which implement his research into practical tools for data analysis. McGonigle currently supervises PhD students in Mathematical Sciences and is accepting new PhD applications. Key areas of application include signal processing, financial time series, and environmental data analysis. His recent publications highlight advancements in wavelet-based trend estimation, multiscale variance analysis, and nonparametric change point detection techniques for multivariate settings.

Volodymyr NovykovView profile

Senior Lecturer

• Empirical Finance
• Machine Learning
• Asset Pricing
• +4 more

Volodymyr Novykov is a Senior Teaching Fellow at the Bond Business School, Bond University. His research focuses on the intersection of empirical finance and machine learning, including asset pricing, deep learning applications in investment management, and predictive modeling. He holds a PhD from Bond Business School and has a Master of International Finance from Kyiv National Taras Shevchenko University. His work contributes to the UN Sustainable Development Goals in education and financial inclusion. Education: PhD (Bond Business School), Master of Accounting (Bond University), Master of International Finance (Kyiv), Bachelor of Economics & International Relations (Kyiv) Research interests span financial economics, machine learning techniques, and their applications in capital markets. He actively supervises HDR students exploring these intersections. Recent work includes empirical studies on neural networks in finance and algorithmic puzzle solutions using limit theorems. He has presented at international conferences like the World Finance Conference and Bond Business School's HDR Poster Competition.

• Stochastic Analysis
• Numerical Methods for PDEs
• High-Dimensional Approximations
• +3 more

Dr. Tuan Anh Nguyen is a researcher at the Faculty of Mathematics of Universität Bielefeld, Germany. He is affiliated with the Collaborative Research Centre (SFB) 1283, specifically sub-project B10, which focuses on high-dimensional Markov processes in cones. His research interests include stochastic analysis, numerical methods for partial differential equations (PDEs), and overcoming the curse of dimensionality through advanced techniques like multilevel Picard approximations and deep neural networks. Dr. Nguyen's work bridges theoretical mathematics and applied computational methods, addressing challenges in stochastic control, mean-field systems, and high-dimensional stochastic differential equations (SDEs). His contributions to numerical analysis emphasize scalable algorithms for semilinear PDEs and backward SDEs, leveraging machine learning frameworks to tackle dimensionality constraints. He collaborates on interdisciplinary projects within the university's strategic research area of the Mathematical World , focusing on stochastic processes, mathematical finance, and real-world system modeling. His research has been recognized through affiliations with leading projects like SFB 1283 and contributions to top-tier journals in computational mathematics.

• Statistics
• Fuzzy Logic
• Time Series Forecasting
• +5 more

Abel Rubio Fornes is a researcher affiliated with the Faculty of Mathematics at the Universitat de València, specifically in the Department of Statistics and Operational Research. He earned his PhD in 2020 with a thesis on fuzzy prediction models and algorithms, supervised by Dr. Enriqueta Vercher González and Dr. José Domingo Bermúdez Edo. Institution: Universitat de València School: Faculty of Mathematics Department: Department of Statistics and Operational Research PhD: 2020, Universitat de València His research focuses on statistical modeling, fuzzy logic, and computational finance, particularly in forecasting portfolio returns using advanced time series techniques. His work bridges mathematical statistics with practical financial applications, emphasizing uncertainty quantification and predictive accuracy. The available publications show a strong trend in applying fuzzy systems to financial forecasting, with a secondary contribution to computational physics methods in an earlier work. This indicates an interdisciplinary profile with a core in applied statistics and optimization. He has not received any scientific awards mentioned in the provided data. Rubio Fornes has advised or collaborated with researchers in quantitative finance and has co-authored significant works, though no formal students are listed. His research is supported by collaborations within the statistics and operational research community at his institution. He is associated with research groups focusing on statistical modeling and financial applications, though specific lab names are not provided.

Leonor Santiago PintoView profile

Assistant Professor

• Operations Research
• Arc Routing Problems
• Vehicle Routing
• +7 more

Leonor Santiago Pinto is an Assistant Professor in the Department of Mathematics at ISEG, University of Lisbon, specializing in Operations Research. She has been a faculty member since 1983 and previously taught at the University of Macau (1991–1998). She is actively involved in teaching and coordinates the Master’s program in Quantitative Methods for Economic and Business Decision-Making. Her research focuses on combinatorial optimization, particularly arc routing problems with applications in urban logistics, waste collection, parking enforcement, and conservation biology. She employs mathematical programming and heuristic methods to solve real-world logistical challenges. Her recent publications highlight a strong trend in applying arc routing models to municipal services, with emphasis on solution attractiveness, trip balancing, and hybrid matheuristics. Her work bridges theoretical optimization with practical implementation in urban environments. PhD in Mathematics Applied to Economics and Management, ISEG (2004) She has supervised numerous Master’s theses on topics including waste collection, scheduling, logistics, and optimization in healthcare and sports. She has co-supervised a doctoral thesis and continues to mentor students in quantitative decision-making. Leonor Pinto is affiliated with ISEG’s research initiatives in data and policy modeling, contributing to applied projects in urban sustainability and public service optimization.

• Air Transport
• Aviation Management
• Airline Business Models
• +12 more

Professor Zheng Lei is a Professor of Aviation at Swinburne University of Technology, School of Engineering, where he serves as Chair of the Department of Aviation within the Faculty of Science, Engineering and Technology. He holds a PhD in Air Transport and Tourism Management from the University of Surrey and has previously held academic roles at the University of Surrey and Cranfield University. He also serves as a Visiting Professor at the Civil Aviation University of China and is the founder and President of the Institute for Aviation Research, a think tank focusing on aviation in China. His research expertise lies in airline business models, air transport market analysis, regulation and policy, and aviation’s role in regional competitiveness. His work emphasizes real-world impact, advising airlines, airports, regulators, and aviation authorities globally. His fields of research include transportation, logistics, supply chains, and urban and regional planning. The 15 most recent publications reflect a consistent focus on aviation economics, policy, and digital transformation. Key themes include aviation resilience during the pandemic, market liberalization, airline competition, digital technologies like AI and blockchain, service quality assessment via NLP, and sustainable connectivity. His work employs advanced econometric, modeling, and data analytics methods to inform policy and industry practice. Professor Lei has secured significant external research funding from government and industry, including grants from the Department of Foreign Affairs and Trade, Department of Infrastructure, and Civil Aviation Safety Authority, focusing on hydrogen in aviation, alternative fuels, and advanced air mobility. He actively supervises PhD and Master’s students on topics such as hydrogen adoption, autopilot liability, advanced air mobility, airport security, and pilot success prediction, demonstrating a strong commitment to training the next generation of aviation professionals. He has led or contributed to numerous research projects and contracts, including 'Hydrogen to the Skies' and 'Regional AAM Surrogate Trial,' working with partners such as Textron Systems and government agencies. His leadership in both academic and applied research underscores his role as a key figure in contemporary aviation studies.