Find Academic Supervisors

• Stochastic Processes
• Probability Theory
• Mathematical Modeling
• +4 more

Professor Vitali Wachtel of Bielefeld University's Faculty of Mathematics specializes in advanced stochastic processes, probability theory, and their applications in mathematical modeling. Since 2021, he holds a W3 Professorship and serves as Principal Investigator in CRC 1283 'Taming uncertainty and profiting from randomness and low regularity in analysis, stochastics and their applications' since 2023. Chaired Examination Boards for Bachelor & Master Business Mathematics Member, Bielefeld Graduate School in Theoretical Sciences Research focus: Markov processes, random walks in cones, branching processes Research Trends: His recent work spans critical multitype branching in random environments (2025), asymptotic expansions for conditioned random walks (2024), and invariance principles for integrated processes. He explores connections between stochastic processes, combinatorial structures, and risk modeling with level-dependent premiums. Awards: Feodor Lynen Research Fellowship (2017), Alexander von Humboldt Foundation Teaching: Coordinates modules including 'Stochastic Processes' (24-M-PT-STP) and 'Introduction to Probability Theory' (24-B-EW-5). Active in curriculum development and academic governance through multiple university committees.

Ali KashaniView profile

Senior Lecturer

• Sustainable and low-carbon concrete
• Cementitious materials
• Robot-aided construction
• +8 more

Dr. Ali Kashani is a Senior Lecturer at the University of New South Wales (UNSW) within the School of Civil and Environmental Engineering. His research focuses on sustainable and low-carbon concrete materials, robot-aided construction (particularly 3D printing), and Circular Economy-aligned applications. Leadership in cementitious materials innovation Expertise in 3D printing for construction Advocate for waste valorisation and carbon capture Dr. Kashani has secured approximately $7 million in research funding and holds a patent in lightweight concrete foam. His work spans 70+ publications with 9,000+ citations, including media coverage in the Sydney Morning Herald and The Fifth Estate. He actively contributes to professional organizations such as MECLA, RILEM, and ASTM. Recent research trends include AI and optimization algorithms for sustainable concrete mix design, chloride diffusion modeling, and 3D printing performance analysis. His publications often address waste material integration, durability assessment, and eco-friendly construction practices. Scientific Awards: National and NSW Awards for 'Excellence in Concrete' (Technology and Innovation) from the Concrete Institute of Australia Churchill Fellowship for Digital Construction and 3D Printing sponsored by AVJennings Dr. Kashani serves as Co-Chair of the cement and concrete working group at MECLA and contributes to RILEM and ASTM committees. His email is ali.kashani@unsw.edu.au , and his office is located in the Civil Engineering Building (H20), Level 2, Room CE204, UNSW.

• Financial Mathematics
• Actuarial Mathematics
• Stochastic Processes
• +7 more

Thorsten Schmidt is Professor of Mathematical Stochastics at the University of Freiburg, succeeding Prof. Ernst Eberlein in the summer semester of 2015. He also serves as Senior Financial Engineer at MathFinance. Previously, he held professorships at Chemnitz University of Technology (2008-2015), Technical University Munich (2008), and University of Leipzig (2004 onwards). From 2017-2019, he was a Research Fellow at the Freiburg Institute for Advanced Studies (FRIAS) in a joint research group with the University of Strasbourg and USIAS on the topic of Linking Finance and Insurance. His research focuses primarily on financial and actuarial mathematics, stochastic processes, and statistics, with recent work on machine learning methods and their applications in financial mathematics and AI regulation. In Freiburg, his goal with his young team is to tackle complex challenges with improved mathematical models and apply these methodologies to various fields. Key Research Areas: Financial mathematics and credit risks Pricing and hedging of derivative financial products Statistics of stochastic processes Energy markets and nonlinear filter theory Machine learning applications in finance and insurance His recent publications show a strong trend toward integrating machine learning with traditional mathematical finance, particularly in risk management, insurance-finance arbitrage, and robust financial modeling. His work increasingly addresses ethical considerations in AI applications within finance, reflecting his broader interest in responsible AI development. Notable Awards: IDA Award Finance (2015) FRIAS-USIAS Research Fellow (2017/2018) IDA Award Machine Learning and AI (2020) MAPFRE Research Grant (2020) Luis Bachelier Fellow (2021) As Editor-in-Chief of Statistics and Risk Modeling and Associate Editor for Mathematical Finance and International Journal of Theoretical and Applied Finance, Schmidt plays a significant role in academic publishing. He leads the CRC 'Small Data' research center with Harald Binder, focusing on medical problems where disease progression must be estimated with few data points per patient. His LeanAI project, funded by the Vector Foundation, explores the connection between machine learning and theorem-proving software LEAN, aiming to develop AI that can translate between mathematics and formal proof systems. His laboratory work centers around the application of stochastic methods combined with machine learning to solve problems in finance and insurance where data is limited ('Small Data' initiative), with significant funding from DFG (€12 million for CRC Small Data) and the Carl Zeiss Foundation.

Partha Narayan MishraView profile

Senior Lecturer

• Geotechnical Engineering
• Soft Soil Improvement
• Unsaturated Soil Mechanics
• +4 more

Dr. Partha Narayan Mishra is a Senior Lecturer at the School of Civil Engineering, The University of Queensland, with expertise in geotechnical engineering and electromagnetic soil characterization. He holds a PhD in Geotechnical Engineering (2020) and dual master's/bachelor's degrees (2015) from National Institute of Technology Rourkela, India. PhD Thesis: Soft soil characterization and improvement for reclaimed land application (2020) Dual Degree: B.Tech. Hons. in Civil Engineering and M.Tech. in Geotechnical Engineering His research focuses on soft soil improvement, unsaturated soil mechanics, electromagnetic characterization of geomaterials, biomediated geotechnical engineering, and clay barrier systems for waste disposal. He has published 30+ articles in top-tier journals and conferences, with recent work on mine waste utilization, MSE wall stability, and electromagnetic dewatering techniques. He co-supervises 2 PhD and 5 Master's students at UQ, having previously guided 1 PhD, 1 Master's, 2 Bachelor's, and 3 summer research theses. He initiated the global AGERP lecture series (2020), reaching 125+ countries, and holds teaching certifications from the Higher Education Academy (UK). Professional affiliations include the Australian Geomechanics Society, ISSMGE, IGS, and ASCE. He has reviewed 50+ journal articles and held leadership roles in UQ committees.

• Geometallurgy
• Compositional Data Analysis
• Geostatistics
• +3 more

Dr. Raimon Tolosana Delgado is a Research Fellow at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), affiliated with the Helmholtz Institute Freiberg for Resource Technology. He leads research in predictive geometallurgy and statistical analysis of mineral resources, focusing on translating geological data into processing insights. His research integrates geostatistics , compositional data analysis (CoDa) , and machine learning to model ore behavior and resource potential. Key areas include: Predictive geometallurgy for forecasting ore/waste behavior Bayesian statistics for parameter estimation and uncertainty analysis Development of R-based tools (e.g., compositions and gmGeostats packages) for mineral data analysis Particle-based process modelling for mineral separation optimization Recent publications emphasize machine learning integration (e.g., neural networks for geophysical tensor fields), tailings reprocessing (3D geostatistical assessment of resource potential), and advanced statistical methods for compositional data. A consistent trend involves enhancing predictive accuracy in mineral processing through multi-source data fusion. Dr. Tolosana Delgado coordinates the development of technology platforms for geometallurgical data analysis, including databases and interfaces for industrial applications. His work bridges ore geology, mineral processing, and metallurgy to optimize resource efficiency.

Eric T. AndersonView profile

Professor

• Pricing Strategy
• Retail Analytics
• Customer Loyalty
• +5 more

Eric T. Anderson is the Polk Bros. Chair in Retailing and Professor of Marketing at Northwestern University's Kellogg School of Management. He serves as Chair of the Marketing Department and Director of the Kellogg-McCormick MBAi Program. Anderson holds a Ph.D. from MIT Sloan School of Management and has held academic positions at the University of Chicago and University of Rochester. His research focuses on pricing strategies, retail analytics, AI/ML applications, customer loyalty, and channel management. He teaches courses in pricing, retail analytics, and marketing strategy at Kellogg. Education: PhD in Management Science, MIT Sloan School of Management (1995) MS in Engineering-Economic Systems, Stanford University (1989) BS in Electrical Engineering, Northwestern University (1988) Research Interests: Anderson's work bridges quantitative and behavioral marketing. He explores pricing dynamics, retail innovation, and the impact of digital tools on consumer decisions. His recent studies analyze alternative data for credit scoring, backorder effects, and AI-driven analytics. His research has appeared in top journals like Management Science and Marketing Science . Teaching & Recognition: A four-time winner of the Sydney Levy Award for teaching excellence, Anderson is known for courses like Pricing and Retail Analytics. He advises Canadian Tire and LiftLab, and serves on editorial boards for Management Science and Journal of Marketing Research . His work often collaborates with industry, yielding practical insights for firms like FTD and NASCAR. Grants & Labs: Leads the Center for Global Marketing Practice at Kellogg. His research has been funded by the Marketing Science Institute and industry partnerships. Active in doctoral training, Anderson coordinates the Marketing Ph.D. program and mentors emerging scholars.

• Extreme Value Statistics
• Financial Risk Management
• Mathematical Statistics
• +7 more

Chen Zhou is a Full Professor of Mathematical Statistics and Risk Management at the Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam. He is a member of the Research Advisory Committee of Erasmus School of Economics and actively contributes to academic leadership and research governance. His research focuses on extreme value statistics and financial risk management , with significant contributions to the theoretical and applied understanding of extreme events in financial and statistical contexts. His work bridges mathematical rigor with practical applications in finance and econometrics. The recent publications highlight a strong trend in advancing methodologies for extreme value estimation, including bootstrapping techniques, tail copula modeling, dimension reduction for extremes, and semi-supervised frameworks. These works are published in high-impact journals such as the Journal of the American Statistical Association , Bernoulli , and the Journal of Finance , indicating broad disciplinary relevance across statistics, econometrics, and finance. Editorial work: Editor, Extremes (since 2015) He teaches in the Bachelor program of Econometrics and Management Science and the MSc program in Quantitative Finance, and is affiliated with the Tinbergen Institute. He has supervised multiple doctoral students, reflecting his active role in academic mentorship and research training. Chen Zhou leads a research network focused on extreme value theory, systemic risk, and statistical inference, collaborating with leading scholars in the field. His work continues to shape methodological developments in the analysis of rare and high-impact events.

Bei JiangView profile

Associate Professor

• Biostatistics
• Functional Data Analysis
• Statistical Machine Learning
• +6 more

Dr. Bei Jiang is an Associate Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta , Canada. She holds the Canada CIFAR AI Chair and is affiliated with the Alberta Machine Intelligence Institute (Amii) . Her academic journey includes a PhD in Biostatistics (2014) from the University of Michigan, MS (2008) and BS (2004) from the University of Alberta and Beijing University of Technology, respectively. Current Positions : 2021–Present (Associate Professor), 2022–Present (CIFAR AI Chair) Past Appointments : Assistant Professor (2015–2021), Postdoctoral Fellow at Columbia University (2014–2015), Research Assistant at University of Michigan (2009–2013) Research Interests : Dr. Jiang specializes in methods for joint modeling of longitudinal and health outcome data , Bayesian hierarchical modeling , functional and imaging data analysis , and statistical machine learning . Her work integrates kernel machine regression , differential privacy , and synthetic data generation to address challenges in heterogeneous health data and neuroimaging. Scientific Awards : Highlights include the 2015 SAMSI New Research Fellow , multiple Rackham Conference Travel Awards (2013, 2012), and prestigious NSERC scholarships (2009–2012). She has also received the J Gordin Kaplan Graduate Award (2008) and Statistical Society of Canada Travel Award (2008). Grants : $375,000 (CIFAR AI Chairs, 2022–2027), $480,000 (MITACS Accelerate, 2022–2025), and $210,000 (Canadian Statistical Sciences Institute, 2022–2025) Students and Postdocs : She mentors numerous PhD , MSc , and Postdoctoral Fellows , including Junxi Zhang (2023–Present), Enze Shi (2022–Present), and former advisees like Wenxing Guo (now Lecturer at University of Essex) and Yafei Wang (Assistant Professor at University of Alberta).

• Extreme Value Theory
• Applied Mathematics
• Gaussian Processes
• +5 more

Marie Kratz is a Full Professor at ESSEC Business School (Cergy, France), affiliated with the CREAR - Center of Research in Econo-finance and Actuarial Sciences on Risk . Her work bridges theoretical and applied domains in extreme value theory , heavy-tailed distributions , and risk management , with applications in finance, cybersecurity, and neuroscience. Research Focus : Extreme value theory, risk concentration, cyber risk modeling, Gaussian random fields, and pro-cyclicality in financial risk measures. Collaborations : Active collaborations with Michel Dacorogna, Marcel Bräutigam, and Sibsankar Singha on cyber risk and financial applications. Methodologies : Development of the Normex method for aggregated heavy-tailed risks, hybrid Gaussian-Pareto models, and near-explosive random coefficient autoregressive models. Awards and Recognition : No specific awards mentioned in the text.

Mawuli Kouami SegnonView profile

Researcher

• Econometrics
• Time Series Analysis
• Financial Forecasting
• +5 more

Dr. Mawuli Kouami Segnon is a researcher at the Chair of Empirical Economics, School of Business and Economics, University of Münster. His work focuses on econometric modeling, financial time series analysis, and volatility forecasting across various domains including cryptocurrencies, energy markets, and macroeconomic indicators. Research interests include: Development of advanced volatility models (GARCH, multifractal, regime-switching) Applications to financial markets, energy economics, and macroeconomic policy High-frequency data analysis and mixed-frequency forecasting Count data modeling with conditional heteroscedasticity Portfolio risk management using copula and multifractal approaches Recent publications demonstrate expertise in: Geopolitical risk impacts on stock volatility Comparative analysis of realized variance measures Inflation uncertainty modeling in G7 countries Electricity price volatility in Australian markets Bitcoin market forecasting Historical economic data analysis Current projects (since 2020) involve: Innovative economic/financial time series forecasting Financial market volatility modeling Applications of multifractal structures in econometrics

• Agent-Based Computational Economics
• Economic Networks
• Macroeconomics
• +3 more

Giorgio Fagiolo is a Full Professor of Economics at Sant'Anna School of Advanced Studies. His work spans agent-based computational economics, economic networks, and macroeconomic policy analysis. University: Sant'Anna School of Advanced Studies (Scuola Superiore Sant'Anna) Department: Economics Email: giorgio.fagiolo@sssup.it Research interests focus on agent-based modeling , macroeconomic instability , and climate-economy interactions . He develops computational models to study industrial dynamics, financial integration, and policy design in complex systems. Key themes: Endogenous growth cycles, R&D network stability, and green transition policies. Methodological emphasis: Empirical validation of agent-based models and nonlinear economic dynamics. Scientific awards include collaboration with leading institutions like ETH Zurich, Columbia University, and OFCE Sciences Po. His publications appear in journals such as Journal of Economic Dynamics and Control and Ecological Economics .

David ThesmarView profile

Professor

• Corporate Finance
• Financial Intermediation
• Entrepreneurship
• +1 more

David Thesmar is the Franco Modigliani Professor of Financial Economics and Professor of Finance at MIT Sloan School of Management. He holds a BA in physics and economics from École Polytechnique and a PhD from the Paris School of Economics. Thesmar has served on the council of economic advisors to the French prime minister (2007-2013) and is a scientific adviser to the European Systemic Risk Board. His research examines corporate finance, financial intermediation, entrepreneurship, and behavioral economics, with recent focus on financing constraints and systemic risk in banking. He has received the Brattle Group Distinguished Paper Prize for his contributions to financial economics.

Helen LuView profile

Associate Professor

• Applications of AI in Accounting and Finance
• Valuation
• Return Predictability
• +3 more

Helen Lu is an Associate Professor of Accounting and AI at Vlerick Business School and a Senior Lecturer at the University of Auckland (FinTech Lead for the Master's in Business Analytics). She holds a PhD in Finance from Massey University, an MBA from London Business School, a Master's in Economics (Macroeconomics) from Peking University, and a Bachelor's in Computer Science Engineering from Northern Jiaotong University. Her research focuses on integrating AI into accounting and finance, including valuation, return predictability, ESG disclosure, executive succession, and FinTech disruptions. Before academia, Lu worked in investment banking at Credit Suisse and Deutsche Bank, specializing in capital raising and cross-border M&A in Asia-Pacific. Her work bridges academic research and industry practice, particularly in leveraging AI to solve complex financial and accounting challenges. She has published in top journals like the Journal of Accounting Research , Journal of Banking and Finance , and Journal of International Money and Finance . Her recent publications emphasize AI-driven valuation methods, ESG disclosure analysis, and the interplay between executive transitions and financial reporting. Her articles often highlight the application of machine learning to traditional finance problems, such as tail risk assessment and anomaly strategy correlations. Lu’s expertise spans multiple domains, with a focus on transforming data-driven technologies into actionable insights for financial decision-making. She actively contributes to academic discourse on sustainability metrics, particularly green asset valuation and corporate governance during crises.

Zhenzhen FanView profile

Assistant Professor

• Asset Pricing
• Financial Derivatives
• International Finance
• +3 more

Zhenzhen Fan is an Assistant Professor of Finance at the Lang School of Business and Economics, University of Guelph (Guelph, Canada). She holds a PhD from the University of Amsterdam and previously served as an Assistant Professor at the University of Manitoba (Winnipeg, Canada). Her research focuses on theoretical/empirical asset pricing, financial derivatives, international finance, and textual analysis in finance. Education: PhD in Finance, University of Amsterdam (Netherlands) Her work explores topics such as currency risk premiums, macroeconomic announcement returns, and post-crisis financial market dynamics. She is affiliated with the FIRM lab and teaches courses like FIN 3100 Corporate Finance and FIN 3000 Investments. Funding: SSHRC Insight Development Grant (2023-current) Canadian Derivatives Institute Grant (2018-2020) Upcoming research includes examining market disagreement effects on macroeconomic announcement returns, to be presented at the Eastern Finance Association conference (Philadelphia, April 2024).

Matteo FasioloView profile

Senior Lecturer

• Generalized Additive Models
• Electricity Demand Forecasting
• Covariance Matrix Modeling
• +3 more

Dr. Matteo Fasiolo is a Senior Lecturer in the School of Mathematics at the University of Bristol, specializing in Statistical Science. His research focuses on advanced statistical modeling with significant applications in electricity demand forecasting and medical statistics, leveraging Generalized Additive Models (GAMs) as a core methodology. His primary research interests span: Generalized Additive Models and their extensions for complex data structures Covariance matrix modeling for high-dimensional energy forecasting Probabilistic forecasting techniques for uncertainty quantification Statistical machine learning including variational inference and contrastive learning Applications in electricity grid management and medical diagnostics Recent publications (2023-2025) reveal a dual focus: developing scalable statistical methods for electricity net-demand prediction in Great Britain using additive covariance models, and applying distributional regression to medical challenges like kidney function decline and cardiovascular risk prediction. His work on SoftCVI demonstrates innovation in variational inference, while extensions to GAMs address both mean modeling and full distributional forecasting. Dr. Fasiolo has supervised at least one student as indicated by university records. His research outputs include 16 publications and 2 publicly available datasets, reflecting active contributions to methodological statistics and domain-specific applications in energy systems.