Find Academic Supervisors

Jochen LeidnerView profile

Visiting Professor

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +4 more

Professor Jochen Leidner is a Visiting Professor in the Department of Computer Science at the University of Sheffield and a Research Professor for Explainable and Responsible AI at Coburg University of Applied Sciences, Germany. He has held leadership roles including Director of Research at Thomson Reuters and Refinitiv, and has founded companies like Polygon Analytics and KnowledgeSpaces. His academic background includes degrees from the University of Erlangen-Nuremberg, University of Cambridge, and a PhD in Informatics from the University of Edinburgh. Research focuses on AI ethics, natural language processing, information extraction, and geoinformatics. Notable contributions include work on question-answering systems (QED/ALYSSA), spatial toponym resolution algorithms, and risk-mining frameworks. Awards include the ACM SIGIR Doctoral Consortium Award and twice winning Thomson Reuters Inventor of the Year for patents. He has taught at institutions across Europe and advises EU funding bodies (FP7/Horizon). Holds multiple patents in information retrieval and mobile computing. Active in industry collaborations, blending academic research with real-world applications in finance, legal tech, and supply chain analytics.

Bram C.M. CappersView profile

Lecturer

• Visual Analytics
• Cybersecurity
• Data Science
• +3 more

Bram C.M. Cappers is a University Lecturer in the Department of Mathematics and Computer Science at Eindhoven University of Technology. His work focuses on visual analytics, cybersecurity, and predictive analytics applied to dynamic heterogeneous networks. He holds a Master’s (2014) and PhD (2018) in Computer Science from TU/e, with doctoral research on security visual analytics for cyber threat analysis. Education: Bachelor in Software, Algorithms, Control Systems (Eindhoven University of Technology, 2012) Master in Software, Algorithms, Control Systems (Eindhoven University of Technology, 2014) PhD in Computer Graphics (Interactive Visualization of Event Logs for Cybersecurity, 2018) Research Interests: Developing predictive models for energy networks, visual analytics tools for cybersecurity incident response, and anomaly detection in complex systems. His work extends to applying these techniques in healthcare, finance, and energy sectors through startups like CodeNext21 and TIBO Energy. Key Contributions: Eventpad: Award-winning visual analytics framework for malware analysis and network intrusion detection Predictive analytics for VoIP fraud prevention (KPN partnership) Co-founder of two ventures commercializing academic research Awards: ICT.Open Award (2018) for traffic data anomaly detection Elsevier’s 30 onder de 30 (2020) for innovation leadership European Venture Program Best Executive Summary (2018) Grants & Activities: Received €150K Demonstrator grant (2018) Contributed to SECREDAS EU project (2018-2021) Speaker at Blackhat USA 2018 and multiple invited talks on cybersecurity

Patrick PunView profile

Associate Professor

• Applied Mathematics
• Financial Mathematics
• Quantitative Finance
• +7 more

Patrick Pun is an Associate Professor in the Division of Mathematical Sciences at Nanyang Technological University (NTU), Singapore, serving as Assistant Chair (MSc Programs) and Director of the MSc in FinTech program within the School of Physical and Mathematical Sciences. His academic journey includes a Ph.D. and M.Phil. from the Chinese University of Hong Kong (CUHK) and a B.Sc. from Nankai University. Professor Pun’s research focuses on the intersection of applied mathematics and finance, with methodologies spanning stochastic controls, nonlinear partial differential equations (PDEs), robust optimization, and machine learning. His work addresses challenges in portfolio optimization, derivatives pricing, risk management, and financial data analysis. He has also contributed to interdisciplinary areas such as nanoparticle characterization and epidemiological modeling (e.g., during the COVID-19 pandemic). Award-winning scholar, Pun holds the 2016 Nicola Bruti Liberati Prize from the Bachelier Finance Society and the CUHK Young Scholars Thesis Award. He is an ad-hoc reviewer for leading journals including Automatica , SIAM Journal on Financial Mathematics , and Quantitative Finance . His professional roles include membership in the Academic Council of the Global Digital Economy Forum. Pun’s teaching and academic leadership include oversight of master’s programs and curriculum development. His research outputs emphasize innovative solutions to time-inconsistent problems, high-dimensional financial data challenges, and the integration of machine learning with traditional financial models. His recent work explores transformer-based generative models, quantum algorithms for financial PDEs, and reinforcement learning applications in portfolio management.

Martin HerdegenView profile

Associate Professor

• Mathematical Finance
• Probability Theory
• Stochastic Processes
• +5 more

Martin Herdegen is a Reader in Financial Mathematics at the Department of Statistics, University of Warwick. He previously served as a postdoc at ETH Zürich under Johannes Muhle-Karbe and holds a PhD in Mathematics from ETH Zürich (2014) under Martin Schweizer. His research focuses on equilibrium theory, utility maximization, stochastic processes, and risk measures, with applications to financial bubbles and market microstructure. Herdegen’s academic career includes supervising multiple PhD students (e.g., Florian Gutekunst, Andreea Popescu) and postdocs (e.g., Nazem Khan). His research group explores topics like ρ-arbitrage, recursive utility, and liquidity provision under adverse conditions. He has contributed to foundational work on strict local martingales and their implications for financial markets. Publications span leading journals such as Finance and Stochastics , Mathematical Finance , and Annals of Applied Probability , addressing equilibrium models with transaction costs, optimal investment strategies, and risk measurement techniques. His work frequently integrates stochastic analysis and control theory to solve practical finance problems. Herdegen’s research also intersects with reinforcement learning applications in trading, as seen in the Mbt-gym framework for limit order book simulations. His contributions emphasize rigorous mathematical foundations while addressing real-world market frictions and liquidity dynamics.

Jichun LiView profile

Professor

• Mathematical Modeling
• Scientific Computing
• Numerical Analysis
• +4 more

Jichun Li is a Professor in the Department of Mathematical Sciences at the University of Nevada, Las Vegas. His research focuses on mathematical modeling, scientific computing, and numerical analysis with applications to electromagnetism and metamaterials. He specializes in developing advanced numerical methods like Finite Element Methods (FEM), Discontinuous Galerkin (DG), and Finite-Difference Time-Domain (FDTD) schemes for simulating wave propagation in complex media such as metamaterials and photonic crystals. Key research areas include metamaterials (e.g., hyperbolic metamaterials, cloaking devices), electromagnetic wave manipulation (e.g., surface plasmon polaritons on graphene), and computational techniques for solving Maxwell’s equations in dispersive and nonlinear media. His work addresses challenges like numerical stability, superconvergence analysis, and implementation of Perfectly Matched Layers (PML) for absorbing boundary conditions. Recent efforts involve integrating deep learning with numerical PDE methods for financial applications and advancing time-domain simulations for metamaterial cloaking and optical black holes. He has contributed to software tools like MATLAB-based edge element codes for metamaterial modeling. His research bridges theoretical analysis and practical simulation, with applications in photonics, quantum optics, and computational electromagnetics. Notable achievements include over 150 peer-reviewed articles, methodological innovations in numerical electromagnetics, and active participation in interdisciplinary collaborations at the Center for Applied Math & Statistics. His work emphasizes high-order methods and rigorous mathematical validation for engineering and scientific problems.

Angel OteroView profile

Assistant Professor

• Artificial Intelligence in Financial/IT Audits
• Robotic Process Automation (RPA)
• Information Systems Auditing
• +3 more

Angel Otero is an Assistant Professor and Program Chair for Accounting and Finance Online Programs at the Nathan M. Bisk College of Business , Florida Institute of Technology. With over 20 years of industry experience including a 10+ year tenure at Deloitte & Touche as Senior Manager, he bridges academic research with practical expertise in public accounting, financial auditing, IT consulting , and information systems auditing . Ph.D. in Information Systems (Nova Southeastern University) M.S. in Software Engineering (Florida Tech) B.S. in Accounting (Pennsylvania State University) His research focuses on artificial intelligence (AI) and robotic process automation (RPA) in audit contexts, information systems/technology auditing , and information security audits . Publications analyze AI's impact on financial audits, blockchain implementation frameworks, and fuzzy logic-based security control assessment models. Recent work explores AI integration in accounting departments, RPA applications in financial statement audits, and cybersecurity frameworks for financial institutions. Key methodological approaches include grey systems theory , fuzzy logic evaluation , and multi-criteria analysis for IT control optimization.

Md Al Masum BhuiyanView profile

Assistant Professor

• Applied Statistics
• High Frequency Data Analysis
• Machine Learning
• +6 more

Dr. Md Al Masum Bhuiyan is an Assistant Professor in the Department of Mathematics and Statistics at Austin Peay State University, part of the College of STEM. He holds a Ph.D. and two Master's degrees in Applied Mathematics from The University of Texas at El Paso (UTEP) and a Bachelor's in Mathematics from the University of Dhaka. Dr. Bhuiyan specializes in Applied Statistics, high-frequency data analysis, machine learning, and stochastic modeling, with expertise in analyzing diverse datasets including financial markets, geophysics, meteorology, and public health. His educational background includes certifications in Big Data Analytics and SAS Base Programming. His research focuses on applying advanced statistical and computational methods to complex systems, such as financial volatility estimation, earthquake-seismic data analysis, and environmental monitoring. He teaches courses like Stochastic Processes, Probability, and Elements of Statistics at both undergraduate and graduate levels. Dr. Bhuiyan's work integrates interdisciplinary approaches, combining mathematical rigor with real-world applications. His publications span high-impact journals like Physica A and AIMS Environmental Science , addressing topics from ozone concentration prediction to volcanic activity modeling. His research trends emphasize predictive analytics, volatility modeling, and algorithmic solutions for geophysical and financial systems. While no formal awards are listed, his extensive publication record reflects significant contributions to computational science and applied mathematics. His teaching and research roles highlight a commitment to advancing data-driven methodologies across multiple disciplines.

Hoang Hiep NGUYENView profile

Assistant Professor

• Machine Learning in Finance
• Bankruptcy Prediction
• Sustainable Finance
• +3 more

Hoang Hiep NGUYEN is an Assistant Professor of Finance at EM Normandie since 2024. Previously, he held roles as a Lecturer at Foreign Trade University (Vietnam), Research and Teaching Assistant at Paris-Panthéon-Assas University, and Visiting Lecturer at Paris School of Business. He earned his PhD in Management Sciences (Finance) from Université de Rennes in 2023. His research focuses on machine learning applications in bankruptcy prediction and sustainable finance, with notable work on explainable AI models, environmental factors in financial distress, and macroeconomic uncertainty impacts. He teaches Financial Analysis and Accounting at EM Normandie. Key research contributions include studies on bankruptcy prediction using Shapley additive explanations, Google Trends-based uncertainty indices, and ensemble machine learning approaches. His work has been presented at conferences like the International Risk Management Conference and the French Finance Association (AFFI). No scientific awards are explicitly mentioned in the provided information. His academic dissemination includes video lectures on portfolio management and machine learning applications in finance.

Douglas MillerView profile

Professor

• Applied Econometrics
• Social Policy
• Public Policy Analysis
• +3 more

Douglas Miller is a Professor at Cornell University, affiliated with both the Brooks School of Public Policy and the Economics Department. His work focuses on applied econometrics and social policy, particularly policies impacting demographically and economically vulnerable populations. He holds a B.A. from the University of California, Santa Cruz, and an M.A. and Ph.D. from Princeton University. Miller’s research examines the interplay between economic conditions and health outcomes, including studies on tax policy, social welfare programs like Head Start, and pro-cyclical mortality trends. He also develops statistical tools to enhance public policy analysis, such as methods for cluster-robust inference and synthetic control estimators. His work bridges econometric methodology with practical policy evaluation, emphasizing evidence-based decision-making. Key research themes include understanding how economic fluctuations affect mortality rates, assessing the long-term impacts of early childhood interventions, and refining statistical techniques for policy evaluation. His contributions to cluster-robust inference have become foundational in applied econometrics, while his analyses of tax auditing and Head Start’s lasting effects highlight his focus on real-world policy applications.

António Palma Dos ReisView profile

Professor

• Digital Transformation
• Information Systems
• Robotic Process Automation (RPA)
• +7 more

António Palma Dos Reis is a Professor at the ISEG School of Economics and Management at the University of Lisbon, specializing in Management Science and Information Systems. He holds a PhD in Management Science from the University of Wisconsin-Milwaukee (1996), along with MS (1993) and BS (1989) degrees in Management and Information Systems. His professional roles include leadership positions such as Dean and President of the ADVANCE research unit. His research focuses on digital transformation, robotic process automation (RPA), blockchain applications, and technology adoption in organizations. Notable studies include analyzing RPA's value for enterprises and blockchain's role in online gambling. He has advised over 30 master’s students on topics like cloud security, RPA implementation, and AI-driven decision systems. Reis has served on the scientific council of the Open University of Lisbon and contributed to institutional governance as a member of ISEG’s Senate. His work bridges theoretical frameworks (e.g., TOE model) with practical applications in procurement systems, mobile business, and financial decision-making. His publications span 30+ years, covering strategic performance analysis, e-procurement systems, and collaborative decision-making. Current research emphasizes organizational ambidexterity and post-AI societal impacts, reflecting a dual focus on technological innovation and managerial strategy.

Yingyao HuView profile

Professor

• Econometrics
• Empirical Industrial Organization
• Labor Economics
• +4 more

Yingyao Hu is the Krieger-Eisenhower Professor of Economics and Vice Dean for Social Sciences at Johns Hopkins University. He holds a PhD in Economics from Johns Hopkins University (2003) and has been affiliated with the university since 2007. Previously, he was an assistant professor at the University of Texas at Austin (2003–2007). His research focuses on econometrics, empirical industrial organization, and labor economics, with a particular emphasis on measurement error models, latent variables, and dynamic discrete choice models. He has contributed to leading journals like American Economic Review , Econometrica , and Journal of Econometrics . Professor Hu has pioneered methods in nonparametric identification and estimation, addressing challenges in unobserved variables and misclassification errors. His work on labor economics includes correcting biases in unemployment rate measurements and analyzing China’s economic dynamics. He is a Fellow of the Journal of Econometrics and co-edited a special issue on measurement errors. Awards include the Denis J. Aigner Award (2022–2023) and the Kuznets Prize (2010–2012). Education: PhD (2003), MA in Economics (2001), MSE in Mathematical Sciences (2001) – Johns Hopkins University; Tsinghua University (B.E., 1994); Fudan University (M.A., 1997). Research Interests: Machine learning applications in econometrics, production function estimation, and policy analysis using satellite data. Awards: Denis J. Aigner Award, Kuznets Prize. Grants & Leadership: Oversees faculty policies as Vice Dean; coordinates departmental chair rotations and faculty mentoring. His lab, the JHU Workshop on Measurement Errors & Latent Variables, hosts interdisciplinary collaborations. Hu’s work bridges econometric theory with practical applications, influencing policy and empirical analysis in labor markets, industrial organization, and macroeconomic growth.

Erick DelageView profile

Professor

• Optimization under Uncertainty
• Operations Research
• Robust Optimization
• +7 more

Erick Delage is a Professor in the Department of Decision Sciences at HEC Montréal, holding the Canada Research Chair in Decision Making Under Uncertainty. He is a member of the Group for Research in Decision Analysis (GERAD) and an associate academic member of MILA. His research focuses on optimization under uncertainty, robust and stochastic optimization, machine learning, and risk management, with applications in finance, energy systems, and transportation. Delage holds a Ph.D. in Electrical Engineering from Stanford University, where he worked with renowned scholars like Andrew Y. Ng and Yinyu Ye. His teaching includes courses on Quantitative Risk Management, Decision Analysis, and Robust Optimization at institutions like HEC Montréal, Politecnico di Milano, and EPFL. He has supervised numerous PhD and master's students, leading to impactful contributions in areas like distributionally robust optimization and deep reinforcement learning for financial engineering. Delage's work emphasizes bridging theory and practice, with notable contributions to contextual optimization methods, energy transition pathways, and risk-averse decision-making. His research has been recognized with awards such as the Nicholson Award (2008) and membership in the Royal Society of Canada's College of New Scholars (2020). His laboratories and collaborations include the Supply Chains and Mobility research cluster funded by IVADO, focusing on data-driven decision-making for resilient systems. Key grants include leadership in energy transition optimization and robust supply chain frameworks.

• Machine Learning
• Deep Learning
• Computer Vision
• +7 more

Dr. Guy C. Hembroff is an Associate Professor in the College of Computing at Michigan Technological University, serving as the founding director of the MS in Health Informatics Program and director of the Computational Science & Engineering PhD Program. He leads the Biomedical Data Science (BDS) Lab, focusing on healthcare innovation through AI, cybersecurity, and data science. His expertise spans machine learning, medical image analysis, and healthcare interoperability. Education: PhD in Computational Science & Engineering (Michigan Tech), MPA in Public Administration (Northern Michigan University), BS in Finance and Economics (Michigan Tech). Research interests include human health-focused AI/ML models, cybersecurity in healthcare, medical image segmentation, and public health surveillance. His work emphasizes collaboration with medical institutions like Henry Ford Hospital to develop clinical decision support systems and improve disease surveillance. Recent projects include AI-driven fracture risk prediction from knee radiographs and enhancing mental health intervention efficacy through multi-source data integration. Advising includes four doctoral students in areas like medical image analysis, blockchain for patient data security, and cost-effective mental health modeling. His software projects include FHIR-enabled health information exchange systems and Tick-Talk, a crowdsourced tick disease monitoring platform. Labs/Teams: The BDS Lab integrates expertise in medicine, AI, and cybersecurity to tackle healthcare challenges, emphasizing real-world impact through industry and academic partnerships.

G. Jason JolleyView profile

Professor

• Rural Economic Development
• Economic Development Incentives
• Community Resilience
• +5 more

G. Jason Jolley, Ph.D., is a Professor of Rural Economic Development and Director of the MPA program at Ohio University’s Voinovich School of Leadership and Public Service. He also serves as Interim Associate Vice President for Research and Creative Activity, coordinating economic development initiatives across the university. His work focuses on regional economic resilience, energy transition impacts, and workforce policy. Current roles: Professor, MPA Director, Interim Associate Vice President Leadership: Center for Economic Development and Community Resilience Partnerships: Ohio Economic Development Association, JobsOhio Dr. Jolley’s research spans rural economic development, clean energy transition, and public finance. He employs input-output modeling, skillshed analysis, and machine learning to study labor market shifts and policy effectiveness. His recent work examines coal industry decline, broadband workforce needs, and entrepreneurial ecosystems in Appalachia. His publications highlight trends in energy sector transitions, middle-wage job displacement, and technology-driven workforce adaptation. Articles from 2023-2025 analyze carbon tax impacts, skill demand forecasting, and geographic economic disparities, reflecting his focus on data-driven policy solutions for rural regions. Dr. Jolley directs Ohio University’s participation in the Ohio Economic Development Institute and has secured over $13 million in research funding from federal and state agencies. He has served as a Federal Reserve Bank advisor and led Ohio’s redistricting database development, integrating economic and demographic data for policy-making. As an advocate for developmental disability rights, he holds leadership roles in Ohio’s disability services organizations, including the Athens County Board of Developmental Disabilities and The Arc of Ohio. This work stems from his personal experience as a father to daughters with developmental disabilities.

• Data Analytics
• Statistical Learning
• Computational Science
• +4 more

Giuseppe Brandi is an Assistant Professor in Data Science at Northeastern University London and an Honorary Research Fellow at Imperial College London's Centre for Climate Finance & Investment. His research focuses on DataMetrics, a discipline blending Data Analytics, Statistical Learning, and Computational Science to analyze complex systems, particularly in finance and climate contexts. He emphasizes adapting models to data rather than forcing data into pre-existing theoretical frameworks. Dr. Brandi's work spans Climate Data Science, Econometrics, and Quantitative Finance. He critiques rigid theoretical models and advocates for interdisciplinary methods to extract insights from multidimensional data. His recent publications explore multiscaling in financial systems, tensor-based decomposition for multidimensional data, and liquidity contagion in interbank markets. His articles highlight trends in financial risk assessment, systemic risk modeling, and the application of tensor methods to complex datasets. Notably, he develops tools like the DisaggregateTS package for time-series analysis and visual warning systems for financial anomalies. Despite these contributions, no scientific awards are explicitly mentioned in the provided texts. Dr. Brandi's advising and grant activities remain undocumented here, though his academic roles suggest involvement in student mentorship and collaborative research projects. His affiliation with Northeastern's global network positions him to engage with interdisciplinary teams across multiple campuses.