جست‌وجوی استاد و پژوهشگر

Thierry Badardمشاهده پروفایل

دانشیار

• Geospatial Big Data
• GeoAI
• GeoNLP
• +۹ مورد دیگر

Thierry Badard is an Associate Professor at the Department of Geomatics Sciences , Université Laval, where he also serves as Director of the Center for Research in Geospatial Data and Intelligence (CRDIG) . With over 28 years of experience in geospatial science, he leads research initiatives at the intersection of GeoAI , LiDAR processing , and smart city technologies . Director, CRDIG (2016-2022) Steering Committee Member, Big Data Research Centre (CRDM) Researcher, Institute for Intelligence and Data (IID) Research Expertise spans geospatial big data, GeoNLP, and IoT applications for digital twins. His work addresses flood risk modeling , 3D urban analytics , and environmental monitoring through AI-driven solutions. Recent publications focus on contrastive learning for LiDAR segmentation and geospatial ontologies for early warning systems. Grant Leadership includes collaborative projects on smart insurance analytics (2018-2025), Arctic bioaerosol research (2019-2025), and Quebec-Morocco digital twin partnerships (2022-2023). He has advised 15+ graduate students in geomatics and related fields.

• Accounting
• Sustainable Development
• Fintech
• +۵ مورد دیگر

Professor Hakim BEN OTHMAN holds a Professorship in Accounting at ICN Business School (France), with 25 years of academic experience spanning research, teaching, and professional practice. His work focuses on Fintech, financial reporting standards (IFRS), corporate governance, and social responsibility disclosures. He has held leadership roles including President of the Research Committee at the American University of Malta (AUM) and chaired the Accounting Group at Tunisia’s first U.S.-style business school. He is an accredited trainer with ITTCC and has served on editorial boards for four ABS/ABDC-ranked journals. Education: He earned a PhD in Accounting from the University of Tunis (2005), followed by two Habilitation à Diriger des Recherches (France/Tunisia). He holds an MBA from ISCAE (1998) and a Bachelor’s in Accounting from Carthage University (1995). Research Interests: His work bridges accounting practices with technological advancements (blockchain/Fintech), sustainability (UN SDGs), and cross-cultural studies on tax evasion/corruption. Notable contributions include machine learning analyses of E-government efficacy and cultural impacts on financial behaviors. He has been awarded the Emerald Literati Prize (2016/2017) and the U.S. IVLP program for entrepreneurship development. Teaching: He teaches courses on blockchain technologies, financial data analysis, and accounting standards across institutions like ICN (France), PSU (Saudi Arabia), and the American University of Malta. Recent courses include blockchain strategies at the Paris La Defense campus and fintech’s role in sustainable finance. Professional Experience: Formerly a consultant at Deloitte and KPMG, he led ACCA training programs and participated in AACSB accreditation processes. He has supervised 9 PhD students and reviewed numerous doctoral theses internationally, serving on Tunisia’s National Examiner Board for academic hiring.

• Business Focused Predictive Analytics
• Social Media for Business Transformation
• Management of Innovation
• +۵ مورد دیگر

Indranil Bose is a Distinguished Professor and Head of the Area of Excellence in AI, Data Science, and Business at NEOMA Business School, with a Ph.D. in Management from Purdue University. He specializes in business analytics, big data, and digital transformation, leveraging expertise from prior roles at IIM Calcutta, University of Hong Kong, University of Florida, and UT Arlington. Education: Ph.D. in Management (Purdue University, 1997) M.S. (Purdue University) M.S. (University of Iowa) B.Tech (IIT Kharagpur) Research Focus: His work spans predictive analytics, social media impact on business, innovation management, cybersecurity, fintech, and AI ethics. Key themes include consumer behavior in digital ecosystems, AI-driven decision systems, and ethical implications of emerging technologies. Publication Trends: Recent articles emphasize AI ethics, digital misinformation, and predictive modeling in finance/healthcare, reflecting a focus on societal impacts of technology. Methodologies include deep learning, sentiment analysis, and causal inference. Awards & Recognition: Winner, EFMD 2017 Case Competition Ranked 29th globally for Information Systems research productivity (2005–2014) Editorial Leadership: Senior Editor for Decision Support Systems and Pacific Asia Journal of the AIS ; Associate Editor for Information & Management , Journal of the AIS , and Communications of the AIS .

• Stochastic Control
• Backward Stochastic Differential Equations (BSDEs)
• Actuarial Science
• +۶ مورد دیگر

Anis Matoussi is a Professor of Applied Mathematics at Le Mans University and serves as the Director of the Institut du Risque et de l'Assurance du Mans. He coordinates the master's program in Actuarial Science and leads multiple research initiatives, including ANR DREAMeS (2021-2025) and ITCA (Groupama, Fondation du Risque). Role: Professor, Applied Mathematics Institution: Le Mans University Research Leadership: Director of Institut du Risque et de l'Assurance, Head of Master Actuarial Science His research focuses on stochastic control, backward stochastic differential equations (BSDEs), and their applications in finance, insurance, and energy systems. He has developed numerical methods for second-order BSDEs and studied stochastic nonlinear PDEs, maximum principles for SPDEs, and extended mean field control models. Recent projects include the application of deep learning to forward utilities via ergodic BSDEs and multivariate risk measures. Matoussi has supervised numerous PhD students, including current advisees Zakaria Bensa (industrial thesis with Natixis) and Lucas Da Silva (co-supervised with Caroline Hillairet). Former students like Achraf Tamtalini (Bank of America) and Jing Zhang (Fudan University) hold prominent positions globally. His work includes collaborations on smart grids, control of electrical systems, and robust utility maximization under uncertainty. Publications span journals in applied mathematics, optimization, probability, and financial mathematics, with recent emphasis on numerical schemes and probabilistic representations.

• Financial Econometrics
• Time Series Analysis
• Volatility Modeling
• +۴ مورد دیگر

Jeroen ROMBOUTS is a Professor at ESSEC Business School (France) and holds the Full Professor position of the Accenture Strategic Business Analytics Chair since 2017. He joined ESSEC in 2013, previously serving as Associate Professor at HEC Montreal (2004–2012). His research focuses on financial econometrics, volatility modeling, and machine learning applications in financial markets. He holds a Ph.D. in Econometrics from the Catholic University of Louvain (2004) and has held visiting professorships at numerous institutions, including the University of Melbourne, Aarhus University, and Tilburg University. Education: PhD in Econometrics (2004), Catholic University of Louvain; Master's degrees in Statistics (2001), Econometrics (2000), and Economics (1999), all from the same institution. He is also a Researcher at the Finance and Insurance Lab (CREST) since 2014 and serves on editorial boards of journals like Quantitative Finance and International Journal of Forecasting . Research Interests: His work emphasizes volatility modeling, time series analysis, and applications of machine learning to forecast financial markets. Key areas include GARCH models, structural breaks, and cross-temporal forecasting for digital platforms. He has published extensively in top journals such as Journal of Econometrics and International Journal of Forecasting . Articles Overview: Recent contributions include novel methods for cross-temporal forecast reconciliation using machine learning and sparse change-point VAR models. His work bridges econometric theory with practical applications in asset pricing and risk management. Awards: Recipient of the 2024 Risk-Shift award in France. His research has been recognized for advancing methodologies in volatility modeling and financial econometrics. Advising & Grants: While no specific grants are listed, his roles as a researcher and editor highlight significant contributions to the academic community. He advises on policy and industry applications of his models through consulting roles in financial econometrics and macroeconomic forecasting. Labs & Teams: Affiliated with the Finance and Insurance Lab (CREST) and leads the Information Systems, Data Analytics, and Operations department at ESSEC. Collaborates with global institutions on projects involving high-frequency data and platform economics.

• Inventory Management
• Logistics
• Supply Chain Management
• +۴ مورد دیگر

TRAN Minh Tam is an Assistant Professor at NEOMA Business School, France, specializing in Management Science and Operations Research. He holds a PhD in Industrial Engineering from INSA Lyon and dual Master’s degrees in Statistical and Financial Engineering, Data Science, and Mathematics of Insurance, Economics, and Finance from Paris Dauphine University, PSL University, and École Normale Supérieure de Paris. Research Focus: Digital transformation of supply chains, with emphasis on blockchain, AI, and data-driven decision-making Key Topics: Dual/multi-channel retailing, pricing strategies, stochastic demand modeling, omnichannel optimization Methodologies: Machine learning, operations research, game theory His work addresses cost reduction, service level improvements, and inventory accuracy challenges through quantitative models. Publications appear in journals like the International Journal of Production Economics and at conferences including INFORMS and ISIR. He previously conducted postdoctoral research at CentraleSupélec’s Industrial Engineering Laboratory and Future Cities Lab.

Meryem Schalckمشاهده پروفایل

استادیار

• Data Science
• Artificial Intelligence
• Insurance Sector
• +۳ مورد دیگر

Meryem Schalck is an Assistant Professor of Data Science at IPAG Business School in Nice, France. She earned her PhD in Economic Sciences from the University of Orléans (2022) and has over 15 years of professional experience in the insurance sector as a Data Scientist. Her research focuses on applying AI and machine learning techniques to financial fraud detection, survival analysis, and econometric models in insurance. Research and Publications: Her recent work includes developing fraud scores for automobile insurance using cross-data analysis and predicting SME failures in France with machine learning methods, published in journals like Research in International Business and Finance and Applied Economics . Teaching Expertise: She teaches courses on AI, digital technologies, and statistical analysis for business management across IPAG's PGE, BBA, and MSc programs. Her pedagogical interests include NO CODE tools and business games. Professional Background: Prior roles include Senior Manager at Addactis (pricing software expertise), consultant at Fraeris, and actuarial roles at GI Analytics (Aviva) and Tricast, specializing in predictive modeling and SAS-based statistical tools.

• Actuarial Science
• Machine Learning
• Applied Statistics
• +۱۰ مورد دیگر

Olivier Lopez is a Professor of Actuarial Sciences at the Institut Polytechnique de Paris (ENSAE Paris), where he is affiliated with the CREST Laboratory (Finance - Actuarial). He serves as co-director of the Master's program in Statistics, Finance and Actuarial Sciences at the same institution. Previously, he directed the Institute of Statistics of Sorbonne Université (ISUP) from 2016 to 2023 and currently holds the position of General Secretary of the French Institute of Actuaries since June 2024. His research focuses on the intersection of actuarial science, machine learning, and risk analysis, particularly addressing emerging risks such as cyber threats and climate-related challenges. He specializes in duration models, copula models, and high-dimensional data analysis in insurance contexts. Olivier Lopez leads the CARE research chair on emerging risks with Allianz France under the Fondation du Risque and co-leads the "Cyber Insurance Financialization" (CyFi) project with Caroline Hillairet and Citalid, funded by BPI France. His work emphasizes statistical modeling and its applications to evolving insurance challenges. Scientific Awards: Fellow of the French Institute of Actuaries

Manuel Moralesمشاهده پروفایل

دانشیار

• Financial Mathematics
• Actuarial Science
• Machine Learning in Finance
• +۷ مورد دیگر

Manuel Morales is an Associate Professor at the Department of Mathematics and Statistics , Faculty of Arts and Sciences , University of Montreal. His research focuses on Financial Mathematics , Actuarial Science , and Applied Machine Learning in banking and responsible investment.

Louis Duvivierمشاهده پروفایل

مدرس ارشد

• Network modeling
• Graph analysis
• Community detection
• +۹ مورد دیگر

Louis Duvivier is a Senior Lecturer at the University of Grenoble Alpes since October 2024, affiliated with the Science Department Drôme Ardèche. He is associated with the Coordination, Cooperation & Control of Complex Systems (CO4SYS) team within the Systems Design and Integration Laboratory (LCIS). Former ATER at École Centrale de Lyon (2023–2024) Postdoctoral Fellow at ENS Lyon (2022–2023) ATER at Institute of Financial and Insurance Science (2021–2022) His research focuses on network modeling , graph analysis , and statistical inference , particularly in stochastic block models , community detection , and temporal graph analysis . His recent work explores probabilistic validation techniques and geometric interpretations of network structures. Publications demonstrate expertise in Bayesian inference , model selection , and sensor network estimation . Earlier contributions to distributed estimation and link prediction date back to 2019. He teaches courses in complementary computing , algorithms , numerical analysis , and statistics at both bachelor's and master's levels.

Romain Deguestمشاهده پروفایل

دانشیار

• Finance
• Operations Research
• Asset Allocation
• +۲ مورد دیگر

Romain Deguest is an Associate Professor at IÉSEG School of Management, France, affiliated with the LEM research institute. He holds dual PhDs in Operations Research from Columbia University and Applied Mathematics from École Polytechnique Paris. His research focuses on quantitative finance, particularly portfolio optimization, risk allocation mechanisms, and derivatives pricing. Research interests span: Advanced portfolio construction techniques under real-world constraints Empirical analysis of fixed income markets Risk decomposition methodologies for multi-asset portfolios Machine learning applications in predictive modeling Blockchain applications in financial contracts Recent publications show strong focus on duration-constrained bond portfolios, risk parity frameworks, and machine learning approaches to predictive regressions. His work frequently intersects with asset liability management challenges faced by institutional investors. Professional experience includes roles as Head of Research at Fundvisory and quantitative positions at EDHEC Business School and HSBC France. He maintains active industry collaborations through applied research in investment solutions design.

Peggy Cenac-Guesdonمشاهده پروفایل

مدرس

• Statistics
• Probability
• Random Walks
• +۳ مورد دیگر

Peggy Cenac-Guesdon is a Lecturer-Researcher at the University of Burgundy, affiliated with the Institute of Mathematics of Burgundy (IMB) and the UFR Sciences and Technology. Her research focuses on probability theory, stochastic processes, and their applications to biological sequence analysis. Education: PhD in Applied Mathematics (2006, Université Paul Sabatier Toulouse III), HDR (Habilitation à Diriger des Recherches). Research Interests: Cenac's work spans variable-length Markov chains, persistent random walks, and stochastic algorithms for analyzing multidimensional data. She applies these methods to biological sequences via Chaos Game Representation (CGR), enabling novel pattern detection and taxonomic classification. Her theoretical contributions include central limit theorems for martingales and optimization techniques for risk indicators. Key Publications: Recent papers address multidimensional persistent random walks, variable-length memory chains, and stochastic algorithms for geometric medians in Hilbert spaces. These works intersect probability theory, dynamical systems, and computational biology. Projects: She developed the MyCGR library in Objective-Caml for DNA sequence analysis using CGR, linking algorithmic design with statistical modeling. Her collaborations span mathematics, computer science, and actuarial risk modeling.

• Dependence modeling (copulas)
• Credit risk
• Risk management
• +۲ مورد دیگر

Jean-David Fermanian is a Professor of Finance and Statistics at ENSAE Paris and a researcher at CREST. His work focuses on financial econometrics, dependence modeling, risk management, credit derivatives, and financial applications of machine learning. Research Interests: Dependence modeling (copulas) Credit risk Risk management Multivariate dynamic models Machine learning in finance/insurance Academic Affiliation: Institut Polytechnique de Paris ENSAE-CREST Education: Graduate of Ecole Normale Supérieure Graduate of ENSAE PhD in Statistics from University Paris 6

Lylia Abroukمشاهده پروفایل

استاد پژوهشی

• Semantic Web
• Community Detection
• User Modeling
• +۴ مورد دیگر

Lylia Abrouk is a Research Professor in the Data Science department at the University of Burgundy . Her work focuses on leveraging semantic technologies, machine learning, and ontologies to address challenges in financial fraud detection and decision support systems. Her research integrates: Semantic Web for knowledge representation and ontology population Community Detection in collaborative networks User Modeling for personalized decision support Machine Learning in financial analytics and automotive data Recent publications highlight her contributions to: Ontology-driven fraud detection in interbank transactions Decision trees for cost-sensitive financial modeling Knowledge extraction using hybrid learning approaches Data analytics for automotive industry applications She collaborates extensively within the Data Science team, applying semantic modeling to real-world problems in banking and insurance sectors.

Jérôme Lelongمشاهده پروفایل

استاد

• Computational Finance
• Stochastic Optimization
• Monte Carlo Methods
• +۳ مورد دیگر

Jérôme Lelong is a Professor at Grenoble Institute of Technology - ENSIMAG, Université Grenoble Alpes, and Director of AMIES. He is a member of the DAO team at Laboratoire Jean Kuntzmann, where he leads the development of the open-source numerical library PNL. He also maintains the LaTeX extension for Visual Studio Code (LaTeX Workshop) and vscode-latex-basics. His research focuses on: Computational finance and stochastic optimization Advanced Monte Carlo methods and parallel computing Stochastic modeling for financial and industrial applications Machine learning approaches for option pricing and risk management Lelong's publications demonstrate consistent focus on stochastic methods in finance, with recent emphasis on machine learning integration. His work bridges theoretical mathematics with practical applications in derivatives pricing, risk analysis, and high-performance computing. He actively advises doctoral students, including: Valentin Reis (co-directed, defended 2018) Zineb El Filali Ech-Chafiq (CIFRE Natixis, defended 2022) Tom Picard (CIFRE Nexialog Consulting, defended 2023) William Thévenot (CIFRE SCOR, ongoing) Ghada Ben Youssef (CIFRE Exiom Partners, ongoing) At ENSIMAG, he co-heads the Financial Engineering major and teaches specialized courses including FX derivatives, Monte Carlo methods for finance, and C++ implementation techniques for pricing models.