جستجوی استاد راهنمای دانشگاهی

Yuki Tanakaمشاهده پروفایل

استاد

• Financial Accounting
• ESG Disclosure
• Environmental Management
• +۵ مورد دیگر

Yuki Tanaka is a Professor at the Department of Contemporary Business, Faculty of Economics, Hosei University . Her research focuses on financial accounting and ESG disclosures , examining how corporate sustainability reporting impacts decision-making and organizational value. PhD in Commerce from Hitotsubashi University (2012) Specializes in empirical research using econometrics, text mining, and field studies Active in Japan Bankers Association and Trust Companies Association of Japan Research Themes include corporate ESG disclosures, climate change risk management, and AI applications in sustainability evaluation. She leads projects like "AI-based ESG Information Analysis" (JSPS Grant-in-Aid (B), 2022-2025) and "Climate Change Disclosure Content Analysis" (JSPS Grant-in-Aid (C), 2020-2023). Professional Memberships : Japan Accounting Association Social Accounting Society of Japan Japan Investor Relations Association

Hugo Paquetمشاهده پروفایل

پژوهشگر

• Probabilistic Programming
• Categorical Semantics
• Game Semantics
• +۴ مورد دیگر

Hugo Paquet is a Researcher at INRIA Paris and a member of the ANTIQUE team at École Normale Supérieure , PSL University. He completed a PhD in Computer Science (2015–2019) at the University of Cambridge under Glynn Winskel , focusing on concurrent game semantics for probabilistic programming. His postdoctoral work includes positions at LIPN, Paris (2022–2024, funded by a Marie Skłodowska-Curie Award) and University of Oxford (2020–2022). He has contributed to conferences including LICS , ESOP , FSCD , and POPL . Education : PhD in Computer Science (University of Cambridge, 2019) Research Interests : Probabilistic programming (semantics, inference algorithms, nonparametric models), categorical semantics (game semantics, concurrency models, adjunctions), combinatorial species, and 2-dimensional categories. Teaching : Category Theory (2023–2024), Bayesian Statistical Probabilistic Programming (2021–2022), Lambda-calculus and Types (2020–2021), and small-group teaching at Cambridge (Logic, Discrete Mathematics, Semantics). Awards : Marie Skłodowska-Curie Award under the Paris Region Fellowship Programme Labs : INRIA Paris, ANTIQUE team (2024–present)

• Nonparametric Statistics
• High-Dimensional Statistics
• Inverse Problems
• +۳ مورد دیگر

Cristina Butucea is a Professor of Statistics and Machine Learning at ENSAE-CREST, Institut Polytechnique de Paris . She was nominated an IMS Fellow (2019) for her contributions to nonparametric and high-dimensional statistics. She has co-organized major conferences like Fréjus 2018 , Luminy 2019-2020 , and Oberwolfach 2021 , and serves as Associate Editor for ALEA . Fields of interest: Nonparametric statistics, quantum statistics, differential privacy, inverse problems, machine learning. Awards: IMS Fellowship, conference organization leadership. Research trends: Focuses on optimal estimation under privacy constraints, quantum state reconstruction, high-dimensional inference, and adaptive nonparametric methods. Email: Cristina.Butucea@ensea.fr | Cristina.Butucea@ip-paris.fr

Helmut Meissمشاهده پروفایل

استادیار

• Biological Pest Control
• Weed Management
• Sustainable Agriculture
• +۳ مورد دیگر

Helmut Meiss serves as an Assistant Professor at the University of Lorraine's University Institute of Technology (IUT) Nancy-Brabois, where he directs the DUT Biology with Agronomy option study program. His research focuses on conservation biological control of crop pests and weeds through natural enemies within innovative cropping systems, bridging ecological science and agricultural practice. Dr. Meiss's work emphasizes biological pest control and weed management in sustainable agriculture, investigating how landscape features, crop rotations, and biodiversity enhance ecosystem services. His research integrates field studies with predictive modeling—particularly machine learning—to develop practical tools for farmers, as evidenced by his leadership in the ARENA project which establishes observation networks for natural pest regulation. Analysis of his 15 most recent publications reveals consistent themes in cereal aphid regulation, weed dynamics in cropping systems, and data-driven approaches to agri-environmental indicators. His work demonstrates a strong trajectory toward generic predictive models for pest control and the integration of biocontrol into farm-level decision-making, with increasing emphasis on ecosystem service quantification since 2017. Dr. Meiss co-supervises PhD candidate Eric STELL on predicting conservation biological pest control in cereal crop mixtures. His involvement in the ARENA project indicates collaborative grant-funded research, though specific funding sources aren't detailed in available materials. He actively contributes to farmer advisory systems through biodiversity indicators and crop management tools. As part of a multidisciplinary research group organized around three thematic axes, he participates in the ARENA network for pest biocontrol assessment and engages in public outreach including school workshops, biodiversity exhibitions, and science-society initiatives like the 'Grand Nancy Terre de pollinisateurs' project.

Nabil Kahaleمشاهده پروفایل

دانشیار

• Financial Derivatives
• Monte Carlo Methods
• Optimization
• +۳ مورد دیگر

Nabil Kahale is an Associate Professor of Finance at ESCP Business School in Paris. His research focuses on financial derivatives, Monte Carlo methods, optimization, and machine learning. He holds a PhD in theoretical computer science from MIT (1993) and an HDR (French habilitation) from Université Paris 1 Panthéon-Sorbonne (2020), enabling him to supervise PhD students. His academic career includes prior roles in theoretical computer science and consulting for banks. He has published widely in top journals such as Mathematical Finance , Management Science , and SIAM Journal on Computing . Education: Bachelor of Science in Engineering, École Polytechnique (1987) PhD in Theoretical Computer Science, MIT (1993) HDR in Economics, Université Paris 1 Panthéon-Sorbonne (2020) Research Interests: His work bridges finance and computational methods, emphasizing practical applications of stochastic models and algorithmic efficiency. Key areas include derivative pricing, risk management, and the integration of machine learning into financial systems. Professional Contributions: He has served as a consultant for banking institutions and a referee for the French Ministry of Economy and Finance. His research also intersects with social and economic policy analysis, such as evaluating the economic impact of public health measures. Labs/Teams: While no specific lab affiliation is mentioned, his collaborations span interdisciplinary teams in finance, computer science, and applied mathematics through his publications and consulting work.

Christophe Charrierمشاهده پروفایل

استاد

• Digital Image Forensics
• Video Forensics
• Biometrics
• +۷ مورد دیگر

Christophe Charrier is a Full Professor in Forensics and AI at Université de Caen Normandie, affiliated with GREYC UMR CNRS 6072 and IUT Grand Ouest Normandie's Multimedia and Internet Department (Dept. MMI). He obtained his PhD in Computer Science from Université Jean Monnet (Saint-Etienne) in 1998, followed by an HDR (Habilitation à Diriger des Recherches) in 2011 from Université de Caen Normandie. His academic journey includes roles as a Postdoctoral Researcher at Université Laval (1998-2001), Associate Professor at IUT Saint-Lô (2001), and Visiting Scholar/Professor positions at University of Texas at Austin (2008) and University of Sherbrooke (2009-2011). His research focuses on Digital Image and Video Forensics (e.g., deepfake detection), Image/Video Quality Assessment , Computational Vision , and Biometrics (fingerprint quality, template update, presentation attack detection). He leads the SAFE research group since 2016 and collaborates with the E-payment & Biometrics team at GREYC. His work integrates machine learning for quality metrics, biometric system evaluation, and forensic analysis. Recent publications highlight advancements in deepfake detection , 3D mesh quality assessment , and biometric security . Articles span journals like Intelligent Service Robotics (2024), IEEE Access (2024), and conferences such as CORESA (2024) and Cyberworlds (2023-2024). His studies on fingerprint systems, behavioral biometrics, and environmental impacts on data quality underscore his interdisciplinary approach. Scientific Awards : Best PhD Paper Award (ASONAM 2022) Best Full Paper Award (CW2022) He has mentored 14 PhD students since 2003, including notable alumni like Xinwei Liu (Zhejiang Wanli University) and Antoine Cabana (ALTEN, Toulouse). His projects span biometric certification, latent space manipulation, and 3D mesh evaluation, often in collaboration with institutions in Canada, Norway, and Morocco.

Vassilis Christophidesمشاهده پروفایل

استاد

• Databases
• Web Information Systems
• Big Data Processing
• +۹ مورد دیگر

Vassilis Christophides is a Professor of Computer Science at the University of Crete and holds an advanced research position at Inria Paris, where he leads work in the MiMove team. His research spans databases, web information systems, big data processing, and IoT analytics, with a strong emphasis on entity resolution, data integration, and scalable data mining. He has supervised numerous research projects funded by the European Union and the Greek State, and has published over 130 articles in top-tier conferences and journals. Research Interests: His primary research areas include Databases, Web Information Systems, Big Data Processing and Analytics, and Information Systems for the Internet of Things. He also focuses on entity resolution, knowledge graphs, streaming data, and explainable AI, particularly in the context of anomaly detection and fairness-aware data systems. His recent work explores hybrid attention models for entity alignment and causal analysis in time series classification. Recent Research Trends: Analysis of his recent publications (2021–2025) reveals a strong focus on entity resolution with fairness constraints, explainable anomaly detection, and adaptive scheduling in IoT edge analytics. He also investigates deepfake detection, crop type mapping using satellite data, and structural bias in knowledge graphs, demonstrating a broad and impactful research portfolio at the intersection of data management and machine learning. Scientific Awards: 2004 SIGMOD Test of Time Award Best Paper Award, ISWC 2003 Best Paper Award, ISWC 2007 Advising and Grants: While specific student names are not listed in the provided texts, Christophides has co-authored numerous papers with researchers such as Vasilis Efthymiou, Ioannis Tsamardinos, and Nikolaos Myrtakis, suggesting active mentorship. He has been the scientific coordinator of multiple EU and national research projects, indicating substantial grant leadership and project management experience. Labs and Teams: He is affiliated with the MiMove team at Inria Paris, a research group focused on mobility and data-intensive systems. His work bridges academic and applied research, leveraging Inria’s infrastructure for large-scale data experimentation and innovation in IoT and edge computing environments.

Anna Korbaمشاهده پروفایل

استادیار

• Machine Learning
• Nonparametric Statistics
• Kernel Methods
• +۵ مورد دیگر

Anna Korba is an Assistant Professor at École Polytechnique, specifically affiliated with ENSAE/CREST in the Statistics Department since September 2020. She is also a co-administrator of the Master Data Science program at École Polytechnique. Her academic journey has positioned her as a leading researcher in machine learning, with particular expertise in kernel methods, optimal transport, and statistical optimization. Dr. Korba received her PhD from Telecom ParisTech in 2018 under the supervision of Prof. Stephan Clémençon. Prior to her current position, she was a postdoctoral researcher at University College London's Gatsby Computational Neuroscience Unit working with Arthur Gretton from December 2018 to August 2020. Her academic foundation includes a Master's degree in Machine Learning and Computer Vision (MVA) from ENS Cachan and ENSAE in 2015. Anna Korba's research primarily focuses on machine learning with emphasis on kernel methods, optimal transport, optimization, particle systems, and preference learning. Her work bridges theoretical statistics with practical machine learning applications, particularly in developing novel sampling and optimization methods. She has made significant contributions to understanding Wasserstein gradient flows, density ratio estimation, and variational inference techniques. Her publication record demonstrates a strong trajectory in top-tier machine learning conferences including ICML, NeurIPS, AISTATS, and ICLR. Her research shows a clear evolution from foundational work on ranking and preference learning during her PhD to more recent contributions in Wasserstein-based optimization, sampling methods, and deep probabilistic modeling. The interdisciplinary nature of her work connects statistics, optimization theory, and practical machine learning applications. Top 10% Oral Presentation at AISTATS 2022 Top 15% Long Oral Presentation at ICML 2021 She actively mentors PhD students and postdoctoral researchers, currently advising seven PhD candidates and having successfully guided several alumni to prestigious positions. Dr. Korba also contributes to the academic community through her role in administering the Master Data Science program and collaborating with researchers across institutions worldwide. As part of the CREST research center, Dr. Korba works within a vibrant team of researchers focused on statistics, machine learning, and their applications to economic and social sciences. Her research group includes current PhD students and postdocs working on various aspects of her research interests, creating a dynamic environment for advancing the field of statistical machine learning.

Mehdi Khamassiمشاهده پروفایل

استاد پژوهش

• Decision Making
• Reinforcement Learning
• Cognitive Neuroscience
• +۵ مورد دیگر

Mehdi Khamassi is a Research Director at the French National Center for Scientific Research (CNRS), assigned to the Institute of Intelligent Systems and Robotics (ISIR) at Sorbonne University in Paris, France. He holds an engineering background in computer science (specializing in AI and statistical modeling) from the National School of Computer Science for Industry and Business (2003), a Cogmaster in cognitive science from Pierre and Marie Curie University (2003), and a PhD in cognitive neuroscience from UPMC/Collège de France (2007). Recruited by CNRS in 2010, he co-organizes the Symposium of Biology of Decision-Making (SBDM) and co-directs the modeling major for the Cogmaster program. His research integrates computational modeling , neuroscience experiments , and robotic systems to study decision-making and learning mechanisms. Key interests include: Reinforcement learning in biological and artificial systems Role of social/non-social rewards in adaptive behavior Ethical implications of autonomous decision-making in AI Neuro-robotic models of hippocampal-prefrontal interactions Recent publications (2023-2025) demonstrate strong focus on reinforcement learning paradigms, AI alignment with human values, neurorobotics, and computational neuroscience. Work frequently bridges machine learning theory with empirical validation in biological systems or robotic platforms. He leads research within the ACIDE team at ISIR, exploring adaptive coordination of learning strategies in brains and robots. Current collaborations include NTUA (Greece), University of Oxford, and University of Trento.

Andreas Honeckerمشاهده پروفایل

استاد

• Condensed Matter Physics
• Strongly Correlated Electron Systems
• Magnetocaloric Materials
• +۴ مورد دیگر

Andreas Honecker is a Professor at the Theoretical Physics and Modeling Laboratory (CNRS UMR 8089) of CY Cergy Paris Université, where he has been employed since September 2014. He currently serves as Adjoint Director of the Institut des Sciences et Techniques (since April 2023) and was previously Director of the Physics Department (October 2020-April 2023). He also co-directs the Master Program in Physics at CY Cergy Paris Université. Professor Honecker's research focuses on condensed matter physics, particularly strongly correlated electron systems, quantum magnetism, and magnetocaloric materials. His work bridges theoretical physics with practical applications in quantum information and low-temperature refrigeration. He has made significant contributions to understanding quantum phase transitions, frustrated spin systems, and the magnetocaloric effect, with a notable Nature publication in 2021 on critical points in SrCu 2 (BO 3 ) 2 . His research activities are characterized by a strong emphasis on numerical methods for many-body systems, including quantum Monte Carlo techniques, density matrix renormalization group approaches, and advanced diagonalization methods. He has organized numerous international workshops on quantum materials, magnetocaloric effects, and quantum information, reflecting the interdisciplinary nature of his work. Among his notable recognitions are the APS Outstanding Referee award (2019) and being named a distinguished referee of The European Physical Journal (2015). He previously held a prestigious Heisenberg fellowship from the Deutsche Forschungsgemeinschaft (2007-2011). Honecker has extensive experience in academic service, including membership in the Conseil National des Universités (section 29, 2017-2023) and the Commission de la formation et de la vie universitaire at Université de Cergy-Pontoise (2016-2019). His collaborative work spans institutions across Europe, including previous positions at Göttingen University, ETH Zürich, and TU Braunschweig.

• Quantitative Finance
• Machine Learning in Finance
• Sustainable Finance
• +۳ مورد دیگر

Guillaume COQUERET is a Professor of Finance and Data Science at emlyon business school since 2018 and Director of the AIM Institute, which coordinates research and teaching in artificial intelligence applied to management. His academic qualifications include an HDR (2022) from Université Lumière Lyon 2 and a PhD in Business Administration from ESSEC Business School (2012). His research focuses on quantitative finance, machine learning in capital markets, sustainable finance, and heterogeneous agent models. He previously served as a Quantitative Researcher at EDHEC-Risk Institute (2013–2015). Education : 2022: HDR, Université Lumière Lyon 2 2012: PhD in Business Administration, ESSEC Business School 2008: Master in Probability and Finance, Université Pierre et Marie Curie (Paris 6) 2007: Master in Management, ESSEC Business School Research Interests : Machine learning applications in finance, factor investing, climate risk modeling, ESG integration, and algorithmic portfolio strategies. Publications : Over 30 peer-reviewed articles in journals such as The Journal of Portfolio Management , European Journal of Operational Research , and Quantitative Finance . Notable works include studies on climate betas, biodiversity premiums, and supervised learning in equity investing. Books : Machine Learning for Factor Investing: Python Version (2023) Perspectives in Sustainable Equity Investing (2022) Awards : None explicitly mentioned, but recognized for contributions to quantitative finance and AI in management. Labs/Teams : Leads the AIM Institute, fostering AI-driven research in management and finance.

Etienne Méminمشاهده پروفایل

استاد

• Stochastic Modeling
• Geophysical Flows
• Fluid Mechanics
• +۳ مورد دیگر

Etienne Mémin is a Research Director (Full Professor status) at Inria and leads the Odyssey research group, which is affiliated with multiple institutions including University of Rennes, IRMAR, Ifremer, LOPS, UBO, IMT Atlantique, and Lab-STICC. He serves as a Visiting Professor at the Department of Mathematics, Imperial College London (2020–2026) and is the Principal Investigator of the ERC STUOD grant. His research spans the intersection of geophysical sciences, fluid mechanics, computational sciences, and applied mathematics, focusing on stochastic modeling of fluid flows, data assimilation, and uncertainty quantification. He has developed frameworks for stochastic geophysical flows, coarse-scale simulations, and robust motion estimation techniques. Recent publications highlight his work on stochastic Navier-Stokes equations, ensemble forecasting, and data assimilation for ocean and atmospheric models. He has applied these methods to numerical weather prediction, turbulence analysis, and real-time flow reconstruction using sparse measurements. Scientific Awards: ERC STUOD grant PhD Students: Francesco Tucciarone (ERC STUOD, NEMO code) Benjamin Dufée (Ensemble Kalman filters, ATER position) Berenger Hug (Stochastic Navier-Stokes analysis, teaching) Antoine Moneyron (Stochastic ocean models) Collaborations: Imperial College London (D. Crisan, S. Laizet), Zhejiang University (S. Cai, C. Xu), MétéoFrance (P. Arbogast, O. Pannekoucke), Ifremer (B. Chapron), IRSTEA Lyon (L. Pénard), IRMAR (R. Lewandovsky), University of Buenos Aires (G. Artana), ISSI Beijing (T. Corpetti).

• Machine Learning
• Game Theory
• Economics
• +۶ مورد دیگر

Vianney Perchet is an Associate Professor and Permanent Member at CREST, ENSAE Paris, specializing in the intersection of machine learning, game theory, and economics. His research spans theoretical foundations to practical applications in recommender systems and user behavior modeling, with a concurrent part-time role as principal researcher at Criteo AI Lab focusing on exploration efficiency. His research interests bridge mathematics, computer science, and economics, emphasizing reinforcement learning, social learning, online matching, bandit problems, and auction theory. Key contributions address optimal algorithm convergence rates, non-clairvoyant scheduling, fair resource allocation, and asynchronous multiplayer bandits, demonstrating strong interdisciplinary integration across theoretical and applied domains. Recent publications at NeurIPS, ICML, and AISTATS reveal a cohesive trend toward learning-augmented algorithms for scheduling and allocation problems, with growing emphasis on fairness constraints, communication limitations, and partial prediction scenarios. This work consistently connects theoretical guarantees with real-world economic and technical applications. No scientific awards were explicitly documented in the provided materials. Perchet advises 5 active PhD students on reinforcement learning, social learning, and online matching/bandits, while 4 former students have completed doctorates on bandits and auction theory. His industry collaboration with Criteo AI Lab provides practical validation for theoretical frameworks in recommender systems. As a core CREST research center member, he actively participates in academic initiatives including the Mediterranean Game Theory Symposium, Games and AI Summer School, and Learning in Games workshops, fostering cross-institutional collaboration in algorithmic economics.

Sylvain Faisanمشاهده پروفایل

استادیار

• Image Processing
• Machine Learning
• Geometry
• +۶ مورد دیگر

Sylvain Faisan is a permanent Assistant Professor at ICube - MIV (University of Strasbourg, France). His research focuses on image processing, statistical modeling, and geometry, with applications in medical imaging and neuroscience. He works on advanced methodologies integrating machine learning and mathematical frameworks. Key Research Areas: Polarimetric image processing, retinal image registration, 3D statistical model comparison, topology-preserving image deformation, and fMRI brain mapping Technical Expertise: Bayesian inference, non-local means filtering, reversible jump MCMC algorithms, causal modeling, and constrained optimization His publications demonstrate interdisciplinary applications in optics, biomedical imaging, and computational anatomy. He contributes to developing algorithms that maintain physical admissibility and topological integrity in complex imaging problems.

Jamal Atifمشاهده پروفایل

استاد

• Artificial Intelligence
• Responsible AI
• Privacy Preservation in Machine Learning
• +۴ مورد دیگر

Jamal Atif is a Professor at Paris-Dauphine University and holds multiple significant leadership positions including Project Manager for 'Data Science and Artificial Intelligence' at the Institute of Information Sciences and their Interactions (INS2I) of the CNRS, Deputy Scientific Director of 3IA PRAIRIE, Head of the MILES team/project at LAMSADE (UMR CNRS-Université Paris-Dauphine), Co-leader of the Transverse Artificial Intelligence Program at PSL University, and Director of the Dauphine Numérique program. Professor Atif's primary research focuses on the foundations of responsible artificial intelligence, with specific expertise in privacy preservation in machine learning, robustness of deep learning algorithms to malicious attacks, causality, and explainability. His work bridges theoretical foundations with practical applications in security and reliability of AI systems. He has developed innovative approaches to address adversarial vulnerabilities in machine learning models and has made significant contributions to privacy-preserving techniques in data analysis. His publication record demonstrates a consistent focus on robust and trustworthy AI systems, with recent work exploring differential privacy in clustering, adversarial robustness, and explainable AI. The research spans theoretical foundations in logic and knowledge representation to practical applications in finance, healthcare, and computer vision. His publications appear in top-tier venues including Machine Learning journal, Neural Information Processing Systems, and International Joint Conferences on Artificial Intelligence. Scientific Awards: Recipient of two awards from the North American Society of Radiology for his thesis work Professor Atif has co-supervised or is currently supervising around fifteen doctoral students, demonstrating his commitment to mentoring the next generation of AI researchers. His leadership extends to directing major institutional programs including Dauphine Numérique and the Transverse Artificial Intelligence Program at PSL University, where he shapes strategic research directions in AI. He leads the MILES team/project at LAMSADE, which focuses on foundational aspects of machine learning and artificial intelligence. The team's research spans theoretical aspects of learning algorithms to practical applications requiring robust and reliable AI systems, with particular emphasis on security and privacy considerations in modern machine learning deployments.