جست‌وجوی استاد و پژوهشگر

Adriana Tapusمشاهده پروفایل

استاد

• Robotics
• Human-Robot Interaction
• Machine Learning
• +۷ مورد دیگر

Adriana Tapus is a Full Professor at ENSTA Paris, affiliated with Institut Polytechnique de Paris, leading the Autonomous Systems and Robotics Laboratory (SAR) within the Computer Science and Systems Engineering Unit (U2IS). She holds an HDR (Habilitation) and a PhD from EPFL, Switzerland, with postdoctoral experience at USC. Her research focuses on socially assistive robotics, human-robot interaction (HRI), and personalized therapy for individuals with physical/cognitive impairments. She directs the IP Paris Doctoral School and coordinates national/international projects like the EU-funded ENRICHME and SWEET. Education: PhD in Mobile Robotics, EPFL (2005) Habilitation (HDR), ENSTA Paris (2011) M.S. Computer Science, University Joseph Fourier Engineer, Politehnica University of Bucharest Research Interests: Tapus pioneers socially assistive robotics, integrating machine learning, human modeling, and multimodal communication (verbal/non-verbal/para-verbal). Her work addresses adaptive therapies for vulnerable populations using robotics, physiological data interpretation, and context-aware interaction. Key themes include: Human-robot cooperation and trust Emotion recognition and expression Personalized rehabilitation systems AI ethics and human-centered design Publications: Over 150 articles, with recent work exploring humor in HRI, teleoperation trust models, and cross-cultural intelligent vehicles. Notable 2025 contributions include studies on robot laughter efficacy and multimodal facial expression frameworks. Awards: 2025: 4 IROS papers accepted 2016: 25 Women in Robotics recognition 2010: Romanian Academy Award Multiple conference best paper awards (RO-MAN, ICRA, etc.) Advising & Grants: Supervised over 20 PhD students and led projects like EU Horizon 2020 ENRICHME. Current students focus on teleoperation dynamics, robot humor, and haptic interfaces. Active in editorial roles (IJSR, THRI) and conference organization (HRI General Chair 2019). Labs/Teams: Founder of RoboticsByDesign lab and co-initiator of the Hi! Paris interdisciplinary AI center. The SAR lab develops systems for healthcare, education, and human-robot collaboration.

• Supply Chain Management
• Transportation Systems
• Logistics
• +۴ مورد دیگر

Dr. Jérôme Verny is an Associate Professor specializing in transport, logistics, and supply chain management. He is the founder and director of the research institute in innovative transport and logistics, as well as the co-founder of the DISC Master's program (Digital & Innovative Supply Chain) in Paris and the Mobility Accelerator. His expertise spans digitalization in logistics, blockchain applications, and sustainable development. Educated at the University of Lille Nord de France (PhD in Economics and Management) and engineering schools like École Nationale des Ponts et Chaussées, he advises both public institutions (OECD, EU) and private enterprises on transportation strategies. Research Interests: Dr. Verny focuses on supply chain innovation, digital transformation, and strategic logistics. His work integrates blockchain technology for supply chain transparency, optimizes last-mile delivery in urban environments, and analyzes the impact of geopolitical actors like China on global trade networks. He also explores sustainable practices in transportation, including CO2 reduction strategies and pandemic response logistics. Awards: Recipient of the 2009 OECD-FIT Young Researcher Prize in Transport. His contributions bridge academic research with practical applications in industry, policy, and international trade. Key Activities: Co-founded the DISC Master’s program and leads research initiatives on blockchain adoption, Arctic shipping routes, and Mediterranean trade dynamics. He actively contributes to conferences such as the International Association of Maritime Economists (IAME) and publishes in journals like Structural Change and Economic Dynamics and International Journal of Shipping and Transport Logistics . Labs/Teams: Directs the Institut de Recherche en Transport et Logistique Innovante and collaborates with institutions like the OECD and European Commission on transport policy. His interdisciplinary approach involves engineering, economics, and data science to address global supply chain challenges.

Alexandre RUBESAMمشاهده پروفایل

دانشیار

• Finance
• Asset Pricing
• Financial Econometrics
• +۵ مورد دیگر

Alexandre RUBESAM is an Associate Professor at IÉSEG School of Management (France), specializing in Finance with a focus on asset pricing, financial econometrics, and quantitative trading. He holds a Ph.D. in Finance from Cass Business School (UK), an MSc in Statistics from the State University of Campinas (Brazil), and a Bachelor in Statistics from the same university. Education: Ph.D., Finance, Cass Business School, UK (2008) MSc., Statistics, State University of Campinas, Brazil (2004) Bachelor, Statistics, State University of Campinas, Brazil (2001) His research interests span behavioral finance, risk management, machine learning applications in finance, and portfolio optimization. Notably, he explores topics like market herding during crises, volatility forecasting, and the low-beta anomaly through behavioral lenses. Prof. Rubesam has authored influential papers on information transmission in financial markets, risk parity strategies, and the efficacy of linear models in volatility prediction. His work bridges theoretical finance with practical applications, such as developing machine learning-based portfolio construction methods for emerging markets. Awards: 2007 Dimitris N. Chorafas Foundation Prize 2006 Best Paper Award, Cass Business School His professional roles include Chief Risk Officer at Itaú-Unibanco (2013–2017) and Quantitative Researcher/Trader at Principia Capital Management (2009–2011). He is a member of LEM (Laboratory of Economics and Management) and teaches courses on financial programming, risk management, and portfolio analysis.

• statistical machine learning
• optimization
• sparse methods
• +۵ مورد دیگر

Francis Bach is a Professor and researcher at INRIA, leading the SIERRA project-team since 2011, which is part of the Computer Science Department at Ecole Normale Supérieure (ENS) within PSL Research University. His work bridges CNRS, ENS, and INRIA as a joint research effort. Elected to the French Academy of Sciences in 2020, he currently runs the ERC project SEQUOIA following his previous ERC project SIERRA (2009-2014). His research spans statistical machine learning with focus on optimization, sparse methods, kernel-based learning, neural networks, graphical models, and signal processing. Bach completed his Ph.D. in Computer Science at U.C. Berkeley under Professor Michael Jordan, followed by work at Ecole des Mines de Paris and the WILLOW project-team at INRIA/ENS/CNRS (2007-2010). His recent book "Learning Theory from First Principles" was published by MIT Press in December 2024. Bach's publication record shows consistent high-impact contributions across machine learning theory and applications, with recent work focusing on conformal prediction, diffusion models, optimization theory, and learning theory foundations. His research demonstrates strong connections between theoretical guarantees and practical algorithms, with applications spanning generative modeling, robust optimization, and statistical inference. Elected to French Academy of Sciences (2020) ERC project SIERRA (2009-2014) ERC project SEQUOIA (current) Author of "Learning Theory from First Principles" (MIT Press, 2024) Bach actively mentors numerous PhD students and postdocs, with many alumni now holding faculty positions at institutions like EPFL, Ecole Polytechnique, University of Washington, and University of Montreal. His teaching includes advanced courses on learning theory at ENS's Master's programs. He regularly presents tutorials at major conferences including COLT, NeurIPS, and ICML, demonstrating his leadership in the theoretical machine learning community.

• Machine Learning
• Artificial Intelligence
• Bioinformatics
• +۹ مورد دیگر

Florence d'Alché-Buc is a Professor at Télécom Paris (Institut Polytechnique de Paris), holding an Isaac Newton Institute Simons Chair (2025) and leading the Data Science and Artificial Intelligence for Digitalized Industry & Services (DSAI) Chair. She heads the Image, Data, and Signal Department and is part of the Signal, Statistics, and Learning (S2A) team at the LTCI laboratory. Her research focuses on machine learning, bioinformatics, and industrial applications, emphasizing kernel methods, structured prediction, and reliable AI. Education: Previously a professor at Université d’Evry and deputy director of the IBISC lab. Co-director of the Paris-Saclay Data Science Master and creator of specialized AI programs (e.g., Certificate of Specialized Studies in AI). Research highlights include contributions to operator-valued kernel methods, graph prediction, and frugal AI. She actively collaborates with institutions like Inria, École Polytechnique, and industry partners (Airbus, Engie, etc.). Notable roles: Scientific director of Digicosme Labex, Ellis Fellow, and board member of IVADO (Montreal). Her recent work addresses AI explainability, robustness, and sustainability, including projects on interpretable networks and energy-efficient models.

Joseph Salmonمشاهده پروفایل

پژوهشگر

• Machine Learning
• Optimization
• Data Science
• +۳ مورد دیگر

Joseph Salmon is a Senior Researcher at Inria (Team Iroko) in Montpellier, working with the Pl@nNet team. He previously served as a Full Professor at Université de Montpellier from 2018 to 2024 and was a Junior member of the Institut Universitaire de France (IUF) from 2021 to 2024. His research focuses on machine learning, optimization, and data science, with applications in citizen science and crowd-sourcing. He leads the doctoral program 'Statistics and Data Science' at Université de Montpellier. Education: Ph.D. in Statistics and Image Processing (2010) from Université Paris Diderot, under supervision of Dominique Picard and Erwan Le Pennec. Earlier roles include Assistant Professor at Telecom Paris (2012-2018), postdoctoral work at Duke University (2011-2012), and visiting positions at UW Statistics (2018) and the Simons Institute (2022). Research interests span isotonic regression, convex optimization algorithms (e.g., PAVA), statistical learning theory, and applications in environmental AI (Pl@ntNet). His work bridges theory and practice, emphasizing scalable algorithms for high-dimensional problems. Key contributions include advancements in PAVA convergence analysis, Slope penalty optimization, and peer-reviewed frameworks for crowdsourced data. Grants include ANR VITE (variable importance/explainability) and CaMeLOt (Cooperative Machine Learning Optimization). Labs/Teams: Active in Inria's Iroko team and collaborates with Pl@ntNet's AI development. Maintains the STATLEARN conference and ML-MTP initiative in Montpellier.

• Social Stratification
• Social Demography
• Health and Wellbeing
• +۳ مورد دیگر

Patrick Präg serves as an Associate Professor of Sociology at CREST/ENSAE (part of the Institut Polytechnique de Paris) and holds an associate faculty position at Oxford University’s Nuffield College. His research integrates quantitative methods and survey data to address social stratification, demography, health, and work-family dynamics. He earned his PhD in Sociology from the ICS/University of Groningen in 2015. His work has been recognized with the Aage B. Sørensen Award (2014). Education: PhD in Sociology, ICS/University of Groningen (2015) Master’s Thesis: Nonresponse to Items on Self-Reported Delinquency, University of Hamburg Research Interests: Patrick’s work explores the interplay between social structure and individual wellbeing. Key topics include: Intergenerational mobility and health outcomes Work-life balance and gender inequalities Pandemic-driven societal changes Algorithmic bias in social science Subjective socioeconomic status measurement Grants & Collaborations: He contributed to the EU-funded ‘Families and Societies’ project, producing deliverables on assisted reproduction and demographic consequences. His research often involves cross-national datasets and methodological innovation, with replication materials shared via Open Science Framework (OSF). Labs & Teams: He is affiliated with CREST (Center for Research in Economics and Statistics) and collaborates with institutions like the Max Planck Institute for Demographic Research (MPIDR) and the Nuffield College. His work frequently intersects with interdisciplinary teams addressing health, education, and labor market policies.

Thomas Walterمشاهده پروفایل

استاد

• Biomedical Image Analysis
• Machine Learning
• Computational Pathology
• +۴ مورد دیگر

Thomas Walter is a Professor at Mines ParisTech and Director of the Centre for Computational Biology (CBIO) , a research group affiliated with the Institut Curie and INSERM . His work focuses on applying Machine Learning and Computer Vision to biomedical image analysis, particularly in high-content screening and computational pathology . He also serves as Deputy Director of the Computational Oncology (U1331) unit and leads the Statistical Learning and Modeling of Biological Systems team. PhD in Medical Image Analysis (2003, Mines ParisTech) Postdoctoral work at EMBL (European Molecular Biology Laboratory) Director of CBIO since 2018 Holder of a PRAIRIE Chair (Paris Artificial Intelligence Research Institute) since 2019 Dr. Walter's research bridges biomedical imaging , machine learning , and cancer genomics . Key areas include: Statistical reconstruction of biological networks Prediction of tumor progression at genomic/transcriptomic levels Development of deep learning methods for cell cycle analysis Integration of multi-omics data for precision oncology Tools for spatial transcriptomics (e.g., autoFISH, RNA2seg) Recent publications highlight his work in spatial transcriptomics , immunotherapy outcome prediction , and deep learning for digital pathology . His team has developed open-source tools like FISH-quant and pyHiM for single-molecule RNA imaging analysis. Scientific Honors: PRAIRIE Chair (2019) for AI research in life sciences Dr. Walter actively contributes to teaching deep learning for image analysis in multiple graduate programs across France, including courses at Mines ParisTech , Université Paris-Saclay , and Institut Curie . His software tools (FISH-quant, pyHiM) and methodological frameworks (e.g., Cut-Detector, PointFISH) have become standard resources in bioimage informatics.

Loris D'Antoniمشاهده پروفایل

دانشیار

• Programming Languages
• Software Verification
• Program Synthesis
• +۴ مورد دیگر

Loris D'Antoni is an Associate Professor in the Department of Computer Science and Engineering at the University of California at San Diego (UCSD) . He is also a Visiting Academic at Amazon Web Services (AWS) . His research focuses on helping people write trustworthy software through techniques in program synthesis, formal verification, and machine learning robustness. Bachelor and Master in Computer Science from University of Torino (2008, 2010) PhD in Computer Science from University of Pennsylvania (2015) His research integrates programming languages , automata theory , and formal methods to ensure software reliability. Recent work explores semantics-guided synthesis and specification-aligned LLMs , with applications in network security, machine learning fairness, and automated code repair. Key trends in his publications include program synthesis , formal verification , and trustworthy AI systems . He has contributed to tools like AutomataTutor and SemGuS , a framework for customizable synthesis problems using constrained Horn clauses. Phillip R. Certain-Gary D. Sandefur Distinguished Faculty Award NSF CAREER Award Microsoft Research Faculty Fellowship Google and Facebook Faculty Awards Best Paper Award at ICDCN 2023 Distinguished Paper Award at SBES 2021 D'Antoni actively contributes to academic community service as a committee member in PLDI , OOPSLA , POPL , and CAV . He leads the Programming Systems Group at UCSD and collaborates with SemGuS research team on synthesis frameworks.

Pierre Marionمشاهده پروفایل

پژوهشگر

• Deep Learning Theory
• Optimization of Neural Networks
• Generative Models
• +۲ مورد دیگر

Pierre Marion is a Researcher at INRIA Paris, working within the Sierra research team since September 2025. His work focuses on the theoretical foundations of deep learning and he is beginning to explore applications of AI in mathematics. Marion has established collaborations across multiple institutions including EPFL, Sorbonne Université, and Google DeepMind. His educational background includes: Engineering degree from École polytechnique (2015-2018) with specialization in Applied Mathematics Master's degree from Sorbonne Université (2019-2020) PhD from Sorbonne Université (2020-2023) under the supervision of Gérard Biau and Jean-Philippe Vert Postdoctoral research at EPFL (2024) supervised by Lénaïc Chizat Marion's research interests primarily focus on the theory of deep learning, where he investigates the optimization and statistical properties of various neural network architectures. His work spans from shallow networks to complex generative models, with a particular emphasis on understanding the mathematical foundations that govern deep learning performance. Recently, he has begun exploring applications of AI in mathematical research, aiming to bridge the gap between theoretical machine learning and mathematical discovery. His research often combines rigorous theoretical analysis with practical implications for training deep neural networks. Analysis of Marion's recent publications reveals several key trends in his research. He has made significant contributions to understanding the role of large learning rates in optimization dynamics, demonstrating how they can accelerate convergence in logistic regression and prevent memorization in score-based generative models. His work on attention mechanisms has provided theoretical guarantees for their effectiveness in specific tasks like single-location regression and clustering. Additionally, Marion has extensively studied the connections between residual networks and neural ordinary differential equations , establishing generalization bounds and exploring scaling properties in the large-depth regime. His earlier work included contributions to natural language processing and quasi-Monte Carlo methods, reflecting a broad mathematical foundation that informs his current deep learning research. Marion has received several notable scientific awards: Runner-up PhD Award of AFIA (French Association for Artificial Intelligence) in 2024 Google PhD Fellowship in 2022 Ecole polytechnique Grand Prize of Research Internships in 2018 As an advisor, Marion currently supervises PhD student Yu-Han Wu (since 2024), with whom he has co-authored multiple publications on large learning rates and denoising score matching. Previously, he co-supervised several Master's students including Seorim Park, Yerkin Yesbay, and Nathan Doumèche. Marion has been actively involved in the machine learning community through conference organization (NeurIPS@Paris meetups), session chairing (ICSDS 2022), and extensive reviewing activities. He has served as a reviewer for top journals including JASA and Annals of Statistics, and conferences including NeurIPS and ICLR, where he was recognized as a top reviewer at NeurIPS 2023. Marion is a member of the Sierra research team at INRIA Paris, which focuses on machine learning theory and applications. He has also collaborated with researchers at CREST (Center for Research in Economics and Statistics), as evidenced by his participation in seminars organized by Anna Korba and Karim Lounici. His work often bridges theoretical computer science, statistics, and applied mathematics, reflecting the interdisciplinary nature of modern machine learning research.

Claude DELPHAمشاهده پروفایل

استاد

• Signal Processing
• Fault Diagnosis and Prognosis
• Electrical Engineering Systems
• +۴ مورد دیگر

Claude DELPHA is a Full Professor at Université Paris Saclay, affiliated with CentraleSupélec’s Laboratoire des Signaux et Systèmes (L2S). He holds an IEEE Senior Member status and has been with L2S since 2001. His expertise spans signal processing, fault diagnosis, electrical engineering systems, and machine learning. He leads the Modelling and Estimation team (GME) at L2S and oversees engineering admissions at Polytech Paris Saclay. Education: PhD in Instrumentation & Measurements and Signal Processing from Université de Metz, with a focus on intelligent sensor systems. Graduate degree in Electrical and Signal Processing Engineering. Research Interests: Multidimensional/statistical signal processing, fault diagnosis/prognosis (modeling, detection, estimation), electrical systems (drives, converters, PV), data hiding (watermarking), and pattern recognition (machine/deep learning). Active in energy systems, industry 4.0, and health/biology applications. Professional Roles: Director of GME research team, Polytech admissions lead, member of Polytech’s executive and academic boards, and IUT department council member. Engaged in labs like SYCOMORE and ILOCOS. Publications: Over 200 works since 2015, focusing on fault diagnosis in electrical systems, photovoltaic modules, bearings, and tidal turbines. Key methods include Kullback-Leibler divergence, Jensen-Shannon divergence, Mahalanobis distance, and PCA-based approaches. Awards: Not explicitly listed in provided texts.

Hugues Aschardمشاهده پروفایل

پژوهشگر

• Statistical Genetics
• Genome-Wide Association Studies (GWAS)
• Microbiome Research
• +۷ مورد دیگر

Hugues Aschard is a Principal Investigator and Structure Manager at the Pasteur Institute in Paris, where he leads research in statistical genetics, microbiome analysis, and computational genomics. He is the principal investigator of the MicMat project, the EpiGenCOV Consortium, and several bioinformatics software initiatives including JASS, RAISS, and MGMM. Research Interests: Statistical and computational methods in genetics Genome-wide association studies (GWAS) Gene-environment interactions Microbiome and host genetics in inflammatory bowel disease Genetic epidemiology of infectious diseases like COVID-19 Development of open-source tools for multi-trait and summary-statistic analysis Recent Research Trends: His recent publications and projects emphasize integrative genetic modeling, multi-trait analysis across diverse populations, and the development of novel computational methods to handle missing data and improve SNP discovery. His work bridges statistical innovation with biological and clinical applications in complex diseases. Scientific Contributions: Development of JASS, RAISS, and MGMM software tools Leadership in large-scale consortia like EpiGenCOV Advancing methods for cross-ancestry genetic studies Advising and Collaboration: He supervises multiple PhD students and postdoctoral fellows, including Christophe Boetto, Antoine Auvergne, and Lucas Chataigner. He collaborates with major institutions such as APHP and CNRGH. His team includes research engineers and administrative staff, indicating an active and well-supported research group. Laboratories and Teams: He is a key member of the Biomaterials and Microfluidics team at the Pasteur Institute, where he contributes to interdisciplinary research involving Bayesian decision processes and genetic modeling.

• Statistics
• Learning Theory
• High-dimensional statistics
• +۴ مورد دیگر

Jaouad Mourtada is an Assistant Professor in the Department of Statistics at ENSAE/CREST, École Nationale de la Statistique et de l'Administration Économique since September 2020. Previously, he was a postdoctoral researcher at the Laboratory for Computational and Statistical Learning at the University of Genoa (2019-2020). He completed his PhD in Statistics at École Polytechnique under the supervision of Stéphane Gaïffas and Erwan Scornet. His educational background includes a Master's degree in Mathematics with specialization in Probability and Random Models (2016), a Master's degree in Fundamental Mathematics (2015), and a Bachelor's degree in Mathematics from Pierre and Marie Curie University and École Normale Supérieure (2013). Dr. Mourtada's research focuses on the intersection of statistics and learning theory, with particular interest in high-dimensional statistics, online learning, and density estimation. His work explores the complexity of prediction and estimation problems through rigorous theoretical analysis. His research spans statistical learning theory, robust statistics, and the theoretical foundations of machine learning algorithms. His publication record since 2017 demonstrates consistent contributions to top-tier venues in statistics and machine learning, with recent work focusing on universal coding, aggregation methods, robust regression, and the theoretical analysis of kernel methods and random forests. His research shows a progression from online learning and expert aggregation to more complex statistical learning problems involving high-dimensional data and model misspecification. He teaches courses including Statistical Learning Theory for Master 2 Data Science students and Probability Theory at ENSAE. His teaching spans theoretical foundations of machine learning and core probability concepts for advanced statistics students.

• Computational Statistics
• Machine Learning
• Monte Carlo Methods
• +۴ مورد دیگر

Alain Durmus is a Professor at École Polytechnique, affiliated with the applied mathematics department (CMAP). His research focuses on computational statistics, machine learning, and stochastic methods, including Monte Carlo algorithms, Bayesian inference, and optimization. He explores topics such as Markov chain Monte Carlo (MCMC), stochastic approximation, and generative models. His work emphasizes theoretical guarantees for algorithms like Langevin Monte Carlo and Hamiltonian Monte Carlo, with applications to high-dimensional Bayesian inference and inverse problems. Key contributions include hypocoercivity analysis of piecewise deterministic MCMC processes, convergence guarantees for stochastic gradient methods, and the development of efficient sampling techniques. He has also contributed to Bayesian imaging and federated learning through works like the QLSD algorithm. Awarded the Best Student Paper Award at ICASSP 2020 for his work on the Sliced-Wasserstein distance. His teaching spans mathematical statistics, stochastic methods, and probability at École Polytechnique and ENS Paris-Saclay. He has also contributed to conferences and workshops on topics ranging from MCMC convergence to optimization in machine learning.

• Asset Pricing
• Investment Strategies
• Portfolio Management
• +۴ مورد دیگر

Adam Zaremba is an Associate Professor in the Finance & Accounting department at MBS. His expertise spans asset pricing, investment strategies, portfolio management, international finance, and capital allocation. Prior to joining MBS in 2020, he was affiliated with the University of Dubai (UAE) for three years. Education: PhD in Finance from Poznan University of Economics and Business (2012) His research integrates artificial intelligence with financial markets, focusing on factor investing, cryptocurrency dynamics, ESG strategies, and climate finance. Recent work includes machine learning applications in factor return prediction, cross-sectional cryptocurrency interactions, and non-standard errors in crypto analytics. Themes in his publications highlight advancements in momentum investing , capital allocation , and risk modeling across traditional and emerging markets. He has contributed to journals like the Journal of Finance , Journal of Financial Economics , and Review of Finance .