جستجوی استاد راهنمای دانشگاهی

Ioan Todincaمشاهده پروفایل

استاد

• Graph Algorithms
• Theoretical Computer Science
• Exact Algorithms
• +۶ مورد دیگر

Ioan Todinca is a Professor of Computer Science at the University of Orléans, France, affiliated with the LIFO (Laboratoire d'Informatique Fondamentale d'Orléans) research laboratory. His academic career spans over two decades, with significant contributions to theoretical computer science, particularly in graph algorithms and distributed computing. Faculty of Science, University of Orléans LIFO Research Laboratory Member of Institut thématique pluridisciplinaire Modélisation, Systèmes, Langages (since 2014) Former director of MIPTIS doctoral school (2012-2014) Former head of Computer Science degree program (2007-2011) Former leader of LIFO Graphs, Algorithms and Computational Models team (2008-2012) Todinca's research focuses primarily on graph algorithms, with expertise in exact algorithms (moderately exponential), parameterized algorithms, and algorithms for specific graph classes. He has made significant contributions to techniques involving tree decompositions, treewidth, minimal separators, and potential maximal cliques. More recently, his work has expanded into distributed algorithms, especially in communication-constrained models like the broadcast congested clique. His research bridges theoretical foundations with practical algorithmic approaches for NP-hard problems. The analysis of his recent publications reveals a strong trend toward distributed computing problems, particularly in congested network models. His work spans from fundamental graph theory problems (cycle detection, graph modification) to applications in quantum computing and model checking. The consistent focus on communication complexity, verification, and efficient algorithms across different computational models demonstrates his ability to adapt theoretical computer science principles to emerging computational paradigms. Todinca has supervised numerous PhD students and has been actively involved in the theoretical computer science community through conference organization and editorial work. His publications appear consistently in top-tier venues including SIAM Journal on Computing, Algorithmica, and proceedings of major conferences like STACS, WG, and DISC. Teaching responsibilities include algorithms, graph theory, and discrete structures for undergraduate and graduate students, with previous experience teaching databases, programming, and software engineering. His educational materials are hosted on the university's Celene platform.

Renaud Detcherryمشاهده پروفایل

مدرس

• Low-dimensional Topology
• Quantum Topology
• 3-manifolds
• +۵ مورد دیگر

Renaud Detcherry is a Lecturer (Maître de Conférences) at the University of Burgundy, affiliated with the Institute of Mathematics of Bourgogne (UMR 5584 CNRS) within the Faculty of Science and Technology. He has held this position since September 2020 and is part of the Geometry, Algebra, Dynamics and Topology (GADT) research team. His office is located in the Mirande Building (Office 314, Wing A) at the university's Dijon campus. His research focuses on low-dimensional topology and quantum invariants, including: 3-manifold topology, colored Jones polynomials, Witten-Reshetikhin-Turaev TQFT, quantum representations of mapping class groups, skein modules, and connections between quantum invariants and classical topology through various conjectures (Witten, volume, AJ, AMU). He also investigates Dehn surgeries, cosmetic surgery conjecture, and homological representations of surface diffeomorphism groups. Detcherry's recent publications demonstrate a consistent focus on quantum topology invariants and 3-manifold theory, particularly skein modules and their relationships with geometric structures. His work frequently explores connections between quantum algebra and classical topological concepts, with significant contributions to understanding the representation theory aspects of mapping class groups and the geometric properties of quantum invariants. He actively organizes academic events, including co-organizing the upcoming 'New Perspectives on Skein Modules' conference at CIRM (2025) and previously co-organizing the GDR Tresses meeting on 'Geometric and Quantum Topology' (2021) and a working group on modular surface groups (2021-2022).

Mathieu Fontaineمشاهده پروفایل

دانشیار

• Machine Listening
• Speech Enhancement
• Speaker Separation
• +۹ مورد دیگر

Mathieu Fontaine is an Associate Professor in Machine Listening at Télécom Paris , affiliated with the LTCI Lab within the IDS Department (Information, Data, Signal). His research focuses on machine listening for speech and audio signal processing. PhD in Informatics (2019), Lorraine University Master in Applied and Fundamental Mathematics (2015), Poitiers University BSc in Fundamental Mathematics (2013), Rennes University Fontaine's research spans speech enhancement , speaker separation , source localization , and music source separation using heavy-tailed probabilistic models and deep Bayesian networks , with applications in augmented reality . He has expertise in Python , signal processing , and machine learning (80% proficiency). His recent publications (2024) include work on diffusion models for speech synthesis , room acoustics estimation from 3D meshes , robust audio scene analysis , and direction-aware speech processing . Earlier publications (2022-2023) explore flow-based NMF , alpha-stable representations , and adaptive beamforming in multiparty environments. Fontaine collaborates with the S2A team and ADASP group at LTCI Lab. His work integrates probabilistic modeling with deep learning to address challenges in real-world audio processing, including reverberation, noise, and complex acoustic environments.

Nate Fosterمشاهده پروفایل

استاد

• Programming Languages
• Networking
• Formal Verification
• +۳ مورد دیگر

Nate Foster is a Professor of Computer Science at Cornell University and a Visiting Researcher at Jane Street . During the 2023-24 academic year, he served as a Visiting Professor at EPFL in the Data Center Systems Laboratory. His research bridges Programming Languages and Networking , focusing on formal verification, data plane programming, and language design for networked systems. His recent work includes developing symbolic automata for network verification (e.g., Active Learning of Symbolic NetKAT Automata , StacKAT ), creating efficient verifiers like KATch , and advancing type safety in data plane programming (e.g., SafeP4 ). These efforts span formal methods, algorithm optimization, and practical networked systems. Notable scientific awards include: NSF CAREER Award Sloan Research Fellowship ACM SIGCOMM Rising Star Award ACM SIGPLAN Robin Milner Award He actively contributes to PLDI, POPL, OOPSLA, and ICFP, serving as author, editor, and committee member. His GitHub repositories include tools for academic website templates and programming language research codebases.

• Machine Learning
• Information Fusion
• Data Clustering
• +۳ مورد دیگر

Julien Ah-Pine is a lecturer at Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS) under Université Clermont Auvergne , with affiliations at Institut national polytechnique Clermont Auvergne and École des Mines de Saint-Étienne . He also holds a Researcher position at CNRS. Research Interests His work spans machine learning , information fusion , aggregation functions , and multi-criteria decision support , with a focus on complex data types like graphs , functional data , and multi-view datasets . Recent publications emphasize anomaly detection in spectral data streams , online learning , and interpretable AI for industrial applications. Selected Publications 2025 work on OnlineBootKNN introduces a novel framework for real-time spectral anomaly detection, while 2024 research explores multiple kernel methods in functional data classification. Earlier studies cover graph-based clustering , relational data mining , and linguistic network models for NLP tasks. Laboratory & Collaborations Works within LIMOS laboratory at Université Clermont Auvergne, collaborating with institutions like Mines Saint-Étienne and CNRS. Key partnerships include Nicolas Rojas Varela and Engelbert Mephu Nguifo on data stream analysis projects.

Hachem Kadriمشاهده پروفایل

استاد

• Machine Learning
• Kernel Methods
• Functional Data Analysis
• +۵ مورد دیگر

Hachem Kadri is a Professor of Artificial Intelligence in the Department of Computer Science at Aix-Marseille University and Head of the Data Science Department at LIS Lab since 2023. He was previously an Assistant Professor at the same institution from 2012 to 2019 and conducted postdoctoral research at INRIA Lille and LAGIS. He holds a PhD in Electrical Engineering from the National Engineering School of Tunis (2008) and completed his HDR (Habilitation) at Aix-Marseille University in 2019 on operator-valued kernel methods. His research spans machine learning with core expertise in kernel methods, functional data analysis, statistical learning theory, deep learning, and quantum machine learning, bridging theoretical foundations with applications in signal processing and quantum computing. Recent publications (2022-2024) demonstrate a strategic shift toward quantum machine learning and C*-algebraic frameworks, with significant contributions in implicit regularization for deep tensor factorization, quantum perceptron algorithms, and operator-theoretic kernel extensions. Key works appear in ICML, NeurIPS, and AISTATS, highlighting interdisciplinary innovation at the intersection of algebra, functional analysis, and ML. Professor Kadri leads the ANR Starting Grant QuantML (2019-2024) as Principal Investigator and co-led the Franco-Tunisian OPERA project (2019-2023). He was nominated to the National Universities Council (CNU) for 2024-2027 and elected Head of LIS Data Science Department in 2023, reflecting his leadership in French academia. As a core member of the QARMA machine learning group at LIS Lab, he co-organized the inaugural Quantum Machine Learning workshop at ECML-PKDD 2022 and serves as Vice-president of SSFAM (Société Savante Francophone d’Apprentissage Machine), actively shaping the European ML research landscape.

Sylvain Arlotمشاهده پروفایل

استاد

• Statistical Learning
• Non-Parametric Statistics
• Data-Driven Tuning of Learning Algorithms
• +۵ مورد دیگر

Sylvain Arlot is a Professor at the Mathematics Department of Université Paris-Saclay, affiliated with the Probability and Statistics team at Laboratoire de Mathématiques d'Orsay. He leads the Celeste INRIA Saclay project-team and is a junior member of the Institut Universitaire de France (IUF) since 2020. His research focuses on statistical learning theory, non-parametric methods, model selection, and change-point detection. Arlot has contributed to foundational work on cross-validation, penalization techniques, and random forests. He co-organizes the Séminaire Palaisien and serves as an associate editor for the Annales de l'Institut Henri Poincaré B. Education: PhD in Mathematics from Université Paris-Sud (2007), HDR (Habilitation) from Université Paris Diderot (2014). Research Interests: Core areas include statistical learning theory, resampling methods (e.g., cross-validation and bootstrap), and applications in high-dimensional data analysis. His work bridges theoretical guarantees with practical algorithm design, emphasizing data-driven model selection and robust estimation techniques. Grants & Projects: Leads the PEPR IA Project Causali-t-AI (2023–2028) and was a member of the ANR Fast-Big project (2018–2023). He coordinates the math-AI program under Labex Mathématique Hadamard. Awards: Junior IUF membership (2020–2025). Labs/Teams: Heads the Celeste team at INRIA Saclay, collaborating on statistical machine learning and data science challenges.

Simon Lacoste-Julienمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization
• Deep Learning
• +۵ مورد دیگر

Simon Lacoste-Julien is an Associate Professor at Université de Montréal, affiliated with the Department of Computer Science and Operations Research (DIRO). He also serves as the Associate Scientific Director of Mila – Quebec Institute of Artificial Intelligence and holds the position of Vice President Lab Director at Samsung SAIT AI Lab Montreal (SAIL). His research focuses on machine learning, optimization, and their applications in areas like deep learning, generative models, causality, and computer vision. Lacoste-Julien has held academic positions at INRIA in Paris and has a PhD from UC Berkeley, with postdoctoral work at the University of Cambridge. He teaches advanced graduate courses on probabilistic graphical models and structured prediction. His work includes contributions to optimization algorithms (e.g., Frank-Wolfe methods), causal discovery, and generative models. Lacoste-Julien has supervised numerous students and postdocs, and his awards include being a CIFAR Fellow and Canada CIFAR AI Chair. His research spans theoretical foundations and practical applications, with a strong emphasis on scalable and efficient machine learning techniques.

Binoy Ravindranمشاهده پروفایل

استاد

• Computer Systems
• Software Verification
• Concurrency
• +۷ مورد دیگر

Binoy Ravindran is a Professor at Virginia Tech’s College of Engineering, Department of Electrical and Computer Engineering, leading the Systems Software Research Group (SSRG). His research focuses on computer systems, emphasizing security, performance, concurrency, distributed systems, and real-time computing, with recent work in software verification and heterogeneous-ISA platforms. Key projects: Low-level Reasoning Machine (LLRM), Popcorn Linux, LibrettOS, Hyflow, HermiTux, SlimGuard, HydraVM, KairosVM. He has co-authored 15+ papers from 2025 to 2022, spanning venues like ASPLOS, POPL, PLDI, VEE, PPoPP, and MIDDLEWARE, with awards including ACM Distinguished Scientist and eight Best Paper Awards. Service roles: Editorial Boards (IEEE Transactions on Cloud Computing, ACM TECS), Program Co-Chair (ACM Systor 2025), Committee memberships across ASPLOS, PLDI, and more.

Hervé Cardotمشاهده پروفایل

استاد

• Functional Data Analysis
• Stochastic Algorithms
• Survey Sampling
• +۱۰ مورد دیگر

Hervé Cardot is a Professor of Statistics and team leader of the Statistics, Probability, Optimization and Control (SPOC) group at the Institut de Mathématiques de Bourgogne (IMB) , University of Burgundy. His research spans functional data analysis, stochastic algorithms for robust estimation, survey sampling methodology, and applied statistics across diverse fields. Research Interests : Functional data analysis, online principal component analysis, zero-inflated regression, survey sampling, nonparametric estimation Key Applications : Agriculture, climatology, energy consumption, food science, remote sensing, medical imaging Recent Work includes: Developing statistical frameworks for analyzing categorical trajectories using multivariate functional PCA (2025) Advancing bias-robust estimation for functional data in survey sampling (2020) Creating online stochastic algorithms for high-dimensional robust estimation (2017) Applying semi-Markov models to sensory analysis (2018) Teaching at the University of Burgundy includes courses on statistical modeling, big data analysis, and applied statistics for economics and psychology. He supervises PhD students in statistics and co-edits Statistics and Probability Letters .

Ludovic Sacchelliمشاهده پروفایل

پژوهشگر

• Control Theory
• Sub-Riemannian Geometry
• Optimal Control
• +۷ مورد دیگر

Ludovic Sacchelli is an Inria researcher (CR) affiliated with the McTAO team at the Centre Inria d'Université Côte d'Azur and the Laboratoire J.A. Dieudonné of Université Côte d'Azur. His research spans control theory, sub-Riemannian geometry, and mathematical neuroscience, focusing on optimal control, observers, and estimation problems. His work on sub-Riemannian manifolds and control systems includes stabilization techniques for non-uniformly observable systems and applications to UAV control, neural fields, and bioprocess modeling. He has contributed to heat kernel analysis, line fields interpolation, and geometric models for sound processing. His recent publications address topics like distributed state estimation in neural models, polynomial state-affine control systems, and geometric algorithms for orientation field interpolation. He has also explored observability singularities in bilinear systems and stabilization of weakly contractive systems. Teaching roles include instructing Measure Theory , Stochastic Processes , and applied mathematics at institutions such as Université Côte d'Azur, Polytech Nice, Lehigh University, and École Polytechnique. His mentorship includes supervising a Masters research project on numerical implementation of line fields interpolation in 2019.

Anna Korbaمشاهده پروفایل

استادیار

• Machine Learning
• Bayesian Inference
• Sampling Methods
• +۴ مورد دیگر

Anna Korba is an Assistant Professor at CREST-ENSAE Paris within the Statistics Department. She holds an ENSAE Engineering degree in Data Science and a Master's in Mathematics, Vision & Learning (MVA) from ENSAE Paris. Her career includes a Ph.D. in Machine Learning at Télécom ParisTech, followed by a postdoctoral position at UCL's Gatsby Unit. Her research focuses on sampling techniques, Bayesian inference, optimal transport, and generative modeling, with recent work on constrained sampling and fairness integration. She contributes to collaborative efforts at the intersection of machine learning, dynamical systems, and PDEs. Notably, she co-presented tutorials on Wasserstein gradient flows at ICML 2022. Her work addresses unsolved challenges in sampling efficiency and fairness constraints. She is actively involved in CREST research initiatives and academic mentorship.

• Machine Learning
• Statistical Learning
• Bayesian Methods
• +۵ مورد دیگر

Pierre ALQUIER is a Professor at ESSEC Business School (Singapore) since 2023, specializing in statistical learning and machine learning. Previously, he held professorships at ENSAE Paris (2014–2019) and the University of Dublin (2012–2014). He earned his PhD in Mathematical Statistics from Pierre and Marie Curie University in 2006, with a focus on advanced statistical methodologies. His research centers on Bayesian methods, PAC-Bayes bounds, high-dimensional data analysis, and robust estimation, with applications in quantum computing and time series. He has authored over 60 peer-reviewed articles, including influential works on kernel mean embeddings and meta-learning. Alquier has received the 2019 Best Paper Award at the Asian Conference on Machine Learning. He actively contributes to academic leadership, serving as an associate editor for leading journals like the Journal of Machine Learning Research and organizing international workshops. His educational contributions include co-supervising multiple doctoral theses on topics like robust Bayesian inference and non-negative matrix factorization. Education: PhD in Mathematical Statistics (2006), Pierre and Marie Curie University MSc in Probability Theory and Statistics (2003), Pierre and Marie Curie University Diploma in Statistician-Economist (2003), ENSAE Research Focus: Machine learning theory, PAC-Bayes bounds, Bayesian computation, high-dimensional statistics, quantum tomography, and time series forecasting. Grants & Activities: Member of key academic societies (IMS, SFdS), reviewer for top conferences (NeurIPS, ICML), and organizer of workshops on approximate Bayesian inference and high-dimensional data analysis. His recent work emphasizes robust regression, meta-learning, and the theoretical foundations of deep learning, often addressing challenges in dependent data and model misspecification. He has developed R packages like regMMD for robust statistical estimation.

François Bachocمشاهده پروفایل

استادیار

• Statistics
• Machine Learning
• Mathematical Statistics
• +۳ مورد دیگر

François Bachoc is an Assistant Professor at the Toulouse Mathematics Institute and the University Paul Sabatier, where he has held a tenured position since 2015. He is a Junior Member of the Institut universitaire de France (IUF) (2024–2029). His research focuses on Statistics, Machine Learning, and Gaussian Processes , with applications in industrial and interdisciplinary domains. He earned his Ph.D. in Statistics from the CEA and Université Paris VII (2013), followed by a postdoctoral position at the University of Vienna (2013–2015). He completed a Habilitation (HDR) at University Paul Sabatier in 2018. His work emphasizes uncertainty quantification, Bayesian methods, and optimal design of experiments. Bachoc leads several funded projects, including the ANR Project GAP (€205k) and the AI Chair UQPhysAI (€350k). He teaches courses on asymptotic statistics, machine learning, and Gaussian processes at the graduate level. His contributions span theoretical advancements and practical applications in coastal flood modeling, sensitivity analysis, and computational statistics.

• Machine Learning
• Artificial Intelligence
• Bioinformatics
• +۷ مورد دیگر

Florence d’Alché-Buc is a full professor at Télécom Paris (Institut Polytechnique de Paris) since 2014, leading research in machine learning and AI. She heads the Image, Data, Signal (IDS) Department and holds the Data Science and Artificial Intelligence for Digitalized Industry and Services (DSAIDIS) Chair. Her work focuses on kernel methods, structured prediction, reliable AI, and bioinformatics applications. She co-leads the Master’s in Data Science at Université Paris-Saclay and pioneered the PASCAL Network’s Challenges program. Key roles include Scientific Director of Digiscome Labex and ELLIS Fellow. Research emphasizes frugal AI, reducing environmental impact, and enhancing model explainability. Recent contributions include work on metabolite identification via kernel-based methods and AI sustainability. She collaborates with institutions like Columbia University and co-organized the MLSE 2020 conference. Awards include the Simons Chair (2025) and leadership roles in AI ethics and regulation initiatives.