جستجوی استاد راهنمای دانشگاهی

Manik Varmaمشاهده پروفایل

استاد مدعو

• Machine Learning
• Deep Learning
• Information Retrieval
• +۱۶ مورد دیگر

Manik Varma is a Distinguished Scientist and Vice President at Microsoft Research India, and an Adjunct Professor at the Indian Institute of Technology Delhi. He is a Fellow of the Indian Academies of Science (IASc, INSA, NASI), the Indian National Academy of Engineering (INAE), and the Association for Computing Machinery (ACM). He has received prestigious awards such as the Shanti Swarup Bhatnagar Prize and Microsoft Gold Star Award. Education : BSc in Physics from St. Stephen's College (David Raja Ram Prize) BA in Theoretical Physics from the University of Oxford (Rhodes Scholar) DPhil in Computer Vision and Machine Learning from the University of Oxford (University Scholar) Post-doctoral Fellow at the Mathematical Sciences Research Institute (MSRI), Berkeley Visiting Miller Professor at UC Berkeley His research focuses on Machine Learning (Extreme Classification, Resource-efficient ML, Supervised Learning), Information Retrieval (Computational Advertising, Dense Retrieval, Recommender Systems), and Computer Vision (Image Search, Object Recognition). Recent work includes graph-regularized encoders, label variance reduction, and multimodal classification frameworks. His publications span extreme classification algorithms like NGAME , SiameseXML , and DECAF , with applications in IoT, web search, and recommendation systems. He leads a research group at Microsoft Research India and advises PhD students at IIT Delhi. Scientific Awards : Shanti Swarup Bhatnagar Prize (Government of India) Microsoft Gold Star and Achievement Awards WSDM 2019 Best Paper Prize BuildSys 2019 Best Paper Runner-up Fellow of ACM, IASc, INSA, NASI, INAE He has supervised numerous PhD students, including Sonu Mehta and Suchith Prabhu, and collaborates with institutions like Microsoft Research India, IIT Delhi, and UC Berkeley. His research has led to scalable solutions for billion-label classification and resource-constrained IoT applications.

• Optimization
• Complex Networks
• Statistical Inference
• +۳ مورد دیگر

Stefano NASINI is an Associate Professor at the University of Lille 3, specializing in Quantitative Methods within the Economics and Mathematics Sciences. He holds a HDR (Habilitation à Diriger des Recherches) from the University of Lille 3 (2021), a Ph.D. in Statistics and Operations Research from the Polytechnic University of Catalonia (2015), and a Master in Statistics (2011). His research focuses on optimization, complex networks, statistical inference, and microeconomic applications. He has held academic positions including a post-doctoral role at IESE Business School (2014–2016) and a visiting researcher role at the University of Lisbon (2014). His work spans scheduling optimization, network analysis, financial contagion modeling, and energy system planning. Key contributions include specialized algorithms for large-scale optimization problems and frameworks for decentralized portfolio management. He is a member of the LEM research group and teaches courses in optimization, econometrics, and social network analysis at the Grande École and MSc levels. Publications highlight interdisciplinary applications, including network-based diffusion models, multi-market financial strategies, and dynamic choice analysis. His research bridges theoretical advancements in operations research with practical challenges in economics, energy, and transportation systems. No scientific awards are explicitly listed in the provided materials. His advising roles and grants are not detailed here, but his extensive publication record reflects active collaboration within academic and applied domains.

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.

Tom Schrijversمشاهده پروفایل

استاد

• Programming Languages
• Functional Programming
• Type Systems
• +۶ مورد دیگر

Tom Schrijvers is a Professor at the Department of Computer Science in the Faculty of Engineering Science at KU Leuven, Belgium. He leads the Programming Languages Group within the Declarative Languages and Artificial Intelligence (DTAI) research group. His research focuses on programming languages, particularly functional and logic programming, with special emphasis on Haskell, type systems, and algebraic effects. His research interests include: Functional Programming, especially Haskell Type Systems and Type Theory Algebraic Effects and Handlers Logic Programming, particularly Prolog Constraint Programming Domain-Specific Languages Programming Language Theory Prof. Schrijvers' recent research has focused on effect systems, staged programming, and language composition. His work on algebraic effect handlers has been particularly influential, providing new insights into how effects can be modularly composed and handled in functional languages. He has also made significant contributions to the understanding of type classes and their implementation in Haskell. His publications demonstrate a consistent focus on practical applications of programming language theory, with work spanning from foundational type theory to applied domain-specific languages for areas like fluorescence microscopy. His research often bridges the gap between theoretical programming language concepts and practical implementation concerns. Prof. Schrijvers has supervised numerous PhD students to completion, including Pieter Wuille, Benoit Desouter, George Karachalias, Steven Keuchel, Amr Saleh, Alexander Vandenbroucke, and Ruben Pieters. He currently supervises PhD students Klara Mardirosian, César Santos, Gert-Jan Bottu, Koen Pauwels, Birthe van den Berg, and Roger Bosman. His research group has received funding from various sources including EU projects like GRACeFUL. The Programming Languages Group at KU Leuven, which he leads, focuses on functional (Haskell) and logic (Prolog, Datalog, CLP) programming languages, as well as general programming language theory. The group has been active in numerous research projects and collaborations across Europe.

• 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.

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.

• 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.

Elena Asparouhovaمشاهده پروفایل

استاد

• Theoretical Financial Economics
• Experimental Finance
• Asset Pricing
• +۷ مورد دیگر

Elena Asparouhova is a Professor of Finance at the David Eccles School of Business , University of Utah. She holds a doctorate in Social Sciences from the California Institute of Technology and a Masters in Statistics from Sofia University , Bulgaria. Her research focuses on theoretical and experimental financial economics , including asset pricing theory , experimental finance , general equilibrium theory , and econometrics . Recent work examines financial market competition under delegation , information percolation in dark markets , and human-robot interaction in trading . Best Paper Award, Journal of Financial Markets (2025) Best Paper Award, Review of Finance (2024) Best Paper, Behavioral Finance and Capital Markets Conference, Australia (2025) Continuous National Science Foundation funding for 10+ years Contact: e.asparouhova@utah.edu , University of Utah, Salt Lake City, Utah 84112.

Clément FERNANDESمشاهده پروفایل

مدرس

• Hidden Markov Models
• Data Segmentation
• Image Segmentation
• +۳ مورد دیگر

Clément FERNANDES is a Lecturer at Telecom SudParis, part of the SAMOVAR laboratory under Institut Polytechnique de Paris. His research focuses on statistical modeling and unsupervised learning techniques, particularly applying hidden Markov chains to data and image segmentation. He holds a PhD in Discrete Mathematics from Institut Polytechnique de Paris (2022), where his thesis explored Triplet Markov Chains in image segmentation. His recent work includes advancing non-stationary data segmentation methods and developing forecasting models using Gaussian Markov frameworks. Though no explicit grants or awards are mentioned, his publications reflect contributions to approximate reasoning and applied statistics in engineering contexts. He is affiliated with the SAMOVAR laboratory and maintains an active publication record in top-tier journals like International Journal of Approximate Reasoning .

Jean-Baptiste Alayracمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• Multimodal Learning
• +۳ مورد دیگر

Jean-Baptiste Alayrac is a Researcher at DeepMind, focusing on structured learning from video and natural language. His academic background includes a PhD from the Sierra and Willow groups at Ecole Normale Supérieure and Telecom ParisTech, where he explored machine learning and computer vision. He has held teaching roles as a Teaching Assistant at Ecole Normale Supérieure and other universities, contributing to courses in statistical machine learning and mathematics. His research interests span multimodal learning, vision-language models, self-supervised learning, and efficient retrieval systems. Notable projects include the Flamingo model for few-shot learning and the Perceiver IO architecture for structured data processing. He has also contributed to foundational works like HowTo100M, leveraging large-scale video-text embeddings. Alayrac's publications emphasize cross-modal interactions, with key contributions in adversarial robustness, layered video representations, and weakly supervised learning. His work often bridges computer vision and natural language processing, with applications in instructional video analysis and cross-lingual translation.

Xujia ZHUمشاهده پروفایل

دانشیار

• Uncertainty Quantification
• Surrogate Modeling
• Stochastic Simulators
• +۴ مورد دیگر

Xujia ZHU is an Associate Professor at CentraleSupélec, Paris-Saclay University, affiliated with the Laboratory of Signals and Systems (L2S). His research focuses on uncertainty quantification, surrogate modeling, stochastic simulators, and reliability analysis. He holds an engineer’s degree in Mechanics from École Polytechnique (2015), a Master’s in Computational Mechanics from TU Munich (2017), and a Ph.D. from ETH Zurich (2022). He was a postdoctoral researcher at ETH Zurich until 2023. Education: Ph.D., Chair of Risk, Safety, and Uncertainty Quantification, ETH Zurich, 2022 Master’s (high distinction), Computational Mechanics, Technical University of Munich, 2017 Engineer’s Degree, Mechanics, École Polytechnique, 2015 Research Interests: Xujia’s work bridges numerical simulations and statistics, addressing topics like uncertainty propagation, sensitivity analysis, and surrogate modeling for stochastic systems. Key areas include polynomial chaos expansions, Bayesian active learning, and applications in seismic fragility analysis. Publications: His recent work emphasizes emulation techniques for stochastic simulators, multi-fidelity methodologies, and Bayesian active learning strategies in reliability analysis. Key themes include sparse polynomial chaos expansions and latent variable modeling. Labs/Teams: Affiliated with L2S, he collaborates on transversal projects in energy, industry, and health, leveraging interdisciplinary approaches in uncertainty quantification and computational modeling.

Éric Tanterمشاهده پروفایل

استاد

• Programming Languages
• Gradual Typing
• Software Engineering
• +۹ مورد دیگر

Éric Tanter is a Full Professor in the Computer Science Department (DCC) at the University of Chile, where he also leads the PLEIAD Lab. He holds an Inria International Chair (2025–2029) and is an Associate Researcher at the Millenium Institute for Foundational Research on Data (IMFD). His academic leadership includes prior roles as Director and Deputy Director of the DCC, and Coordinator of the PhD Program in Computer Science. His research centers on programming languages and software engineering, with a strong focus on gradual typing, type systems, program verification, and secure programming. He explores both theoretical foundations and empirical practices, aiming to bridge the gap between static and dynamic language paradigms. His recent work emphasizes gradual verification, refinement types, and applications in proof assistants and security. The trends in his recent publications show a deep engagement with foundational aspects of gradual and dependent typing, often aiming to enhance the reliability and security of software systems. These works frequently involve formalization in proof assistants like Coq and explore applications in differential privacy, secure interoperability, and symbolic execution. Inria International Chair (2025–2030) Best Paper Awards at POPL 2019, OOPSLA 2018, ICFP 2018, MSR 2011, AOSD 2010, SBLP 2008, DAIS 2006 Most Influential/Most Notable Paper Awards at DLS, Programming, DLS Facebook Research Testing and Verification Award (2018) Google Faculty Research Awards (2015, 2016) Best Professor Award, University of Chile (2011) Éric Tanter has advised numerous PhD and Master’s students, many of whom have gone on to publish influential work in top venues. He has led multiple research projects funded by FONDECYT, ANID, INRIA, and other national and international agencies. His service includes editorial roles in journals such as the Journal of Functional Programming and Science of Computer Programming, and extensive participation in program committees of major conferences like POPL, ICFP, and OOPSLA. He leads the PLEIAD Lab at the University of Chile, a research group focused on programming languages, software engineering, and formal methods. The lab fosters collaboration with international institutions and emphasizes both theoretical rigor and practical impact.

Lionel Truquetمشاهده پروفایل

مدرس

• Statistics for dependent data
• Time series analysis
• Markov chains
• +۲ مورد دیگر

Lionel Truquet is a Lecturer-Researcher in Statistics at ENSAI (École Nationale de la Statistique et de l'Administration Économique), where he focuses on Statistics for dependent data and Time series analysis . He serves as a Director of Research and has contributed significantly to fields like Markov chains and nonlinear dynamics . Research Interests: Time series models for ecological and economic data Statistical inference for categorical and discrete-valued processes Ergodic properties of Markov chains in random environments Mixing conditions for nonstationary processes Perturbation techniques in stochastic modeling Recent Publications: His work spans nearest neighbor sampling , multivariate autoregressive models , and mixing properties of count processes , with applications in ecology and econometrics. Key trends include nonparametric methods for high-dimensional data and stationarity analysis in time-varying systems. Scientific Awards: TJALLING C. KOOPMANS ECONOMETRIC THEORY PRIZE (2021–2023) for groundbreaking work on multivariate count autoregressions.