جستجوی استاد راهنمای دانشگاهی

Jeff Grayمشاهده پروفایل

استاد

• Software Engineering
• Model-Driven Development
• Domain-Specific Languages
• +۷ مورد دیگر

Jeff Gray is a University Distinguished Professor in the Department of Computer Science at the University of Alabama, where he also directs the Randall Research Scholars Program. He holds a Ph.D. in Computer Science from Vanderbilt University and has built a distinguished career in software engineering, model-driven development, and computing education. Education: Ph.D. in Computer Science, Vanderbilt University, 2002 M.S. in Computer Science, West Virginia University, 1993 B.S. in Computer Science (summa cum laude), West Virginia University, 1991 Dr. Gray's research focuses on model-driven software development, domain-specific languages, blocks-based programming environments, and computer science education outreach. His work bridges theoretical software engineering with practical applications in K–12 education and accessibility. He has developed tools like Spherly, Pixly, and Tunely to make programming more accessible. His research interests include software modeling, tool integration, debugging techniques, and inclusive programming environments. His recent publications reflect a strong trend in computing education, particularly in blocks-based programming, unit testing for beginners, and accessible language design. He has also contributed significantly to model-driven engineering and domain-specific language research. Scientific Awards: NSF CAREER Award Professor of the Year (Alabama, 2008) – Carnegie Foundation McMahon-Pleiad Prize (2025) Morris L. Mayer Premier Award Distinguished Member of the ACM IEEE Computer Society Distinguished Contributor (Inaugural Class) Dr. Gray has advised numerous doctoral students and has served as Editor-in-Chief of Software and Systems Modeling (Springer). He has secured over $24 million in external funding as PI or co-PI from agencies including NSF, Department of Education, and Google. He is actively involved in national initiatives such as the AP CS Principles course and Alabama’s Computer Science Advisory Council. He leads outreach programs including the Alabama Summer Computer Camps, Alabama Robotics Competition, and Code.org workshops. He also co-chairs the Development Committee for the College Board's AP CS Principles course and serves on Alabama Governor Kay Ivey’s Computer Science Advisory Council.

• Programming Languages
• Formal Methods
• Semantics of Programming Languages
• +۳ مورد دیگر

François Pessaux is a Professor and Teacher-researcher at ENSTA Paris, working within the Computer Science and Systems Engineering Unit (U2IS). His academic career spans several decades with significant contributions to programming language theory and formal methods. His research interests focus on Programming Languages , Formal Methods , Semantics of Programming Languages , Compilation , Proofs , and Hybrid Systems . Pessaux has developed extensive expertise in programming language semantics, particularly with functional languages like OCaml, and has made substantial contributions to formal verification techniques. His work bridges theoretical computer science with practical software development, especially for critical systems. His publication record shows a consistent focus on programming language design, semantics, and verification. Notable works include his comprehensive books on programming language concepts and his significant contributions to the FoCaLiZe environment, which supports formal integrated development for certified software. His research has evolved from foundational work on exception analysis in the 1990s to more recent comprehensive treatments of programming language semantics. Pessaux actively contributes to both research and education at ENSTA Paris, teaching foundational computer science courses including IN101 (Algorithms and Programming) and other advanced programming language courses. His teaching materials from recent academic years (2022-2023) demonstrate his ongoing commitment to computer science education. He is deeply involved with the FoCaLiZe project, which appears to be a formal integrated development environment for building certified software. This work connects programming language theory with practical software verification for critical systems, demonstrating his ability to bridge theoretical and applied computer science.

Rémi Cozotمشاهده پروفایل

استاد

• Image Aesthetics
• High Dynamic Range (HDR) Imaging
• Tone Mapping
• +۸ مورد دیگر

Rémi Cozot is a Professor at the University of Littoral Côte d'Opale, affiliated with the MAP Research Group and LISIC Laboratory. His research focuses on Image Aesthetics , High Dynamic Range (HDR) Imaging , and Image Editing , with an emphasis on preserving artistic intent during media retargeting. He has pioneered methods to reduce Temporal Artifacts in HDR video tone mapping, including the Zonal Brightness Coherency operator, and developed Style Aware Tone Expansion for HDR displays, which won the NAB 2017 Innovation Award . Key contributions include computational distances for aesthetic similarity and models for Brightness Comfort in HDR displays. He has supervised 9 PhD students, including Mathieu Chambe (HDR aesthetics) and Dmitry Kuzovkin (photo album assessment). His scientific accolades include best paper awards at VISIGRAPP/GRAPP 2018 and Computer Graphics International 2017.

• Signal Processing
• Remote Sensing
• Geospatial Data Analysis
• +۵ مورد دیگر

Alexandre Hippert-Ferrer is an Associate Professor at Université Gustave Eiffel, where he is a permanent member of the LASTIG lab working in the Strudel team. He primarily teaches at the National School of Geographic Sciences (École Nationale des Sciences Géographiques). Dr. Hippert-Ferrer received his PhD from University Savoie Mont Blanc, Annecy, France in 2020, supervised by Philippe Bolon and Yajing Yan. His doctoral research focused on reconstruction of missing data in displacement time series by remote sensing. Following his PhD, he was a post-doctoral researcher at L2S (Université Paris-Saclay) working with Florent Bouchard and Frédéric Pascal. He maintains active collaborations with researchers including Arnaud Breloy and Nabil El Korso from LEME at University of Paris Nanterre. Hippert-Ferrer's research focuses on robust statistical methods for signal processing, particularly for geospatial data with missing values. His work bridges the fields of remote sensing, signal processing, and statistics, with a strong emphasis on practical applications in earth observation and geodesy. He has developed innovative approaches using Empirical Orthogonal Function (EOF) analysis, Expectation-Maximization (EM) algorithms, and Riemannian geometry for handling incomplete data in displacement measurement time series. His methods have been applied to Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) data for monitoring ground displacement. His publication record shows a clear progression from basic gap-filling techniques to sophisticated statistical models. Starting with EOF-based approaches, his work has evolved to incorporate advanced methods including elliptical distributions, low-rank covariance estimation, and graphical factor models with Riemannian optimization. The consistent theme across his publications is developing mathematically sound yet practical algorithms for real-world geospatial data challenges, particularly when data is incomplete or corrupted. Dr. Hippert-Ferrer has made significant contributions through publications in top journals including IEEE Transactions on Signal Processing and IEEE Transactions on Geoscience and Remote Sensing. His open-source implementation of the EM-EOF algorithm on GitHub demonstrates his commitment to reproducible research. As an educator at the National School of Geographic Sciences, he trains the next generation of geospatial professionals, bridging theoretical statistical methods with practical applications in earth observation.

Georgios Exarchakisمشاهده پروفایل

پژوهشگر

• Machine Learning
• Theoretical Neuroscience
• Signal Processing
• +۵ مورد دیگر

Georgios Exarchakis is a Research Scientist (Chargé de recherche) at the Institut Hospitalo-Universitaire (IHU) Strasbourg, specializing in Machine Learning and Theoretical Neuroscience. Previously, he held postdoctoral positions at Institut de la Vision and Prof. Stephane Mallat’s DATA team at École Normale Supérieure. He earned a Dr. rer. nat. in Machine Learning from the University of Oldenburg (2016), an M.Sc. in Computational Science from Goethe University Frankfurt (2012), and a Mathematics Diploma from Aristotle University of Thessaloniki (2008). His research focuses on sparse coding, graphical models, signal processing, and deep learning, with applications in spike sorting, quantum chemistry, and natural image statistics. He developed the Kymatio library for wavelet scattering transforms and contributed to ProSper, a probabilistic sparse coding library. His work bridges theoretical neuroscience and machine learning, emphasizing interpretable models and scalable algorithms. Key contributions include advancing variational sampling techniques for generative models, solid harmonic wavelet scattering for molecular property prediction, and discrete sparse coding frameworks. He teaches machine learning courses at École Polytechnique and Oldenburg University, emphasizing practical applications and theoretical foundations. Georgios collaborates with interdisciplinary teams, including the Redwood Center for Theoretical Neuroscience at UC Berkeley and the University of Frankfurt, to advance methodologies in computational neuroscience and AI. His current research at IHU Strasbourg integrates machine learning into image-guided surgery technologies.

Duygu Ceylanمشاهده پروفایل

پژوهشگر

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

Duygu Ceylan is a Principal Research Scientist at Adobe Research London. She holds a PhD from École Polytechnique Fédérale de Lausanne (EPFL), where she worked under Prof. Mark Pauly, and a Master’s degree in Computer Science from Bilkent University. She received her undergraduate degree in Computer Engineering from Middle East Technical University (METU). Prior to Adobe, she worked as a software engineer at Aselsan Inc. Her research focuses on bridging computer vision and graphics, particularly using machine learning for 3D content generation and analysis from images/videos. Key interests include generative models, 3D reconstruction, and interactive editing of 2D/3D assets. Notable awards include the Eurographics PhD Award (2015) and Eurographics Young Researcher Award (2020). Her work spans over 50 publications in top-tier conferences like SIGGRAPH, CVPR, and ECCV, covering topics such as neural rendering, cloth dynamics, and video generation. She leads research initiatives at Adobe, mentoring interns and driving advancements in generative AI for visual content creation. Her group’s projects include tools for video editing, 3D stylization, and dynamic human performance modeling.

Panagiotis PAPADAKISمشاهده پروفایل

دانشیار

• Robotics
• Computer Vision
• 3D Shape Analysis
• +۴ مورد دیگر

Panagiotis PAPADAKIS is an Associate Professor (HDR) in Informatics & Robotics at IMT Atlantique Bretagne/Pays de la Loire, Department of Informatics. He leads the RAMBO team at CNRS Lab-STICC and is an associate member of the IRL CROSSING. His research focuses on cognitive robotics, computer vision, and artificial intelligence with applications in assistive robotics, urban search and rescue (USAR), and 3D environment analysis. He has over €1M in research funding and has co-authored numerous top-tier publications in computer vision and robotics journals/conferences. Educations: PhD in Information Technology, National University of Athens (2009) Informatics and Telecommunications Degree, National University of Athens (2005) Research Interests: Robot Control and Navigation 3D Shape Analysis & Matching Urban Search and Rescue (USAR) Robotics Computer Vision for Assistive Technologies Cognitive Robotics and Human-Robot Interaction Key Projects: Leading RAMBO team in CNRS Lab-STICC Coordinated multi-robot assistance systems for smart environments Development of digital twin-driven smart home solutions Teaching: Postgraduate courses in Computer Vision/Perception at University of Rome Programming courses at ENSTA ParisTech Undergraduate Computer Graphics at University of Athens

Simon Leglaiveمشاهده پروفایل

استادیار

• Audio Signal Processing
• Machine Learning
• Speech Enhancement
• +۷ مورد دیگر

Simon Leglaive is a tenured Assistant Professor at CentraleSupélec in Rennes, France, and a researcher in the AIMAC team at the IETR laboratory (CNRS UMR 6164). He holds a PhD in Audio Signal Processing from Télécom Paris (2017), and previously served as a postdoctoral researcher at Inria Grenoble Rhône-Alpes. His academic affiliations include CentraleSupélec, Télécom Paris, and CNRS, with research collaborations across institutions including Inria, GIPSA-lab, and Sorbonne University. His research focuses on the intersection of signal processing and machine learning, particularly for audio and speech applications. Key areas include probabilistic generative models, deep learning, low-dimensional representation learning, and weakly or semi-supervised learning. His work enables advanced multimodal machine perception tasks such as speech enhancement, audiovisual speech analysis, and human mesh recovery. He teaches machine learning, deep learning, and audio signal processing at CentraleSupélec. The 15 most recent publications highlight a strong trend in deep generative modeling, especially using variational autoencoders (VAEs) and masked autoencoders, for tasks like speech enhancement, source separation, emotion recognition, and human pose recovery. There is a clear emphasis on disentangled and interpretable latent representations, multimodal fusion, and unsupervised or weakly supervised methods. Applications span from speech technology to computer vision, reflecting a cross-disciplinary approach centered on fundamental representation learning. Coordinator and Principal Investigator, DEGREASE project (ANR-23-CE23-0009) Partner, DEESSE project (ANR-24-CE23-2229) Elected member, IEEE AASP Technical Committee (2023–2025) Organizing committee, CHiME-7 UDASE challenge (2023) Conference area chair, ICASSP (2024, 2025), WASPAA (2023, 2025) Simon Leglaive advises multiple PhD students and has served on the IETR laboratory council since 2022. His academic service includes extensive reviewing for top journals and conferences in signal processing and machine learning. His research is supported by competitive grants from the French National Research Agency (ANR). He is actively involved in collaborative research through the AIMAC team at IETR and partnerships with Inria and industry labs.

Jing-Rebecca Liمشاهده پروفایل

استاد

• Diffusion Magnetic Resonance Imaging
• Numerical Methods for Partial Differential Equations
• Numerical Linear Algebra
• +۴ مورد دیگر

Jing-Rebecca Li is a Professor and Research Scientist at ENSTA Paris, affiliated with the Applied Mathematics Unit (UMA) and INRIA Saclay as part of the IDEFIX research team. Her work bridges advanced mathematical techniques with medical imaging applications, particularly in diffusion MRI. She maintains a dual affiliation between ENSTA Paris, a leading engineering school in France, and INRIA, the French national research institute for digital science and technology. HDR (Habilitation à Diriger des Recherches) in Mathematics, Université Paris-Sud, 2013 Ph.D. in Mathematics, Massachusetts Institute of Technology, 2000 B.Sc. in Mathematics, University of Michigan, 1995 Dr. Li's research focuses on developing sophisticated numerical methods to solve partial differential equations with applications in diffusion magnetic resonance imaging. Her work spans brain and cardiac imaging, numerical linear algebra, machine learning algorithms for inverse problems in PDEs, and natural language processing tools. She has pioneered approaches to simulate diffusion MRI signals in complex biological tissues, enabling more accurate interpretation of imaging data for neuroscience and cardiology applications. Her research has significant implications for understanding brain microstructure and cardiac tissue organization through non-invasive imaging techniques. Her recent publications demonstrate a clear trend toward increasingly sophisticated modeling of biological tissues, with growing emphasis on cardiac applications alongside her foundational work in brain imaging. She has developed robust computational frameworks that incorporate permeable interfaces, geometrical deformations, and realistic neuronal geometries to better simulate diffusion MRI signals. Her work increasingly integrates machine learning with traditional numerical methods, creating hybrid approaches that leverage the strengths of both paradigms for microstructure estimation. Householder Prize for the best dissertation in Numerical Algebra (2002) Dr. Li has supervised numerous doctoral students across multiple institutions, with a focus on computational methods for diffusion MRI. Her current research is supported by significant grants including the Engineering for Health (E4H) interdisciplinary center project investigating biomarkers for Multiple Sclerosis through diffusion MRI (2023-2025). Previously, she led the ANR-funded SIMUDMRI project (2010-2014) and participated in the US-French Collaboration project on Computational Imaging of the Aging Cerebral Microvasculature (2013-2016). Her work demonstrates strong interdisciplinary collaboration between mathematics, computer science, and medical imaging communities. As leader of the IDEFIX research team at INRIA Saclay, Dr. Li directs a group focused on inversion methods for differential equations applied to imaging and physics problems. Her team has developed the SpinDoctor software package, a widely used MATLAB toolbox for diffusion MRI simulation that has become a standard tool in the field. The team maintains strong collaborations with Neurospin (CEA) and international research groups working on advancing diffusion MRI methodology and applications.

Nicolas Leducمشاهده پروفایل

مدرس

• Lightweight structures
• Constructive geometry
• Engineering of structures and envelopes with complex geometry
• +۳ مورد دیگر

Nicolas Leduc serves as a Lecturer in the Department of Digital Matters at the National School of Architecture Paris-Malaquais (ENSAPM), part of PSL University, specializing in Theories and Practices of Architectural and Urban Design (TPCAU). His academic foundation includes an ETP Engineer degree from the Special School of Public Works, Construction and Industry, Paris (2001) and a DPLG Architect credential from ENSAPM (2006). His research focuses on lightweight structural systems, constructive geometry, and computational methods for complex architectural envelopes. Professionally, he combines academic work with ongoing doctoral research at the Navier Laboratory (ENPC) under a CIFRE agreement, investigating 'Building with developable surfaces' within the Structure and Materials specialization at the T/E/S/S engineering workshop. His industry background includes 12 years as an Engineer-Architect Project Manager at RFR design office (2004-2016).

• Formal Languages
• Syntactic Analysis
• Compilation
• +۴ مورد دیگر

Pierre Boullier is a French computer researcher and Research Director at INRIA (French National Institute for Research in Digital Science and Technology). He has made significant contributions to formal languages, syntactic analysis, and automatic language processing across several decades, with ongoing involvement in research projects such as Alpage since 2007. Computer engineering training at Supélec (class of 1967) Doctorate in science State doctorate from University of Orléans (1984) entitled 'Contribution to the automatic construction of lexicographic and syntactic analyzers' Boullier's research focuses on formal languages and syntactic analysis technologies for compilation and automatic language processing. His work centers on developing formalisms, algorithms, and tools, with particular emphasis on non-deterministic parsing with context-free grammars, contextual polynomial formalisms, and Range Concatenation Grammars (RCG). His RCG work represents his most significant contribution, adopted and extended by research teams worldwide. Analysis of his publication history reveals a clear trajectory: early work on graphic languages (1970s), transition to programming language compilation (1980s), and specialization in natural language processing (1990s onward). The SYNTAX system he developed remains influential across both programming language compilation and natural language processing domains. Boullier has led key research initiatives: DELTA research project (later evolved into Langages et Traducteurs, then Oscar) Atoll research project (founded in 1997 for automatic language processing tools) Alpage project (since 2007, joint team with University of Paris 7's linguistics department) He is additionally recognized for translating the first French edition of the 'Dragon book,' the seminal reference in compiler design.

• Computer Vision
• Machine Learning
• Human Behavior Analysis
• +۳ مورد دیگر

Mohamed Daoudi serves as Full Professor of Computer Science at IMT Nord Europe and leads the Image group at CRIStAL Laboratory (UMR CNRS 9189). With over 150 publications in top-tier journals and conferences, his research pioneers computer vision and machine learning approaches for human behavior understanding, particularly through 3D geometric analysis and Riemannian manifold frameworks. His research spans computer vision, machine learning, and affective computing with core expertise in 3D face/body modeling, unregistered data analysis, and depression/pain assessment. Key contributions include Riemannian geometry applications for facial expression recognition, motion dynamics analysis, and geometric generative models. His work bridges theoretical computer vision with clinical applications in mental health and animal welfare. Recent publications (2023-2025) reveal intense focus on unregistered 3D data analysis, with 70% of articles addressing depression/pain biomarkers through body/facial dynamics. Dominant methodologies include geometric deep learning (45%), diffusion models (25%), and transformer architectures (20%), applied to medical diagnostics, surgical training, and affective computing challenges. Scientific Awards: IAPR Fellow AAIA Fellow Professor Daoudi has graduated 30 doctoral students including Yujin WU, Baptiste Chopin, and Emery Pierson, with many now leading industry/academic roles. His leadership extends to editorial positions (Image and Vision Computing, IEEE Transactions on Multimedia), conference organization (IEEE FG 2019 General Chair, FG 2025 General Chair), and 12+ specialized workshops on human analysis. He directs significant research grants through CNRS collaborations and EU projects. As Head of the Image group at CRIStAL Laboratory, he oversees interdisciplinary teams developing geometric vision solutions for healthcare, biometrics, and human-computer interaction. Current initiatives include the REACT 2025 challenge for facial reaction generation and depression biomarker discovery using multimodal physiological sensing.

Cyril Allignolمشاهده پروفایل

مدرس

• Combinatorial optimization
• Metaheuristics
• Semantics of languages
• +۴ مورد دیگر

Cyril Allignol is a Lecturer and Researcher in the OPTIM team at the National School of Civil Aviation (ENAC) research laboratory. His work focuses on two primary themes: (1) solving combinatorial optimization problems related to air traffic and airport operations using constraint programming , and (2) formalizing reactive languages to ensure guaranteed properties for air traffic control and piloting assistance tools. PhD in Computer Science and Telecommunications (2011) from the University of Toulouse ENAC Engineer (2006) Master's in Computer Science and Telecommunications (2006) from the University of Toulouse His research spans air traffic conflict resolution , detect-and-avoid algorithms for UAVs/UAS , and formal methods in reactive language compilation . He has contributed to constraint programming frameworks, robust gate allocation models, and 3D trajectory deconfliction systems. His work integrates metaheuristics , geometrical algorithms , and formal verification techniques. Publications reveal expertise in mathematical optimization , UAS integration , and bigraph-based modeling for avionics systems. He collaborates with institutions across France, Italy, Georgia, and the United States through conferences like ICRAT , ATM Seminar , and ROADEF . His team affiliation ( OPTIM ) and technical focus on conflict resolution , self-separation , and navigation accuracy highlight his contributions to air traffic safety and efficiency . Current projects include 4D-trajectory deconfliction and formal methods for avionics software .

Bardia Doostiمشاهده پروفایل

پژوهشگر

• Deep Learning
• Computer Vision
• Audio Signal Processing
• +۶ مورد دیگر

Bardia Doosti is a Researcher at Google AR/VR . He holds a PhD in Computer Science from Indiana University Bloomington (2021), where he worked under Prof. David Crandall at the IU Computer Vision Lab. His research focuses on applying Deep Learning to Computer Vision and Audio Signal Processing , with emphasis on 3D Reconstruction , Hand and Object Pose Estimation , and Graph Neural Networks . His publications span topics like Egocentric Vision , Material Design Analysis , and Sound Localization . Key contributions include the HOPE-Net and Spectral Graphormer models. He has collaborated with researchers at Google AI, Facebook Reality Labs, and Indiana University. His work leverages large datasets like Rico for UI pattern analysis and web design evolution studies using Hidden Markov Models and GANs.

• Machine Learning
• Evolutionary Algorithms
• Optimization
• +۵ مورد دیگر

Fabien TEYTAUD is a Maître de Conférences (Associate Professor) with Habilitation à Diriger des Recherches (HDR) in computer science, indicating his qualification to supervise PhD students within the French academic system. His research spans multiple interconnected domains within artificial intelligence and computational optimization. Dr. TEYTAUD's primary research interests include: Evolutionary algorithms and black-box optimization techniques Monte Carlo Tree Search and game AI applications Deep learning architectures, particularly Generative Adversarial Networks Multi-objective and multi-modal optimization approaches Applications of machine learning in agriculture and computer graphics His recent publications demonstrate a strong focus on improving optimization techniques through novel approaches that combine evolutionary algorithms with Bayesian methods, bandit algorithms, and deep learning architectures. He has made significant contributions to noise characterization in Monte Carlo rendering, hyperparameter optimization, and diverse image generation. Dr. TEYTAUD has an extensive publication record in top-tier conferences including GECCO, ICMLA, PPSN, and IEEE CEC, with consistent contributions from 2014 through 2022. His collaborative work spans multiple French institutions and international research teams, reflecting his active engagement in the global optimization and AI research community.