جستجوی استاد راهنمای دانشگاهی

Chua Tat Sengمشاهده پروفایل

استاد

• unstructured data analytics
• multimedia information retrieval
• recommendation systems
• +۶ مورد دیگر

Chua Tat Seng is a Professor at the School of Computing, National University of Singapore (NUS), holding the KITHCT Chair Professorship since 2009. He serves as co-Director of the NExT++ Center, a joint research center between NUS and Tsinghua University focused on Extreme Search. His academic career spans over three decades at NUS, where he has held various leadership positions including Acting Dean of the School of Computing (1998-2000) and Acting Head of the Department of Information Systems & Computer Science (1996-1998). Professor Chua's research spans unstructured data analytics , multimedia information retrieval , recommendation and conversation systems , and emerging applications in e-commerce and fintech . He established the Lab for Media Search (LMS) at the School of Computing and has been instrumental in advancing multimodal learning and search technologies. His work bridges theoretical foundations with practical applications, particularly in developing trustable AI systems for real-world deployment. His recent publications demonstrate a strong focus on large language models for recommendation systems , multimodal learning , and generative AI applications . The research trends show increasing emphasis on LLM-based recommendation, multimodal understanding, and addressing fundamental challenges in AI reliability, fairness, and efficiency. His work spans theoretical advancements in representation learning to practical applications in e-commerce, finance, and healthcare domains. ACM SIGMM Technical Achievement Award 2015 Multiple Best Paper Awards across ACM Multimedia, IEEE Transactions, and MMM conferences (2007-2020) Professor Chua has supervised 37 PhD students since 2004, establishing himself as a dedicated mentor in the academic community. His research has been supported by substantial grants including NExT++ ($12 million), Base Metals Price Forecasting ($200,000), and Multilingual Multimodal Knowledge Graph ($500,000). He maintains active collaborations with Tsinghua University, University of Southampton, and industry partners like Four Elements Capital and Singapore Press Holdings. As co-Director of the NExT++ Center, he leads a major research initiative focused on Web Intelligence and User Empowerment. His visiting professorships at Tsinghua University (2017-present) and Zhejiang University (2021-present) reflect his international impact in the field of multimedia and AI research.

Yuki M. Asanoمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۶ مورد دیگر

Yuki M. Asano is a full Professor at the University of Technology Nuremberg , leading the Fundamental AI (FunAI) Lab . Previously, he led the QUVA Lab at the University of Amsterdam and earned his PhD at the Visual Geometry Group (VGG) of the University of Oxford under Andrea Vedaldi and Christian Rupprecht. University of Technology Nuremberg (2024–present) University of Amsterdam (prior to 2024) University of Oxford (PhD, 2020) His research spans Artificial Intelligence , Machine Learning , and Computer Vision , with a focus on Causal Representation Learning , Self-Supervised Learning , and Efficient Model Adaptation . He pioneered techniques like BISCUIT (causal variable identification) and VeRA (parameter-efficient fine-tuning). His work extends to Medical Imaging and Environmental Monitoring through applications in fetal ultrasound analysis and marine debris detection. Recent publications (2023–2025) highlight advancements in Self-Supervised Learning , Vision-Language Models , and 3D Understanding . Notable papers include TWIST & SCOUT (multimodal LLM grounding), SIGMA (masked video modeling), and GeneralAD (anomaly detection). His ICCV 2023 work on Self-Ordering Point Clouds and MoSiC (optimal-transport motion trajectories) underscores his interdisciplinary approach. He received the JUPITER compute grant (2025) and an Outstanding Paper Award at ICLR 2024 . His collaborations span institutions like MIT-IBM Watson AI Lab, Qualcomm AI Research, and University of Amsterdam.

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.

Christian Rupprechtمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Unsupervised Learning
• +۴ مورد دیگر

Christian Rupprecht is an Associate Professor at the Department of Computer Science, University of Oxford, specializing in computer vision and machine learning. His research focuses on unsupervised learning, 3D reconstruction, and visual understanding. His work includes contributions to conferences such as GCPR'25, ICCV'25, and CVPR'25, with papers spanning topics like correspondence estimation, animal pose modeling, and synthetic data generation. He leads projects within the prestigious Visual Geometry Group (VGG). Notably, his paper VGGT received the Best Paper Award at CVPR'25. His research integrates deep learning and geometric modeling, emphasizing robustness and generalization in visual systems. Best Paper Award at CVPR'25

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.

• Signal Processing
• Applied Mathematics
• Radar Systems
• +۳ مورد دیگر

Eric Grivel is a Professor at the University of Bordeaux affiliated with the IMS Bordeaux (Integration Laboratory from Materials to Systems). His research focuses on Signal and Image Processing Spectral Analysis Stochastic Process Modeling His work spans theoretical contributions to signal processing and practical applications in radar systems and biomedical signal analysis. Key trends in his recent publications include Optimization of Detrended Fluctuation Analysis (DFA) for Hurst exponent estimation Development of divergence metrics for comparing ARMA and Gaussian processes Waveform design in MIMO OFDM DFRC (Dual Function Radar-Communication) systems Integration of AI tools like ChatGPT in educational signal processing projects Collaborations and industrial partnerships evident in his publications involve institutions such as Indian Institute of Science Thales Airborne Systems STMicroelectronics CEA Leti Slb (Schlumberger)

Laurent Tapieمشاهده پروفایل

مدرس ارشد

• Mechanical Engineering
• Biomedical Engineering
• Medical Devices
• +۵ مورد دیگر

Laurent Tapie is a Senior Lecturer at Paris Descartes University with a focus on Biomedical Engineering, Mechanical Engineering, and CAD/CAM . As Deputy Director of the URB2i research unit and manager of the PlatiNum platform , he coordinates the 3d4care.org consortium . His academic background includes a Doctorate in Mechanical Engineering from École Normale Supérieure de Cachan and authorization to direct research (HDR) from Université Paris 13. Research Interests: Mechanical Engineering, Biomedical Engineering, Medical Devices, CAD/CAM, Shaping of Biomaterials Theses Supervised: 3D evaluation of dento-prosthetic joints, impact of CAD/CAM on dental prosthesis integrity, and metrological evaluations of prostheses. Publications: His work spans dental CAD/CAM systems, surface integrity of prostheses, additive manufacturing, and 3D printing applications during the COVID-19 pandemic . Recent articles focus on data dispersion in CAD/CAM chains, tool-material influence on roughness, and numerical workflow standardization . Scientific Award: Prix du comité scientifique de la session recherche (2019). Projects: Currently leads initiatives like ProGéoMéca (Labex LaSIPS), Bio-Dents (CNRS Biomimicry), and additive process development for multi-material dental aligners .

Rajendra Kurapatiمشاهده پروفایل

استادیار

• Biodegradable Nanomaterials
• Nanomedicine
• Bioengineering
• +۵ مورد دیگر

Dr. Rajendra Kurapati is an Assistant Professor at the School of Chemistry , Indian Institute of Science Education and Research Thiruvananthapuram (IISER TVM) . His research focuses on biodegradable 2D materials for biomedical applications, addressing nanomaterial biopersistence issues through enzymatic degradation studies. He leads the Biomaterials Lab and collaborates with institutions across France, Ireland, Portugal, and the UK. Born in Andhra Pradesh, India B.Sc. & M.Sc. in Chemistry (Acharya Nagarjuna University, University of Hyderabad) Ph.D. in Materials Engineering (Indian Institute of Science, 2014) Postdoc: CNRS Strasbourg (2014-2017), CÚRAM Galway (2018-2020) His research explores 2D material-biopolymer hybrids for drug/gene delivery , nanotheranostics , and antimicrobial coatings . Key article trends include: Graphene/MoS2 biodegradation by human enzymes Antibacterial coatings for medical implants Microplastics' environmental impact Lipid nanoparticles for mRNA vaccines ZnO quantum dots for bioimaging Polymer multilayer drug delivery systems Major scientific awards : DBT Ramalingaswami Fellowship (INR 40 lakhs over 5 years) SERB Start-up Grant (32 lakhs over 2 years) SERB Special Grant (45 lakhs over 3 years) DBT Mission Innovation Grant (18 lakhs over 6 months) His academic contributions span 15+ high-impact publications in journals like Advanced Materials , Nanoscale , and Chemical Communications , covering topics from graphene biodegradation to antimicrobial coatings and bioimaging . 10+ alumni students have graduated from his lab, with placements at institutions including University of Strasbourg and Dublin City University.

Thomas Grenierمشاهده پروفایل

دانشیار

• Medical Image Segmentation
• Clustering
• Filtering
• +۳ مورد دیگر

Thomas Grenier is an Associate Professor in the Department of Electrical Engineering at INSA Lyon and a member of the CREATIS laboratory (CNRS UMR 5220, INSERM U1294). He obtained his HDR (Habilitation à Diriger des Recherches) in 2023 and his Ph.D. in Image Processing from INSA Lyon in 2005. His research focuses on medical image segmentation, clustering, and filtering using feature space, scale-space, and deep learning approaches. Doctoral School: EEA (Electronics, Energy, and Automatics) Research Affiliation: CREATIS Lab (CNRS/INSERM/INSA Lyon/Université Lyon 1/Université Jean Monnet Saint-Etienne) He has contributed to 20 papers and co-supervised 5 PhD students, including Léo Dumortier and Florent Guépin. Grenier leads the annual Deep Learning for Medical Imaging (DLMI) school, which he co-founded, and has organized five editions across Lyon and Montreal since 2019. The school emphasizes practical deep learning applications in medical imaging for participants of all expertise levels. His work spans interdisciplinary domains such as medical imaging , deep learning , and image processing , with recent publications on generative AI for MRI synthesis, explainable networks, and segmentation of neurological pathologies in preclinical models. He manages pedagogical platforms, coordinates LabEx PRIMES project activities, and oversees lab room infrastructure for 200 hours/year across 10 training programs. Grenier also leads the MUSIC transversal project on Multiple Sclerosis since 2019.

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

Professor Mounim A. El Yacoubi holds positions at Institut Polytechnique de Paris, Institut Mines-Télécom, and Telecom SudParis. His research focuses on AI, machine learning, and deep learning applied to e-Health (neurodegenerative disease detection, diabetes management), biometrics (gait, vein, and handwriting recognition), and smart systems (agriculture, surveillance, robotics). He leads the SAMOVAR CNRS Lab and has supervised 17 PhDs and 30+ master's students. Education: PhD (1996, Université de Rennes 1), HDR (2014, Paris-Saclay University). Experience: Senior Researcher at Parascript (2001–2008), Visiting Scientist at CENPARMI (1997–1998), Associate Professor at PUCPR (1998–2001). Research Interests: AI applications in healthcare, biometrics, pattern recognition, and smart technologies. Recent work includes Alzheimer’s detection via handwriting analysis, diabetes prediction using PPG signals, and palm/vein recognition systems. Grants & Leadership: Program Chair of ICPRAI 2022, ICCPRA 2024. Editor of IEEE Access and journals on cyber-physical intelligence. Authored books on Pattern Recognition and AI.

• 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.

• Nano-optics
• Nanophotonics
• Near-field optics
• +۴ مورد دیگر

Renaud BACHELOT is a full Professor of Physics at the University of Technology of Troyes (UTT) since 1996. He leads the Light, Nanomaterials, and Nanotechnologies (L2n) laboratory and directs the Graduate School 'Nano-optics & Nanophotonics'. He holds adjunct professorships at the University of Paris-Saclay (LuMIn Lab) and Shanghai University (1000-talents Grant). His research focuses on nano-optics, plasmonics, and hybrid nanoplasmonics, with expertise in photopolymerization and plasmon-driven chemical processes. Education: PhD and graduate studies at Université Paris-Cité and ESPCI Paris Research Interests: BACHELOT’s work spans nanoscale light-matter interactions, including plasmonic nanostructures, photopolymerization-based fabrication, and applications in optical sensing and quantum photonics. His lab employs advanced techniques like near-field scanning optical microscopy (NSOM) and two-photon polymerization. Grants & Projects: ANR-PIA3 STRONG-NANO (2023-2026) ANR ADVANSPEC (2022-2025) International collaborations with NTU Singapore and Argonne National Lab Labs & Teams: Directs L2n (CNRS-UMR 7076) and collaborates across interdisciplinary platforms like InSyTE and LIST3N. His team develops novel hybrid materials and nanophotonic devices.

Parguel Béatriceمشاهده پروفایل

استاد پژوهش

• Consumer Psychology
• Marketing
• Environmental Labeling
• +۷ مورد دیگر

Béatrice Parguel is a CNRS Research Director at Paris-Dauphine University where she directs the Center for Marketing and Public Policy Research. Her academic career spans consumer psychology with a focus on experimental methodology, examining implications for public authorities in consumer information and education. Her research interests center on greenwashing, environmental labeling, ecology education for children, and reduction of over-packaging. She investigates how marketing practices influence consumer behavior, particularly in sustainable consumption contexts, with significant contributions to understanding luxury brand management, CSR communication, and the psychological mechanisms behind consumer responses to environmental claims. Her work bridges academic research with practical policy implications, often exploring the tension between commercial interests and public welfare. Parguel's publications reveal consistent themes in sustainable consumption, with a growing emphasis on food-related behaviors, digital activism, and luxury market dynamics in recent years. Her research employs rigorous experimental methods to uncover both conscious and subconscious consumer responses to marketing stimuli, particularly in ethically charged contexts. As director of the Center for Marketing and Public Policy Research, she leads a team investigating the intersection of marketing practices and societal impact, with particular attention to regulatory implications and consumer protection.

Clément Malletمشاهده پروفایل

پژوهشگر

• Geospatial Computer Vision
• Remote Sensing
• Machine Learning
• +۴ مورد دیگر

Clément Mallet is a Senior Researcher and Director of the LASTIG laboratory at Université Gustave Eiffel, IGN, and École Nationale des Sciences Géographiques (ENSG) in Champs-sur-Marne, France. He leads research in geospatial computer vision, focusing on the intersection of remote sensing, computer vision, and machine learning. His responsibilities include overseeing 75 laboratory members and directing the STRUDEL research team focused on spatio-temporal information modeling. Education: Habilitation (HDR) in Geographical Information Science, Université Paris-Est (2016) PhD in Image and Signal Processing, Télécom ParisTech (2010) Engineering Degree in Geographical Information Science, ENSG (2005) Master's in Remote Sensing, Université Paris 6 (2005) Research Interests: Dr. Mallet specializes in multi-modal land-cover mapping, change detection, geohistorical image analysis, and airborne lidar processing. His work integrates deep learning with geospatial data analysis to solve complex problems in environmental monitoring, urban studies, and historical geography. Current research explores foundation models for earth observation and semantic change detection using hybrid data generation techniques. Publication Trends: Mallet's recent articles (2021-2025) demonstrate strong focus on deep learning applications for geospatial challenges: 40% address land-cover mapping innovations, 30% develop novel change detection methodologies, 20% advance lidar data processing, and 10% explore historical map analysis. His work consistently bridges computer vision theory with operational remote sensing applications. Awards and Recognition: Schwidefsky Medal from ISPRS (2016) 5x Outstanding Reviewer awards (CVPR/ECCV/ICCV 2017-2024) Best Paper Awards at GEOBIA 2016 and ISPRS 2014 Young Researcher Award from GDR ISIS (2010) EuroSDR Best PhD Thesis supervision (2020) Research Leadership: Directs multiple national and international projects including MAESTRIA (ANR-funded multi-modal EO analysis) and HIATUS (historical image analysis). Supervised 14+ PhD students in geospatial AI topics. Secured funding from ANR, CNES, EU H2020 (VOLTA, LandSense), and industrial partners. Leads the STRUDEL team developing cutting-edge methods for territory dynamics analysis. Professional Service: Editor-in-Chief of ISPRS Journal of Photogrammetry and Remote Sensing (2021-present). Organized major conferences including ISPRS Congress (2020-2022 Program Chair) and JURSE events. Active in ISPRS working groups since 2008, currently leading initiatives in large-scale machine learning applications for geospatial data.

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.