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Pierre-Etienne MartinView profile

Researcher

• Computer Vision
• Deep Learning
• Optical Flow Analysis
• +6 more

Roles & Affiliations: Pierre-Etienne Martin is a Postdoctoral Researcher & Tech Development Coordinator at the Max Planck Institute for Evolutionary Anthropology (MPI-EVA), Department of Comparative Cultural Psychology. He applies computer vision tools to study human and non-human animal cognition through projects like Zoo Cam Set-Up and BioTIP. Prior to this, he held roles as an ATER (Temporary Teaching and Research Associate) and PhD student at the University of Bordeaux, specializing in spatio-temporal neural networks for action classification in sports like table tennis. Education: PhD in Computer Science (2020): University of Bordeaux, Thesis on fine-grained action detection using spatio-temporal CNNs M.Sc. in Image Processing & Computer Vision (2017): Erasmus Mundus program across Budapest, Madrid, and Bordeaux B.Sc. in Mathematical Engineering (2015): University of Bordeaux Research Interests: Focuses on computer vision applications in psychology and animal behavior, including thermal imaging analysis, fine-grained action classification, and non-invasive data acquisition. Projects include automated primate tracking (Zoo Cam Set-Up), thermal facial/nose segmentation (ApeTI dataset), and child behavior analysis (Quantex project). Publications: Over 20 peer-reviewed papers in journals like Signals and conferences like MediaEval, focusing on CNN-based action recognition in sports and animal studies. Notable contributions include the ApeTI thermal dataset and techniques for stroke classification in table tennis. Grants & Outreach: Scientific advisor at DeepMove (2021-2022). Active in public outreach via YouTube, science festivals, and competitions like Ma thèse en 180 secondes . Engages schools through discussion programs like Declics. Labs & Teams: Leads tech development for MPI-EVA's Comparative Cultural Psychology lab, collaborating on projects like CASE (animal self-testing environments) and BioTIP (thermal imaging tools for behavioral research).

Chun-Hao Paul HuangView profile

Researcher

• 3D Visual Perception
• Computer Vision
• Machine Learning
• +3 more

Dr. Chun-Hao Paul Huang is a research scientist at Adobe Research London, previously a postdoctoral researcher at the Perceiving Systems department of the Max Planck Institute for Intelligent Systems. He holds a Ph.D. from Technische Universität München (TUM) and degrees from National Cheng Kung University (NCKU). His research focuses on 3D visual perception, human body reconstruction, and marker-less motion capture, with contributions to computer vision and machine learning. Notable awards include Best Paper Finalist at CVPR 2022 and a Summa cum Laude doctoral distinction. Education: Ph.D. (TUM, 2016), M.Sc. (NCKU, 2010), B.Sc. (NCKU, 2008) Collaborations: Disney Research Zurich, Microsoft Research Redmond, INRIA Morpheo Team Labs: Perceiving Systems Lab (MPI-IS), CAMP Group (TUM) His work spans 4D modeling, spatio-temporal analysis, and cross-domain applications in medical and augmented reality systems.

• Computer Vision
• Machine Learning
• Video Analysis
• +3 more

Laura Sevilla is a Reader (Associate Professor) at the School of Informatics, University of Edinburgh, and an ELLIS Scholar. Her research focuses on understanding the visual world in motion, encompassing video representations, high-level perceptual tasks, and joint learning of video and language. She actively organizes workshops, including 'What is Motion For?' at ECCV 2022 and 'Computer Vision for Global Challenges' at CVPR 2019, aiming to address humanitarian and global issues through vision technology. Her academic journey includes leadership roles such as Program Chair at BMVC 2021 and Area Chair at CVPR 2021 and 2024. She has received the Google Scholar Award 2022. Laura mentors a dynamic team of PhD students, including Dr. Shreyank N Gowda (now at Oxford), Anil, Gen, Davide, and Kiyoon, whose work spans video analysis, temporal dynamics, and efficient learning methods. Her outreach efforts emphasize accessibility, such as developing vision tools for the blind and educational workshops in the Philippines. Key contributions include the ALBA tracking method, FASTER video classification, and the Temporal Dataset benchmark. Her research bridges theoretical advancements with practical applications, fostering collaboration across disciplines within the top-ranked School of Informatics.

Manudeo SinghView profile

Research Fellow

• Surface water dynamics
• Remote Sensing
• Wetland hydrogeomorphology
• +4 more

Manudeo Singh is a Research Fellow at the Department of Geography and Earth Sciences, Aberystwyth University. He holds prestigious fellowships including the Newton International Fellowship (2023) and Alexander von Humboldt Fellowship (2021–2023). His research focuses on surface water dynamics, remote sensing applications, wetland hydrogeomorphology, and climate change impacts in the Ganga Plains and other regions. Singh completed his PhD at IIT Kanpur, India, and has contributed extensively to understanding floodplain wetland dynamics, hydrological connectivity, and environmental monitoring using Earth Observation techniques. Research Interests: Surface water dynamics and wetland health Remote Sensing and GIS applications Climate Change impacts on hydrological systems Geomorphological connectivity frameworks Awards: Newton International Fellowship (2023) Alexander von Humboldt Fellowship (2021–2023) Advising & Grants: While no formal advisee names are listed, his research is supported by fellowships and collaborations with institutions like the University of Potsdam, Germany. His work integrates multi-source remote sensing data and geostatistical methods to address environmental challenges in India and globally. Labs/Teams: Collaborates with international teams on wetland restoration and disaster management, leveraging tools like Google Earth Engine and R packages for large-scale analyses.

William W.S. WeiView profile

Professor

• Time Series Analysis
• Forecasting Methods
• Statistical Modeling
• +5 more

William W.S. Wei is a Professor of Statistics at Temple University’s Fox School of Business and Management, where he has served since 1974. Previously, he chaired the Department of Statistics (1982–1987) and directed graduate programs in Statistics. He holds a B.A. in Economics (National Taiwan University, 1966), a B.A. in Mathematics (University of Oregon, 1969), and an M.S. (1972) and Ph.D. (1974) in Statistics from the University of Wisconsin, Madison. His research focuses on time series analysis, forecasting, and multivariate statistical modeling, with contributions to aggregation effects, outlier detection, and high-dimensional dimension reduction. He authored seminal books like Time Series Analysis (1990, 2006) and Multivariate Time Series Analysis and Applications (2019). His work emphasizes practical applications across economics, finance, and environmental science. Dr. Wei is a Fellow of the American Statistical Association and Royal Statistical Society, and served as 2002 ICSA President. He has received Temple’s 2016 Musser Award and two Lifetime Achievement Awards (2014, 2024). He has advised numerous Ph.D. students now in academia and industry. Beyond academia, he contributes to community education and leadership roles in Taiwanese Hakka organizations.

• Complex Geometry
• Kähler Geometry
• Stability of Algebraic Varieties
• +5 more

Chi Li is an Associate Professor in the Department of Mathematics at Rutgers, The State University of New Jersey. Their research focuses on Complex Geometry, including canonical metrics in Kähler geometry, stability of algebraic varieties, and pluripotential theory. They hold an office in HLL-511 and can be contacted at cl1412@rutgers.edu. Research interests emphasize foundational aspects of geometric analysis with applications to algebraic structures. Recent work explores interdisciplinary applications in AI, such as anomaly detection, cross-lingual NLP, and large language model optimization. Key contributions include frameworks for domain-oriented model compression and dynamic routing in mixed LLMs. Publications span 2020–2025, addressing topics from turbulence forecasting to spatio-temporal recommendation systems. Notable trends include integration of geometric principles with machine learning methodologies. Extensive contributions to log anomaly detection (GLAD, Cat), time series meta-contrastive learning, and graph-based cross-lingual transfer. Active in developing interpretable imitation learning and uncertainty quantification techniques for in-context learning.

Fei DouView profile

Assistant Professor

• Machine Learning
• Federated Learning
• Wireless Sensor Networks
• +3 more

Fei Dou is an Assistant Professor in the School of Computing at the University of Georgia, which operates under the Franklin College of Arts and Sciences. He joined UGA in August 2023 and serves as Graduate Program Faculty. His office is located in the Boyd Research and Education Center. He holds a Ph.D. and M.S. in Computer Science and Engineering from the University of Connecticut, both completed in 2023. Dr. Dou's research spans several key areas of computer science with particular focus on: Machine learning systems : Federated learning, reinforcement learning, and self-supervised techniques Wireless sensing : Healthcare monitoring using WiFi signals and contactless sensing Networked systems : Underwater sensor networks and secure communication protocols Applied AI : Education technology, precision agriculture, and indoor positioning His recent publications (2021-2025) demonstrate strong emphasis on federated learning adaptations for resource-constrained environments, healthcare applications using novel sensing methodologies, and security solutions for underwater networks. A consistent theme involves developing efficient machine learning techniques that operate in challenging real-world conditions while preserving privacy and security.

• Computer Science
• Geometric Algebra
• Remote Sensing
• +3 more

Wen Luo is a Professor at Peking University's School of Computer Science, affiliated with the State Key Laboratory of Multimedia Information Processing. His work spans Computer Science , Geometric Algebra , and Remote Sensing , with a focus on Medical Imaging and GIS Applications . Current affiliation: Peking University Research areas: Geometric Algebra, Spatio-Temporal Analysis, Medical Informatics His recent publications highlight trends in Deep Learning for medical diagnosis, Geospatial AI , and Remote Sensing innovations. Collaborations include Zhaoyuan Yu, Linwang Yuan, and Fei Deng.

Guy De TréView profile

Associate Professor

• Computational Intelligence
• Data Quality
• Decision Support Systems
• +7 more

Guy De Tré is an Associate Professor at the Department of Telecommunications and Information Processing within Ghent University's Faculty of Engineering and Architecture. He leads the Database, Document and Content Management (DDCM) research group and focuses on computational intelligence in information systems, with expertise in bi-polarity handling, uncertainty modeling, and multi-valued logic systems. Primary Affiliation: Ghent University Research Focus: Data quality, fuzzy querying, spatio-temporal modeling Key Contributions: Foundational work in possibilistic databases and explainable AI His research combines theoretical and applied approaches to information management systems. Theoretical work includes: Bipolarity and uncertainty handling in databases Multi-valued logic frameworks Interval B-tree indexing for possibilistic data Applied research spans: NoSQL database optimization Decision support systems Contextualized machine learning 3D/4D modeling for geological resources Recent publications show increasing focus on explainable AI, with multiple works on contextualized support vector machine classification and orthographic similarity measures for graph-based data representations. His work bridges database theory with practical applications in data quality assessment, medical informatics, and cultural heritage projects like the Byzantine Book Epigrams database. Research Group: Leads the DDCM group at Ghent University, specializing in: Database management innovation Content modeling techniques Fuzzy logic implementations Temporal data indexing Intelligent information systems

Emmanuel GOBETView profile

Professor

• Machine Learning
• Data Science
• Risk Modeling
• +5 more

Emmanuel GOBET is a Professor of Applied Mathematics at École Polytechnique, leading the SIMPAS research team and the Financial Mathematics group. He holds roles as Scientific Leader of the Stress Test: RISK Management and Financial Steering Chair (2018–present) and former Head of the Applied Mathematics Department (2020–2023). He co-directs the Master's program in Probability and Finance since 2010 and is involved in pedagogical coordination at Hi! PARIS (2021–2024). His research focuses on machine learning, data science, stochastic processes, and their applications in climate, energy, finance, and blockchain. Education includes a PhD in Probability (University Paris 7, 1996–1998), Master’s in Statistics (University Paris 7, 1995–1996), and an engineering degree from École Polytechnique. He has held academic positions at Grenoble Institute of Technology (2005–2010) and prior roles as Assistant Professor at École Polytechnique and University Paris 6. Research interests span extreme value analysis, Monte Carlo methods, risk modeling, and stochastic control. Notable projects include the ANR BLOCKFI (Blockchain & Decentralized Finance, 2024–2029) and leadership in conferences like CLIFIRIUM and GenHack data challenges. His work bridges theoretical advancements with practical applications in climate finance, energy systems, and financial risk management. Publications emphasize methodological innovations in stochastic simulation, risk assessment, and generative modeling of extreme events. Over 20 PhD students have been advised, contributing to fields like quantitative finance, stochastic control, and climate-related risk analysis. Current research priorities include climate transition pathways, energy systems optimization, and decentralized finance frameworks.

• High-dimensional Statistics
• Machine Learning
• Signal Processing
• +5 more

Karim Lounici is a Professor at the Centre de Mathématiques Appliquées (CMAP) of Ecole Polytechnique. His research focuses on high-dimensional statistics, machine learning, signal/image processing, probability theory, and optimization. He explores theoretical foundations and practical applications in areas like stochastic processes, quantum tomography, and dynamical systems. His work bridges mathematical rigor with computational methods, addressing challenges in data analysis, uncertainty quantification, and algorithmic efficiency. Recent contributions include equivariant representation learning, robust covariance estimation, and learning Koopman operators for dynamical systems. Publications span topics like neural conditional probability, spatio-temporal Monge alignment, and domain adaptation, reflecting expertise in both statistical theory and applied machine learning. No scientific awards explicitly mentioned. No advising or grant details provided in text. Affiliated with CMAP, a leading applied mathematics research center at Ecole Polytechnique.

Rudy MorelView profile

Research Fellow

• Artificial Intelligence
• Machine Learning
• Physics
• +5 more

Rudy Morel is a Research Fellow at the Center for Computational Mathematics of the Flatiron Institute in New York. His work focuses on AI for Science, developing models like transformers for spatio-temporal systems in physics and stochastic processes in finance. He completed his PhD in Mathematics at École Normale Supérieure (2020-2023), advised by Prof. Stéphane Mallat and co-advised by Prof. Jean-Philippe Bouchaud. His research explores multi-scale processes, compact models, and applications of AI to limited-data domains. Education: PhD in Mathematics, École Normale Supérieure (2020-2023) MS in Probability and Finance, Sorbonne Université (2018-2019) BSc in Mathematics, École Normale Supérieure of Rennes (2015-2018) Research focuses on: AI-driven physics simulations and surrogate models Wavelet scattering spectra for non-Gaussian time-series analysis Path shadowing methods for stochastic prediction Multi-scale data modeling in finance and physics Key collaborations include the Polymathic AI initiative and the EconophysiX chair (CFM/Polytechnique), with visiting research at Rice University (2022).

Stephane CholetView profile

Researcher

• Artificial Intelligence
• Affective Computing
• Machine Learning
• +3 more

Stephane Cholet is a Researcher and Temporary Teaching and Research Associate (ATER) in Computer Science at the University of Strasbourg. He is affiliated with the ICube Laboratory of Engineering, Computer Science and Imaging Sciences, and teaches in the Department of Computer Science within the Faculty of Mathematics and Computer Science. His research focuses on artificial intelligence and affective computing, particularly in emotional state classification, biomedical image segmentation, and spatio-temporal data analysis. Education includes a PhD from the University of the Antilles on 'Automatic assessment of emotional and depressive states: towards a system for preventing psychosocial risks' (2019), an MSc in Computer Science (2015), and professional experience in project management and student affairs. He has taught courses on computer architecture, algorithms, and human-machine interactions across undergraduate years. His work integrates interdisciplinary approaches, blending machine learning with healthcare applications, and他曾担任法国安的列斯群岛大学的学生生活副主管（2017-2019）并参与多个协会的管理工作。

Amir ShabaniView profile

Lecturer

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +6 more

Amir Shabani is a Lecturer in the School of Sustainable Energy Engineering at Simon Fraser University (SFU). He holds a Ph.D. in System Design Engineering from the University of Waterloo (2011), an M.Sc. in Electrical and Electronics Engineering from Iran University of Science and Technology (2004), and a B.Sc. in the same field (2001). His research focuses on Artificial Intelligence, Machine Learning, Computer Vision, and Smart City technologies, with emphasis on applications in interactive robotics, affective computing, and IoT systems. Dr. Shabani teaches courses such as SEE 231 (Electronic Devices and Systems), SEE 332 (Power Systems Design and Analysis), and SEE 333 (Network and Communication Systems). His academic interests span System Design (e.g., AI, Robotics, Data Structures) and Electronics (e.g., Embedded Systems, IoT). He actively contributes to smart building automation, renewable energy systems, and human-centric technologies like social robotics for elderly care. His publications address advanced topics including edge computing for social robots, facial emotion recognition, and intelligent occupancy detection in smart environments. His work bridges theoretical computer vision with practical engineering solutions for sustainable energy and urban infrastructure.

• Numerical Linear Algebra
• Scientific Computing
• Image Reconstruction
• +5 more

Misha Kilmer is the William Walker Professor of Mathematics at Tufts University, with a secondary appointment in Computer Science. She also serves as Deputy Director of ICERM at Brown University and co-PI of Tufts TRIPODS Institute. Her research focuses on numerical linear algebra, scientific computing, and image reconstruction/restoration. Kilmer has held leadership roles, including Chair of Tufts' Mathematics Department (2013-2019) and has received prestigious recognition, including SIAM Fellowship (2019) and the Tufts Undergraduate Teaching Award (2001). Education: PhD in Applied Mathematics, University of Maryland (1997) MA in Mathematics, Wake Forest University (1994) BS in Mathematics, Wake Forest University (1992) Research Interests: Kilmer's work emphasizes tensor decompositions, iterative methods, and model reduction. She develops algorithms for high-dimensional data analysis, imaging applications, and inverse problems. Her techniques bridge computational mathematics and engineering challenges, with applications in biomedical imaging, environmental monitoring, and machine learning. Publications Trends: Recent work highlights advancements in tensor-based neural networks, dynamic image reconstruction, and efficient algorithms for large-scale data. Key contributions include randomized algorithms for tensor decompositions and compressed sensing techniques for multi-modal data. Awards: SIAM Fellow (2019) Tufts Undergraduate Initiative in Teaching Award (2001) Advising & Grants: Kilmer has secured funding from NSF, NIH, IARPA, DARPA, and IBM. She co-chairs major SIAM conferences and serves on editorial boards for journals like SIAM Review and La Matematica . Labs/Teams: Active in Tufts' Data Intensive Studies Center (DISC) and co-leads the TRIPODS Institute. Collaborates internationally on projects involving tensor analysis and computational science.