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Manik VarmaView profile

Adjunct Professor

• Machine Learning
• Deep Learning
• Information Retrieval
• +16 more

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.

• Supply Chain Management
• Transportation Systems
• Logistics
• +4 more

Dr. Jérôme Verny is an Associate Professor specializing in transport, logistics, and supply chain management. He is the founder and director of the research institute in innovative transport and logistics, as well as the co-founder of the DISC Master's program (Digital & Innovative Supply Chain) in Paris and the Mobility Accelerator. His expertise spans digitalization in logistics, blockchain applications, and sustainable development. Educated at the University of Lille Nord de France (PhD in Economics and Management) and engineering schools like École Nationale des Ponts et Chaussées, he advises both public institutions (OECD, EU) and private enterprises on transportation strategies. Research Interests: Dr. Verny focuses on supply chain innovation, digital transformation, and strategic logistics. His work integrates blockchain technology for supply chain transparency, optimizes last-mile delivery in urban environments, and analyzes the impact of geopolitical actors like China on global trade networks. He also explores sustainable practices in transportation, including CO2 reduction strategies and pandemic response logistics. Awards: Recipient of the 2009 OECD-FIT Young Researcher Prize in Transport. His contributions bridge academic research with practical applications in industry, policy, and international trade. Key Activities: Co-founded the DISC Master’s program and leads research initiatives on blockchain adoption, Arctic shipping routes, and Mediterranean trade dynamics. He actively contributes to conferences such as the International Association of Maritime Economists (IAME) and publishes in journals like Structural Change and Economic Dynamics and International Journal of Shipping and Transport Logistics . Labs/Teams: Directs the Institut de Recherche en Transport et Logistique Innovante and collaborates with institutions like the OECD and European Commission on transport policy. His interdisciplinary approach involves engineering, economics, and data science to address global supply chain challenges.

Jean PonceView profile

Professor

• Computer Vision
• Image Processing
• Machine Learning
• +3 more

Jean Ponce is a Professor at Ecole Normale Supérieure - PSL and a Global Distinguished Professor at New York University's Courant Institute and Center for Data Science. He serves as Scientific Director of PRAIRIE Interdisciplinary AI Research Institute and co-founded Enhance Lab, commercializing super-resolution imaging software. His research focuses on computer vision, machine learning, robotics, and image processing. Ponce has held roles at Inria, MIT, Stanford, and the University of Illinois, and is an IEEE and ELLIS Fellow. He has served as chair of major conferences like CVPR, ECCV, and ICCV, and authored the textbook 'Computer Vision: A Modern Approach.' Research interests include statistical models for exoplanet detection, neural networks for 3D reconstruction, and self-supervised learning. His work combines theoretical foundations with practical applications in astrophysics, robotics, and imaging. Notable awards include the IEEE CVPR Longuet-Higgins Prize (2016, 2020) and ICML Test-of-Time Award (2019). Key projects include Enhance Lab's high dynamic range imaging and PRAIRIE's interdisciplinary AI initiatives. Ponce's articles explore cutting-edge topics like neural object priors, geodesic motion planning, and satellite image analysis. His contributions bridge academic research and industrial applications, emphasizing both fundamental theory and real-world impact.

Chao LiuView profile

Researcher

• Surgical Robotics
• Teleoperation
• Haptics
• +2 more

Chao Liu is a Research Scientist at CNRS (French National Center for Scientific Research) since 2008, affiliated with the DEXTER team and the Department of Robotics, LIRMM at University of Montpellier, France. He earned his Ph.D. in Electrical & Electronic Engineering from Nanyang Technological University, Singapore (2006). Current research focuses on surgical robotics , haptics , teleoperation , and nonlinear control theory with applications in computer vision. His work addresses challenges in robotic-assisted telesurgery, including: Stable and transparent human-robot interaction through wave variable compensators and passivity filters Physiological motion compensation using spatio-temporal LSTM and dual Kalman filters EMG-based motion recognition for surgical skill assessment 3D soft-tissue reconstruction with stereo-endoscopes and deep learning Dr. Liu leads European and French projects like: TS2RT (CNRS-funded): Safer teleoperation with motion compensation ROBACUS (ANR-funded): Needle positioning with MPC control HaTUMoCo (CNRS-funded): Haptic teleoperation with uncertainty handling ARAKNES (EU-funded): Microrobotic systems for endoluminal surgery Scientific honors include Senior Member of IEEE and Member of Sigma Xi . He supervises Ph.D. and Master's students working on topics such as concentric tube robot optimization, haptic teleoperation, and EMG-based force estimation. Dr. Liu serves on IEEE Technical Committees for Telerobotics and Haptics , and as Technical Editor of IEEE/ASME Transactions on Mechatronics.

• Theoretical Deep Learning
• Uncertainty Quantification
• Reliable Artificial Intelligence
• +10 more

Gianni Franchi is an assistant professor at ENSTA Paris , affiliated with the Computer Science and Systems Engineering Unit (U2IS) . His work focuses on theoretical deep learning , with a strong emphasis on uncertainty quantification, robustness, and explainability in machine learning models. Current affiliation: ENSTA Paris (U2IS) Academic rank: Assistant Professor Key collaborators: David Filliat, Emanuel Aldea, Andrei Bursuc, Antoine Manzanera His research spans uncertainty quantification , explainable AI , and reliable machine learning . He investigates methods like Bayesian neural networks, ensemble approaches, and deterministic uncertainty models. His work also addresses domain adaptation , self-supervised learning , and autonomous systems , particularly in trajectory forecasting and semantic segmentation for autonomous driving. Recent publications analyze probabilistic modeling for robustness, symmetry-aware Bayesian methods , and multi-modal datasets like InfraParis. He develops frameworks like Torch-Uncertainty and benchmarks such as MUAD for uncertainty types in autonomous driving. Key themes: Uncertainty Quantification Deep Learning Theory Autonomous Systems Explainable AI Dataset Creation Bayesian Methods

• Operations Research
• Combinatorial Optimization
• Artificial Intelligence
• +3 more

Omar Rifki is an Associate Professor (Maître de Conférences) specializing in combinatorial optimization and artificial intelligence applications. His research bridges theoretical computer science with practical logistics challenges, focusing on routing problems, process mining, and machine learning integration for complex decision systems. His core research interests include phase transitions in NP-hard problems, vehicle routing optimization under time constraints, and healthcare process modeling. Rifki's work demonstrates a consistent pattern of integrating reinforcement learning with traditional optimization techniques to solve large-scale real-world problems in transportation and logistics, with particular emphasis on spatio-temporal data effects and collaborative systems. Analysis of his 15 publications (2019-2025) reveals three dominant research thrusts: (1) Fundamental studies of combinatorial problem hardness using phase transition frameworks, (2) Practical applications of deep reinforcement learning in vehicle routing and taxi assignment, and (3) Healthcare process optimization through advanced process mining techniques. His work consistently addresses scalability challenges in real-world implementations while maintaining theoretical rigor. No scientific awards were documented in the provided materials. His collaborative work with researchers like Christine Solnon and Thierry Garaix indicates active participation in European operations research communities, though specific grant details remain unreported. Rifki's research shows increasing integration of graph theory and machine learning in transportation applications, particularly evident in his Lyon City case studies on autonomous ride-sharing systems.

Nadjib BRAHIMIView profile

Associate Professor

• Supply Chain Management
• Production Planning and Scheduling
• Operations Research
• +4 more

Nadjib Brahimi is an Associate Professor at Rennes School of Business, specializing in Supply Chain Management and Operations Research. He holds a PhD in Production and Logistics Systems from Université de Nantes and has extensive academic experience, including roles as Academic Head of the Supply Chain Management & Information Systems department (2019–2020) and Programme Manager of the MSc in Data and Business Analytics. His expertise spans Production Planning, Supply Chain Optimization, and Reconfigurable Manufacturing Systems. Education: PhD in Production and Logistics Systems, Université de Nantes, 2004 M.Sc. in Automatic Control and Applied Computing, Ecole Centrale de Nantes, 2001 Engineering Degree in Electronics and Electrical Engineering, National Institute of Electronics and Electrical Engineering, Boumerdes, Algeria, 1999 Research Focus: His work emphasizes optimization of production systems, reconfigurable manufacturing, and sustainable supply chains. Key areas include task reassignment in assembly lines, biomass supply chain design, and disruption resilience strategies. Key Contributions: Over 40 peer-reviewed articles in journals like International Journal of Production Economics and European Journal of Operational Research . Developed robust optimization models for supplier selection and production routing. Advocated for circular economy principles in hybrid manufacturing systems. Labs/Teams: He collaborates with research groups on reconfigurable manufacturing systems and bioenergy supply chain design, contributing to projects in France, UAE, and Algeria.

Youssef TLICHEView profile

Assistant Professor

• Supply Chain Management
• Logistics
• Quantitative Methods
• +3 more

Youssef TLICHE is an Assistant Professor in Supply Chain Management and Logistics at EM Normandie, where he has been since 2021. He previously held an assistant professorship at Université Le Havre Normandie (2019–2020). He serves as Academic Director of the Graduate programs in Supply Chain Logistics Innovations. He holds a PhD in Management Sciences from Université Le Havre Normandie (2020) and an Engineering degree in Statistics and Data Analysis from ESSAI Tunis (2015). His research focuses on quantitative methods in supply chain management, including collaborative forecasting, decentralized supply chain optimization, and blockchain applications. He also explores decarbonization strategies in maritime logistics and the impact of AI on e-commerce. Key contributions include work on demand inference, inventory optimization, and blockchain in finance. His recent articles address topics such as AI-driven recommendation systems, wind-powered vessel financing, and Industry 4.0’s impact on supply chain vulnerability. No scientific awards are listed. His advising and grant activities remain unspecified in the provided text.

François GouletteView profile

Professor

• 3D Point Cloud Processing
• LiDAR Perception
• Autonomous Systems
• +7 more

François Goulette is a Professor and Deputy Director of the Computer Science and Systems Engineering Unit (U2IS) at ENSTA Paris, part of Institut Polytechnique de Paris. His research focuses on 3D point cloud processing, LiDAR perception, and autonomous systems within the Robotics Center (CAOR). His primary research interests lie in 3D point cloud processing , LiDAR perception , and autonomous systems . His work spans fundamental algorithm development to practical applications in autonomous driving, cultural heritage digitization, and robotics. He has made significant contributions to domain generalization of LiDAR perception, semantic segmentation of 3D point clouds, and point cloud registration techniques. The analysis of his recent publications reveals a strong focus on domain generalization for LiDAR perception systems, with multiple papers addressing challenges in 3D semantic segmentation across different environments. His work combines multi-scale architectures , unsupervised learning , and dataset creation to advance the state-of-the-art in autonomous systems perception. The research spans both theoretical algorithm development and practical applications in urban environments. François Goulette leads research activities within the Robotics Center (CAOR) at ENSTA Paris. His team develops advanced techniques for 3D environment understanding, with applications in autonomous vehicles, cultural heritage preservation, and industrial robotics. The research combines computer vision, machine learning, and robotics to solve challenging problems in 3D perception and scene understanding.

• Supply Chain Management
• Operations Research
• Sustainable Operations
• +4 more

Dr. Reza Alikhani is an Assistant Professor of Operations and Supply Chain Management at the MBS School of Business in France. He holds a PhD in Production and Operations Management from the University of Tehran and has held postdoctoral and visiting research positions at EM Normandie Business School and DePaul University. His primary research focuses on applying operations research methodologies to optimize supply chain and logistics systems, with expertise in supply chain resilience, sustainable operations, and collaborative logistics. Education and Positions: PhD in Production and Operations Management, University of Tehran, Iran (2018) Postdoctoral Researcher at EM Normandie Business School Visiting Research Fellow at DePaul University, Chicago Research Interests: Dr. Alikhani’s work emphasizes supply chain resilience , sustainable operations , and collaborative logistics optimization . His studies often integrate fuzzy logic, stochastic programming, and multi-objective models to address uncertainties and strategic decision-making in supply chains. Recent trends in his publications highlight advancements in concurrent resilience strategies and cross-industry collaboration for logistics efficiency. Collaborations and Industry Links: He actively collaborates with international companies to enhance operational efficiency through innovative supply chain solutions. This bridges academic research with real-world applications, particularly in retail logistics and humanitarian relief considerations. Professional Contributions: Dr. Alikhani serves as an ad hoc reviewer for top-tier journals such as Production and Operations Management and European Journal of Operational Research , contributing to the rigor of supply chain literature.

Camilla FioriniView profile

Associate Professor

• Computational Fluid Dynamics
• Numerical Analysis
• Partial Differential Equations
• +4 more

Camilla Fiorini is an Associate Professor at the National Conservatory of Arts and Crafts (CNAM) in Paris, where she conducts research at the Mathematical and Numerical Modeling Laboratory (M2N). She serves as Principal Investigator for the ANR-funded SPARCL project (2025-2029) focusing on structure-preserving reduced order models for conservation laws. Her academic background includes a PhD in Applied Mathematics from the University of Versailles and both MSc/BSc degrees in Mathematical Engineering from Politecnico di Milano. Her research centers on computational fluid dynamics, numerical analysis of PDEs, and sensitivity methods, with specific applications in uncertainty quantification and reduced order modeling. Current projects develop novel approaches for conservation laws that maintain structural properties while improving computational efficiency and reliability. Fiorini's publication record demonstrates consistent focus on sensitivity analysis techniques for complex fluid systems, shock-capturing methods, and uncertainty propagation in hyperbolic PDEs. She received the SMAI-GAMNI PhD Award 2019 (French ECCOMAS Award) for her doctoral dissertation on sensitivity analysis for nonlinear hyperbolic systems. As Principal Investigator of the SPARCL project, she leads a team developing new reduced basis construction techniques for conservation laws. Fiorini actively advises graduate researchers including PhD students Nathalie Nouaime (2021-2024) and Nicolas Lepage (2022-present), plus multiple Master's candidates. Her research group collaborates with institutions including Inria, Sorbonne University, ONERA, and CEA. Current projects include ANR JCJC-funded SPARCL and ANR AHEAD initiatives. She leads the SPARCL research group at M2N laboratory, collaborating with researchers including Alessia Del Grosso, Iraj Mortazavi, and Taraneh Sayadi on reduced order modeling techniques. The team focuses on developing computationally efficient ROMs that preserve physical structures in conservation laws.

Yohan PETETINView profile

Associate Professor

• Monte Carlo methods
• Bayesian filtering
• Hidden Markov models
• +5 more

Yohan PETETIN is an Associate Professor at Telecom SudParis (Institut polytechnique de Paris) in the CITI Department. His research focuses on Bayesian filtering, Monte Carlo methods, hidden Markov models, and multi-object tracking. He has authored over 20 peer-reviewed articles since 2011, with notable contributions in IEEE Transactions on Signal Processing and other top venues. His work bridges statistical signal processing with machine learning applications. PhD: Algorithmes de restauration bayésienne mono- et multi-objets dans des modèles Markoviens (2013, Telecom SudParis) HDR: Generative models for time series data (2023, Institut polytechnique de Paris) Research interests emphasize sequential Monte Carlo algorithms, particle filtering optimizations, and deep learning integration for time-series analysis. Recent work explores expressivity comparisons between recurrent neural networks and hidden Markov models. Teaching includes courses on probabilistic graphical models, Bayesian filtering, and deep learning across undergraduate and graduate programs at Telecom SudParis and affiliated institutions.

Slim EssidView profile

Professor

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +7 more

Slim Essid is a Full Professor at Télécom Paris, leading the Audio Data Analysis and Signal Processing (ADASP) group. He holds a Doctorat (Ph.D.) and Habilitation from Université Pierre et Marie Curie (UPMC). With 15+ years of research experience, he has advised 15 PhD graduates and currently co-advises 10 others. His work focuses on machine learning, signal processing, and multimodal systems, publishing over 150 peer-reviewed papers. He serves as a reviewer for top journals/conferences (e.g., IEEE Transactions) and research funding agencies. Education: State Engineering Degree, École Nationale d’Ingénieurs de Tunis (2001) M.Sc. (D.E.A.) in Digital Communication Systems, École Nationale Supérieure des Télécommunications, Paris (2002) Ph.D., Université Pierre et Marie Curie (2005) Habilitation (HDR), UPMC (2015) Research Interests: Multimodal learning, self-supervised representations, audio-visual segmentation, music structure analysis, domain generalization, and speech enhancement. Recent publications highlight innovations like TACO (training-free sound-prompted segmentation) and CLOUDS (domain-generalized semantic segmentation framework using foundation models). His work bridges audio processing with vision and language models, emphasizing unsupervised/zero-shot approaches. Key achievements include state-of-the-art methods in sound event detection, speaker diarization, and music segmentation. He collaborates with 14 post-docs and leads projects funded by French/EU agencies.

• Optimization
• Operational Research
• Electric Mobility
• +3 more

Imene ZAIDI is a Teacher-researcher at CESI engineering school in France, affiliated with the Engineering and Digital Tools research team. Her work bridges computer science and operational research with practical applications in sustainable transportation systems. Her academic background includes: Doctorate in Optimized management of electric vehicle fleet charging from University of Haute-Alsace (2022) Engineering Diploma in Computer Systems from ex.INI Algiers (2018) ZAIDI's research centers on optimization challenges in electric mobility, particularly electric vehicle charging scheduling. She develops computational approaches including heuristics, metaheuristics, and exact algorithms to address grid capacity constraints, demand satisfaction, and energy maximization in unbalanced power systems. Her work integrates operational research with real-world transportation and energy infrastructure challenges. Analysis of her 2020-2024 publications reveals consistent focus on preemptive scheduling, computational complexity, and multi-objective optimization in electric vehicle ecosystems. The research demonstrates increasing sophistication from foundational scheduling models to grid-integrated solutions addressing three-phase power imbalances and capacity limitations. She teaches computer science courses including Object-oriented programming, Algorithms, Databases, Embedded systems, Web development, and Information systems to engineering and bachelor's students across multiple academic levels. The Engineering and Digital Tools research team at CESI provides the collaborative framework for her investigations into decision support systems for sustainable mobility and energy-efficient transportation solutions.

Toufik AZIBView profile

Professor

• Energy conversion systems
• Multi-physical optimization
• Embedded energy management
• +7 more

Toufik AZIB is a Full Professor and scientific coordinator of the ECMS (Energy and Conception of Mechatronic Systems) research theme at ESTACA Engineering School in France. He leads a team of 6 teacher-researchers and 13 PhD students, focusing on optimal design of power electronics and energy management for hybrid power systems. His work bridges academic research and industrial applications in sustainable mobility, with strong collaborations across Europe and Algeria. Dr. AZIB received his Electrotechnical Engineering Diploma from the University of Setif, Algeria in 2006, followed by an M.Sc. in Electrical Engineering from ENSEM-INPL, France in 2007. He earned his Ph.D. in electrical engineering from the University of Paris South XI in 2010 and completed his HDR (Habilitation à Diriger des Recherches) from the University of Paris Saclay in 2021. His academic journey reflects a strong foundation in both theoretical and applied electrical engineering. His research focuses on the modeling, control, and optimal design of embedded energy systems under multi-physical constraints (electrical, thermal, electromagnetic compatibility, volume, reliability). He specializes in energy management strategies for hybrid systems combining fuel cells, batteries, and ultracapacitors, with applications in electric vehicles and the 'more electric aircraft.' His work integrates numerical and experimental approaches to develop methodologies for pre-dimensioning and real-time energy management, addressing challenges in sustainable transportation. Analysis of Dr. AZIB's recent publications reveals a strong trend toward multidisciplinary design optimization for automotive applications, particularly electronic throttle systems. His research increasingly incorporates knowledge management techniques and addresses reliability considerations in hybrid power source design. There's a clear progression from fundamental energy management strategies to sophisticated eco-driving solutions for electric vehicles, reflecting the evolving demands of sustainable mobility. Best Paper Award for 'Structure and Control Strategy for a Parallel Hybrid Fuel Cell/Supercapacitors Power Source' at IEEE VPPC'09 Dr. AZIB has supervised numerous PhD and Master's students across multiple institutions in France, Algeria, and Colombia. He leads significant research projects including MIMe (Module d'Intégration et de simulation Mécatronique), ECOS Nord (Eco-driving strategies for electric motorcycle), and AmCoAIR (improving air quality in vehicle cabins), securing funding from national and international sources. His work demonstrates strong industry collaboration with partners like Valeo, PSA, and Renault. As experimental platforms coordinator since 2012, Dr. AZIB oversees 10 specialized experimental facilities at ESTACA's S2ET-Paris Saclay research pole, including those for autonomous electric vehicles, drones, electric machines, and power modulators. His team regularly develops proof-of-concept demonstrators to validate research findings, such as the Formula Student electric vehicle and the 'Electric Appeal' streamliner project, demonstrating practical applications of their theoretical work.