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Zhiyang ShenView profile

Associate Professor

• Environmental and Energy Economics
• Efficiency and Productivity Analysis
• Banking Performance Evaluation
• +4 more

Zhiyang Shen is an Associate Professor at the IÉSEG School of Management, specializing in Economics with a focus on Environmental and Energy Economics, Efficiency Analysis, and Sustainable Development. He holds a Ph.D. in Economics from the University of Lille 1 (2016) and an HDR (Habilitation) in Economics from the University of Lille (2023). His research explores the intersection of digital technology, energy transitions, and environmental policy, with a particular emphasis on green productivity and climate resilience in developing economies. Education: 2023: HDR in Economics, University of Lille, France 2016: Ph.D. in Economics, University of Lille 1, France 2013: Master in Public Economics and Public Finance, University of Rennes, France 2012: Master of Business Administration, Rouen Business School, France Research Interests: Shen’s work centers on measuring and improving environmental and economic efficiency through advanced methodologies like Data Envelopment Analysis (DEA). He investigates topics such as energy transitions, carbon neutrality strategies, and the role of digital technologies in fostering sustainable growth. His studies often address policy-relevant questions in China and other Belt and Road countries. Publications: His recent work highlights themes such as green productivity decomposition, energy security, and the impact of digitalization on energy efficiency. Key contributions include analyzing agricultural productivity in Belt and Road nations and evaluating renewable energy policies in China. Professional Experience: 2022–present: Associate Professor, IÉSEG School of Management 2020–2022: Assistant Professor, Beijing Institute of Technology 2013–2022: Research and Teaching roles at IÉSEG 2017–2020: Economic Analyst/Manager, China Ex-Im Bank 2009–2011: Civil Servant, Bengbu Municipal Bureau of Finance Labs/Teams: Member of the Laboratory of Economics and Management (LEM) at IÉSEG.

• Software Architecture for AI
• Distributed Algorithms
• Photogrammetry
• +3 more

Flavien VERNIER is a Professor at Université Savoie Mont-Blanc, affiliated with Polytech Annecy-Chambéry and the LISTIC laboratory. His research focuses on software architecture for AI, distributed systems, and photogrammetry applications in geophysical observation. He leads the ReGaRD research group and has coordinated projects like ANR AIACOPAHV and EFIDIR. Affiliations: LISTIC Laboratory, Polytech Annecy-Chambéry. Roles: Head of the IDU program at Polytech Annecy-Chambéry (since 2022), Responsible for Robotics Projects (2011–2024), and coordinator of ANR and AAP USMB projects. Research Interests: Wise Objects (WO) framework for self-learning systems, distributed computing optimization, and fusion of radar imaging data for displacement measurement. His work integrates software engineering principles with AI-driven knowledge modeling. Projects: ANR AIACOPAHV (2020–2025), ANR EFIDIR (2007–2011), VIP Mt Blanc (2014–2019), and AAP USMB initiatives on connected objects and knowledge modeling. Education: PhD in Computer Science (2001–2004, LIFC), HDR (2021), prior roles include Associate Professor (2006–2022) and teaching/research assistant positions at universities in Nancy and Belfort.

• Quantum Information Theory
• Network Information Theory
• Machine Learning Theory
• +3 more

Padakandla Arun is a Professor in the Communication Systems department at EURECOM. His research focuses on quantum information theory, network communication, and coding theory, with recent work on multi-terminal quantum channels and machine learning applications in quantum measurement systems. Affiliation: EURECOM - Communication Systems Academic Rank: Professor Research Interests Prof. Padakandla investigates quantum communication protocols, classical-quantum hybrid systems, and structured code design for multi-user channels. His work bridges information theory with quantum mechanics, emphasizing achievable rate regions and measurement simulation techniques. Scientific Contributions Key trends in his publications include: Quantum MAC and interference channel analysis PAC learning frameworks for POVM hypothesis classes Algebraic structures in network information theory Contact Email: Arun.Padakandla@eurecom.fr

Nicolas BallierView profile

Professor

• Corpus prosody
• Neural machine translation
• Automatic analysis of learner corpora
• +5 more

Nicolas Ballier is a Professor at Université Paris Cité (formerly Université Paris Diderot), where he teaches and conducts research in linguistics and digital humanities. Previously, he taught at the Université de Rouen and Paris 13. His work focuses on the intersection of linguistics, computational methods, and language learning technologies, with particular expertise in neural machine translation and speech processing. His primary research interests include: Corpus prosody Neural machine translation Automatic analysis of learner corpora Digital humanities Epistemology of linguistics (third revolution of grammatisation) Interpretability of neural machine translation systems Representation of speech in Whisper audio models Dr. Ballier's recent work explores how computers transform linguistic data (the 'third revolution of grammatisation'), with a focus on neural machine translation interpretability and speech analysis using large language models like Whisper. His research bridges theoretical linguistics with practical applications in language learning and translation technologies, particularly focusing on how these technologies can be made transparent and useful for translators and language learners. His 15 most recent publications (2022-2024) demonstrate a strong focus on neural machine translation interpretability, Whisper applications for language assessment, and learner corpus analysis. The publications span multiple prestigious venues including EAMT, ACL, LREC-COLING, and specialized journals in speech technology and computational linguistics, showing consistent productivity and impact in his fields. Dr. Ballier has been PI or team member on numerous European-funded projects including DOKTORAND (2012-2016), KVARK project (2014-2026), PHC Ulysses (2019), multitraiNMT (2021), and LT-LIder project (2024-Nov 2026). He has developed platforms like PAPTAN for neural machine translation experiments and MAKE-NMT VIZ for investigating machine translation interpretability. He has supervised PhD students through collaborative projects and has been involved in research initiatives like DLLA (Deep Learning for Language Assessment) exploring CEFR levels with keylog data, Neuroviz (2021-2022), and SPECTRANS (2020-2022) focusing on specialized neural translation and probing information flow in neural networks.

Thomas Debris-AlazardView profile

Assistant Professor

• Code-based Cryptography
• Lattice-based Cryptography
• Post-Quantum Cryptography
• +5 more

Thomas Debris-Alazard is a research scientist at Inria within the Grace project-team and a part-time assistant professor at École Polytechnique. His work focuses on code-based and lattice-based cryptography, with significant contributions to post-quantum cryptographic design and analysis. PhD in Code-Based Cryptography, Sorbonne University (2019) His research interests include code-based and lattice-based cryptography, post-quantum security, quantum algorithms, and information theory. He has made foundational contributions to the understanding of decoding problems, learning with errors, and the design of secure cryptographic primitives resistant to quantum attacks. The recent publications highlight a strong focus on the theoretical underpinnings of code-based cryptography, including hardness reductions, decoding algorithms, and the security of post-quantum signature schemes. His work bridges classical and quantum computational models, with an emphasis on provable security and cryptanalysis. Best Paper Award at Asiacrypt 2019 He has advised PhD students Maxime Bombar and Pierre Loisel. He is the principal investigator of the ANR JCJC COLA project and coordinator of the Wave signature scheme for NIST standardization. His teaching at École Polytechnique includes courses on code-based cryptography, information theory, and quantum computing.

Derya MalakView profile

Assistant Professor

• Information Theory
• Distributed Computing
• Communication Theory
• +12 more

Derya Malak is an Assistant Professor at EURECOM in the Communication Systems Department, with an adjunct position at Rensselaer Polytechnic Institute (RPI) in the Department of Electrical, Computer, and Systems Engineering. She leads the ERC-funded SENSIBILITÉ project, focusing on distributed computing of nonlinear functions over communication networks. Education: Ph.D. in Electrical and Computer Engineering, University of Texas at Austin, 2017 M.S. in Telecommunication Engineering, Koç University, 2013 B.S. in Electrical and Electronics Engineering, Middle East Technical University (METU), 2010 Minor in Physics, METU Her research bridges information theory, coding, and distributed computing, with emphasis on reducing communication costs through structured data encoding. She explores distributed matrix multiplication, function computation over networks, caching optimization, and secure computation, leveraging tools from graph theory and stochastic modeling. Her work has significant implications for content delivery networks, distributed machine learning, and efficient data transmission. Her recent publications focus on distributed computation using structured coding schemes and multi-server frameworks, demonstrating substantial gains over classical approaches. These works are unified by a theme of optimizing computation under limited bandwidth and correlated data structures. Scientific Awards: ERC Starting Grant (2022) Best Paper Award, WiOpt 2023 Best Paper Award, WiOpt 2022 Harold N. Trevett Award (student advisee) She advises PhD students, including Aiden Chen, and has secured major grants from the European Research Council (ERC), Agence Nationale de la Recherche (ANR), National Science Foundation (NSF), Rensselaer-IBM AI Research Collaboration, and DARPA Dispersive Computing. She teaches Information Theory and Advanced Wireless Communications at EURECOM, and previously taught Digital Communications and Signal Processing at RPI. She leads the FASS student seminar series and organizes workshops such as the Workshop on Distributed Computing, Optimization & Learning. Her lab focuses on theoretical and practical aspects of distributed computation, with strong ties to computer science and information theory.

• Chaos Control
• Nonlinear Dynamics
• Power Electronics
• +3 more

Cristina MOREL is a Permanent lecturer-researcher at ESTACA since 2020, specializing in electrical engineering and control systems. Previously, she held academic positions at ESEO (2016-2020) and the IUT of Angers (2000-2020), and began her career at the Technical University of Cluj-Napoca (1992-1999). Her research focuses on nonlinear dynamics, chaos control, and applications in power electronics and renewable energy systems. Dr. MOREL holds a Doctorate in Automatics and Applied Computer Science from the University of Angers (2005) and a Habilitation to Direct Research (2016). She earned her Graduate Engineer degree in Automation and Computer Science from the Technical University of Cluj-Napoca (1992), where she was Vice-major of her promotion. 2016: Habilitation to Direct Research in Electronics, University of Angers 2005: Doctorate in Automatics and Applied Computer Science, University of Angers 1992: Graduate Engineer in Automation and Computer Science, Technical University of Cluj-Napoca Her research spans chaos control , nonlinear dynamics , and their applications in power electronics and renewable energy systems . She pioneered anticontrol of chaos to reduce electromagnetic interference in power converters and developed novel maximum power point tracking (MPPT) algorithms for photovoltaic systems under partial shading. Current work focuses on fault diagnosis for electric vehicle powertrains using entropy analysis and neural networks. Recent publications (2022-2025) show strong focus on chaos applications for MOSFET thermal management, entropy-based fault diagnosis in inverters, and energy storage optimization for electric vehicles and drones. There is clear evolution toward data-driven approaches (neural networks) for fault detection and real-world applications in e-mobility. Dr. MOREL has received several academic honors early in her career: Winner of the National University Assistant Competition (Romania, 1996) Winner of the National University Preparation Competition (Romania, 1993) Winner of the national entrance exam to the Polytechnic School of Cluj-Napoca (1987, 15th out of 700 candidates) She has supervised five PhD theses and three Master's research projects on topics including fault diagnosis in polyphase machines, energy management for battery-ultracapacitor vehicles, and MPPT for photovoltaic systems. Her research is supported by collaborations with University of Angers, ESEO, and ESTACA laboratories. Dr. MOREL led the Automation and Electrical Engineering research group at ESEO (2016-2018) and the Energy and Environment option (2016-2020), conducting experimental work in power electronics and control systems laboratories focused on converter design and EV powertrain validation.

Vincent BARRAView profile

Professor

• Data Analysis
• Image Processing
• Video Processing
• +6 more

Vincent BARRA is a full professor in Computer Science at the LIMOS lab of Clermont-Auvergne University and CNRS. He serves as Vice dean of Clermont-Auvergne-INP (education and training), Assistant director of LIMOS, and Artificial Intelligence Special Advisor for Clermont-Auvergne University. He is also a member of the scientific board and Executive committee of MIAI Cluster. His academic work is primarily conducted through ISIMA, the engineering school in computer science of Clermont-Auvergne INP. Professor BARRA's research focuses on data analysis from both methodological and applicative perspectives. His work spans image and video processing, mesh processing, computational geometry, and machine/deep learning techniques. His interdisciplinary research connects computer science with applications in medical imaging, geothermal systems, polymer dynamics, mental health diagnostics, semiconductor manufacturing, and environmental monitoring. His publications demonstrate consistent innovation in developing algorithms for segmentation, feature extraction, and pattern recognition across diverse domains. The analysis of his recent publications reveals strong trends toward deep learning applications in specialized domains. His work shows increasing focus on medical applications (depression diagnosis, preterm birth prediction), geoscience (geothermal systems analysis), and industrial applications (semiconductor metrology, structural health monitoring). A consistent theme across his research is the development of robust, domain-specific deep learning architectures that address unique challenges in data analysis across different scientific fields. Professor BARRA maintains active collaborations across multiple institutions and disciplines, as evidenced by his extensive co-authorship network. His work bridges theoretical computer science with practical applications in diverse fields, demonstrating a commitment to solving real-world problems through innovative computational approaches. He leads research within the LIMOS laboratory, which focuses on computer science, modeling, and systems optimization.

Sam GoreeView profile

Assistant Professor

• computer vision
• human-computer interaction
• digital humanities
• +4 more

Sam Goree is an assistant professor of computer science at Stonehill College, specializing in the intersection of computer vision, human-computer interaction, and digital humanities. His work critically examines how AI systems evaluate aesthetic quality and human-centered aspects of AI, with particular focus on subjectivity in machine learning models and evaluation methodologies. PhD in Informatics from Indiana University's Luddy School of Informatics, Computing and Engineering Goree's research explores aesthetic phenomenon problems in computer vision, particularly image aesthetic quality assessment. He investigates how subjective human perceptions of beauty and aesthetics can be modeled and evaluated in AI systems, challenging traditional approaches that assume a universal standard of beauty. His work draws from feminist theory, philosophy of science, and media archaeology to develop more human-centered evaluation methods for AI systems. Goree is particularly interested in how subjectivity manifests in both data and models, and how this affects algorithmic bias. His recent publications reveal a strong trend toward human-centered evaluation of AI systems, with increasing focus on subjectivity, personalization, and philosophical foundations of aesthetics in computer vision. Goree's work bridges technical computer vision research with critical humanities perspectives, examining how historical contexts shape current AI practices. He has developed novel evaluation frameworks that incorporate qualitative human feedback, moving beyond traditional quantitative metrics to better understand how AI systems interact with human perception and values. Goree actively engages with teaching challenges in the era of generative AI, developing pedagogical approaches that help students develop critical thinking skills while navigating new AI tools. His teaching philosophy emphasizes maintaining human-centered learning experiences even as AI transforms educational landscapes. He advocates for assignments that require contextual understanding and creativity, which are difficult for AI to replicate. His research is organized around understanding the "aesthetic gap" - the difference between information extractable from image pixels and the feelings images evoke in humans. Goree's lab explores how to build more transparent and accountable AI systems that acknowledge their subjective nature rather than claiming false objectivity.

Marc ChaumontView profile

Associate Professor

• Visual Data Analysis
• Remote Sensing
• Steganography
• +10 more

Marc Chaumont is an Associate Professor at the University of Nîmes since 2005 and a senior researcher at LIRMM Montpellier. He holds an HDR (Habilitation à Diriger des Recherches) and is an IEEE Senior Member. Since October 2024, he has been an associate collaborator at IRISA laboratory in Vannes, France. His career spans academic research, teaching, and interdisciplinary collaborations with institutions like MARBEC, CIRAD, and CNRS. PhD in Computer Science from IRISA Rennes (2003) Engineer Diploma from INSA Rennes (1999) His research focuses on visual data analysis , remote sensing , and digital forensics , particularly in steganography and steganalysis . He has contributed to creating large-scale databases like LSSD , a 2 million JPEG image dataset for deep-learning steganalysis. His work extends to AI applications in marine ecology, medical imaging, and poverty estimation from satellite data. Recent publications highlight collaborations across disciplines, including transformer models for socioeconomic indicator prediction, 3D fish tracking in coral reefs, and self-supervised encoder pretraining for chronic wound segmentation. He has supervised interns like Ayman El Mannouy (2024) and Abir Zahi (2023) in projects related to weakly supervised segmentation and marine biodiversity. IEEE Senior Member (2020) Top-3% Best Reviewer at IEEE ICIP 2020 Marc has taught courses ranging from signal processing to image compression since 1999. His technical contributions include software tools for image databases and steganalysis algorithms. He actively participates in European projects like DFUC'2024 and ConvEntion for astronomical data classification.

Nicolas DuguéView profile

Assistant Professor

• Graph Mining
• Complex Networks
• Text Mining
• +2 more

Nicolas Dugué is an Assistant Professor at the University of Le Mans, affiliated with the LIUM and EIAH teams. His research focuses on data mining, complex networks, and text mining, with applications in social network analysis, community detection, and interpretable embeddings. Education: PhD in Computer Science from LIFO (University of Orléans), Postdoc at LORIA (Nancy, France). His work spans graph mining, supervised learning, and clustering, particularly in social networks and scientific data analysis. Recent publications emphasize interpretable embeddings, community filtering, and modularity optimization in directed networks. He contributes to open-source tools like SINr for word and graph embeddings and explores interdisciplinary applications in medical research and scientometrics. Affiliated with LIUM (Laboratory of Computer Science of Le Mans) and EIAH (Education, Information, and Human-Computer Interaction) teams.

Peter DalyView profile

Professor

• Management Communication
• Leadership Studies
• Management Education and Learning
• +3 more

Peter Daly is a Professor of Management/Leadership at EDHEC Business School 's Management & Humanities faculty. His expertise spans Management Communication , Leadership , and Business Education Pedagogy . Fluent in Irish, English, French, and German, Daly combines linguistic proficiency with educational innovation.

• Entrepreneurship
• Organizational Behavior
• Leadership Development
• +4 more

Fabrice CAVARRETTA is an Associate Professor of Leadership and Intrapreneurship at ESSEC Business School, France. His academic positions include roles as Assistant Professor (2008–2014) and current Associate Professor since 2014. He holds a PhD in Management from INSEAD and additional degrees from Harvard, Stanford, and École Polytechnique. Education: Bachelor of Arts in Mathematics, École Polytechnique (1988) Master of Science in Computer Science, ENSTA (1991) Master of Business Administration, Harvard Business School (1996) PhD in Management, INSEAD (2008) Habilitation to Supervise Research (HDR), Université de Lille (2021) Research focuses on managerial paradigms, entrepreneurial heuristics, and extreme leadership contexts. Key interests include: Entrepreneurship doctrines and resource effects Leadership in extreme environments Heuristic emergence processes Organizational behavior dynamics Award highlights include the 2014 Outstanding Contribution Award and multiple paper distinctions. Active in interdisciplinary research, he collaborates on AI projects exploring entrepreneurial beliefs and ecological transitions. Serves on editorial boards and advisory committees for organizations like the French government's ExpertiseFrance and Educapital. Teaching includes leadership, entrepreneurship, and organizational behavior courses across executive education and graduate programs. Directs PhD seminars and innovation-focused curricula.

François BachocView profile

Assistant Professor

• Statistics
• Machine Learning
• Mathematical Statistics
• +3 more

François Bachoc is an Assistant Professor at the Toulouse Mathematics Institute and the University Paul Sabatier, where he has held a tenured position since 2015. He is a Junior Member of the Institut universitaire de France (IUF) (2024–2029). His research focuses on Statistics, Machine Learning, and Gaussian Processes , with applications in industrial and interdisciplinary domains. He earned his Ph.D. in Statistics from the CEA and Université Paris VII (2013), followed by a postdoctoral position at the University of Vienna (2013–2015). He completed a Habilitation (HDR) at University Paul Sabatier in 2018. His work emphasizes uncertainty quantification, Bayesian methods, and optimal design of experiments. Bachoc leads several funded projects, including the ANR Project GAP (€205k) and the AI Chair UQPhysAI (€350k). He teaches courses on asymptotic statistics, machine learning, and Gaussian processes at the graduate level. His contributions span theoretical advancements and practical applications in coastal flood modeling, sensitivity analysis, and computational statistics.

Simon TheveninView profile

Assistant Professor

• Production Management
• Optimization Methods
• Production Scheduling
• +4 more

Simon Thevenin is an Assistant Professor in the Automation, Production, and Computer Sciences Department at IMT Atlantique in France since 2018. He holds a Ph.D. from the University of Geneva (2015) and previously worked as a Postdoctoral researcher at HEC Montreal and an Algorithm Expert at Quintiq. His research focuses on optimization methods for production management, including scheduling, planning, and manufacturing line design. He leads projects such as the EU-funded ASSISTANT (2020-2023), ALICIA (2023-2025), and ACCURATE (2023-2026), advancing AI-driven solutions for sustainable manufacturing and supply chain resilience. His work integrates machine learning, robust optimization, and digital twin technologies to enhance production systems' adaptability and efficiency. Education: Ph.D. in Production Systems Scheduling, University of Geneva (2015) Teaching/Research Assistant, University of Geneva (2010-2015) Research Interests: His research emphasizes optimization under uncertainty, reconfigurable manufacturing systems, and AI applications in production. He explores topics like lot-sizing models, equipment lifecycle management, and circular manufacturing ecosystems. Grants/Projects: ASSISTANT (EU-funded, 2020-2023): AI for production digital twins ALICIA (EU-funded, 2023-2025): Circular production resource ecosystems ACCURATE (EU-funded, 2023-2026): Supply chain resilience against disruptions Labs/Teams: Part of the LS2N research lab (Modelis team) at IMT Atlantique, specializing in logistics and industrial optimization.