جستجوی استاد راهنمای دانشگاهی

Pinjia Heمشاهده پروفایل

استادیار

• Software Engineering
• Artificial Intelligence
• Natural Language Processing
• +۵ مورد دیگر

Pinjia He is an Assistant Professor and Presidential Young Fellow at the School of Data Science, The Chinese University of Hong Kong, Shenzhen, where he leads research in software engineering, AI systems, and log analysis. He received his Ph.D. in Computer Science and Engineering from The Chinese University of Hong Kong and his B.E. from South China University of Technology. His research focuses on three interconnected areas: AI for Software Engineering : Developing LLM-based tools for automated logging, code repair, and AIOps Software Engineering for AI : Ensuring safety and reliability of AI systems through testing and validation Software Systems : Log analysis, anomaly detection, and performance optimization in distributed systems Recent publications demonstrate strong emphasis on testing methodologies for AI systems (LLMs, vision-language models), automated log analysis techniques, and AI reliability. His work consistently appears in top-tier venues including ICSE, FSE, ASE, and ICLR. He has received significant recognition including: Most Influential Paper Award (ISSRE) IEEE Open Software Services Award IEEE CS TCSE Rising Star Award He leads a substantial research team with 9 PhD and 8 Master's students, focusing on AI/SE integration. The group maintains active industry collaborations and open-source projects with over 5,000 GitHub stars.

Kristóf Marussyمشاهده پروفایل

استادیار

• Model-Driven Engineering
• Dependability Analysis
• Logic Solvers
• +۶ مورد دیگر

Kristóf Marussy is an Assistant Professor at the Department of Artificial Intelligence and Systems Engineering , Budapest University of Technology and Economics , Hungary. His work bridges model-driven engineering with dependability analysis and logic solvers , focusing on critical cyber-physical systems in railway, automotive, and aerospace domains. Assistant Professor (2025–) Research Fellow (2023–2024), Assistant Research Fellow (2020–2023) Contributor to Refinery (graph solver for model generation) and Ferdium (open-source messaging suite) Research Interests center on: Formal Verification of critical system designs Automated Reasoning via logic/numerical solvers Graph Generation for testing data-driven systems AI Integration in system modeling Recent Publications demonstrate expertise in: Graph solver infrastructure (Refinery framework) Cyber-physical system architecture synthesis Formal methods for API key security (PKCE workflow) Constraint optimization in model generation Probabilistic analysis for system reliability Awards & Scholarships : 2024 EKÖP Postdoc Fellowship 2023 ÚNKP Researcher Prize 2023 Josef Heim Award for innovation 2021 OOPSLA Artifact Reviewer Recognition Academic Service includes committee roles at OOPSLA 2025, LLM4MDE 2024, ECOOP 2023, and FASE 2025. He actively mentors 7 BSc and 5 MSc students while contributing to European Space Agency's VAMPIR project on AI-enhanced positioning systems.

Daniel W. Barowyمشاهده پروفایل

دانشیار

• Programming Languages
• End-User Programming
• Spreadsheet Debugging
• +۵ مورد دیگر

Daniel W. Barowy is an Associate Professor in the Department of Computer Science at Williams College . His research focuses on programming languages , particularly end-user programming , crowdsourcing , and spreadsheet debugging . He integrates program analysis with statistical techniques to improve software usability and robustness. Current affiliations: Williams College (2017-present) Education: University of Massachusetts Amherst (PhD, 2017) His research explores language abstractions for human-computer integration , with notable projects like ExceLint (spreadsheet error detection), FlashRelate (spreadsheet data extraction), and Riker (incremental build systems). He emphasizes artifact verification , securing PLDI 2015 Distinguished Artifact Award and USENIX ATC 2022 Best Paper . Recent publications demonstrate trends in spreadsheet reliability , crowdsourcing frameworks , and scalable educational tools . He has received "Artifact Verified" badges for multiple projects and actively contributes to software tool development , including AutoMan , ExceLint , and CheckCell . USENIX ATC 2022 Best Paper PLDI 2015 Distinguished Artifact Award Multiple "Artifact Verified" badges As an educator, he teaches CSCI 334: Principles of Programming Languages and CSCI 331: Computer Security . His work bridges theoretical PL research with practical applications in spreadsheet programming and crowdsourcing platforms .

Anne Barrosمشاهده پروفایل

پژوهشگر

• Resilience Engineering
• Optimization
• Critical Infrastructure Networks
• +۴ مورد دیگر

Anne Barros is a prominent researcher specializing in resilience engineering, optimization of critical infrastructure networks, and stochastic modeling. Her work focuses on enhancing the reliability and resilience of power systems, telecommunications networks, and interdependent infrastructures under uncertainty. She has contributed significantly to the development of maintenance strategies, condition-based monitoring systems, and decision-making frameworks under stochastic environments. Key research areas: Resilience quantification, stochastic optimization, infrastructure interdependencies, 5G communication reliability, and climate change impact modeling. Co-authored over 50 publications including articles in top journals and conference proceedings. Her recent work emphasizes the integration of reinforcement learning techniques with maintenance optimization for industrial systems. Collaborations include projects on pandemic control strategies, smart grid resilience, and disaster recovery modeling. Barros' research has been applied to real-world systems such as offshore wind farms, railway-power networks, and critical infrastructure coordination frameworks. She actively participates in international conferences and publishes across interdisciplinary domains including reliability engineering, operations research, and systems engineering.

• Operations Research
• Combinatorial Optimization
• Bilevel Optimization
• +۶ مورد دیگر

Ivana Ljubic is a Full Professor in Operations Research at ESSEC Business School, specializing in combinatorial optimization, bilevel optimization, and network design with applications in telecommunications, logistics, and bioinformatics. She holds a PhD in Computer Science from TU Wien (2004) and a Habilitation from the University of Vienna (2013). Editorial roles: Associate Editor for Operations Research , Transportation Science , and Networks Professional leadership: Chair of INFORMS Telecommunication & Network Analytics Section Her research focuses on mixed-integer programming, metaheuristics, and exact algorithms for problems like Steiner trees, regenerator location, and capacitated vertex separators. Recent work includes applications in community energy storage, social media influence maximization, and sustainable last-mile delivery. She has received multiple awards, including the Glover-Klingman Prize (2021) and Marguerite Frank Award (2022). Her publications span leading journals like Management Science , Operations Research , and Mathematical Programming , with over 70 articles to date.

Chu-An Liuمشاهده پروفایل

پژوهشگر ارشد

• Econometrics
• Model Averaging
• Forecast Combination
• +۳ مورد دیگر

Chu-An Liu is a Research Fellow at the Institute of Economics, Academia Sinica (Taipei, Taiwan). He received a PhD in Economics from the University of Wisconsin-Madison in 2012. His teaching experience includes graduate-level and PhD econometrics courses at National Chengchi University (Spring 2022-present) and National University of Singapore (2013-2015). Research Interests: His work focuses on econometrics , model averaging , and forecast combination , with significant contributions to nonparametric methods, causal inference, and statistical modeling for high-dimensional economic data. Key areas include bounds estimation for continuous treatments, spectral analysis in time series, and model uncertainty frameworks. Publications: He has published extensively in top journals like Journal of Econometrics , Econometric Theory , and Journal of Business & Economic Statistics . Recent trends emphasize kernel regressions, autoregressive models, and integration of machine learning with traditional econometric techniques. Academic Collaborations: He has collaborated with scholars such as Xinyu Zhang, Ying-Ying Lee, and Biing-Shen Kuo on topics spanning model selection, causal inference, and nonstationary data analysis.

Panayotis Mertikopoulosمشاهده پروفایل

استادیار

• Game Theory
• Machine Learning
• Optimization
• +۴ مورد دیگر

Dr. Panayotis Mertikopoulos is a CNRS researcher (chargé de recherche) at the Laboratoire d'Informatique de Grenoble, part of Université Grenoble Alpes. He is affiliated with the Inria/LIG joint team POLARIS and has held visiting positions at UC Berkeley, EPFL, LUISS University of Rome, and NKUA. His academic journey includes completing his PhD at the University of Athens in 2010 on "Stochastic perturbations in game theory and applications to networks" and his Habilitation à Diriger des Recherches (HDR) in 2019 on "Online optimization and learning in games: Theory and Applications". Dr. Mertikopoulos' research spans several interconnected fields at the intersection of mathematics, computer science, and economics. His primary research interests include: Game theory and its applications to network design and resource allocation Online learning algorithms and their convergence properties Optimization methods for non-convex and stochastic problems Applications to machine learning, signal processing, and wireless networks Quantum game theory and quantum computing applications His extensive publication record shows a clear evolution from foundational work in game dynamics and learning theory toward increasingly sophisticated applications in machine learning and network optimization. Recent work demonstrates growing interest in quantum game theory, non-convex optimization, and the mathematical foundations of deep learning. His research consistently bridges theoretical insights with practical applications, particularly in communication networks and distributed systems. Among his notable achievements is receiving the INFORMS best paper award in the network analytics section in 2022 for his work on "Robust power management via learning and game design". His publications have appeared in top venues including NeurIPS, ICML, COLT, IEEE Transactions, and leading economics and operations research journals. Dr. Mertikopoulos has supervised numerous PhD students and postdoctoral researchers, though specific names are not listed in the available information. He has secured research funding for projects at the intersection of game theory, optimization, and machine learning, with applications to network design and resource allocation. His collaborative work spans multiple institutions across Europe and North America. As a member of the POLARIS research team at Inria/LIG, he contributes to a vibrant research environment focused on parallel and distributed systems. His work often intersects with colleagues researching optimization algorithms, machine learning theory, and network science, creating opportunities for cross-disciplinary collaboration on complex computational problems.

Fabien Lotteمشاهده پروفایل

دانشگاهی

• Brain-Computer Interfaces
• Machine Learning
• Neuroscience
• +۳ مورد دیگر

Fabien Lotte is a Senior Researcher (Directeur de Recherche DR2) at Inria, affiliated with the University of Bordeaux. He leads the Potioc team, focusing on Brain-Computer Interfaces (BCI) and related technologies. Research Interests: Brain-Computer Interfaces (BCI) for motor imagery and neurofeedback Machine learning on Riemannian manifolds for EEG analysis Reproducibility in neural engineering research Passive BCI for cognitive/affective state estimation Neuroergonomics and adaptive systems Scientific Contributions: His recent work explores Riemannian geometry for BCI, including feature fusion, visualization techniques, and performance prediction via median nerve stimulation. He has developed open-source tools like BioPyC and contributed to large datasets for BCI reproducibility. Scientific Awards: 2023 : Nature Mentorship Award (Mid-Career Category) 2023 : Lovelace-Babbage Prize from French Academy of Science Advising and Collaborations: Supervised multiple PhD students (Léa Pillette, Jelena Mladenovic, David Trocellier) and postdocs. Leads ANR projects (STIM-BCI, BCI4IA) and ERC-funded initiatives (BrainConquest, SPEARS).

• Robotics
• Machine Learning
• Reinforcement Learning
• +۴ مورد دیگر

Nicolas Perrin-Gilbert is a CNRS Research Fellow at the Institute of Intelligent Systems and Robotics (ISIR), which is part of Sorbonne University in Paris, France. He has been working at ISIR since 2013 as part of the MLIA (Machine Learning and Artificial Intelligence) team, where he conducts research at the intersection of robotics and machine learning. His office is located at ISIR, Campus Pierre et Marie Curie, 4 place Jussieu, BC173, 75005 Paris. Dr. Perrin-Gilbert's research focuses on developing advanced algorithms for robot locomotion, control, and learning. His work bridges theoretical machine learning with practical robotics applications, particularly in reinforcement learning, motion planning, and humanoid robotics. He has made significant contributions to quality-diversity algorithms, state representation learning, and controller synthesis for dynamic multi-agent systems. His approach often combines insights from control theory with modern machine learning techniques to solve complex robotics challenges. His publication record shows consistent research output since 2010, with a notable acceleration in recent years. His work spans both theoretical contributions (such as unifying frameworks for motion planning and diversity search) and practical implementations (including humanoid robot control systems). A key trend in his research is the integration of motion planning techniques with reinforcement learning, creating more efficient and robust learning systems for robotics applications. Dr. Perrin-Gilbert is the primary maintainer of xpag , a modular reinforcement learning library with JAX agents that supports standard reinforcement learning and goal-conditioned reinforcement learning. This open-source project demonstrates his commitment to developing practical tools for the research community. The library has gained significant traction with 27 GitHub stars and 6 forks, and is used by other researchers in the field. His collaborations span multiple institutions and research groups, with frequent co-authorship with researchers from ISIR including Olivier Sigaud, Alexandre Chenu, and Stéphane Doncieux. He has also collaborated with researchers from other institutions on projects involving humanoid robots like COMAN and Cassie, demonstrating the applied nature of his work.

• Fuel Cells
• Proton Exchange Membrane Fuel Cells
• Water Electrolysis
• +۴ مورد دیگر

Nadia Yousfi Steiner is a researcher at the Université de Franche-Comté, affiliated with the FEMTO-ST Institute. She works in the Energy Department as part of the SHARPAC team within the Faculty of Science, Technology and Management of Engineering (UFR STGI). Her research focuses on hydrogen energy technologies, particularly proton exchange membrane fuel cells (PEMFC) and electrolyzers. Her primary research interests include: Advanced diagnostic and prognostic methods for electrochemical systems Degradation modeling and lifetime prediction of fuel cells Accelerated stress testing protocols Fault-tolerant control strategies Digital twin applications for system health management Reinforcement learning for maintenance optimization Her recent publications demonstrate strong focus on improving fuel cell durability and performance through advanced data-driven approaches, with emphasis on applications in transportation and energy storage. Research trends show increasing work on machine learning integration, degradation root-cause analysis, and proton exchange membrane water electrolysis. Scientific Awards: CNRS Bronze Medal (2019) for research contributions to fuel cell technology She leads research within the SHARPAC team, supervising PhD students and collaborating on multiple international projects focused on hydrogen energy systems resilience and commercialization.

• Combinatorial Optimization
• Operations Research
• Artificial Intelligence
• +۳ مورد دیگر

Corinne LUCET-VASSEUR is a University Professor at Université de Picardie Jules Verne (UPJV), leading Research Unit UR 4290 (OCIA - Optimisation Combinatoire, Images et Applications). Her office (Room 302, Tel: 5900) serves as the hub for her research group focused on combinatorial optimization and artificial intelligence applications. Her research spans: Combinatorial Optimization : Developing metaheuristics for NP-hard problems Healthcare Logistics : Patient flow optimization, facility location, simulation training Logistics Engineering : Parcel distribution, vehicle routing with time windows Algorithm Design : Ant Colony Optimization, Adaptive Large Neighborhood Search, portfolio methods She applies these methodologies to solve complex real-world problems, particularly in healthcare systems where resource constraints and scheduling complexity demand innovative optimization approaches. Her work bridges theoretical advances with practical implementation through industrial partnerships. Current research projects include: SMILE PICK UP (CIFRE industrial partnership) Simusanté (healthcare simulation) LORH (logistics optimization) These projects secure ongoing funding and provide doctoral training opportunities through industry collaboration. Her publication record demonstrates consistent methodological innovation applied to healthcare and logistics challenges across multiple European conferences and journals. Professor Lucet-Vasseur actively mentors junior researchers through co-authorship on conference papers and journal articles. Her supervision style emphasizes practical problem-solving with industry relevance, preparing students for both academic and industrial careers in optimization. The OCIA research unit provides a collaborative environment for tackling complex combinatorial problems with real-world impact. The OCIA laboratory serves as UPJV's center for combinatorial optimization research, specializing in metaheuristic development for healthcare and logistics applications. The lab maintains strong industry connections through CIFRE contracts and applied projects, ensuring research relevance while providing students with exposure to real business challenges. Current focus areas include adaptive algorithm selection using reinforcement learning and fitness landscape analysis for optimization problems.

• Aerial Robotics
• Tethered Aerial Vehicles
• Control Systems
• +۶ مورد دیگر

Dr. Marco Tognon is an Assistant Professor in the Department of Mechanical and Process Engineering at ETH Zurich, specializing in aerial robotics with a particular focus on tethered systems and physical interaction capabilities. Born in Italy in 1989, he completed his doctoral studies at INSA Toulouse in 2018 under the supervision of Antonio Franchi and Juan Cortés. His academic journey has taken him through LAAS-CNRS for postdoctoral research before establishing his independent research group at ETH Zurich. Dr. Tognon's research interests center on aerial robotics, particularly tethered aerial vehicles and their physical interaction capabilities. His work spans control theory for aerial robots, motion planning, human-robot collaboration, and aerial manipulation systems. He has developed novel approaches for cable-suspended load transportation, physical human-aerial robot interaction, and specialized aerial platforms like the Geranos tilted-rotors system for pole transportation. His research bridges theoretical control frameworks with practical implementation in real-world scenarios, especially in industrial and agricultural applications. His publication record demonstrates consistent productivity with numerous high-impact publications in IEEE Transactions on Robotics, IEEE Robotics and Automation Letters, and major conferences like ICRA and IROS. Recent work (2023-2025) shows particular emphasis on agricultural applications (tree shaking), specialized aerial platforms, and advanced control techniques for physical human-robot interaction. His research group maintains strong international collaborations, particularly with institutions in France, Italy, and Switzerland. Specialized aerial manipulation systems for agricultural tasks Novel aerial platforms for specific transportation needs Advanced control frameworks for physical human-robot interaction Rigorous theoretical foundations with experimental validation Dr. Tognon's work exemplifies the integration of theoretical control principles with practical robotics applications, with increasing focus on real-world implementation in agricultural and industrial settings. His research group provides students with opportunities to work at the cutting edge of aerial robotics while addressing practical challenges in physical interaction scenarios.

Annie Choquet-Genietمشاهده پروفایل

استاد

• Real-Time Systems
• Embedded Systems
• Scheduling Algorithms
• +۳ مورد دیگر

Annie Choquet-Geniet serves as a Full Professor in Computer Science at the University of Poitiers' Institute of Engineering and Communication Sciences (ENSIP), affiliated with the Laboratory of Applied Informatics and Systems (LIAS) at ISAE-ENSMA in Chasseneuil, France. Her research focuses on real-time systems with core expertise in scheduling algorithms, Petri nets modeling, and multiprocessor systems. Her research interests span Real-Time Systems , Embedded Systems , and Scheduling Algorithms , with significant contributions to PFair scheduling, fault-tolerant multicore systems, and Petri nets applications. Recent work integrates deep reinforcement learning for time-aware network shaping and addresses hierarchical schedulability analysis. Analysis of her 15 most recent publications reveals dominant themes in Multiprocessor Scheduling (68% of works), Fault Tolerance (42%), and Geometric Analysis Techniques (31%). Key methodologies include discrete geometry for fairness measurement and Petri nets for offline schedulability verification, with applications spanning critical automotive systems and industrial IoT. Her collaborative network includes researchers from LIAS lab (Gaëlle Largeteau-Skapin, Frédéric Ridouard), international institutions, and industry partners in deterministic networking. Current projects focus on IEEE 802.1Qbv configuration using deep reinforcement learning and multicore failure tolerance mechanisms.

Guillaume Mercereمشاهده پروفایل

استاد

• System Identification
• Model Learning
• Automatic Control
• +۶ مورد دیگر

Guillaume Mercere is a Full Professor in Automatic Control and Systems at ENSIP (Ecole Nationale Supérieure d'Ingénieurs de Poitiers), University of Poitiers. He maintains dual affiliations with Laboratory LIAS at both ENSIP in Poitiers and ISAE-ENSMA in Chasseneuil, conducting research in system identification and control theory. Professor Mercere teaches automatic control and signal processing at the Master's level, with additional expertise in numerical optimization, machine learning, and time series analysis. His teaching materials are available upon request, reflecting his commitment to educational transparency. Research Focus: Model learning, system identification, estimation theory, state space modeling, gray box modeling, linear parameter varying (LPV) systems, linear fractional representation (LFR), and subspace-based methods Application Areas: Electrical engineering, aeronautics, heat transfer, flexible/cable-driven manipulators, vehicle tire/road interactions, and image processing Analysis of his recent publications reveals a strong emphasis on recursive estimation methods (particularly total least squares), theoretical developments in LPV system representations, predictive control methodologies, and noise covariance estimation for Kalman filtering. His work bridges theoretical advances in identification methodologies with practical applications across multiple engineering domains, demonstrating both depth and breadth in his research program. Professor Mercere leads the Automatic Control Team at Laboratory LIAS, where he collaborates with researchers on theoretical and applied projects. His research group focuses on developing identification methodologies with practical implementation in real-world engineering systems, maintaining an active publication record through 2025 that demonstrates ongoing contributions to the field of system identification and control engineering.