جستجوی استاد راهنمای دانشگاهی

Gao Jieمشاهده پروفایل

استاد

• Algorithms
• Wireless Networks
• Social Networks
• +۳ مورد دیگر

Jie Gao is a Professor of Computer Science at Rutgers University, serving as the Graduate Program Director. Her research focuses on algorithms, computational geometry, wireless networks, and social networks. She holds an office in Hill 411 and can be reached at jg1555@rutgers.edu. Her work emphasizes geometric algorithms, privacy-preserving techniques, and network optimization. Professor Gao teaches advanced courses such as Computational Geometry (CS529), Design and Analysis of Data Structures and Algorithms (CS513/CS514), and Introduction to Discrete Structures (CS205). Her courses blend theoretical foundations with practical applications in geometric computing and algorithm design. Her research group explores geometric algorithms, distributed systems, and privacy-preserving data analysis. Notable projects include developing efficient algorithms for unit-disk graphs, security game theory for patrol scheduling, and methodologies for interpreting health data from wearable devices. She actively contributes to the field through interdisciplinary collaborations, addressing challenges in both theoretical and applied computing domains.

• Operational Research
• Optimization Methods
• Logistics and Supply Chain Management
• +۷ مورد دیگر

Manuel Iori is a Full Professor at the Department of Engineering Sciences and Methods, University of Modena and Reggio Emilia (UNIMORE), Italy. His primary research focuses on operational research, optimization methods, and logistics systems. He specializes in vehicle routing problems, scheduling algorithms, and decision support systems with applications in industrial automation, healthcare, and service industries. Iori is actively involved in teaching advanced optimization courses for engineering students, emphasizing practical applications in data-driven decision-making and simulation. Research Interests: His work addresses complex optimization challenges such as multi-trip vehicle routing with time windows, scheduling under resource constraints, and tool switching in manufacturing systems. He integrates machine learning and metaheuristics to develop innovative solutions for logistics, production planning, and healthcare operations. Collaborations with industry partners (e.g., pharmaceutical distributors, printing companies) ensure practical relevance of his research. Teaching: Iori teaches courses like Optimization Methods for Data-Driven Engineering Processes , Models for Logistics and Production Optimization , and Methods and Algorithms for Optimization in Digital Industries . These courses combine theoretical foundations with hands-on labs using tools like Python, Xpress, and Anylogic. Key Contributions: He developed decision support systems for multi-trip routing in pharmaceutical distribution and supplier selection in facility management. His research on satellite scheduling and attended home delivery systems advances both theoretical and applied domains. As a member of CIRRELT (Canada), he collaborates on logistics optimization projects. Professional Activities: Iori’s work is reflected in over 80 peer-reviewed publications and contributions to conferences. He advises graduate students on optimization challenges and serves as a reviewer for top journals in operations research.

Mario Zanonمشاهده پروفایل

دانشیار

• Optimal Control
• Reinforcement Learning
• Model Predictive Control (MPC)
• +۴ مورد دیگر

Mario Zanon is an Associate Professor at the IMT School for Advanced Studies Lucca, Italy. He holds a B.Sc. in Industrial Engineering (University of Trento, 2008), an M.Sc. in Mechatronics (University of Trento, 2010) and General Engineering (Ecole Centrale Paris, 2010), and a Ph.D. in Electrical Engineering (KU Leuven, 2015). His postdoctoral research (2015–2017) was at Chalmers University of Technology. He joined IMT Lucca in 2018 as Assistant Professor, advancing to his current rank in 2021. His research focuses on Reinforcement Learning, distributed and economic Model Predictive Control (MPC), optimal control of nonlinear systems , with applications in aerospace and automotive domains. He investigates safety-critical systems, autonomous driving, and coordination of vehicles in mixed traffic environments. His work emphasizes theoretical guarantees, computational efficiency, and practical implementation. Recent publications highlight advancements in MPC stability, safe autonomous systems, and optimization algorithms for machine learning. His studies on intersection coordination and disturbance modeling contribute to safer, efficient autonomous vehicle systems. Current research trends include reinforcement learning integration with MPC, data-driven terminal cost design, and scalable optimization for complex systems. Zanon’s work has been applied to real-world scenarios like vaccine distribution optimization during the pandemic and thermal welding modeling. He collaborates with industry partners on autonomous driving and robotics, emphasizing practical, safety-first solutions. Future directions include advancing learning-based MPC and exploring hybrid systems in robotics.

Nina Skorin-Kapovمشاهده پروفایل

دانشیار

• Optimization of Communication Networks
• Wide-Area Optical Networks
• Operations Research
• +۳ مورد دیگر

Dr. Nina Skorin-Kapov is an Associate Professor at the Technical University of Cartagena, associated with the Centro Universitario de la Defensa de San Javier. She holds a PhD in Telecommunications and Informatics from the University of Zagreb and completed a postdoctoral fellowship at Telecom ParisTech. Her research focuses on optimization of communication networks, wide-area optical networks, operations research, and heuristic algorithms. She leads the Computational and Telecommunications Engineering Group (GICOT) and serves on the editorial board of the IEEE/OSA Journal of Optical Communications and Networking. Her academic roles include teaching undergraduate courses on Electromagnetic Exploration Systems and graduate courses on Metaheuristics and Graph Theory. Key research contributions include work on network security, routing algorithms, and capacity planning in optical networks. Notable projects include the Movilidad Ecoeficiente y Conectada para Ciudadanos initiative (2020–2022), funded by IDAE. Her publications span topics like wildfire resource scheduling, drone control systems, and secure optical network design. Dr. Skorin-Kapov’s articles emphasize practical applications of optimization techniques, such as scheduling aerial resources for wildfire management and improving QoE in drone teleoperation. Her work bridges theoretical research with real-world network challenges, particularly in telecommunications and disaster response scenarios.

David Poza Garcíaمشاهده پروفایل

دانشیار

• Additive Manufacturing
• Project Management
• Combinatorial Auctions
• +۱ مورد دیگر

David Poza García is an Associate Professor at the University of Valladolid’s Department of Business Organisation and Market Research. He holds affiliations with INSISOC (GIR UVa, GREX251, UIC086) and professional organizations including ESSA, ADINGOR, and AEIPRO. His certifications include IPMA-D Project Management accreditation. Research focuses on optimization in industrial systems, spanning additive manufacturing, project scheduling, combinatorial auctions, and risk modeling. Key themes include algorithmic efficiency, stochastic analysis, and sustainable operations. Publications emphasize computational methods for industrial challenges, with recent trends in simulation-based scheduling, additive manufacturing economics, and uncertainty management in project frameworks. No advising roles, grants, or lab affiliations are detailed in available texts.

Myra Cohenمشاهده پروفایل

استاد

• Software Engineering
• Search-Based Software Engineering
• Highly-Configurable Software Testing
• +۵ مورد دیگر

Myra B. Cohen is a Professor and the Lanh and Oanh Nguyen Chair in Software Engineering at Iowa State University's Department of Computer Science, part of the College of Engineering. She previously held the Susan J. Rosowski Professorship at the University of Nebraska-Lincoln. Her research focuses on software testing of highly-configurable systems, search-based software engineering, and applications in synthetic biology and bioinformatics. Cohen holds a Ph.D. from the University of Auckland, M.S. from the University of Vermont, and B.S. in Agriculture from Cornell University. Education: Ph.D., Computer Science, University of Auckland, 2004 M.S., Computer Science, University of Vermont, 1999 B.S., School of Agriculture and Life Sciences, Cornell University Research Interests: Testing of cyber-physical systems and scientific software Combinatorial design applications in software assurance Synergies between software engineering and synthetic biology Configuration-aware testing for safety-critical systems Recent Projects: OPERA: Rural broadband solutions through open-source ecosystems Dependable AI-driven computational biology tools for KBase Blockchain fault tolerance modeling and measurement Self-adaptive mechanisms for unmanned aerial systems Awards: NSF CAREER Award AFOSR Young Investigator Award ACM Distinguished Scientist ASE Conference Fellow Leadership Roles: Program Co-Chair: ESEC/FSE 2020, ISSTA 2025 General Chair: ASE 2015 Steering Committee: ASE, ESEC/FSE, ISSTA Labs & Affiliations: Director: LaVA-OPs (Variability-Aware Assurance Lab) Center for Wireless, Communities and Innovation (WiCI) Bioinformatics & Computational Biology Graduate Program

Caroline Chanelمشاهده پروفایل

استاد

• Autonomous Systems
• Decision-Making Algorithms
• Robotics
• +۳ مورد دیگر

Caroline Chanel is a Professor at ISAE-SUPAERO (DCAS), holding the CASAC Chair since 2022. She has extensive experience in research and teaching, with roles including Associate Professor (2019-2024), Research Engineer (2013-2019), and HDR accreditation (2023). Her research focuses on embedded decision-making for autonomous systems, including proactive architectures, multi-robot cooperation, and human-machine interaction. She leads and participates in high-profile projects like SAMURAI (AID), CHIP-GT (ANR-DFG), and HAICO (CASAC Chair). She has supervised numerous PhD students and post-doctoral researchers, collaborating with institutions like ONERA, INRAe, and international partners. Education: PhD in Control and Embedded Systems (University of Toulouse 2013), Master's in Automatic Systems (Université Paul Sabatier 2008-2009), and Engineer Diploma from PUCRS (2002-2008). Awards include the Amelia Earhart Fellowship (2011) and ISAE-SUPAERO Foundation PhD Fellowship (2013). Research interests span decision-making algorithms, multi-agent systems, and neuroergonomics. Key projects address UAV navigation, blockchain integration for autonomous systems, and physiological computing for human-robot interaction. Teaching responsibilities include modules on autonomous systems, control systems, and neuroergonomics at ISAE-SUPAERO's engineering and master's programs. She co-leads the DECISIO doctoral team (ISAE-ONERA) and is involved in executive roles for conferences like EDSYS'.

• Scheduling and Control
• Discrete Event Systems
• Real-Time Systems
• +۳ مورد دیگر

Patrice Bonhomme is a Teacher-researcher at the University of Tours, affiliated with the Polytechnic School of the University of Tours (EPU) and the Fundamental and Applied Computer Science Laboratory of Tours (LIFAT, UR 6300). His primary role involves both teaching and research activities within the Computer Science Department. Patrice holds a PhD from the University of Tours, as evidenced by his 'MEMOIRE DE THESE' published in 2001. His educational background and current position reflect a deep specialization in computer science and engineering disciplines. His research interests focus on Scheduling and Control , Discrete Event Systems , and Real-Time Systems , with particular emphasis on Petri Nets as a modeling framework. He explores advanced control strategies, state estimation techniques, and fault diagnosis methodologies for time-critical systems. Key areas include energy optimization, decentralized control policies, and symbolic analysis of Petri Net-based systems. In recent years, his work has centered on developing novel algorithms for P-Time Petri Nets, addressing challenges in unobservable transitions and partial observability. He has contributed to both theoretical advancements and practical applications in manufacturing and real-time system optimization. Patrice has been actively involved in international conferences, presenting papers at IFAC and IEEE events. Notably, he served as a reviewer for the T-ASE journal (2004-2005) and contributed to proactive/reactive control methodologies for time-critical systems. His research demonstrates a consistent trajectory toward enhancing system efficiency through formal methods and algorithmic innovations. He is part of the Computer Science Laboratory Team at the University of Tours, specializing in the control of discrete event systems. No scientific awards are explicitly listed in his profile, and he has no documented advisees.

• Scheduling
• Flexibility
• Robustness
• +۳ مورد دیگر

Carl Esswein is a Lecturer-researcher in the Computer Science Department of the Polytechnic School of the University of Tours (EPU), affiliated with the Fundamental and Applied Computer Science Laboratory of Tours (LIFAT). His work focuses on scheduling problems with objectives centered on flexibility and robustness. Doctoral thesis, François Rabelais University of Tours (2003) DEA Computer Science, François Rabelais University of Tours (2001) Diploma in Computer Science Engineering for Industry (DIII), François Rabelais University of Tours (2001) Esswein's research explores the intersection of scheduling, flexibility, and robustness in operational research. His publications address multi-criteria optimization, robust shop scheduling, and algorithm design for complex scheduling scenarios. He has contributed to methodological advancements such as revisiting Branch and Bound strategies and analyzing trade-offs between flexibility and makespan efficiency. His work spans theoretical and applied domains, including enumeration complexity in multicriteria scheduling, optimization algorithms for production planning, and computational methods for constraint satisfaction in manufacturing systems. Publications highlight interdisciplinary applications in industrial engineering and computer science. Carl Esswein is actively associated with the LIFAT laboratory, where he collaborates on fundamental and applied research in computer science. His affiliations include the Computer Science Department of Polytech'Tours and the University of Tours.

Majid Eskandarpourمشاهده پروفایل

دانشیار

• Operations Management
• Supply Chain Network Design
• Sustainable Logistics
• +۱ مورد دیگر

Majid Eskandarpour is Associate Professor at IÉSEG, specializing in Operations Management. His research focuses on sustainable supply chain network design, logistics optimization, and renewable energy operations. Dr. Eskandarpour holds a Ph.D. in Management Sciences from Ecole des Mines de Nantes, a Master's in Industrial Engineering from Tarbiat Modares University, and a Bachelor's from Azad University. His research explores multi-objective optimization in healthcare networks, disruption management in distribution systems, and maintenance routing for offshore wind farms. Methodologically, he develops exact algorithms and metaheuristic approaches for complex logistics problems. Before academia, he held industry positions as Production Planning Manager at Iran Booster Co and industrial planning roles at Solico Group.

Rabia NESSAHمشاهده پروفایل

استاد

• Game Theory
• Operations Research
• Economics
• +۳ مورد دیگر

Rabia NESSAH is a Full Professor of Quantitative Methods at IÉSEG School of Management in Lille, France. She holds a Ph.D. in Industrial Systems and Optimization (2005) from Technologic University of Troyes and an HDR in Economics and Mathematics (2013) from University of Paris Est. Her academic career includes prior roles as Lecturer at Technologic University of Troyes (2002–2006) and Université de Tizi-Ouzou, Algeria (2001–2002). Her research focuses on game theory, optimization, and scheduling algorithms, with notable contributions to equilibrium solutions (Berge, Nash, and hybrid variants), production scheduling models, and mathematical economics. She has published extensively in journals like European Journal of Operational Research , Journal of Mathematical Economics , and Mathematical Social Sciences , and co-authored books on revolutionary choice theories and voting paradoxes. NESSAH teaches courses in quality control, production planning, and optimization methods at the undergraduate level. Her work bridges theoretical advancements in game theory with practical applications in operations research and economic modeling.

Frits de Nijsمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Renewable Energy Systems
• Reinforcement Learning
• +۳ مورد دیگر

Frits de Nijs is a Research Fellow in the Department of Data Science & AI at Monash University. His work focuses on advancing artificial intelligence techniques for energy systems optimization, multi-agent decision-making, and sustainable infrastructure. He has contributed to projects such as the Human-in-the-loop Microgrid Project and RACE for 2030 CRC, addressing challenges in renewable energy integration and smart grid management. Key research areas include reinforcement learning applications for energy curtailment, HVAC control systems, and resource-constrained multi-agent planning. His projects often bridge AI methodologies with real-world energy challenges, aiming to enhance grid reliability, carbon efficiency, and fair resource allocation. Notable collaborations involve researchers in machine learning, sustainable energy, and building automation. He has actively participated in 6 major research projects since 2020, including initiatives on AI-ready buildings and probabilistic multi-agent learning environments. His publications span conferences like AAAI, NeurIPS, and IEEE Power & Energy Society events, emphasizing peer-reviewed contributions to energy-AI intersections.

Nikolay Mehandjievمشاهده پروفایل

استاد

• Enterprise Information Systems
• Artificial Intelligence
• Supply Chain Management
• +۵ مورد دیگر

Nikolay Mehandjiev is a Professor of Enterprise Information Systems and Academic Director of the Data Visualisation Observatory at Alliance Manchester Business School, University of Manchester. He specializes in enabling non-technical audiences to understand complex systems like AI, service-based systems, and industrial robotics through immersive visualizations. His research is grounded in collaborations with major industries including Airbus, Comau, and SAP. He has managed over €8M in projects, including the €10M DIGICOR initiative with Airbus and Comau. Notable achievements include the 2018 BAM Best Developmental Paper Award and the 2019 EMCIS Best Paper Award. His work focuses on supply chain collaboration, Industry 4.0, and digital transformation. Recent projects explore digital twins in the metaverse, voice assistant technologies, and adaptive manufacturing systems. Supervising 18 research projects, he emphasizes practical applications of AI and data analytics in healthcare, manufacturing, and sustainability. Professional activities include visiting appointments at SAP Research Labs, University of Sussex, and University of New South Wales. He actively contributes to academic conferences and policy reviews, advancing both theoretical and applied research in enterprise systems.

David McAllesterمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

David McAllester is a Professor at the Toyota Technological Institute at Chicago (TTIC) and holds a part-time Professor position at the University of Chicago's Department of Computer Science. He earned his B.S., M.S., and Ph.D. from MIT (1978, 1979, 1987). His research spans Artificial Intelligence, Machine Learning, and Theoretical Computer Science , with notable contributions to automated theorem proving (Ontic system), reinforcement learning, probabilistic programming, and computer vision. He is a Fellow of AAAI (since 1997) and has received multiple test-of-time awards for seminal papers in AI planning, constraint solving, and computer vision. Key Contributions: Developed the Ontic verification system for mathematical proofs. Pioneered conspiracy numbers in game tree search (influenced Deep Blue). Co-authored foundational work on policy gradient methods in reinforcement learning. Advanced PAC-Bayesian learning theory and co-training methods. Teaching: Teaches TTIC31230 (Fundamentals of Deep Learning), emphasizing mathematical rigor and research skills in computer vision, NLP, and reinforcement learning. Labs/Teams: Co-founded TTIC's research initiatives in AI and machine learning. Collaborates with industry and academia on foundational AI challenges. Awards: AAAI Fellow (1997) Test-of-Time Awards (AAAI, ICLP, CVPR)

Odile Bellenguezمشاهده پروفایل

استاد

• Computer Science
• Ethics in Technology
• Operations Research
• +۳ مورد دیگر

Professor Odile Bellenguez is a faculty member in the Automation, Production and Computer Sciences department at IMT Atlantique. Her work focuses on integrating ethical considerations into algorithm design, particularly in personnel scheduling and decision support systems. She holds a University Diploma in Philosophy from Université Paris Nanterre, enhancing her interdisciplinary approach. Her research bridges computer science, discrete mathematics, and social sciences, with collaborations in sociology, psychology, and management science. Notable projects include co-supervising a thesis on ethical inclusion in healthcare scheduling and developing fair nurse rostering algorithms. She also pioneered a philosophy-infused framework for algorithmic ethics, addressing fairness and equity in automated systems. Professor Bellenguez actively engages in pedagogy, designing courses that teach ethical implications of algorithms through case studies like traffic navigation and workforce scheduling. Her recent work emphasizes cross-disciplinary dialogue to address societal challenges posed by algorithmic systems. Her contributions span over 50 publications, with a focus on scheduling optimization (RCPSP variants, nurse rostering) and ethical frameworks for automated decision-making. She plans to host a summer school for PhD students exploring ethics in algorithmics.