Tamás Tettamanti is a Professor at Budapest University of Technology and Economics, serving as Deputy Head of the Department of Control for Transportation and Vehicle Systems. He holds a PhD (2013), Habilitation (2023), and DSc (2023) in Transportation Engineering, with expertise in road traffic modeling and control. 2007–2010: PhD Student 2010–2013: Assistant Lecturer 2014–2018: Senior Lecturer 2019–2024: Associate Professor 2025–present: Full Professor Education: High School Graduation (2001), DEUG (2004), MSc in Transportation Engineering (2007), Jazz Trumpet Graduate (2008) PhD (2013), Habilitation (2023), DSc (2023) Research focuses on road traffic modeling, intelligent transportation systems (ITS), autonomous vehicle integration, and emission-aware traffic control. His work bridges theoretical developments with real-world applications, including wireless traffic light systems and deep learning for urban mobility peaks. Scientific Awards: BME PhD Research Prize (2012) Literary Awards from Hungarian Scientific Association for Transport (2013, 2017, 2021, 2023) Bolyai János Research Scholarship (2017–2020) Michelberger Master Prize (2022) BME Jubilee Medal (2023) He has led major projects such as Dynamic Adaptive Traffic Control Services (2020–2024) and Deep Learning Anticipated Urban Mobility Peaks (DARUMA) (2021–2024), with industry collaborations including Google-BME Traffic Lab.
Maurice Gagnaire is a Full Professor at Télécom Paris in the Computer Science and Networks (Infres) department, affiliated with the Networks, Mobility and Services (RMS) research team and the Information Processing and Communication Laboratory (LTCI). He has contributed extensively to optical network design, cloud computing, and network virtualization. Education: Engineering degree from Télécom SudParis, Master's in Computer Systems (Paris VI), Ph.D. (Télécom ParisTech), HDR (University of Versailles) Research Interests : His work focuses on translucent WDM networks , green networking , dynamic resource allocation , and physical layer impairments . Key projects include traffic grooming , failure detection , and energy-aware routing . Publications & Awards : He co-authored Springer's Traffic Grooming for Optical Networks and received the IBM Faculty Award 2014 . His research spans optical access systems , cloud brokering , and multi-layer traffic engineering . Scientific Honors: IBM Faculty award, Chevalier de l'Ordre des Palmes Académiques Academic Service : He served as expert for NSF (USA), IEEE, and ARCEP. His leadership includes coordinating the RMS research group and leading the Optimization and Networking Cluster.
Lan Wang is a Professor in the Department of Computer Science at the University of Memphis, temporarily assigned as a Program Director at the NSF. She holds a PhD in Computer Science from UCLA (2004). Her research focuses on Internet architecture, network security, and wireless sensor networks, with major grants from NSF, NIST, and DoD, including a $15M NSF-funded 'Named Data Networking' project. She has served as Department Chair (2016-2023) and is an IEEE Senior Member. Dr. Wang's research emphasizes scalable, reliable, and secure Internet infrastructure. She has pioneered advancements in named-data networking (NDN), including protocols for adaptive forwarding, secure access control, and efficient traffic management. Her work extends to applications in smart cities, public safety, and healthcare data sharing. She has received prestigious awards including the Willard R. Sparks Eminent Faculty Award (2022) and Dunavant Professorship (2021). Her teaching spans courses like Networking and Information Assurance, Advanced Computer Networks, and Wireless and Mobile Computing. She actively promotes gender diversity in CS, co-organizing events like the 'Networking Networking Women' panel at SIGCOMM. Her grants total over $15M, including NSF’s NDN project and university-funded initiatives. She advises the Women in Computing student chapter and has served on over 50 conference committees.
Max Klimm is an Assistant Professor for Discrete Optimization at Technische Universität Berlin, affiliated with Faculty II – Mathematics and Natural Sciences and the Department of Mathematics. He leads the research group in Discrete Optimization and holds editorial roles at journals like the International Journal of Game Theory and Operations Research Forum . His academic journey includes a PhD in Mathematics from TU Berlin (2012), followed by roles as an Assistant Professor at Humboldt-Universität zu Berlin and Head of the Junior Research Group at the Einstein-Center for Mathematics. His research focuses on mathematical optimization, game theory, and mechanism design applied to multi-agent systems in traffic, telecommunications, and economics. Recent work addresses equilibrium computation in congestion games, stochastic optimization, and algorithmic challenges in network design. Key projects include Combinatorial Network Flow Methods for Gas Markets and the Math+ projects on mechanism design and evolutionary models for networks. Teaching responsibilities include courses on Discrete Optimization, Algorithmic Game Theory, and introductory mathematical courses. His research has been funded by DFG, Einstein Center, and Math+ initiatives. Notable contributions include advancements in parametric flow algorithms, impartial selection mechanisms, and reconstructing historical road networks using cost-benefit models.
Jing Li is an Assistant Professor at the Department of Computer Science in the Ying Wu College of Computing at New Jersey Institute of Technology. She holds a Ph.D. in Computer Science from Washington University in St. Louis (2017), advised by Chenyang Lu and Kunal Agrawal. Ph.D. in Computer Science, Washington University in St. Louis (2017) M.S. in Computer Science, Washington University in St. Louis (2014) B.S. in Computer Science, Harbin Institute of Technology, China (2011) Her research spans Real-Time Systems , Parallel Computing , Reinforcement Learning for System Design , and Scheduling Theory . Current projects include NSF-funded work on real-time systems with parallel resources and ARPA-E/IBM collaborations on reinforcement learning for converter design. Recent publications focus on AI-driven scheduling , parallel task optimization , and reinforcement learning applications in traffic control and circuit design. Key venues include AAAI, RTSS, PPoPP, and RTAS. Outstanding Achievement in Research (2022) Outstanding Paper Awards at RTSS (2018), RTAS (2016), ECRTS (2013) Turner Dissertation Award (2017) She advises graduate students in real-time systems and parallel computing, mentors NSF/ARPA-E projects, and leads professional services as TPC member and workshop organizer.
Xinwei Wang is a Professor at Nanjing University of Aeronautics and Astronautics with an extensive publication record spanning over two decades. Their research primarily focuses on autonomous systems, UAV trajectory planning, medical imaging, and computer vision applications. Wang has established significant collaborative networks with researchers including Yan Zhou, Liang Sun, Xichao Su, and Lei Wang across multiple Chinese institutions. Wang's research interests center on the intersection of robotics, artificial intelligence, and practical engineering applications. Their work demonstrates particular expertise in UAV coordination systems, medical diagnostic technologies, and underwater imaging solutions. Recent publications reveal a growing emphasis on explainable AI systems for medical applications and safety-critical trajectory planning for autonomous vehicles. The publication trends show a consistent output of high-impact research, with recent work increasingly focusing on practical implementations of AI systems in medical diagnostics, autonomous vehicle navigation, and aerospace applications. Wang's research bridges theoretical control systems with real-world engineering challenges, particularly in safety-critical domains requiring precise motion planning and reliable decision-making. Wang has contributed significantly to both theoretical frameworks in optimal control and practical implementations in medical imaging and autonomous systems. Their work on UAV cooperative task assignment and flight deck operations demonstrates strong connections to aerospace engineering applications, while medical imaging research shows interdisciplinary collaboration with healthcare professionals.
Qinglong Han is the Pro-Vice Chancellor (Research Quality) and a Distinguished Professor at Swinburne University of Technology in Melbourne, Australia. He previously held academic and leadership roles at Griffith University and Central Queensland University. His research focuses on networked control systems, multi-agent systems, time-delay systems, smart grids, and unmanned vehicles. He is a Fellow of IEEE, IFAC, and multiple other institutions, and has received prestigious awards including the IEEE Dr.-Ing. Eugene Mittelmann Achievement Award (2024) and Norbert Wiener Award (2021). His research interests span control engineering, applied mathematics, and artificial intelligence. Notable contributions include secure platooning control for autonomous vehicles, resilient control under cyber-physical threats, and optimization of industrial systems. He leads editorial roles in journals like IEEE Transactions on Industrial Informatics and IEEE/CAA Journal of Automatica Sinica. His work emphasizes interdisciplinary applications in smart grids, robotics, and industrial automation. Dr. Han has supervised numerous PhD students in areas like networked control and vehicle dynamics. He has secured grants from ARC and NSFC for projects on networked control systems and renewable energy integration. His achievements include multiple best paper awards and recognition as a Clarivate Highly Cited Researcher in Engineering and Computer Science.
Prof. Otto Anker Nielsen is a Full Professor and Head of the Transport Modelling Division at the Technical University of Denmark (DTU), within the Department of Technology, Management and Economics. He specializes in transport modelling, with 27 years of expertise across passenger and freight transport, and has led over 55 projects at national and EU levels. His work focuses on route choice models, road pricing, public transport optimization, and sustainable mobility. Education: Ph.D. in Transport Modelling, DTU (1992–1994) M.Sc. in Civil Engineering, DTU (1987–1991) Research Management Education, Copenhagen School of Business (CBS) (2008) Research Interests: His research spans transport modelling methodologies, including dynamic traffic assignment, bicycle route choice, and integrated transport systems. He emphasizes sustainable development goals, electrification, and automation in transport. Recent work includes studies on transit-oriented development (TOD), travel time reliability, and crowdshipping optimization. Grants & Projects: Over the last five years, he has secured €9.5 million in grants, leading projects like Quantra (quantum computing in transport) and IPTOP (public transport optimization). He oversees DTU’s inter-departmental transport center and advises international transport models (e.g., Sweden, Norway). Awards: INFORMS Railway Application Section 2016 Student Paper Award (2016) Member of the Danish Academy of Technical Sciences Advising: Supervised 25 PhD students (14 since 2010), 100+ MSc, and 70+ BSc theses. Active in academic leadership, including editorial roles and international conference committees. Labs/Teams: Leads the Transport Modelling Division at DTU and collaborates with global institutions on projects like the European TransTools model. His team develops tools such as the OTM model and Copenhagen-Ringsted Transport Model.
Rainer Kolisch is a Professor of Operations Management at the TUM School of Management, Technical University of Munich , where he has held a chair since 2002. He currently serves as Head of the Operations & Technology Department (since 2024) and previously as Dean of the QTEM Masters Network (2016–present) and Vice Dean of International Affairs (2007–2020). His career includes academic roles at Technical Universities of Dresden (Full Professor, 2002) and Darmstadt (Associate Professor, 1999–2002). Affiliations: TUM School of Management, Technical University of Munich Editorial Roles: Editor-in-Chief of OR Spectrum (2014–2020), Member of editorial boards for journals like International Journal of Production Research Research Interests focus on Airport Operations Management , Health Care Operations Management , Project Management and Scheduling , and Engineer-to-Order Manufacturing . His work addresses dynamic scheduling, resource allocation, and robust optimization in transportation and healthcare systems, with recent studies on electric vehicle charging networks and agile project management. Scientific Awards include: Best Teaching Award (2022) Excellence in Reviewing (2022) OMEGA Best Paper Award (2021) Handelsblatt Research Recognition (2005) DFG Habilitation Fellowship Advising spans numerous PhD and Master’s students , including Christopher Bersch, Robert Brachmann, and Giacomo Dall'Olio. His grants likely include DFG funding, though specifics are not detailed here.
Keivan Navaie is a Professor of Intelligent Networks at Lancaster University’s School of Computing and Communications. He serves as a member of the Independent Scientific Advisory Committee at the Alan Turing Institute, overseeing the £100 million BridgeAI programme, and previously as Principal AI Technology Advisor to the UK Information Commissioner’s Office (ICO). He is recognized with Fellowships from the Institution of Engineering and Technology (IET), Chartered Engineer status in the UK, Senior Fellowship of the Higher Education Academy (HEA), and the IEEE Young Investigator Award. Research Focus: Wireless communications, mathematics, artificial intelligence, 6G networks, blockchain technology, edge computing, cognitive radio networks, and non-orthogonal multiple access (NOMA). Supervision: Actively supervises PhD students in areas like wireless communications and mathematical modeling. Projects: Involved in distributed learning, blockchain integration, 6G research, and spectrum sharing systems. Awards: IEEE Young Investigator Award, Fellow of IET, Chartered Engineer, Senior Fellow of HEA.
Richard Weber is Professor at the Statistical Laboratory, University of Cambridge. His research centers on applied probability with applications to queueing systems, scheduling optimization, and search problems. Key research areas include development of optimal resource allocation strategies and analysis of stochastic systems. Recent publications focus on: Stability conditions and optimization in queueing networks Optimal scheduling policies for parallel processing systems Search theory and resource allocation problems Performance analysis of stochastic systems
Dr. Mark Raadsen is a Postdoctoral Research Fellow at the University of Sydney's Institute of Transport and Logistics Studies (ITLS) within the Business School. His research focuses on the supply side of transport, particularly dynamic/static traffic assignment methods, traffic flow theory, and algorithm design. He co-founded the open-source transport planning tool PLANit . Mark holds an MSc from Twente University (Netherlands) and a PhD from the University of Sydney. **Education:** MSc in Computer Science, Twente University, Netherlands PhD in Transport Planning, University of Sydney **Research Interests:** Combines academic rigor with practical applications, emphasizing aggregation/decomposition methods in multi-scale/multi-modal transport contexts. His work integrates sustainability aspects into transport models to aid policy-making. Key areas include variable speed limits, traffic simulation, and intelligent transport systems. **Teaching:** Contributes to courses such as Strategic Transport Planning, Traffic and Mobility Management, and Quantitative Methods in Transport. His teaching bridges theoretical frameworks with real-world applications. **Awards & Recognition:** While no explicit awards were listed, his work has led to commercial adoption and tangible impacts in transport systems globally. **Labs/Projects:** Co-lead of the PLANit project, an open-source platform for transport planning. His research has influenced both academic discourse and industry practices through models like the link transmission framework.
Roles & Affiliations: Full Professor at the Department of Computer Science, University of Pisa. Served as Vice Director of the Department (2020–2024). Active in editorial roles for journals like Networks and Computers & Operations Research . Education: Laurea in Information Science (University of Pisa, 1985, cum laude), PhD in Computer Science (University of Pisa, 1990). Qualified as Full Professor in Operations Research (2013). Research Focus: Specializes in Combinatorial Optimization, Robust Optimization, Network Design, and Logistics. Key contributions include green wireless networks, home care optimization, and vehicle routing. Awarded the Best Paper in Omega (2018) and recognized for seminal work in Discrete Applied Mathematics (1998). Teaching: Teaches courses in Operations Research, Logistics, and Network Optimization at both undergraduate and graduate levels, including international programs. Grants & Projects: Led national and international projects, including PNRR initiatives on sustainable mobility and IREAD-4.0 for warehouse optimization. Collaborated with industries like Softec S.r.l. and Siemens. Professional Activities: Organized major conferences (e.g., INOC 2009). Served on editorial boards and scientific committees for AIRO, INOC, and other networks. Active in mentoring doctoral students and fostering academic-industrial partnerships.
Kamesh Munagala is an Associate Professor of Computer Science at Duke University, where he has been employed since 2004. His research spans theoretical computer science with applications in e-commerce, databases, data analysis, and networks. He serves as an Area Editor for PeerJ Computer Science and has held leadership roles including Director of Graduate Studies for the Duke CS department from 2012 to 2015. Education: PhD in Computer Science from Stanford University (2003) BTech from IIT Bombay (1998) Munagala's research focuses on algorithm design and discrete optimization, particularly in scenarios with uncertain inputs. His work encompasses approximation algorithms, online algorithms, and algorithmic game theory. Recent research has concentrated on two main themes: (1) Persuading optimizers or learners toward certain objectives through information revelation and pricing, and (2) Ensuring fairness to groups based on proportionality and stability in resource allocation and societal decision making contexts. His theoretical work has practical applications in designing data networks, facility location, data center scheduling, ad slot allocation, ride-share scheduling, and civic budgeting. Analysis of Munagala's recent publications reveals a strong trend toward interdisciplinary research at the intersection of computer science, economics, and social choice theory. His work shows increasing sophistication in handling fairness constraints across multiple dimensions, particularly in societal decision-making contexts like school assignment, participatory budgeting, and redistricting. There is also a notable expansion into the emerging area of large language models for combinatorial optimization, reflecting the field's evolution toward AI-assisted algorithmic solutions. His research consistently bridges theoretical foundations with real-world applications requiring nuanced approaches to resource allocation under uncertainty. Scientific Awards: NSF CAREER Award (2008) Alfred P. Sloan Research Fellowship (2009) Best paper award at WWW 2009 conference Munagala has served as Director of Graduate Studies for the Duke CS department from 2012 to 2015 and was a Visiting Research Professor at Twitter in 2012. His research has been supported by prestigious grants including the NSF CAREER award. He actively mentors students, with many of his publications featuring student co-authors across his two main research thrusts. His work addresses fundamental challenges in computing efficient solutions under uncertainty, designing pricing and incentive mechanisms for selfish agents, and developing fairness-preserving algorithms for societal decision making. Munagala leads research projects in two broad categories: proportionality in resource allocation and societal decision making, and asymmetric information and persuasion in learning and optimization. His work on proportionality explores fairness concepts like proportionality or core-stability across various societal contexts, while his research on asymmetric information addresses how parties with incomplete information collaborate or compete to achieve optimization objectives through strategic information revelation.
Sekhar Somenahalli is a Senior Lecturer at University of South Australia 's UniSA STEM school, affiliated with the School of Natural and Built Environments . His work spans transport planning, urban analytics, and sustainable mobility systems, with a focus on walkability, traffic modeling, and public transport optimization. Research Themes : Walkability Assessment : Developed novel metrics for measuring land-use mix and pedestrian infrastructure quality Traffic Modeling : Pioneered multi-tier frameworks like the Tactical Adelaide Model Public Transport Innovation : Investigated bus-based transit corridors and interchange efficiency Shared Mobility : Identified regulatory and infrastructural barriers to sharing economy services His recent publications analyze census-based mobility prediction, spatiotemporal traffic patterns, and socio-demographic walkability variations. Despite extensive grant funding (e.g., CRC for Low Carbon Living), no specific awards are documented in the provided materials. He supervises research students and applies GIS extensively in urban infrastructure analysis.
