Chenguang Liu is a researcher at Institut Polytechnique de Paris , France, with a focus on computational methods, machine learning, and control systems. His work bridges theoretical and applied research across multiple domains including computer vision, signal processing, and operations research. Key research areas: Machine Learning, Computer Vision, Computational Physics, Control Theory Notable publication trends: Develops novel algorithms for parallel computing, peridynamic modeling, and real-time systems Recent contributions include Bayesian neural networks for gas-bearing prediction, multi-agent reinforcement learning for UAV swarms, and domain adaptation techniques in object detection. He collaborates extensively with researchers in electrical engineering and applied mathematics disciplines.
Guillaume Ducoffe is an Associate Professor at the Faculty of Mathematics and Informatics, University of Bucharest, Romania, and a Senior Research Scientist at the National Institute of Research and Development in Informatics (I.C.I.), Romania. He is also affiliated with a joint research team between ICI and the Research Institute of the University of Bucharest (I.C.U.B.). Previously, he was a PhD student at Université Côte d'Azur, France, under the supervision of David Coudert, within the COATI project-team at Inria Sophia Antipolis. PhD, Université Côte d'Azur, France (2016) Master's Thesis, MPRI-ENS Cachan (2013) His research centers on algorithmic graph theory , with emphasis on computation in large graphs , including parameterized algorithms for problems like Diameter and Maximum Matching. He investigates metric tree-likeness in real-life networks through Gromov hyperbolicity, which has implications for routing efficiency and congestion. His work extends to information propagation using game-theoretic models such as coloring and hedonic games, and to online targeting detection , where he develops theoretically sound algorithms to uncover sensitive attribute targeting on the web via reductions to PAC learning of k-juntas. He also explores combinatorial topics like proper connectivity and Randic indices with applications in cryptography and chemistry. The analysis of his recent publications reveals a strong trend in theoretical computer science , particularly in the design and complexity of graph algorithms, structural graph properties, and their applications in network science and privacy. His work bridges pure graph theory with practical concerns in data centers, web transparency, and social networks. Guillaume Ducoffe has made significant contributions to both journal and conference literature, publishing in venues such as Discrete Applied Mathematics , SIAM Journal on Discrete Mathematics , ACM SIGMETRICS , and USENIX Security . His research is highly interdisciplinary, combining insights from algorithms, game theory, and network analysis. He actively supervises and mentors students, though specific names are not listed in the provided materials. He has been involved in research grants, including a postdoc grant from I.C.U.B., and has collaborated with prominent researchers such as David Coudert, Nicolas Nisse, Augustin Chaintreau, and Roxana Geambasu. His teaching includes core courses such as Data Structures and Algorithms , Advanced Graph Algorithms , and Advanced Programming Techniques at the University of Bucharest. Guillaume Ducoffe is a key member of collaborative research initiatives, including the joint ICI-I.C.U.B. team and the former COATI project at Inria. His work continues to advance the theoretical foundations of graph algorithms while addressing pressing issues in web transparency and network design.
Pranabendu Misra is an Assistant Professor in the Department of Computer Science at Chennai Mathematical Institute (CMI), India. He previously held postdoctoral positions at the Max Planck Institute for Informatics, Germany, and the University of Bergen, Norway. His research is centered in theoretical computer science with strong contributions to algorithms and complexity. Current Affiliation: Assistant Professor, Department of Computer Science, Chennai Mathematical Institute, India Previous Positions: Postdoctoral Fellow, Max Planck Institute for Informatics; Researcher, University of Bergen Education: PhD from Institute of Mathematical Sciences (IMSc), Chennai, advised by Prof. Saket Saurabh; M.Sc. and B.Sc. from CMI in Computer Science and Mathematics Pranabendu's research interests span a broad spectrum of algorithmic theory, including Graph Theory , Parameterized Complexity , Approximation Algorithms , Matroids , Algebraic Methods , Derandomization , Algorithmic Game Theory , Streaming and Dynamic Graphs , and increasingly, AI/ML and Deep Learning . His work combines deep theoretical analysis with practical algorithmic insights. His recent publications in top-tier venues like SODA, STOC, ICALP, and EC reveal a strong trend toward developing meta-theorems, kernelization techniques, and FPT approximation algorithms for fundamental graph problems such as vertex cover, feedback vertex set, connectivity augmentation, and disjoint paths. He also explores fair division in algorithmic game theory and fault-tolerant network design. His scientific honors include: Google India Research Award (2022) SERB Startup Research Grant (2022) Fujitsu Research Grant (2023) Pranabendu actively contributes to academic advising and teaching, offering courses such as Advanced Machine Learning , Approximation Algorithms , and Parameterized Algorithms . He has collaborated extensively with leading researchers including Daniel Lokshtanov, Saket Saurabh, Meirav Zehavi, and Kurt Mehlhorn. While specific grant details are limited, his funded projects suggest strong support for research in algorithms and AI/ML. He teaches at CMI and has previously taught at the Max Planck Institute. He is affiliated with a vibrant research group focusing on algorithms and complexity, working on both foundational and applied algorithmic problems. His recent interest in AI/ML indicates an expanding research scope bridging theoretical guarantees with modern machine learning applications.
Amit Kumar is a Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Delhi . He teaches several core and advanced courses including Mathematical Programming (COL 756) , Advanced Algorithms (COL 758) , Approximation Algorithms (COL 754) , Introduction to Automata and Theory of Computation (COL 352) , Design and Analysis of Algorithms (COL 351) , Data Structures (COL 106) , Discrete Mathematics (COL 202) , and Numerical Analysis and Scientific Computing (COL 726) . His office is located in Room 417, Bharti Building, and he uses email amitk@cse.iitd.ac.in for communication. His research lies primarily in combinatorial optimization and online algorithms , with recent work focusing on clustering problems with side constraints, understanding biases in evaluation processes, and online allocation with additional constraints. He has made significant contributions to approximation algorithms, stochastic optimization, and fairness in algorithmic systems. His research interests span theoretical foundations and practical applications in algorithm design. The recent publications (2023–2025) reflect a strong trend in fairness-aware algorithms , learning-augmented online algorithms , and advanced clustering techniques . The work spans top-tier venues such as STOC, FOCS, SODA, ICML, and NeurIPS, indicating high impact and interdisciplinary reach. Topics include bias in evaluation, fair service allocation, consensus clustering, and robust sorting, showing a shift toward socially aware and data-driven algorithmic solutions. Scientific Awards: Best paper award at International Symposium on Algorithms and Computation (ISAAC), 2023 Advising and Grants: While specific students and grants are not listed in the provided texts, Amit Kumar has supervised or co-supervised numerous publications with junior collaborators, suggesting active mentoring. His extensive collaboration network (e.g., with Anupam Gupta, Debmalya Panigrahi, Ragesh Jaiswal) indicates leadership in research projects and likely involvement in funded grants, though specific grant details are not mentioned. Labs and Teams: No specific lab or research group name is mentioned in the provided materials. However, his frequent collaborations and supervision of research projects suggest he is part of or leads a research team in theoretical computer science and algorithms at IIT Delhi.
Giacomo Como is an Associate Professor in the Department of Automatic Control at the Faculty of Engineering, Lund University, Sweden. He has been affiliated with the university since August 2011, contributing significantly to research and education in systems and control theory. His research interests include network dynamics, game theory, applied probability, and distributed control, with applications in cyber-physical systems, transportation networks, and socio-economic systems. His work emphasizes resilience, information flow, and emergent behaviors in large-scale interconnected systems. The recent publications highlight a strong focus on distributed control, routing in dynamical networks, opinion dynamics, and cascading failures. These works integrate concepts from control theory, graph theory, and stochastic processes, demonstrating interdisciplinary depth in analyzing and designing robust networked systems. George S. Axelby Outstanding Paper Award (2015) Project Research Grant from the Swedish Research Council (VR), 2016–2019 Como has supervised master’s theses and taught both undergraduate and PhD courses such as Network Dynamics and Nonlinear Control. He has also been active in academic service, serving as an Associate Editor for IEEE Transactions on Network Science and Engineering and chairing program committees for international workshops. His collaborations span MIT, Yale, Uppsala University, and the Gran Sasso Science Institute, reflecting broad academic engagement. He leads research in large-scale network dynamics, contributing to foundational and applied aspects of control in complex systems, with ongoing impact in both theoretical and practical domains.
Prof. Stefan Minner is a Full Professor of Logistics and Supply Chain Management at the TUM School of Management, Technical University of Munich since 2012. His academic career includes professorships at the University of Vienna (2008–2012) and University of Mannheim (2004–2008), with prior roles at the University of Calgary and University of Magdeburg. He holds a Ph.D. (1999) and Habilitation (2003) from Magdeburg, focusing on supply chain optimization and inventory management. Research focuses on data-driven logistics network design, inventory management under uncertainty, and urban logistics. Key areas include blockchain in supply chains, commodity procurement strategies, and automation in retail operations. He serves as Editor-in-Chief for the International Journal of Production Economics and holds leadership roles in logistics societies like the German Logistics Association (BVL) and International Society for Inventory Research (ISIR). His honors include Fellow of ISIR (2016), Wirtschaftswoche Top Researcher (2020), and the M&SOM Practice-based Research Competition (2019). Research output spans 25+ years, with recent work addressing AI in logistics, sustainable supply chains, and pandemic-era ambulance dispatching systems. Teaching includes courses on inventory management, transportation analytics, and sustainable supply chains. His lab's work integrates optimization, simulation, and empirical methods to solve real-world logistics challenges in automotive, retail, and manufacturing sectors.
Volker Paelke is an Assistant Professor of 3D Geovisualization and Augmented Reality at the Institute of Cartography and Geoinformatics, Leibniz University Hannover. His work focuses on user interface design, mixed reality systems, and geoinformatics applications. He leads research in augmented reality visualization, location-based services, and spatial data interaction. Education background includes a PhD in 3D Illustration Design (2002) and extensive postdoctoral research in mixed reality systems. His interdisciplinary approach bridges computer science, geospatial technologies, and human-computer interaction. Research interests emphasize innovative user interfaces for geovisualization, context-aware systems, and applications of augmented reality in archaeology, education, and navigation. Key projects include the GeoScope mixed-reality device and AR-based navigation tools. Over 50 peer-reviewed publications since 2002 span topics like tag-cloud visualizations, adaptive LIDAR scanning, and agile design methods for mixed reality systems. His work frequently explores integration of software engineering and usability engineering in system development. Labs/Teams: Active in the Institute's Mixed Reality Lab and collaborates with industry partners on geovisualization tools. Advises on EU-funded projects like GeoPilot involving image-based interaction and public participation systems.
Prof. Dr. Harald Räcke is a Professor at the Technische Universität München (TUM) , affiliated with the TUM School of Computation, Information and Technology in the Effiziente Algorithmen department. His research focuses on the design and analysis of algorithms, particularly approximation algorithms, online algorithms, graph partitioning, scheduling, routing in parallel systems, and metric embeddings. He holds a PhD from the University of Paderborn and has held academic positions at institutions including Carnegie Mellon University (USA), Toyota Technological Institute (USA), and the University of Warwick (UK) before joining TUM in 2011. His work has been recognized with prestigious awards, including the Best Paper Awards at STOC 2008 and FOCS 2002. Key research trends in his publications emphasize theoretical computer science and algorithm design, with a focus on optimizing network-related problems such as congestion minimization, balanced partitioning, and oblivious routing. His recent work explores dynamic graph algorithms and sparsification techniques, reflecting advancements in handling large-scale network challenges. Education: PhD in Algorithms and Complexity (2003), University of Paderborn Studies in Computer Science at University of Paderborn Professional Milestones: Postdoc: Carnegie Mellon University (Pittsburgh, USA) and Toyota Technological Institute (Chicago, USA) Assistant Professor at University of Warwick (UK) Joined TUM in 2011 His scientific awards highlight contributions to theoretical computer science: Best Paper Award, 40th ACM Symposium on Theory of Computing (2008) Best Paper Award, 43rd IEEE Symposium on Foundations of Computer Science (2002)
Mathieu Laurière is an Assistant Professor of Mathematics and Data Science at New York University Shanghai, actively contributing to the NYU-ECNU Institute of Mathematical Sciences. His academic trajectory includes a postdoctoral fellowship at NYU Shanghai, a Postdoctoral Research Associate position at Princeton University’s Operations Research and Financial Engineering department, and a Visiting Faculty Researcher role at Google Research (Brain Team, Paris). His educational credentials comprise a Master of Science from Sorbonne University (Paris 6) and École normale supérieure Paris-Saclay (formerly ENS Cachan), followed by a PhD from Université Paris Diderot (Paris 7). Research focuses on Mean Field Games and Mean Field Control , developing numerical methods and machine learning algorithms for large-scale strategic interactions. He bridges stochastic analysis , partial differential equations , and deep learning to address finance, operations research, and environmental challenges like traffic routing and epidemic control. Recent work emphasizes scalability and robustness in multi-agent systems. His 2024-2025 publications reveal a dominant trend toward reinforcement learning for mean field games, featuring convergence guarantees, graphon-based control, Stackelberg formulations for green regulation, and cross-disciplinary links with optimal transport. Key applications target investment strategies, carbon markets, and traffic systems using simulation-free deep learning. No scientific awards or fellowships are documented in the provided sources. As faculty, he mentors graduate students though specific names aren’t listed. His leadership in co-organizing webinars and delivering tutorials (e.g., at INFORMS and AAAI conferences) underscores academic engagement. Research is supported by institutional collaborations with Google Brain and Princeton University, but grant details remain unspecified. He operates within the NYU-ECNU Institute of Mathematical Sciences at NYU Shanghai, building on prior affiliations with Google’s Brain Team and Princeton’s ORFE department. His work integrates teams across machine learning, operations research, and financial engineering for real-world implementations.
Sven O. Krumke is a Professor in the Department of Mathematics at the Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU), where he has been a faculty member since 2004. He currently serves as the Dean of the Faculty of Mathematics, overseeing academic and administrative affairs. His research is centered in combinatorial optimization and algorithmic game theory, with strong applications in logistics, emergency response, and network design. PhD, University of Würzburg (1997) Habilitation, Technical University of Berlin (2002) Professor, RPTU Kaiserslautern-Landau (since 2004) His research interests include combinatorial optimization, approximation and online algorithms, robust scheduling, algorithmic game theory, and graph-theoretic applications. He has led major interdisciplinary projects such as GRK 2982: MIMO (Mathematics of Interdisciplinary Multiobjective Optimization) and ONE PLAN , focusing on optimizing emergency medical services in Rhineland-Palatinate. His work often bridges theoretical computer science and real-world decision-making systems. His recent publications (2016–2022) emphasize robust optimization , pandemic response logistics , decision-support systems in healthcare , and online routing . These works span domains such as vaccine location strategies, ambulance dispatch, car-sharing relocation, and scheduling under uncertainty. The recurring themes are efficiency, resilience, and algorithmic fairness in dynamic environments. Notable scientific contributions include work on network design, flow problems with budget constraints, and game-theoretic models of routing. While no specific awards are listed, his leadership in DFG-funded research groups underscores his academic standing. He actively supervises bachelor's and master's theses and teaches courses such as Grundlagen der Mathematik I: Analysis . He has collaborated with researchers across Germany and internationally, particularly in algorithmic game theory and optimization under uncertainty. Dr. Krumke leads and contributes to research teams in: AG Optimierung (Optimization Research Group) at RPTU GRK 2982: MIMO – interdisciplinary multiobjective optimization ONE PLAN – optimization in emergency medical response
Ralf Borndörfer is a Professor and Head of the Network Optimization Department at the Zuse Institute Berlin (ZIB) , a leading research institution in mathematical algorithmic intelligence. His work focuses on optimizing complex transportation systems, particularly in railway operations, public transit, and air cargo logistics. He leads projects like Timetabling with Duality and Zonotopes, Symmetric Line Planning, and WILSON-LEARN, which address challenges in train scheduling, electric vehicle integration, and predictive maintenance. Key Research Areas : Mathematical optimization, railway timetabling, public transport planning, game theory for toll enforcement, and electric vehicle scheduling. Notable Collaborations : Projects with Deutsche Bahn, BIFOLD, and MATH+ Cluster of Excellence. His recent publications (2023-2025) explore: Non-linear battery modeling in electric bus scheduling Predictive maintenance integration in rolling stock rotations Logic-constrained shortest paths for flight planning Price-sensitive routing in public transport He has contributed to algorithmic frameworks like the Restricted Modulo Network Simplex Method and Bayesian rolling horizon approaches, emphasizing computational efficiency and real-world applicability.
Prof. Dr. Maximilian Schiffer is an Associate Professor at the Professorship of Business Analytics & Intelligent Systems , TUM School of Management , Technical University of Munich. He holds a Ph.D. in Operations Research from RWTH Aachen University (2017) and has previously served as a visiting postdoctoral scholar at Stanford University and a postdoctoral scholar at RWTH Aachen. Education: Ph.D. in Operations Research, RWTH Aachen University (2017) Maximilian’s research focuses on developing operations research and prescriptive analytics methods to address societal mobility and transportation challenges, including electric vehicles , autonomous mobility-on-demand systems , and multi-modal transportation networks . His interdisciplinary work bridges robotics , computer science , and transport engineering . Recent publications highlight advancements in autonomous fleet control , electric vehicle charging infrastructure , and collusion detection in dynamic pricing . Scientific Awards: INFORMS TSL Dissertation Prize GOR Doctoral Dissertation Prize Best Paper Award, IEEE Conference on Intelligent Transportation Systems Maximilian is an associate member of the GERAD research group and serves on the editorial board of OR Spectrum and the TSL Dissertation Prize committee . His lab, the Professorship of Business Analytics & Intelligent Systems , trains students in machine learning, optimization, and future mobility systems.
Dr. David Kuehn is a Senior Research Fellow at the German Institute for Global and Area Studies (GIGA) Institute for Asian Studies and Coordinator of the GIGA Forum. He holds a PhD in Political Science from Heidelberg University, where his dissertation focused on institutionalizing civilian control of the military in new democracies. His academic career includes senior research roles at Heidelberg University (2015-2019) and ongoing positions at GIGA since 2018. MA in Political Science and Modern Chinese Studies from Heidelberg University, Chinese Culture University, and Chinese National Normal University Taipei PhD in Political Science from Heidelberg University Dr. Kuehn's research centers on civil-military relations, democratization, authoritarianism, and comparative research methods. His work explores how militaries influence democratic transitions, the personalization of executive power during crises like the COVID-19 pandemic, and the interplay between military behavior and political stability in Asia and Latin America. Recent publications include analyses of the 2024 South Korean self-coup, military roles in pandemic responses, and methodological critiques of quantitative/qualitative divides. He contributes to debates on executive power centralization in the Global South and has authored a 2023 book Routes to Reform: Civil-Military Relations and Democracy in the Third Wave , awarded the Giuseppe Caforio ERGOMAS Award in 2024. Giuseppe Caforio ERGOMAS Award (2024) Best C&M Working Paper Award (2011) Dr. Kuehn collaborates with researchers from the University of Passau, Heidelberg University, and the Volkswagen Foundation. His teaching includes courses on game-theoretic models, qualitative comparative analysis, and democratic military control at Heidelberg University and ETH Zurich's Swiss Military Academy. He served as coordinator for the ERGOMAS working group on civilian military control (2009-2019) and editor for the journal Democratization's book review section since 2013.
Zhu Han is a Professor at the University of Houston, TX, USA, with a PhD from the University of Maryland, College Park. He is affiliated with the Chinese Academy of Sciences' Aerospace Information Research Institute in Beijing, China. His research spans wireless networks, 6G communication, and machine learning applications in telecommunications. Key areas: Semantic communication, UAV networks, IoT security, quantum networking. Recent publications focus on federated learning, optical IRS for VLC, and resource allocation in LEO satellite systems. His work integrates AI with network security and edge computing. He collaborates extensively on topics like reconfigurable intelligent surfaces, quantum-assisted optimization, and privacy-preserving protocols. No specific awards or student details are provided in the dataset.
Professor Giselher Pankratz serves as Head of University at the Deutsche Bundesbank University of Applied Sciences, where he teaches IT and IT management, process management, and payments systems. His courses include SAP exercises, IT project management, cashless payment instruments, bank management business games, and payment transaction case studies. He earned his economics degree specializing in business informatics from the University of Siegen and completed his PhD at FernUniversität in Hagen under Professor Hermann Gehring, receiving the Stinnes Logistics Award in 2003 for his dissertation. His research spans combinatorial auctions, heuristic decision-making approaches, multi-objective optimization, process management, requirements analysis patterns, cyber laundering technology, and Distributed Ledger Technologies. Professor Pankratz's publication history reveals an evolution from logistics optimization (2000-2010) toward blockchain applications (2019), with consistent focus on combinatorial auctions and transportation systems. His work bridges theoretical operations research with practical financial technology applications, particularly in payment systems and distributed ledgers. Stinnes Logistics Award 2003 (for doctoral dissertation) As a peer reviewer for international journals and coordinator of research projects sponsored by Germany's Federal Ministry of Economics and Technology, he has lectured in Bachelor's and Master's programs at Hagen Institute for Management Studies and Danube University Krems. His academic leadership extends to editing works like 'Intelligent Decision Support' (2008) and 'Intelligente Systeme zur Entscheidungsunterstützung' (2008). While specific lab affiliations aren't detailed, his research involved collaborations at FernUniversität in Hagen and practical industry projects, particularly in transportation logistics and payment systems development.
