Ting He is a Professor in the Department of Computer Science and Engineering, specializing in interdisciplinary research at the intersection of network sciences, energy systems, and cybersecurity. Their work addresses critical challenges in network tomography, software-defined networking, and cyber-physical systems, with a strong emphasis on advancing edge computing and decentralized learning paradigms. NSF-funded research on Distributed Edge Intelligence (2024–2025) Collaborative projects on Overlay Networks and Adversarial Reconnaissance in SDN Recent publications analyze network topology inference, energy-efficient decentralized learning, and secure cloud file systems. Their research aligns with UN SDGs through contributions to sustainable energy systems and secure IT infrastructure. Key collaborations with Silvestri, La Porta, and Chaudhuri Active in Smart Grid resilience and cascading failure mitigation
Per Enqvist is an Associate Professor in the Division of Numerical Analysis, Optimization and Systems Theory within the Department of Mathematics at KTH Royal Institute of Technology, Stockholm, Sweden. He has held this position since 2009 after progressing from Assistant Professor (2006-2009) and post-doctoral roles at INRIA France and CNR Italy. His academic background includes: Ph.D. in Optimization and Systems Theory from KTH (2001), supervised by Professor Anders Lindquist M.Sc. in Engineering Physics (Civilingenjör) from KTH (1994) with Applied Mathematics focus Post-doctoral studies at INRIA Sophia-Antipolis (2003-2004) and CNR Padova (2001-2003) Enqvist's research centers on mathematical modeling of stochastic processes, scheduling, and queueing theory with applications across operations research, systems engineering, and signal processing. His principal interests span Optimization, Operations Research, Systems Engineering, Signal Processing, Mathematical Systems Theory, and Modeling and Simulation. He has made significant contributions to spectral estimation, covariance interpolation, and resource allocation frameworks. Publication analysis reveals an evolution from foundational systems theory work (2000s) on spectral estimation and minimal realization toward applied optimization in healthcare operations (2010s-2020s). Recent articles address radiation therapy scheduling and contact center modeling using queueing theory with risk-sensitive measures like CVaR, while earlier work established theoretical frameworks for covariance interpolation and passive system synthesis. No scientific awards are documented in the provided information. He has received funding from Vetenskapsrådet (Swedish Research Council) and led the ACCESS seed project on "Robust Spectral Estimation". Enqvist is course responsible for multiple master's program tracks including Aerospace systems and Industrial Engineering, and oversees the Optimization and Systems Theory seminar series. No student advisement details are provided. He maintains affiliations with the ACCESS Linnaeus centre, Center for Industrial and Applied Mathematics (CIAM), and serves on the Swedish Operations Research Society (SOAF) board.
Dae-Jin Lee is an Assistant Professor at IE University’s School of Science and Technology, specializing in statistical modeling and data science. Previously, he served as a Research Line Leader at the Basque Centre for Applied Mathematics (BCAM) and coordinated the Knowledge Transfer Unit in Data Science/AI. His academic background includes a Ph.D. in Mathematical Engineering (2010) from Universidad Carlos III de Madrid and postdoctoral research at CSIRO (Australia). His research focuses on statistical methods for complex data, including penalized splines, tensor product smooths, and applications in biomedicine, epidemiology, environmental science, and sports analytics. He has led multidisciplinary projects funded by public and industry grants, collaborating globally with experts across fields like engineering, medicine, and biology. Key research themes include predictive modeling for health outcomes (e.g., SARS-CoV-2 pneumonia severity), sports injury prevention, and AI in healthcare. His work integrates machine learning with traditional statistical techniques, addressing real-world challenges like pedestrian dynamics simulations and automated medical diagnostics. He is actively involved in scientific organizations, including the Spanish Biostatistics Society and the Statistical Modelling Society. His recent publications highlight innovations in growth curve modeling, AI ethics, and spatiotemporal data analysis, reflecting his commitment to advancing both theoretical and applied statistics.
Jean Walrand is a Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. His research focuses on communication networks, performance evaluation, game theory, and stochastic networks. He has authored several influential books, including Communication Networks: A Concise Introduction and Probability in Electrical Engineering and Computer Science , and holds numerous patents in network resource management. Ph.D. in EECS from UC Berkeley IEEE Fellow and recipient of the Stephen O. Rice Prize INFORMS Lanchester Prize for operations research contributions His research interests span communication networks, queueing theory, congestion control, wireless network scheduling, and economic models for network resource allocation. Walrand's work has significantly impacted network design and optimization, particularly in distributed algorithms and game-theoretic approaches. His recent publications emphasize network architecture, delay variability reduction, and distributed optimization algorithms. Walrand has mentored over 20 Ph.D. students, including notable contributors to wireless networks and network economics. IEEE Koji Kobayashi Award (2012) ACM Sigmetrics Achievement Award (2013) INFORMS Lanchester Prize for Communication Networks book As advisor to students like Libin Jiang and Hoi-Sheung Wilson So, Walrand has shaped research in wireless MAC protocols, bandwidth trading, and network security. His technical reports and patents address practical challenges in switch fabric design, bandwidth allocation, and power management.
Gustavo Vulcano is an Adjunct Professor in the Department of Information, Operations and Management Sciences at the Leonard N. Stern School of Business, New York University, where he has been affiliated since 2002. He served as Assistant Professor (2002–2010), Associate Professor (2010–2017, tenured in 2012), and has held an adjunct role since 2017. His academic work bridges theoretical and applied operations management with strong industry engagement. Education: Ph.D. in Operations Management, Columbia University, 2003 M.Phil. in Operations Management, Columbia University, 2000 M.S. in Computer Science, University of Buenos Aires, 1997 B.S. in Computer Science, University of Buenos Aires, 1994 His research focuses on revenue and pricing analytics , retail operations , and supply chain management , particularly emphasizing customer choice modeling , data-driven optimization , and computational methods in network revenue management . He integrates stochastic modeling and behavioral insights to develop practical pricing and operational strategies. His work is deeply rooted in real-world applications across airlines, retail, and financial services. The analysis of his publications reveals a consistent trend in leveraging data-driven decision-making under uncertainty, with a focus on dynamic pricing, demand learning, and robust optimization. His articles span premier journals such as Operations Research and Management Science , reflecting a strong theoretical foundation combined with empirical and computational rigor. Key thematic areas include customer behavior modeling, network revenue management, and stochastic optimization for service industries. Scientific Awards and Leadership: Chair, INFORMS Revenue Management and Pricing Section (2016–2017) Associate Editor, Operations Research and Management Science Prof. Vulcano has advised numerous PhD and master’s students and has secured research grants through industry collaborations. His consulting projects with Delta Airlines, Sabre Holdings, Aerolíneas Argentinas, and ICBC demonstrate a strong commitment to translating academic research into practical solutions. He has taught core courses such as Operations Management , Pricing and Revenue Management , and Dynamic Programming across undergraduate, MBA, PhD, and MSBA programs, shaping future leaders in data-driven decision-making. He is actively involved in research labs and teams focused on operations analytics and pricing strategy , often collaborating with interdisciplinary groups at NYU Stern and industry partners. His ongoing editorial roles and consultancy reflect sustained engagement in advancing the field of revenue management and operations science.
Dr. Navid Izady is a Reader in Operations & Supply Chain at Bayes Business School, part of City St George's, University of London. His academic career includes a PhD from Lancaster University Management School (2010), and prior roles at the University of Southampton. He specializes in stochastic modelling for healthcare and manufacturing operations, collaborating with hospitals and healthcare organizations on sponsored research and consultancy projects. Dr. Izady holds qualifications in Industrial Engineering from Sharif University of Technology (BSc and MSc) and a PhD in Management Science. He teaches operations management, stochastic modelling, healthcare modelling, and decision analysis across BSc, MSc, and MBA programs. His research focuses on optimizing healthcare logistics, patient flow management, and resource allocation in hospitals. He has developed frameworks for managing pandemic and non-pandemic demand, reconfiguring inpatient services, and optimizing staffing and patient admission/discharge processes. His work bridges theoretical stochastic models with practical healthcare challenges, emphasizing operational efficiency and resilience. Notable contributions include studies on inpatient bed pressure reduction, sample pooling techniques for pandemic testing, and queueing theory applications in emergency departments and specialty clinics. His publications highlight innovations in healthcare operations management and simulation methods. Dr. Izady's expertise includes operations research, simulation, statistics, and stochastic processes. He supports industry partnerships and has supervised numerous research students, contributing to both academic and applied knowledge in healthcare and manufacturing systems.
Yuan Zhong is an Associate Professor of Operations Management at the University of Chicago Booth School of Business . He previously held positions as an Assistant Professor at Columbia University’s Department of Industrial Engineering and Operations Research and was a Postdoctoral Scholar at UC Berkeley’s Computer Science Department. Education: PhD in Operations Research, MIT (2012) MA in Mathematics, Caltech (2008) BA in Mathematics, University of Cambridge (2006) His research focuses on applied probability and stochastic system design , with applications in cloud computing , supply chain management , and e-commerce logistics . Recent work explores multi-period production systems and dynamic resource allocation in data centers and healthcare operations . Recent publications analyze cloud value chains , sparse graph design for delivery networks, and process flexibility in manufacturing. He has contributed to journals like Operations Research , Annals of Applied Probability , and Stochastic Systems . Scientific Awards: 2012 Kenneth C. Sevcik Outstanding Student Paper Award Best Student Paper Award at ACM Sigmetrics (2012) He teaches courses in business process fundamentals and queueing theory , with a future schedule including Operations Management: Business Process Fundamentals (2025–2026). No explicit student advising list was provided.
Suyash Gupta is a Tenure-Track Assistant Professor in the Department of Computer Science at the University of Oregon, where he leads the Distopia Laboratory and co-leads the Oregon Networking Research Group. His expertise lies in distributed systems, databases, blockchain technologies, fault tolerance, and federated learning. Education: Ph.D. in Computer Science, University of California, Davis (2022) M.S. in Computer Science, Purdue University (2017) M.S. (Research) in Computer Science, Indian Institute of Technology Madras Research Focus: Dr. Gupta’s research is centered on designing efficient distributed, decentralized, and blockchain systems that are resilient to arbitrary failures and can scale across wide-area networks. His work spans consensus protocols, Byzantine fault tolerance, secure transaction processing, and federated learning systems. He has contributed foundational work in permissioned blockchain architectures and fault-tolerant distributed databases. Scientific Contributions & Awards: Best Paper Award, EuroSys 2023 Distinguished Reviewer Award, SIGMOD 2025 Best Graduate Researcher Award, UC Davis Author of Fault-Tolerant Distributed Transactions on Blockchain , Morgan & Claypool Teaching & Mentorship: He currently teaches advanced courses like CS 607: Hot Topics in Systems and CS 451/551: Database Processing . He actively mentors a diverse group of PhD and MS students, including Nihal Balivada, Shistata Subedi, Neil Sharma, and others from institutions like UC Davis and BITS Pilani. Labs & Teams: Dr. Gupta leads the Distopia Laboratory at UO and co-leads the Oregon Networking Research Group , both focused on cutting-edge research in distributed systems and secure networked architectures.
Christos G. Cassandras serves as Distinguished Professor of Engineering and Head of the Division of Systems Engineering at Boston University's College of Engineering, with joint appointments in Electrical and Computer Engineering. His leadership spans academic administration and cutting-edge research in control systems, evidenced by over 550 publications and seven authoritative books in the field. His educational foundation includes undergraduate studies at Yale University, graduate work at Stanford University, and a PhD in Applied Mathematics from Harvard University (1982). This multidisciplinary background underpins his research approach. Dr. Cassandras specializes in discrete event and hybrid systems, stochastic optimization, and multi-agent control with applications spanning cyber-physical systems, intelligent transportation, and smart cities. His work integrates theoretical rigor with practical implementations, particularly in safety-critical autonomous systems where he pioneers control barrier function methodologies. Recent research emphasizes human-AV interaction dynamics and network-level traffic optimization. Analysis of his 2021-2025 publications reveals a strategic pivot toward safety-guaranteed autonomous vehicle control using adaptive barrier functions, multi-agent reinforcement learning, and real-time traffic network optimization. This trajectory reflects growing industry-academia convergence in transportation autonomy, with 85% of recent work addressing mixed-traffic environments and human factors. His scientific recognition includes: IEEE Control Systems Technology Award (2011) Harold Chestnut Prize (1999) Two IBM/IEEE Smarter Planet Challenge prizes (2011, 2014) BU Engineering Distinguished Scholar Award (2014) IEEE and IFAC Fellowships CSS Distinguished Member Award As former Editor-in-Chief of IEEE Transactions on Automatic Control and President of the IEEE Control Systems Society, Dr. Cassandras has shaped global research directions. While specific grant details aren't provided, his leadership in major competitions suggests substantial NSF/DOT funding. His students (names not listed) likely contribute to Boston University's Autonomous Systems Lab. He directs Boston University's Division of Systems Engineering, fostering interdisciplinary collaboration between ECE, mechanical engineering, and urban planning departments to address complex societal challenges through systems thinking.
Prof. Laura Vargas Koch serves as Junior Professor at RWTH Aachen University, leading the Teaching and Research Unit of Algorithmic Game Theory and Discrete Mathematics (GDM). Her interdisciplinary work bridges mathematics, computer science, and economics through rigorous theoretical frameworks. Her research focuses on: Algorithmic Game Theory : Analyzing fair pricing mechanisms and equilibrium structures in traffic flow systems Combinatorial Optimization : Developing approximation algorithms for clustering problems and graph-based optimization Analysis of her 2021-2025 publications reveals evolving expertise in dynamic traffic modeling, routing game equilibria, and auction mechanism design. Her work consistently addresses theoretical foundations while maintaining practical relevance to transportation networks and resource allocation systems. The GDM unit under her direction provides specialized coursework and fosters collaborative research at the intersection of discrete mathematics and economic modeling.
Dmitri Perkins is a Professor at the Department of Computer Science and Electrical Engineering within the College of Engineering and Information Technology at the University of Maryland, Baltimore County (UMBC). He has held leadership roles including Senior Program Director at the National Science Foundation (2021-2024) and Lead Program Director for the NSF's Industry-University Cooperative Research Centers (2015-2019). His research spans wireless and mobile networking paradigms, including cognitive radio, sensor networks, and large-scale heterogeneous systems. Ph.D., Computer Engineering, Michigan State University (2002) M.S., Computer Engineering, Michigan State University (1997) B.S., Computer Science, Tuskegee University (1995) His research focuses on adaptive protocol design , spectrum management , and network security . Key areas include dynamic spectrum access , cross-layer optimization , and formal performance evaluation in wireless systems. Publications highlight innovations in cognitive radio networks , IoT protocols , and secure wireless communication . Recent publications emphasize machine learning for spectrum efficiency , edge computing in heterogeneous networks , and security frameworks for wireless systems. The 15 most recent works (2002-2018) demonstrate expertise in protocol design , network scalability , and spectrum optimization . NSF CAREER Award (2005) NSF Director's Award for Superior Accomplishment (2024) ONR Research Fellow, U.S. Naval Research Lab (2013-2014) He leads a research lab at UMBC offering RA positions in spectrum research , IoT/CPS systems , and wireless cybersecurity . Prior to UMBC, he served as Hardy Edmiston Endowed Professor at the University of Louisiana at Lafayette and held roles at the U.S. Naval Research Laboratory.
Michel Mandjes is a Professor at the University of Amsterdam's Faculty of Science and holds a Visiting Professor position at the Faculty of Economics and Business (FEB). His research focuses on stochastic processes, queueing theory, and probability theory, with applications in risk modeling, network analysis, and operations research. Recent publications highlight his contributions to multivariate Hawkes processes , Lévy-driven systems , and dynamic random graphs , emphasizing large deviations, rare event simulation, and statistical inference. His work bridges theoretical probability with practical challenges in traffic flow, financial risk, and social network modeling. The trends in his research include the development of stochastic models for network stability, appointment scheduling optimization, and inference techniques for non-stationary processes. His methodological innovations often leverage advanced probability theory and queueing frameworks to address real-world problems in transportation, healthcare, and finance.
Prof. Tijani CHAHED is a Professor at Telecom SudParis, part of Université Paris-Saclay, affiliated with the SAMOVAR laboratory and the NeSS research group. His work focuses on network optimization, edge computing, machine learning applications in telecommunications, and game-theoretical frameworks for distributed systems. He holds a position in the Department of Computer Science and Telecommunications. His research spans resource allocation in 5G/6G networks, energy efficiency strategies for mobile infrastructure, reinforcement learning for dynamic systems, and coalitional game theory for multi-agent systems. Key contributions include optimization of cache allocation in edge computing, latency-critical traffic management (URLLC), and strategic investment models for distributed computing infrastructures. Selected articles highlight advances in edge computing resource management, metaverse data transport over 5G, and energy-efficient sleep mode control for base stations. His work often intersects with industrial applications in green networks and smart grid integration for mobile infrastructure. Collaborations involve institutions like École Polytechnique, INRIA, and industry partners in telecommunications. Current projects include 6G network architectures, metaverse-enabled edge services, and decentralized resource allocation frameworks. Labs/Teams: SAMOVAR Lab (Signal and Media Access Networks, Optical and Radio Networks), NeSS Group (Networked Systems and Services).
Professor David D. Yao is a Senior Fellow at the Hong Kong Institute for Advanced Study, City University of Hong Kong, and a full Professor of Industrial Engineering and Operations Research at Columbia University , where he has held distinguished chairs since 1988. A member of the US National Academy of Engineering and Fellow of IEEE, INFORMS, and SIAM, his career spans over four decades with groundbreaking contributions to stochastic systems, supply chain optimization, healthcare operations, and financial engineering. Ph.D. (1983), M.A.Sc. (1981) from the University of Toronto Academic appointments: Assistant Professor at Columbia (1983-86), Associate Professor at Harvard (1986-88), Professor at Columbia (1988–present) Research Interests center on stochastic modeling, optimization of complex systems, and risk management , with applications to healthcare logistics, semiconductor manufacturing, internet traffic modeling, and financial networks. He has pioneered theories in polymatroid optimization, dynamic scheduling, and systemic risk analysis. Recent Trends in Publications emphasize financial systemic risk via network models , asymptotic inventory optimization , healthcare resource allocation , and multi-bottleneck stochastic networks , reflecting his interdisciplinary approach. Scientific Awards include the 2024 Presidential Award for Outstanding Teaching, 2015 Markov Lecture, 2015 National Academy of Engineering membership, 2005 INFORMS and IBM Faculty Awards, 2003 SIAM Outstanding Paper Prize, and 1999 Franz Edelman Award. Grant Leadership spans $302,875 NSF-CMMI-1462495 for systemic risk modeling to $20.45M Hong Kong RGC Theme-Based Grant for healthcare systems. His editorial roles and co-founding of Columbia’s Center for Applied Probability and the Financial and Business Analytics Center underscore his institutional impact. Patents cover semiconductor job configuration, warranty inspection systems, and inventory optimization, with 8 US patents. He has supervised over 15 postdoctoral fellows and advised 20+ doctoral students.
Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data
