Find Academic Supervisors

• Probability Theory
• Random Spatial Processes
• Last Passage Percolation
• +6 more

Prof Nicos Georgiou is a Professor of Probability Theory and Applications at the University of Sussex, within the School of Mathematical and Physical Sciences. He holds a PhD in Mathematics from the University of Wisconsin-Madison and has held academic positions including Reader, Senior Lecturer, and Lecturer at Sussex, as well as a postdoctoral role at the University of Utah. His research focuses on Probability Theory, with emphasis on random spatial processes, last passage percolation, and applications in queueing theory, epidemiology, and econophysics. Education: PhD in Mathematics, University of Wisconsin-Madison (2011) MA in Mathematics, University of Wisconsin-Madison (2008) BSc in Mathematics, National and Kapodistrian University of Athens (2006) Research Interests: Probability Theory fundamentals and applications Last Passage Percolation (LPP) and its connections to particle systems like TASEP Queueing models with Mittag-Leffler inter-event times Epidemic modeling on complex networks Continuum and thermodynamic limits for wealth-distribution models Key Publications (2020–2025) span topics like exact social contagion dynamics, Mittag-Leffler queuing systems, and TASEP on Galton-Watson trees. His work bridges theoretical insights with real-world applications in traffic modeling, economics, and public health. Grants include EPSRC funding for studying last passage percolation and LMS support for random media research. He has organized conferences and presented at international venues such as BIRS and INI. Teaching responsibilities include modules like Discrete Mathematics and Introduction to Probability, with supervision of undergraduate and graduate projects on topics like sequence alignment and traffic flow models.

• Wireless Communication
• Signal Processing
• Information Theory
• +3 more

Rong-Rong Chen is an Associate Professor in the Department of Electrical & Computer Engineering at the University of Utah's College of Engineering, where she has been employed since August 2003, progressing from Assistant Professor (2003-2011) to her current position (2011-present). Her educational background includes a BS in Applied Mathematics from Tsinghua University (1994), an MS in Mathematics from the University of Illinois at Urbana-Champaign (1996), and a PhD in Electrical Engineering from UIUC (2003). Dr. Chen's research focuses on wireless communication systems , with particular expertise in underwater acoustic communication, signal processing, information theory, and queueing networks. Her work bridges theoretical foundations with practical applications in modern communication systems. Her recent publication on OTFS and OFDM channel estimation demonstrates her continued contribution to advancing wireless communication technologies, particularly in comparing next-generation modulation schemes. Best Paper Award, IEEE Global Communications Conference (2021) NSF Faculty Early Career Development (CAREER) award (2006) University Fellowship for graduate study at UIUC M. E. Van Valkenburg Graduate Research Award for excellence in doctoral research at UIUC Dr. Chen has secured significant research funding including current projects on decentralized intelligent spectrum sharing in UAV networks (SWIFT/DISH-UNET), smart radio environments with reconfigurable intelligent surfaces (REFLECT-MMWAVE), and improving MRI technology through signal processing. She teaches courses including Engineering Probability & Statistics, Information Theory, and thesis research for PhD students.

Mark C. JeffreyView profile

Assistant Professor

• Computer Architecture
• Parallel Computing
• Computer Systems
• +3 more

Mark C. Jeffrey is an Assistant Professor in the Edward S. Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto, cross-appointed to the Department of Computer Science. His research focuses on computer architecture and systems, aiming to simplify parallel programming for software engineers through novel compilers and large-scale multicore architectures. Previously, he was a Research Scientist at Facebook AI Research and earned his PhD from MIT under Daniel Sanchez. He holds a MASc and BASc from the University of Toronto. Education: PhD in Computer Science, MIT (2019) MASc in Computer Engineering, University of Toronto (2011) BASc in Engineering Science, University of Toronto (2009) Research interests include parallel computing, compilers, multicore architectures, and energy-efficient systems. His work bridges hardware-software co-design to enable scalable parallelism in modern systems. Notable awards include the George M. Sprowls PhD Thesis Award (2nd place, 2019), Best Paper at NoCArc 2023, and a Best Paper Award at ARC 2010. He teaches advanced courses in computer architecture and parallel programming (ECE1755, ECE552). His GitHub repository bf-intersect explores Bloom filter configurations for address-set disambiguation, tied to his SPAA 2011 and MASc thesis work.

N. Eva WuView profile

Professor

• Fault-tolerant Control
• Electric Power Systems
• Public Health Interventions
• +5 more

N. Eva Wu is a Professor in the Department of Electrical and Computer Engineering at Binghamton University. She received her MSEE and PhD from the University of Minnesota, with a BSEE from Northwest Telecommunications Engineering Institute. Research Interests : Wu specializes in fault-tolerant control of large-scale dynamic systems, particularly focusing on resilient architecture design for electric power systems through control/sensing placement, real-time monitoring, and hybrid time-event-driven control. Her recent work bridges engineering with public health, including living evidence syntheses on vaccine effectiveness, quarantine impacts, and behavioral interventions. Academic Contributions : Wu's publications span power systems engineering and epidemiological modeling. Her 2025 work includes pulse compression probing for grid resilience, systematic reviews on vaccination uptake, and simulations of epidemiological processes using queueing networks.

• Operations Research
• Supply Chain Management
• Systems Thinking
• +5 more

Nico Vandaele is a Professor at the Faculty of Business and Economics , KU Leuven, with appointments at both the Leuven and Kortrijk campuses. He contributes to research and teaching in operations management, systems thinking, and logistics optimization. His work spans interdisciplinary collaborations with institutes like Leuven One Health , Institute for Rare Diseases , and Institute for Mobility . Faculty of Economics and Business (FEB) Faculty of Business and Economics, Kulak Kortrijk Campus Research Focus: Vandaele specializes in applying operations research and systems thinking to complex logistical challenges, particularly in healthcare and industrial production. Recent projects explore vaccine manufacturing ecosystems , decentralized diagnostic systems for pandemics , and adaptive pandemic preparedness frameworks . His work integrates mathematical modeling, sustainability, and cross-sector collaboration. Projects (Selected): VaxMod (2025-2028): Vaccine manufacturing ecosystem modeling VacxSYS (2024-2027): Systems thinking for vaccination strategies DURABLE (2023-2027): Biomedical-public health lab alliances against epidemics Machine Intelligence-based Radio Access Infrastructure (2025-2028)

Marian CodreanuView profile

Associate Professor

• Wireless Networking
• Signal Processing
• Machine Learning
• +3 more

Marian Codreanu is a Senior Associate Professor at Linköping University's Department of Science and Technology (ITN), within the Communications and Transport Systems (KTS) division. He holds a M.Sc. from the University Politehnica of Bucharest (1998) and a Ph.D. from the University of Oulu, Finland (2007), where his thesis was recognized as the best in technical sciences. He has held roles including Marie Skłodowska-Curie Fellow (2019) and Academy of Finland Research Fellow (2013). His research focuses on information freshness optimization, sparse signal processing, and machine learning applications in wireless networking. Dr. Codreanu’s work spans wireless communications, statistical signal processing, and mathematical optimization. He co-founded the Nordic Workshop on System and Network Optimization for Wireless (SNOW) and served as IEEE Vice Chair (2011–2018). His contributions include over 100 journal/conference papers and co-authored books like Compressed Sensing with Applications in Wireless Networks (2019) and Weighted Sum-Rate Maximization in Wireless Networks (2012). His research group, Wireless Optimization and Analytics, develops models and algorithms for wireless network performance and data analytics. Key projects include B-PREPARED (disaster preparedness) and EAN (electric aircraft networks). His work integrates theoretical advancements with practical applications in smart cities, logistics, and air traffic management.

• Logistics and Supply Chain Management
• Transportation Systems
• Machine Learning Applications
• +5 more

Dr. Bill Du is a Senior Lecturer in Logistics and Supply Chain Management at La Trobe Business School, La Trobe University. He holds a PhD in Control Theory and Control Engineering from Nankai University and has held academic positions at institutions including the National University of Singapore and the University of Tasmania. His research focuses on applying machine learning, simulation, and optimization to transport and logistics systems, with particular emphasis on sustainability (e.g., carbon accounting, decarbonization of transport) and emerging technologies like blockchain and AI. Education: B.E. in Software Engineering (Nankai University, 2004) M.Eng. in System Engineering (Nankai University, 2009) PhD in Control Theory (Nankai University, 2012) Research Interests: AI-driven optimization in logistics Freight transport sustainability Agribusiness value chain risk analysis Blockchain in container shipping Articles Trends: His recent work emphasizes real-time carbon accounting frameworks, blockchain adoption barriers, and stochastic optimization models for maritime operations. He frequently explores interdisciplinary applications of machine learning in transport systems. Awards: Recognized for INFORMS President’s Pick (2015), IBM’s CPLEX industry mention, and a Transportation Research Part E Most Cited Article (2011). Advising & Grants: Total research funding exceeds $1M, with industry collaborators like American President Lines and Kongsberg Digital. Supervised PhD projects on agribusiness logistics and blockchain risks in container transport. Labs/Teams: Active in cross-disciplinary teams addressing UN SDGs (Goals 9,12,13), particularly through projects funded by AgriFutures Australia and Singapore Maritime Institute.

Francesco AmigoniView profile

Researcher

• Robotics
• Multi-Agent Systems
• Machine Learning
• +3 more

Francesco Amigoni is affiliated with the Department of Electronics, Information and Bioengineering (DEIB) at Politecnico di Milano. His research focuses on robotics, multi-agent systems, and machine learning, with applications in autonomous systems, anomaly detection, and energy systems. He has contributed to advancements in multi-agent coordination, path planning, and predictive maintenance through interdisciplinary approaches. Key research areas include: Multi-Agent Systems: Task allocation, cooperative robotics, and deadlock prevention in dynamic environments Machine Learning: Anomaly detection, transfer learning, and neural networks for battery lifecycle analysis Autonomous Robotics: Exploration strategies, SLAM algorithms, and map completeness estimation Data Mining: NLP-driven classification of road accidents and healthcare system analysis His work bridges theoretical foundations with real-world applications, addressing challenges in energy systems, environmental monitoring, and healthcare management. Recent studies highlight innovations in lithium-ion battery prognostics and swarm robotics fault detection.

Jaron SandersView profile

Assistant Professor

• Stochastic Operations Research
• Probability Theory
• Machine Learning
• +7 more

Jaron Sanders is an Assistant Professor in the Department of Stochastic Operations Research at Eindhoven University of Technology (TU/e). He holds M.Sc. degrees in Mathematics and Physics (2012) and a Ph.D. in Mathematics (2016) from TU/e. Previously, he was a post-doctoral researcher at KTH Royal Institute of Technology (2016–2017) and an Assistant Professor at Delft University of Technology (2017–2019). His research focuses on optimizing large complex systems through interdisciplinary approaches combining probability, statistics, operations research, and machine learning. He has received notable awards, including the ASML Graduation Prize (2012) and TU/e PhD Thesis Award (2017). Education: PhD in Mathematics, TU/e (2016) MSc in Mathematics and Physics, TU/e (2012) Research interests include stochastic optimization, Markov chains, random matrices, and applications in quantum computing, wireless networks, and machine learning. His work bridges theoretical foundations with practical systems, such as analyzing cluster detection in sequential data and developing noise-resilient optical neural networks. Key contributions include advancements in Block Markov Chains, spectral analysis, and error accumulation models in quantum circuits. He leads projects like the Waardevol AI initiative and collaborates on spectral concentration in Markov chains. His teaching spans stochastic simulation, decision theory, and mathematical modeling. Awards: ASML Graduation Prize for Mathematics (2012) TU/e PhD Thesis Award (2017) VvS+OR Master’s Thesis Award (2013) He actively participates in conferences like ACM MobiHoc and organizes seminars to foster interdisciplinary collaboration. His research addresses both theoretical challenges and real-world applications, emphasizing scalable solutions for complex systems.

• Healthcare Operations
• Operations Research
• Queueing Theory
• +3 more

Rene Bekker is an Associate Professor in the Department of Mathematics at the Vrije Universiteit Amsterdam (Faculty of Science). His research focuses on healthcare operations, stochastic modeling, and optimization techniques applied to healthcare logistics and scheduling. He actively collaborates with Tonos Care as a partner since 2019, bridging academic research with practical healthcare challenges. Key research interests include queueing theory, appointment scheduling systems, home healthcare routing, and capacity planning in nursing homes. Notable contributions involve integrating queueing-based approaches with optimization algorithms to address complex healthcare operational problems. Recent work emphasizes adaptive strategies for resource allocation and staffing in multitasking service systems. His publications often explore the intersection of mathematical modeling and real-world healthcare systems, addressing issues like patient preference allocation, reduced waiting times, and efficient staff scheduling. While his profile does not explicitly mention awards, his extensive contributions to healthcare operations research are evident through over 45 peer-reviewed articles and supervised PhD theses. Bekker’s teaching spans courses such as Applied Stochastic Modeling and Stochastic Modelling, reflecting his expertise in connecting theoretical frameworks with practical applications. Ancillary activities and collaborations highlight his commitment to translating academic research into actionable solutions for healthcare institutions.

Lin WangView profile

Assistant Professor

• Networked Systems
• Edge Computing
• Cloud Computing
• +5 more

Lin Wang is an Assistant Professor at the Department of Computer Science, Vrije Universiteit Amsterdam, and an Adjunct Professor at TU Darmstadt. His research focuses on networked systems, edge computing, cloud computing, and optimizing computer systems for applications like augmented reality. He holds a PhD from Chinese Academy of Sciences and has postdoctoral experience at SnT Luxembourg. His work contributes to UN Sustainable Development Goals related to innovation and infrastructure. Education: PhD in Computer Science (ICT, CAS), Postdoc at SnT Luxembourg Previous Roles: Head of Smart Urban Networks group at TU Darmstadt's TK Lab (2016-2018) Research interests include edge computing, in-network processing, novel network protocols, and energy efficiency. Recent work addresses latency in edge networks, IoT synchronization, and congestion mitigation in data centers. Supervised 2 PhD theses and collaborates internationally. Active in labs like the Telecooperation (TK) Lab. No ancillary activities declared.

• Formal Methods
• Model-Based Testing
• Process Algebra
• +4 more

Ed Brinksma is a Researcher in the Formal Methods and Tools department at the University of Twente. His work focuses on formal methods, model-based testing, process algebra, and real-time systems verification. He has contributed to foundational research in temporal logic, concurrent systems, and formal verification techniques, with over 148 publications. Brinksma has been actively involved in international conferences and workshops, delivering invited talks on topics like model-based testing and verification methodologies. His research collaborations span theoretical advancements and practical applications in embedded systems, safety-critical systems, and industrial software validation. Key research areas include: formal semantics of concurrent systems, automated test generation frameworks (e.g., Torx), and integration of formal methods with real-time systems. He has edited books and special issues on topics like process algebra and stochastic systems modeling. Brinksma's work bridges theoretical computer science with practical engineering challenges, emphasizing rigorous validation approaches for complex systems. He has supervised 18 academic works and participated in 38 professional activities including keynote lectures at major venues. His research network includes collaborations with institutions globally, particularly in Europe, focusing on probabilistic systems modeling and verification challenges.

Adam ElmachtoubView profile

Associate Professor

• Operations Research
• Machine Learning
• Revenue Management
• +5 more

Adam Elmachtoub is an Associate Professor in the Department of Industrial Engineering and Operations Research at Columbia University, where he holds affiliations with the Data Science Institute and serves as an Amazon Visiting Academic. He earned his B.S. in Operations Research from Cornell University and his Ph.D. in Operations Research from the Massachusetts Institute of Technology (MIT), followed by a postdoctoral position at IBM's T.J. Watson Research Center focusing on Smarter Commerce technologies. His research bridges machine learning, analytics, and operations, with dual specializations: (1) developing ML-driven personalization methods for retail, logistics, and travel industries; and (2) creating novel models for revenue and supply chain optimization in e-commerce and service systems. His work emphasizes practical applications of optimization under uncertainty, data-driven decision-making, and algorithmic fairness. Elmachtoub's publication record demonstrates consistent focus on pricing analytics, revenue management, and operational efficiency, with recent work exploring fairness constraints, reusable resources, and pandemic-responsive systems. His research integrates stochastic modeling, statistical learning, and economic theory to solve complex industrial problems. Notable scientific recognitions include: NSF CAREER Award for foundational contributions to data-driven optimization IBM Faculty Award for industry-relevant research 1st place in INFORMS JFIG Paper Competition Great Teacher Award from the Society of Columbia Graduates Forbes 30 Under 30 in Science honor He maintains active research funding through NSF and IBM collaborations. While specific student advisees are unnamed in available sources, his teaching recognition suggests significant mentoring activities. No explicit details regarding dedicated labs or research teams are documented.

Guodong PangView profile

Professor

• Applied Probability
• Stochastic Control
• Stochastic Networks
• +8 more

Guodong Pang is a Professor of Computational Applied Mathematics and Operations Research at Rice University. He holds a Ph.D. in Operations Research from Columbia University's Department of Industrial Engineering and Operations Research, awarded in 2010. His research focuses on applied probability, stochastic control, stochastic networks, queueing systems, and epidemic models. His work applies to service systems, healthcare, energy systems, telecommunications, and epidemiology, with additional interests in financial engineering, simulation, and numerical methods. Education: Ph.D. in Operations Research, Columbia University (2010) No scientific awards, grants, advised students, or recent publications are listed in the profile. No affiliated labs or teams are mentioned.

Sebastian Perez-SalazarView profile

Assistant Professor

• Operations Research
• Optimization under Uncertainty
• Discrete Optimization
• +4 more

Sebastian Perez-Salazar is an Assistant Professor of Computational Applied Mathematics and Operations Research at Rice University . He is also affiliated with the Ken Kennedy Institute and AI-OWLS at Rice. His research focuses on operations research, discrete optimization, and optimization under uncertainty , with applications in cloud computing and e-commerce. He develops models and algorithms with provable guarantees for decision-making in uncertain environments. Education: Ph.D. in Algorithms, Combinatorics, and Optimization (ACO), Georgia Institute of Technology (2022) M.S. in Applied Math, Universidad de Chile (2016) B.Eng. in Applied Math, Universidad de Chile (2016) Research Interests: His work spans optimization under uncertainty, stochastic scheduling, prophet inequalities, online algorithms, and their applications in cloud systems and revenue management. He emphasizes algorithmic guarantees and practical implementations of theoretical results. Awards: Honorable mention, 2022 INFORMS Optimization Society Student Paper Prize Runner-up, 2019 INFORMS Computing Society Student Paper Prize Teaching: He teaches courses in Linear and Integer Programming , Revenue Management and Pricing , Approximation Algorithms , and Combinatorial Optimization . Labs/Teams: Active contributor to the Ken Kennedy Institute and AI-OWLS at Rice, focusing on interdisciplinary research in optimization and artificial intelligence.