جست‌وجوی استاد و پژوهشگر

• Operations Research
• Discrete Mathematics
• Healthcare Information Systems
• +۳ مورد دیگر

Shanssan Guo is an academic affiliated with Northeastern University in Shenyang, China. Her research focuses on interdisciplinary areas including discrete mathematics, healthcare informatics, operations research, and machine learning. She has co-authored numerous papers in reputable journals such as Discrete Mathematics , IEEE Transactions on Neural Networks , and Information Technology & People . Key research interests include graph theory applications, optimization algorithms, and healthcare technology adoption. Her work spans theoretical contributions (e.g., edge-colored graph cycle analysis) to applied domains like medical imaging reconstruction, cargo stowage optimization, and physician incentive mechanisms in online platforms. Collaborations with industry experts and cross-disciplinary teams highlight her commitment to bridging theory and practice. Publications demonstrate expertise in queueing theory models for resource allocation, reinforcement learning for energy systems, and biomedical data analysis frameworks. Despite prolific output, no specific awards or student mentoring details are explicitly noted in available records.

Matteo Scandellaمشاهده پروفایل

استادیار

• Control Systems
• System Identification
• Machine Learning
• +۳ مورد دیگر

Matteo Scandella is an Assistant Professor at the University of Bergamo , Italy, since February 2024. Previously, he served as a post-doctoral researcher at Imperial College London (2020–2024). He holds a PhD in Control Systems (2019) and advanced degrees in Computer Science Engineering from the University of Bergamo (Bachelor 2014, Master 2016). His research focuses on kernel-based machine learning techniques applied to system identification , nonlinear dynamics , and control systems , with emphasis on aerospace applications and mechatronics. He has developed methods for stable nonlinear system modeling, continuous-time system identification, and data-driven control strategies like SelfMPC. Education: Bachelor Degree in Computer Science Engineering (2014) – University of Bergamo Master Degree in Computer Science Engineering (2016) – University of Bergamo PhD in Control Systems (2019) – University of Bergamo Research interests span kernel methods (e.g., manifold regularization, RKHS), stability analysis of nonlinear systems, and data-driven control . His work bridges theoretical advancements with engineering applications such as health monitoring of aerospace actuators and urban traffic optimization. Recent publications highlight innovations in automated MPC tuning and graph-based system identification techniques. Teaching includes courses like Automatica (6 CFU) and laboratory modules in sustainable industrial systems. His research has been published in top journals like Automatica and conferences such as L4DC and SYSID.

• Wireless Communication
• Wireless Sensor Networks
• Network Coding
• +۳ مورد دیگر

Nadieh Moghadam is an Assistant Professor of Electrical Engineering at the University of San Diego (USD), affiliated with the College of Engineering. She holds a PhD from the University of Louisville (2017), an MS from Iran University of Science and Technology, and a BS from K.N. Toosi University of Technology. Her research focuses on wireless communication, network coding, sensor networks, and embedded systems. She has authored over 15 publications and served as a reviewer for IEEE Transactions journals since 2015. Education : PhD in Electrical Engineering, University of Louisville (2017) MS in Electrical Engineering, Iran University of Science and Technology BS in Electrical Engineering, K.N. Toosi University of Technology (Iran) Research Interests : Dr. Moghadam advances wireless communication systems through innovations in network coding, optimization algorithms, and embedded system design. Her work addresses challenges in multicast efficiency, queue stability, and energy conservation in IoT and sensor networks. She also explores blockchain applications in modern wireless standards like IEEE 802.11ax. Awards : Graduate Dean’s Citation Award (2017) University Fellowship (PhD period) Theobald Scholarship (PhD period) Industry Experience : She co-founded Ivani, a startup developing IoT wireless sensing systems using Bluetooth Low Energy (BLE) and Zigbee protocols. Her team designed low-power occupancy sensing solutions leveraging RF environments.

• Probability Theory
• Random Spatial Processes
• Last Passage Percolation
• +۶ مورد دیگر

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
• +۳ مورد دیگر

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. Jeffreyمشاهده پروفایل

استادیار

• Computer Architecture
• Parallel Computing
• Computer Systems
• +۳ مورد دیگر

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 Wuمشاهده پروفایل

استاد

• Fault-tolerant Control
• Electric Power Systems
• Public Health Interventions
• +۵ مورد دیگر

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
• +۵ مورد دیگر

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 Codreanuمشاهده پروفایل

دانشیار

• Wireless Networking
• Signal Processing
• Machine Learning
• +۳ مورد دیگر

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
• +۵ مورد دیگر

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 Amigoniمشاهده پروفایل

پژوهشگر

• Robotics
• Multi-Agent Systems
• Machine Learning
• +۳ مورد دیگر

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 Sandersمشاهده پروفایل

استادیار

• Stochastic Operations Research
• Probability Theory
• Machine Learning
• +۷ مورد دیگر

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
• +۳ مورد دیگر

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 Wangمشاهده پروفایل

استادیار

• Networked Systems
• Edge Computing
• Cloud Computing
• +۵ مورد دیگر

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
• +۴ مورد دیگر

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.