جست‌وجوی استاد و پژوهشگر

Steffen Bondorfمشاهده پروفایل

استاد

• Network Calculus
• Deterministic Networking
• Performance Modeling
• +۴ مورد دیگر

Steffen Bondorf is a Professor of Distributed and Networked Systems in the Faculty of Computer Science at Ruhr University Bochum, Germany. His research focuses on performance modeling and analysis of deterministic networking, particularly using network calculus to provide rigorous performance guarantees for networked systems. Dr. Bondorf received his B.Sc., M.Sc., and Dr.-Ing. (Ph.D.) in Computer Science from TU Kaiserslautern (TUK), where he was part of the distributed computer systems lab. He was the first student to enroll in a B.Sc. program at TUK before finishing secondary education. After graduation, he held positions as postdoctoral researcher, lecturer and Carl-Zeiss Fellow at TUK, research fellow at the National University of Singapore, and visiting researcher at the University of Toronto. From fall 2018 to fall 2019, he was funded by an ERCIM Alain-Bensoussan fellowship at NTNU Trondheim, Norway, and spent time at INRIA Paris. His research interests include network calculus, deterministic networking, performance modeling, computer networks, distributed systems, and P2P overlays. Dr. Bondorf's work has significantly advanced the field of network calculus, particularly in developing methods for analyzing FIFO feedforward networks, multicast flows, and P2P overlay structures. His recent research has integrated machine learning techniques with network calculus to improve analysis accuracy and efficiency, creating hybrid approaches that leverage the strengths of both methodologies. Dr. Bondorf was appointed as assistant professor at Ruhr University Bochum on October 1, 2019, becoming the university's first hire in the joint tenure-track program for the promotion of young scientists. Since December 1, 2021, he has been a tenured full professor at RUB. Carl-Zeiss Fellow ERCIM Alain-Bensoussan fellowship At RUB, Dr. Bondorf teaches Advanced Topics in Networking, Distributed Systems, Deterministic Network Calculus, and Seminar Distributed and Networked Systems. His research group develops tools and methodologies for deterministic network analysis with applications in industrial networking, real-time systems, and time-sensitive communications. His work bridges theoretical networking principles with practical implementations, contributing to the advancement of deterministic networking standards and protocols.

Juan Camilo Castilloمشاهده پروفایل

استادیار

• Industrial Organization
• Microeconomic Theory
• Market Design
• +۶ مورد دیگر

Juan Camilo Castillo is an Assistant Professor in the Department of Economics at the University of Pennsylvania, focusing on Industrial Organization, Microeconomic Theory, and Market Design. His work bridges theoretical insights with real-world applications in digital platforms, urban transportation, and public health economics. Ph.D., Economics, Stanford University (2020) M.S., Economics, Universidad de Los Andes (2013) B.S., Physics and Industrial Engineering, Universidad de Los Andes (2012) Castillo's research spans two primary domains: Online platforms and digital economy (e.g., market power in web search, service quality in ride-hailing) Market design for social challenges (e.g., vaccine distribution, drug market violence) Recent publications examine platform competition in web search, surge pricing impacts, and pandemic response strategies. His methodological approach combines field experiments, econometric modeling, and network analysis.

Jorg Liebeherrمشاهده پروفایل

استاد

• Computer Networks
• Network Calculus
• Internet of Things (IoT)
• +۶ مورد دیگر

Jorg Liebeherr is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto, holding the Nortel Chair of Network Architecture and Services. His research focuses on computer networks , particularly network calculus , self-organizing networks , protocol design , and traffic scheduling . Education: Diplom-Informatiker (with distinction), University of Erlangen (Germany), 1988 PhD, Computer Science, Georgia Institute of Technology, 1991 His recent work includes low-cost LoRa mesh networks for environmental sensing and mathematical frameworks for traffic control in 5G and IoT systems. Publications span journals like IEEE Internet of Things Journal and conferences such as IEEE Infocom and ACM Sigmetrics . Scientific Awards: IEEE Fellow (2008) Outstanding Service Award, IEEE ComSoc TC on Computer Communications (2006) ACM Sigmetrics Best Student Paper Award (2005) NSF CAREER Award (1996) Advising and Grants: Supervised 15+ theses (MASc/PhD) and secured grants from NSF, Virginia Engineering Foundation, and industry partners. Labs: Leads the Network Research Lab and HyperCast projects, an open-source platform for application-layer internetworking.

Wenlong Jinمشاهده پروفایل

استاد

• Intelligent Transportation Systems
• Traffic Flow Theory
• Transportation Network Analysis
• +۳ مورد دیگر

Wenlong Jin is a Professor in the Department of Civil and Environmental Engineering at the Samueli School of Engineering, University of California, Irvine. His research focuses on intelligent transportation systems , traffic flow theory , and transportation network analysis , with applications to evacuation control, eco-driving algorithms, and connected/autonomous vehicle systems. Ph.D., Applied Mathematics, University of California, Davis (2003) M.S., Applied Mathematics, University of California, Davis (2000) B.S., Automatic Control, University of Science and Technology of China (1998) Recent research trends include: Developing generalized bathtub models for network trip flows and evacuation control Advancing car-following models with safety guarantees for autonomous vehicles Optimizing dynamic pricing schemes for high-occupancy toll lanes Investigating capacity drop phenomena at traffic bottlenecks Creating eco-driving algorithms using vehicle-to-infrastructure communication He actively contributes to transportation network resilience and cybersecurity in intelligent transportation systems .

David A. Goldbergمشاهده پروفایل

دانشیار

• Applied Probability
• Stochastic Processes
• Queueing Theory
• +۶ مورد دیگر

David A. Goldberg is an Associate Professor and Director of Undergraduate Studies in the Operations Research and Information Engineering (ORIE) department at Cornell University's College of Engineering. His research bridges theoretical probability with practical applications in operations management, inventory systems, and queueing networks. Education: Ph.D. in Operations Research, MIT, 2011 B.S. in Computer Science, minors in Applied Math and Industrial Engineering / Operations Research, Columbia University SEAS, 2006 Professor Goldberg's research focuses on advancing theoretical understanding of stochastic systems while developing practical insights for operations management. His work spans applied probability, stochastic processes, queueing theory, inventory models, distributionally robust optimization, and combinatorial optimization. He has made significant contributions to understanding the behavior of complex systems under uncertainty, particularly in many-server queues and inventory management under demand variability. His publications reveal strong trends in asymptotic analysis of stochastic systems, particularly in the Halfin-Whitt regime for queueing systems and in inventory models with large lead times. His work consistently bridges theoretical probability with practical operations management applications, with a strong emphasis on developing models that account for real-world uncertainties while maintaining mathematical tractability. His recent work shows increasing focus on distributionally robust approaches that require minimal assumptions about underlying distributions. Scientific Awards: INFORMS Applied Probability Society Best Publication Award (2019) INFORMS Nicholson student paper competition first place (2019) INFORMS Nicholson student paper competition first place (2015) INFORMS Junior Faculty Interest Group paper competition second place (2015) Professor Goldberg has successfully advised multiple Ph.D. students who have gone on to prestigious academic and industry positions. His research is supported by significant NSF funding, including a CAREER award and a grant for stochastic comparison approaches to parallel server queues. He serves on editorial boards for leading journals including Operations Research and Stochastic Models, and has held leadership positions in the INFORMS Applied Probability Society including Vice-chair (2020-2022) and Council member (2015-2017).

Kostas Magoutisمشاهده پروفایل

استاد

• Distributed computer systems
• Scalable data stream processing
• Internet of Things
• +۳ مورد دیگر

Kostas Magoutis is Professor and Chair of the Computer Science Department at the University of Crete and collaborating researcher with FORTH-ICS. His research focuses on distributed systems, scalable data processing, IoT, and cloud computing, with projects including GreenInCities for urban regeneration and STREAMSTORE for stateful stream processing systems. Research interests include: Distributed computer systems architecture Elastic stream processing platforms Quantum-enhanced computing applications Multi-cloud application lifecycle management Recent publications demonstrate strong focus on federated data systems, quantum computing applications, and IoT-enhanced infrastructure, with consistent output in high-impact conferences and journals. Awards and distinctions: Multiple best paper awards from USENIX conferences Grand Challenge Audience Award at DEBS 2022 Marie Curie Fellowship and IBM Research awards Advises over 20 PhD and MSc students in distributed systems research. Leads multiple EU-funded projects and serves on program committees for top conferences including SOSP, EuroSys, and IEEE BigData. Directs research groups in distributed systems and cloud computing at FORTH-ICS.

• Reinforcement Learning
• Stochastic Dynamic Optimization
• Operations Research
• +۷ مورد دیگر

Somayeh Moazeni is an Associate Professor at the School of Business, Stevens Institute of Technology. She holds a PhD in Computer Science from the University of Waterloo and has held academic appointments including Visiting Associate Professor at Northwestern University and Postdoctoral Research Associate at Princeton University. Her research focuses on Reinforcement Learning, Stochastic Dynamic Optimization, and applications in Energy Markets, Inventory Management, and Algorithmic Trading. She has authored over 30 peer-reviewed articles and serves as an associate editor for INFOR and PLOS One . Education: PhD (Computer Science, 2012), University of Waterloo; Postdoc (Operations Research, 2012-2014), Princeton University Industry Experience: Senior Risk Analyst at RBC (2011-2012), Risk Analyst at BMO (2010) Awards: IEEE Senior Member (2019), Anita Borg Institute GHC Faculty Scholar (2017), MITACS Poster Competition First Place (2009) Her research spans Bayesian Optimization , Resilient Network Design , and Energy Efficiency . Current funded projects include PSEG Foundation grants for energy resilience and NSF funding for distributed energy resource controls. She advises PhD students in Operations Research and Energy Systems and teaches graduate courses in Reinforcement Learning and Financial Engineering. Key Contributions: Developed stochastic optimization frameworks for energy storage, contact center reliability modeling, and risk-aware trading strategies. Her work on sequential learning for consumer-driven demand response programs has advanced smart grid applications.

Amirali Abariمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Computational Decision Making
• +۷ مورد دیگر

Dr. Amirali Abari is an Associate Professor in the Faculty of Business and Information Technology at Ontario Tech University, specializing in Artificial Intelligence, Machine Learning, and Computational Decision Making. His research focuses on decision-making under uncertainty, including preference learning, group recommendation systems, and probabilistic inference. He holds a PhD in Computer Science from the University of Toronto (2016) and was an NSERC Postdoctoral Fellow at the University of Toronto and University of Waterloo. Dr. Abari's research has been recognized with awards including the NSERC Postdoctoral Fellowship and a U.S. patent. He serves on program committees for top AI conferences (e.g., AAAI, IJCAI) and reviews for journals like IEEE Transactions and ACM Transactions. His work bridges computational methods with real-world applications in supply chain optimization, risk management, and sustainable infrastructure planning. Key contributions include integrating machine learning with queueing theory for EV charging stations, stochastic optimization for resilient supply chains, and decision support systems for industrial processes. His research group collaborates with industry partners to apply AI-driven solutions to business analytics and operations challenges. Education: PhD in Computer Science, University of Toronto (2016) Awards: NSERC Postdoctoral Fellowship, Bell Scholarship, Student Best Paper Award Service: Program committees for AAAI, IJCAI; reviewer for IEEE, ACM, and Algorithmica

Sina Ansariمشاهده پروفایل

استادیار

• Operations Research
• Healthcare Operations Management
• Service Systems Optimization
• +۴ مورد دیگر

Sina Ansari is an Assistant Professor at DePaul University's Driehaus College of Business , specializing in Management & Entrepreneurship . He co-directs the MS Business Analytics program and has held academic roles at Northwestern University and Tuck School of Business . Northwestern University (PhD, Industrial Engineering & Management Sciences, 2018) Tuck School of Business (Postdoctoral Research Fellow) His research focuses on optimizing service systems through mathematical models, with applications in healthcare operations, opioid crisis mitigation, and logistics. Key areas include: Dynamic resource allocation Patient scheduling optimization Emergency department operations Humanitarian logistics Recent publications highlight his work in humanitarian operations , healthcare scheduling , and medical decision-making . He has received recognition through editorial roles in journals like Naval Research Logistics and Healthcare Management Science . Professional memberships include: INFORMS (Vice President, Chicago Chapter) Production and Operations Management Society Decision Sciences Institute

Shuangning Liمشاهده پروفایل

استادیار

• Econometrics
• Statistics
• Machine Learning
• +۵ مورد دیگر

Shuangning Li is an Assistant Professor of Econometrics and Statistics at the University of Chicago's Booth School of Business. He holds a Ph.D. from Stanford University's Department of Statistics, advised by Professors Emmanuel Candès and Stefan Wager, and a Bachelor of Science from the University of Hong Kong. Prior to his current role, he was a postdoctoral fellow in Statistics at Harvard University. His research focuses on causal inference, machine learning, and statistical methodology with applications in econometrics, networks, and genomics. **Education:** Ph.D. in Statistics, Stanford University (Advisors: Emmanuel Candès, Stefan Wager) Bachelor of Science, University of Hong Kong **Research Interests:** Causal inference in complex systems (e.g., networks, high-dimensional data) Statistical methods for experimental design and robustness Machine learning applications in genomics and reinforcement learning Randomization-based testing and knockoff filters **Recent Work Trends:** His articles emphasize methodological innovations in causal effect estimation, network interference modeling, and transfer learning. Recent work addresses challenges in stochastic congestion, multi-environment analysis, and cooperative learning frameworks. His 2024 paper advances covariate shift correction for conditional randomization tests, while his 2023 studies explore robustness in model-X inference and dyadic reinforcement learning dynamics. **Advising & Academic Background:** His doctoral training under Candès and Wager shaped his focus on rigorous statistical foundations. He has not yet listed advising relationships in available materials, but his research collaborations span academia and industry.

• Numerical Analysis
• Partial Differential Equations
• Integral Equations
• +۳ مورد دیگر

Mario Annunziato is a Researcher in Mathematics at the Department of Physics, University of Salerno, since 2004. His work focuses on numerical methods for stochastic processes and optimal control. Institution: University of Salerno Department: Department of Physics Academic Rank: Researcher Research Interests include numerical solutions of PDEs and integral equations for stochastic processes, probability density function optimization, and modeling random phenomena. His work addresses positivity, monotonicity, and conservation in discrete PDFs. Article Trends span stochastic control frameworks, computational finance, biophysics applications, and numerical methods for jump-diffusion processes. Key topics involve Fokker-Planck equations, Hamilton-Jacobi-Bellman formulations, and splitting methods. Advising and Grants include teaching Numerical Analysis until 2013 and securing funding from the University of Salerno's FARB program, INdAM-GNCS, and the European Science Foundation's OPTPDE grants. He participated in the STRIKE Marie Curie ITN network. Labs & Teams : Collaborated with Prof. Alfio Borzì at Würzburg University and contributed to open-source tools like MATLAB Central File Exchange for PDP solvers.

Tom van Woenselمشاهده پروفایل

استاد

• Freight Transport
• Logistics
• Operations Research
• +۵ مورد دیگر

Tom van Woensel is a Full Professor of Freight Transport and Logistics at Eindhoven University of Technology (Netherlands), affiliated with the School of Industrial Engineering and Innovation Sciences and the Department of Operations Planning Accounting & Control. He also holds roles as Academic Director of the Global Supply Chain Management program at Antwerp Management School and Director of the European Supply Chain Forum. His research focuses on freight transport, logistics systems, and operations research methodologies, with contributions to over 150 peer-reviewed publications in journals like Transportation Science and European Journal of Operational Research . Education: BSc/MSc in Applied Economic Sciences (Econometrics), University of Antwerp (Belgium) PhD: Queueing Theoretical Approaches for Traffic Flow Networks, University of Antwerp Research Interests: Optimization of transport and logistics networks using integer programming, metaheuristics, and reinforcement learning Urban freight systems, last-mile delivery, and sustainable logistics Supply chain resilience, collaboration in logistics networks, and industry-academia partnerships Applications of AI in solving stochastic transportation problems Awards and Recognition: Outstanding Professor in Supply Chain & Logistics (2021) European Journal of Operational Research Best Review Paper (2016) INFORMS Senior Member (2024) Collaborations and Projects: Leads initiatives like SYNERCIZE (zero-emission construction logistics) and Circulaire stromen (circular logistics) Editorial roles at Transportation Science , OR Spectrum , and Urban Science Active in industry partnerships through the European Supply Chain Forum (75+ multinationals) Labs/Teams: EAISI Mobility (Eindhoven AI Systems Institute) CIRRELT (Montreal, Canada) collaborating member

Philip Brabazonمشاهده پروفایل

مدرس ارشد

• Operational Research
• Logistics
• Mass Customization
• +۵ مورد دیگر

Dr. Philip Brabazon is a Senior Lecturer at the University of Portsmouth, affiliated with the School of Organisations, Systems and People and the Centre for Operational Research & Logistics. His work focuses on operational research, logistics, and mass customization in industries such as automotive and manufacturing. He has published extensively on topics including order fulfillment systems, supply chain management, and production economics. Dr. Brabazon is currently accepting PhD students and has supervised 7 theses. His research explores themes like automotive order-to-delivery processes, virtual build-to-order systems, and the application of simulation in operational systems. He has contributed to understanding mass customization strategies, risk analysis in offshore operations, and safety case frameworks. His work bridges theoretical models with practical industry applications, particularly in optimizing supply chains and production systems. Notable publications include analyses of automotive logistics, long-tail retail dynamics, and Markovian approximations in fulfillment systems. His research has been cited over 100 times in key journals like the European Journal of Operational Research and Production and Operations Management.

Seyed M.R. Iravaniمشاهده پروفایل

استاد

• Stochastic processes
• Queuing theory
• Game theory
• +۷ مورد دیگر

Seyed M.R. Iravani is Professor of Industrial Engineering and Management Sciences at Northwestern University's McCormick School of Engineering, with courtesy appointment at the Kellogg School of Management. His research focuses on applications of stochastic processes, queuing theory, game theory, and social networks to design and control of manufacturing, service operations, healthcare systems, supply chains, and nonprofit systems. His research interests emphasize improving flexibility, coordination, and responsiveness in complex operations systems. Professor Iravani develops mathematical optimization models to analyze optimal system behavior, provide managerial insights, and create practical implementation strategies for real-world operations challenges. His work spans manufacturing systems, healthcare operations, supply chain management, service operations, and nonprofit operations. The analysis of Professor Iravani's publications reveals a consistent focus on operations management challenges across multiple domains. His research employs analytical methods including stochastic processes, game theory, queueing theory, and social networks to address problems in capacity allocation, resource management, supply chain coordination, and service system design. A notable trend is his increasing focus on healthcare operations and nonprofit systems in recent years, while maintaining strong contributions to fundamental operations management theory. Professor Iravani has served on editorial boards of leading journals including Operations Research, Management Science, Service Science, IIE Transactions, and Naval Research Logistics. He has collaborated with organizations such as GM, Ford, Motorola, GE, Northwestern Memorial Hospital, Chicago Food Depository, Facebook, and Roti Mediterranean Restaurants to improve operational performance. He teaches courses including Production Planning and Scheduling, Service Operations Management, Operations, and Operations Management for MBA students. He has also authored the textbook 'Operations Engineering and Management: Concepts, Analytics and Principles for Improvement' published by McGraw Hill in 2020, which has become a standard resource in operations engineering education.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Dr. Kit Yan Chan is a Senior Lecturer at the School of Electrical Engineering, Computing and Mathematical Sciences (EECMS) at Curtin University. His research focuses on Artificial Intelligence, Machine Learning, Deep Learning, and Optimization, with applications in wireless communications, signal processing, and power systems. He has held editorial roles in journals such as Neurocomputing, Sensors, and the International Journal of Ad Hoc and Ubiquitous Computing. His teaching spans courses like Transmission and Interface Design, Advanced Research in AI, and Mobile Cloud Computing. Dr. Chan's work emphasizes interdisciplinary approaches, combining computational intelligence with engineering challenges. He has contributed to over 100 publications in areas such as resource allocation in heterogeneous networks, deep learning for load forecasting, and underwater acoustic communication systems. His research bridges theoretical advancements and practical implementations, addressing real-world problems in telecommunications, energy systems, and smart technologies. His recent projects include optimizing energy efficiency in 5G networks, developing robust power control strategies, and advancing neural network architectures for real-time applications. Collaborations with industry and global institutions underscore his commitment to impactful research.