David Gianazza is a Researcher at the National School of Civil Aviation (ENAC) , focusing on air traffic management and trajectory prediction. His work combines machine learning and operational research to address challenges in aircraft conflict resolution, airspace configuration, and performance modeling. Specializes in neural networks and metaheuristic optimization Key research areas: air traffic complexity , 3D trajectory planning , and workload modeling His 15 most recent articles (2024-2012) demonstrate expertise in ADS-B data analysis, mass/thrust estimation, and hybrid deterministic-stochastic search algorithms. Collaborations include researchers like Nicolas Durand , Richard Alligier , and Jean-Marc Alliot across institutions in France, Netherlands, and USA. Publications highlight applications of ant colony optimization , A* algorithms , and gradient boosting machines to air traffic problems. His work bridges theoretical computer science and practical ATM solutions , with emphasis on safety and efficiency.
Professor Alexander Paz serves as the Transport and Main Roads Chair at Queensland University of Technology (QUT), where he leads research and academic initiatives in transportation engineering. Previously, he was an Associate Professor of Civil Engineering and Director of the Transportation Research Center at the University of Nevada, Las Vegas. His professional credentials include Chartered Professional Engineer and Fellow Engineer status in Australia, along with being a Registered Professional Engineer in Queensland and Licensed Professional Engineer in Nevada. PhD in Transportation and Infrastructure Systems Engineering from Purdue University Over 100 scholarly publications including books, book chapters, and journal articles More than $17 million in research funding securing over 60 research projects Supervision of more than 18 doctoral and research Masters students Professor Paz's research spans multiple critical areas of transportation engineering with a focus on practical applications. His work in traffic safety involves developing methods for crash data collection, advanced analytics for crash estimation, and field testing of safety devices. In congestion management, he develops large-scale dynamic traffic flow models and optimization frameworks. His infrastructure management research focuses on software systems for roadway infrastructure visualization and analytics, while his work in Intelligent Transportation Systems includes frameworks for real-time traveler information and evaluation of ITS technologies. His travel demand research employs statistics and econometric methods to study travel behavior and develop optimization frameworks for model estimation. Analysis of Professor Paz's recent publications reveals a strong trend toward integrating artificial intelligence and machine learning techniques into transportation engineering. His work increasingly combines traditional transportation engineering methods with cutting-edge data analytics, computer vision, and natural language processing approaches. There's a clear emphasis on practical applications with real-world impact, particularly in traffic safety analysis, urban mobility solutions, and sustainable transportation systems. His research demonstrates growing interdisciplinary connections with computer science, data science, and urban planning disciplines. Chartered Professional Engineer in Australia Fellow Engineer in Australia Registered Professional Engineer in Queensland Professional Engineer Licensed in Nevada Three inventions with patents (one granted, one under review, one licensed for commercialization) Professor Paz has successfully secured substantial research funding exceeding $17 million from diverse sources including government agencies and corporate partners. His sponsored research projects total over 60, with major funders including the National Science Foundation, Federal Highway Administration, Nevada Office of Traffic Safety, Queensland Department of Transport and Main Roads, and various transportation authorities. He has supervised more than 18 doctoral and research Masters students, demonstrating significant commitment to academic mentorship. His industry collaborations include partnerships with Verizon, Parsons, and the Nevada Department of Transportation, resulting in practical implementations of his research findings. Notably, one of his traffic safety inventions has been licensed to Rebel Roadway Systems LLC for commercialization. Professor Paz leads research initiatives through the Urban AI Hub at QUT and previously directed the Transportation Research Center at the University of Nevada. His work involves interdisciplinary teams combining expertise in transportation engineering, computer science, data analytics, and urban planning. Current projects include field testing of traffic safety devices, development of AI-powered transportation analytics systems, and smart city transportation solutions. His research group collaborates internationally with institutions in the United States, Australia, and globally through his involvement with the Transportation Research Board of the National Academies.
Alexander Pahr is a doctoral candidate and research associate at the Chair of Production & Supply Chain Management at the Technical University of Munich (TUM). He has been actively involved in academic research since March 2019, contributing to areas such as mathematical modeling, inventory optimization, and deep reinforcement learning applications in food industry contexts. B.Sc. in Information Systems (2019), TUM M.Sc. in Management and Technology (2019), TUM B.Sc. in Global Business Management (2016), University of Augsburg His research focuses on mathematical modeling and optimization , deep reinforcement learning , and food industry supply chains , particularly for managing ameliorating inventory in perishable product environments like port wine and cheese aging. Publications highlight trends in deep reinforcement learning for inventory systems, dynamic scheduling under uncertainty, and stochastic optimization in food industry applications. He has supervised numerous academic projects including Master’s theses on blood platelet inventory, constraint programming for biopharma, and reinforcement learning applications, as well as Bachelor’s theses on perishable inventory management and stochastic programming for assembly lines.
Yihai Chen is an Adjunct Associate Professor in the Department of Computing and Software at McMaster University. His work bridges formal methods in software engineering with healthcare technology applications. Medical device software certification Statistical web testing frameworks Generative AI for image synthesis Formal specification languages (Object-Z, XML, UML) His research spans medical device safety , web application reliability , and educational technology implementation . Recent work (2019) explores LSTM-based workload prediction in cloud environments alongside GAN-driven food dish generation . Key publication trends include: Formal methods in software engineering (2001-2022) Medical software certification (2014) Web testing frameworks (2013-2022) Model transformation techniques (2008) While no explicit awards are listed in available data, his 15 most recent publications demonstrate sustained contributions to software reliability , health informatics , and formal verification challenges.
Wei Wang is an Associate Professor in the Department of Computer Science at The University of Texas at San Antonio (UTSA), part of the College of Sciences. He holds a Ph.D. in Computer Science from the University of Virginia, an M.S. from the same institution, and a B.Eng. from the University of Science and Technology of China. His research focuses on optimizing performance and energy efficiency in large-scale computing systems, particularly in cloud computing, computer architecture, and software engineering. Key areas include compiler design for memory optimization, non-volatile memory integration, and cloud resource management. Research interests span applied AI, computer systems (cloud computing, computer architecture, compilers), and software performance engineering. He leads projects on reducing cloud energy consumption through novel memory systems and improving cloud elasticity through machine learning-driven resource allocation. He co-organized the Explore!STEM summer camp to introduce STEM concepts to students with disabilities, emphasizing hands-on AI programming and autonomous driving technologies. His recent work addresses challenges in cloud 3D rendering efficiency, healthcare monitoring via WiFi, and machine learning applications in geoscience. Teaching responsibilities include courses on computer architecture, parallel programming, and system software design. He pioneered frameworks like CloudBruno for workload prediction and NUMAlloc for NUMA memory optimization, reflecting his focus on practical system-level innovations.
Dr. Reto Bürgin is a Researcher at the ZHAW School of Engineering , specializing in Data Analysis and Statistics . His work spans interdisciplinary applications in agriculture, transportation, healthcare, and risk modeling. Research Focus: Statistical methods, data-driven decision-making, and domain-specific modeling. Projects: Team member in initiatives like DSembedded (data stewardship) and Detailed Meal Forecast (restaurant industry). Research Interests include: Statistical modeling for soil fertility adoption practices in Africa. Space-time demand forecasting in free-floating carsharing systems. Risk adjustment methodologies in Swiss healthcare economics. Development of visualization techniques for longitudinal categorical data. Key Articles reflect trends in computational statistics, agricultural education, and healthcare analytics, with a focus on practical implementations. Collaborations: Partnered with experts across Europe on critical care studies, orthopedic surgical training, and healthcare policy analysis.
Meritxell Pacheco Paneque is a Senior Researcher in the Department of Informatics at the University of Fribourg's Faculty of Management, Economics, and Social Sciences. Her research focuses on integrating advanced discrete choice models and optimization techniques to address complex decision-making problems in transportation and logistics. She holds a Ph.D. and actively contributes to both theoretical and applied research in operations research and transportation science. Her work emphasizes the development of mathematical frameworks that capture demand-supply interactions, particularly in contexts like waste collection routing, passenger satisfaction maximization, and railway disruption management. She employs methodologies such as Lagrangian decomposition, mixed-integer programming, and stochastic modeling to bridge behavioral sciences with optimization challenges. Key research outputs include pioneering work on choice-based optimization models, facility location under penalties, and traffic state estimation using connected vehicle data. Her research has been published in top journals like Transportation Research Part B and Computers & Operations Research . Pacheco Paneque collaborates with institutions and industry partners to advance practical applications of her models. Her ORCID profile (0000-0003-2192-7510) and email meritxell.pacheco@unifr.ch provide access to her full body of work.
Carlos A. Varela is a Professor of Computer Science and Founding Director of the Worldwide Computing Laboratory at Rensselaer Polytechnic Institute. His research focuses on distributed computing systems, concurrent programming models, and cyber-physical systems with applications to aerospace safety and intelligent flight systems. He has authored influential works on actor-based programming languages (e.g., SALSA), PILOTS for aviation data analytics, and MilkyWay@Home for galactic simulations. Varela's contributions span formal verification, middleware design, and adaptive distributed algorithms. He holds an NSF CAREER Award and industry grants from Amazon, Google, and IBM. Education: B.S. with Honors in Computer Science (UIUC) M.S. in Computer Science (UIUC) Ph.D. in Computer Science (UIUC) Research Highlights: PILOTS detects aircraft sensor failures using error signatures; SALSA revolutionized actor-oriented programming; MilkyWay@Home simulates galactic evolution at petascale. Current projects include an Internet of Planes platform for real-time situational awareness and safety envelopes for stochastic flight systems. Awards: NSF CAREER Award (2005–2010), Amazon Cloud Credits (multiple years), Best Paper Nominations (CCGrid, eScience), IBM Eclipse Innovation Awards. Advising & Grants: Supervised 7 PhD and 19 MS students. Research supported by NSF, AFOSR, and industry partnerships. Active in conference leadership (General Chair CCGrid 2016, PC roles in major venues). Labs & Teams: Leads the interdisciplinary Worldwide Computing Lab exploring aero-space informatics, concurrent systems, and distributed computing fundamentals. Collaborates on FAA-mandated ADS-B systems and next-gen flight trajectory algorithms.
Melvin Vooren is an Assistant Professor at the Faculty of Behavioural and Movement Sciences and affiliated with the LEARN! - Learning sciences department at Vrije Universiteit Amsterdam. His research focuses on higher education policy, labor market interventions, and cognitive mechanisms in mental health. He teaches advanced statistics courses across multiple disciplines including Clinical Track, Social and Organizational Psychology, and Pedagogy. Key research interests include: Student retention strategies and academic performance Impact of active labor market policies Cognitive control mechanisms in psychosis Gender disparities in STEM education Pandemic-induced disruptions in education Recent projects include Big Data analysis for mental health and education (2023–2028) and the VU Plan for Success program addressing study delays among bachelor students. He has published widely in journals like Journal of Economic Surveys , Psychological Medicine , and Studies in Higher Education . His work bridges quantitative methods with policy-relevant outcomes, emphasizing evidence-based interventions in education and labor markets.
Ignacio Martín Llorente is a Full Professor (Catedrático) at the Universidad Complutense de Madrid (UCM), leading the Distributed Systems Architecture Research Group and the Data-intensive Cloud Lab . He holds visiting positions at Harvard University as a Visiting Professor in the John A. Paulson School of Engineering and Applied Sciences (SEAS) and as a Visiting Scholar in FAS Research Computing. He earned a Ph.D. in Computer Science from UCM and an Executive MBA from IE Business School. His research focuses on distributed systems, cloud computing architectures, big data processing, edge computing, and federated networking. He pioneered projects like OpenNebula (a cloud management system) and contributed to EU initiatives such as RESERVOIR (cloud virtualization), StratusLab (grid-to-cloud integration), and BEACON (federated networking). He has authored over 169 publications and serves on editorial boards for IEEE Transactions on Cloud Computing and Journal of Grid Computing . He has secured over €100M in EU grants, leading 10 major projects. His work emphasizes transferring research into open-source technologies and commercial products. He advises OpenNebula Systems and has been a consultant for governments and companies like Microsoft. His teaching spans 67 courses in distributed systems, cloud computing, and high-performance computing.
John Williams is a Professor at the Massachusetts Institute of Technology (MIT) in the Resilient Systems & Mobility department. He holds a BA in Physics from Oxford University (1971), an M.S. in Physics from the University of California, Los Angeles (1973), and a Ph.D. in Numerical Methods from Swansea University (1977). His research focuses on Cyber/Physical Security, Information Technology, Web-Based Education Technology, Large-Scale Network Simulation, GeoNumerics of Granular Systems, and Computational Fluid Dynamics. He has contributed to pioneering work on high-performance computing resilience, particle-based numerical methods, and smart grid security. Notably, he received the Best Overall Paper and Best Paper awards at the SpringSim 2011 conference for his work on parallel computation and reservoir characterization. His research integrates computational methods with real-world applications in energy systems, porous media, and cyber-physical systems. Dr. Williams’ academic contributions span over four decades, with a strong emphasis on interdisciplinary collaboration. His lab (geospatial.mit.edu) explores geospatial visualization and big data analytics in cyber-physical systems. He has advised on projects involving container migration in HPC, lattice Boltzmann simulations, and smart grid security protocols. His work bridges theoretical numerical methods with practical engineering challenges, such as optimizing oil reservoir simulations and improving cybersecurity for critical infrastructure. Education: BA Physics (Oxford, 1971), M.S. Physics (UCLA, 1973), Ph.D. Numerical Methods (Swansea, 1977) Affiliations: MIT Resilient Systems & Mobility, Geospatial Lab Awards: Best Paper (SpringSim 2011), Best Overall Paper (SpringSim 2011)
Fanyin Zheng is an Assistant Professor at Imperial College Business School, specializing in Management Science and Business Analytics. She previously held an Assistant Professor position at Columbia Business School. Her research focuses on empirical operations management, business analytics, and applied econometrics, with an emphasis on decision-making in complex service systems like healthcare operations and platform markets. She earned her PhD from Harvard University, Cambridge, United States. Her research explores how individuals and firms leverage data-driven strategies in dynamic environments, particularly in healthcare systems and two-sided platforms. She serves as an Associate Editor for Management Science and Manufacturing & Service Operations Management . Her work addresses challenges such as resource allocation in hospitals, congestion management in transportation networks, and optimal design of marketplaces. Key themes in her publications include healthcare resource optimization, customer preference modeling in transportation systems, and structural estimation of intertemporal externalities in ICU admissions. She actively contributes to advancing methodologies in causal inference and network analysis for complex operational systems.
Andrew Sohn is an Associate Professor in the Department of Computer Science at New Jersey Institute of Technology (NJIT). His research focuses on parallel computing, distributed systems, and high-performance computing, with emphasis on optimizing memory management, virtual machine migration, and cloud infrastructure efficiency. He has led multiple National Science Foundation (NSF)-funded projects, including initiatives on distributed-memory multiprocessors and scalable cloud systems. Education & Background : While specific academic credentials are not detailed, his extensive research output and grant history indicate a strong background in computer science and computational engineering. Research Interests : Dr. Sohn’s work spans distributed memory architectures, multi-processor systems, and cloud resource optimization. His recent studies address challenges in live migration efficiency, energy-efficient VM relocation, and scalable web caching solutions. Grants & Projects : MRI: A PC-based Multithreading Environment (NSF, 1999–2003) U.S.-Korea Cooperative Research on Communication Behavior (NSF, 1997–2000) Performance Studies of the EM-X Multiprocessor (NSF, 1997–2000) Parallelization of Production Systems on EM-4 (NSF, 1993–1993)
Dr. Douglas Down is a Professor in the Department of Computing and Software at McMaster University. His research focuses on performance evaluation, stochastic models, data centers, and scheduling algorithms. He is actively involved in the Digital & Smart Systems research cluster, addressing challenges in healthcare logistics, energy-efficient computing, and resource allocation. Dr. Down teaches CAS 736: Analysis of Stochastic Networks, exploring statistical modeling techniques and current research in stochastic networks. His work integrates machine learning and optimization to solve real-world problems, such as blood product inventory management and thermal-aware data center management. He has published extensively on topics including demand forecasting, inventory policies, and energy-aware workload assignment. While no specific awards are listed, his contributions to operations research and computing systems are evident through his prolific publication record. Dr. Down advises graduate students and collaborates on projects involving healthcare systems, cloud computing, and distributed systems. His research addresses both theoretical and applied challenges, with a focus on decision-making under uncertainty and system optimization.
Okan Arslan is an Associate Professor in the Department of Decision Sciences at HEC Montréal. He holds memberships in the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT) and the Group for Research in Decision Analysis (GERAD). His expertise lies in Operations Research, Combinatorial Optimization, and Large-Scale Network Design. Current research focuses on smart last-mile delivery systems. Education: Ph.D. in Industrial Engineering from Bilkent University. Research Interests: Operations Research, Combinatorial Optimization, Network Design, Logistics, Transportation Systems, and Last-Mile Delivery Optimization. Recent work explores facility location under uncertainty, selective routing frameworks, and crowd-based logistics solutions. Awards include the 2024 Transportation Science Meritorious Service Award (2025), Amazon Last Mile Routing Challenge Third Place (2021), and the HEC Montréal New Researcher Prize (2021). He has also received an Honorable Mention from INFORMS (2019) for work on evasive flow capturing. Teaching includes courses such as Fundamentals of Optimization and Distribution Management. He has supervised 4 master’s theses (e.g., Benders decomposition for facility location, routing optimization) and 2 project supervisions (e.g., workload forecasting at Hydro-Québec). His lab affiliations and collaborations emphasize applied research in transportation and logistics, with a focus on practical industrial applications and network resilience.
