Professor Abdel Lisser is affiliated with CentraleSupélec, where he conducts research in Gif-sur-Yvette, France. His work spans multiple disciplines including stochastic optimization, game theory, and machine learning. Research Interests: Stochastic Optimization, Chance Constrained Optimization, Distributionally Robust Optimization, Stochastic Game Theory, Physics-Informed Neural Networks. His recent publications focus on integrating stochastic programming with deep learning frameworks to address complex optimization problems under uncertainty. Key areas include Markov Decision Processes, joint chance constraints, and applications in autonomous vehicle control and network design. In 2025, he published on single-controller stochastic games, convex approximations for Markov processes, and physics-informed neural networks for nonlinear equations. 2024 contributions include distributionally robust Markov decision processes, neurodynamic optimization, and CNN-based equilibrium prediction in games. Email: abdel.lisser@l2s.centralesupelec.fr Institution: L2S, CentraleSupélec Location: 3 rue Joliot Curie, 91190 Gif-sur-Yvette, France
Sonia Lopez Alarcon is an Associate Professor in the Department of Computer Engineering at the Kate Gleason College of Engineering, Rochester Institute of Technology (RIT). She has been a faculty member since 2009, teaching core courses like Computer Organization and developing quantum computing curricula including the new CMPE-257 undergraduate course and CMPE-757 graduate course. Her research bridges computer architecture and quantum computing with emphasis on practical quantum circuit implementation. Her educational background includes a Bachelor of Physics and Master's in Device Physics from the University Complutense of Madrid (2002), followed by a PhD in Computer Engineering (2009) where she researched cache hierarchy in simultaneous multithreaded architectures. During her studies, she gained industry experience at Lucent Technologies and Fundetel working on integrated circuit design. Dr. Lopez Alarcon's primary research focuses on Quantum Computing and heterogeneous hardware solutions, specifically quantum circuit compilation processes, scalability challenges, and error resilience techniques. She investigates how to translate theoretical quantum algorithms into executable circuits while managing noise and resource constraints, with applications in optimization problems and physics simulations. Her work connects computer engineering principles to emerging quantum technologies. Analysis of her publication timeline shows a strategic shift from traditional computer architecture (2015-2018 cache/HLS research for GPU/heterogeneous systems) to quantum computing (2019-2021). Recent work explores quantum algorithms for combinatorial optimization (Grover's), quantum simulation of physical systems, and machine learning applications, reflecting her adaptation to the rapidly evolving quantum landscape while maintaining her architectural expertise. Her teaching excellence has been recognized through multiple awards: Kate Gleason College of Engineering Exemplary Performance in Teaching Award (2016, 2017, 2020) Computer Engineering Most Effective Teacher Award (2016) She actively mentors graduate students including Mark Danza (MS Computer Engineering candidate 2025), with whom she collaborated on quantum machine learning research featured in Quantum Zeitgeist (May 2025). She contributes to RIT's quantum information science minor launched in 2022, developing curriculum and supervising student research in this emerging field. Dr. Lopez Alarcon leads quantum computing research efforts within RIT's Department of Computer Engineering, collaborating with colleagues like Cory Merkel on quantum algorithm applications. Her work is supported through her personal research website and integration into university-wide quantum initiatives, positioning her at the forefront of academic quantum computing education and research.
Labros Bisdounis is a Professor at the Department of Electrical and Computer Engineering, University of the Peloponnese, Greece. He previously held positions at the Technological Educational Institute of Western Greece, including Associate Professor, Full Professor, and Dean of the School of Technological Applications (2016–2018). He has extensive industry experience as a senior research engineer and project manager at Intracom S.A. (2000–2008), focusing on VLSI circuits and telecom applications. His research interests include CMOS circuit timing/power modeling, low-power/high-speed design, MOSFET modeling, and sensor applications. He has authored over 30 papers with 740+ citations and is an IEEE member. Education: Diploma in Electrical Engineering (1992), University of Patras Ph.D. in Electrical Engineering (1999), University of Patras Research Interests: CMOS circuit timing and power dissipation modeling Deep-submicron/nano-CMOS circuit design MOSFET device modeling Low-power embedded systems and SoC Sensor applications and organic electronics Leadership Roles: Dean of the School of Engineering, University of the Peloponnese (2023–present) Director of Training & Lifelong Learning Centre (2019–2019) Board Member, Hellenic NARIC (2016–2019) Collaborations: Active at the Hellenic Open University as a tutor in Computer Architecture and Digital Systems modules. Co-developed the AETHER framework for pervasive computing and contributed to energy-aware SoC designs for 5 GHz WLANs.
Dr. Markus Zimmermann is a researcher at the Institute of Neuroscience and Medicine (INM-4: Physics of Medical Imaging) at the Research Center Jülich. His work focuses on advancing quantitative MRI techniques, particularly in water content mapping, multiparametric imaging, and ultrahigh-field MRI applications. He contributes to developing methods for eddy current characterization, multi-exponential relaxometry, and rapid whole-brain protocols. His research addresses neurological and medical imaging challenges, including cerebral pathologies and neurobiological implications. Key areas of expertise include MRI parameter estimation, medical imaging algorithms, and the integration of advanced imaging techniques for clinical and neuroscience applications. His projects often involve collaborations to validate methodologies using in vivo/ex vivo experiments and super-resolution reconstruction. Dr. Zimmermann’s work aims to enhance diagnostic precision and understanding of brain physiology through innovative MRI technologies.
Professor Sebastian Stein is a faculty member in the Electronics and Computer Science department at the University of Southampton, specializing in artificial intelligence and multi-agent systems. He holds a PhD from the University of Southampton (2008) and an MEng in Computer Science from the University of Warwick. His research focuses on citizen-centric AI, mechanism design, and applications in smart energy, transportation, and disaster response. He leads or collaborates on projects such as the EPSRC-funded 'Citizen-Centric Artificial Intelligence Systems' and 'Future Electric Vehicle Energy networks supporting Renewables (FEVER)'. Education: PhD in Multi-Agent Systems (University of Southampton, 2008), MEng Computer Science (University of Warwick) Research Groups: Agents, Interaction and Complexity research group His work emphasizes incentive engineering in dynamic systems, sequential decision-making under uncertainty, and societal challenges like smart mobility and electric vehicle infrastructure. Key awards include the Blue Sky Ideas Award (AAMAS-2021) and Best Demonstration Award (AAMAS 2025). He currently supervises multiple PhD students in computer science and engineering.
Garth Gibson is a Professor in the Computer Science Department and Department of Electrical and Computer Engineering at Carnegie Mellon University's School of Computer Science. He serves as Co-Director of the Master of Computational Data Science program and as Associate Dean for Master's Programs. Gibson has been a faculty member at CMU since 1991, after receiving his Ph.D. and M.Sc. in Computer Science from the University of California at Berkeley and a Bachelor of Mathematics in Computer Science and Applied Mathematics from the University of Waterloo. Gibson's research focuses on large-scale parallelism in computer systems, secondary memory system technologies and optimization, scalable file and key-value storage systems, scalable machine learning, and systematic testing for large scale systems. His work bridges theoretical concepts with practical implementations, with a strong emphasis on shepherding technological advances from academic research to commercial reality. He has made significant contributions to RAID technology, network-attached secure disks (NASD), and parallel file systems that have shaped industry standards and products. Gibson's recent publications reveal a strong trend toward data-intensive scalable computing, with increasing focus on machine learning systems, distributed storage solutions, and high-performance computing infrastructure. His research has evolved from foundational storage technologies to address the challenges of petascale and exascale computing environments, with particular attention to the intersection of storage systems and machine learning workloads. The papers demonstrate a consistent theme of addressing system scalability challenges through innovative architectural approaches. Scientific Awards: 2014 Fellow of the IEEE for contributions to the performance and reliability of transformative storage systems 2012 Fellow of the ACM for contributions to the performance and reliability of storage systems 2012 Jean-Claude Laprie Award in Dependable Computing Industrial/Commercial Product Impact Category 2011 SIGOPS Hall of Fame for the SIGMOD88 RAID paper 1999 Reynold B. Johnson Information Storage Award 1999 Allan Newell Award for Research Excellence 1998 Test of Time Award 1991 A.C.M. Doctoral Dissertation Award (tied for second) Gibson has advised numerous graduate students who have gone on to influential positions in both academia and industry, including Swapnil Patil who won first place in the 2010 ACM Graduate Student Research Competition. He has secured significant research funding through initiatives like the DOE Petascale Data Storage Institute and the Intel Science and Technology Center for Cloud Computing. His research has been supported by collaborations with national laboratories including Los Alamos, Sandia, Oak Ridge, Pacific Northwest, and Lawrence Berkeley. Gibson founded CMU's Parallel Data Laboratory (PDL) in 1993, which has grown into a vibrant research community comprising 6-9 faculty members, 2-3 dozen students, and 4-10 staff. The PDL operates with guidance from the Parallel Data Consortium, which includes 15-25 companies interested in parallel data systems. He also founded Panasas Inc. in 1999, a scalable storage cluster company that has deployed technology in national laboratories, energy sectors, and other high-performance computing environments. More recently, Gibson established the Big Learning research group and created the Systems Major curriculum within CMU's Master of Computational Data Science program.
Robson E. De Grande is an Associate Professor in the Department of Computer Science at Brock University, Canada. He holds a PhD from the University of Ottawa (2012) and BSc/MSc degrees from the Federal University of São Carlos, Brazil. His research focuses on vehicular networks, intelligent transportation systems, distributed systems, and cloud computing. He serves on program committees for conferences like DS-RT, MobiWac, and MSWiM, and has organized multiple workshops and special sessions. Education: PhD in Computer Science, University of Ottawa, Canada (2012) MSc and BSc in Computer Science, Federal University of São Carlos, Brazil (2006, 2004) Research Interests: Vehicular Networks (5G, Handover Management) Edge Computing and IoT Performance Modeling/Simulation High-Performance Distributed Systems Intelligent Transportation Systems Publications: Over 100 peer-reviewed articles across journals like IEEE Transactions on ITS, Elsevier Internet of Things, and conferences like IEEE ICC and ACM MobiWac. Recent work emphasizes ML-driven vehicular network optimization and distributed simulation frameworks. Teaching: Teaches Advanced Computer Networks (COSC 4P14), Parallel Computing (COSC 3P93), and graduate-level Mobile Cloud Computing courses. Research Team: Supervises PhD/MSc students and undergraduate researchers in topics like vehicular edge computing, traffic prediction, and simulation systems.
Luca Pavarino is a Professor at the Department of Mathematics, University of Pavia. His research focuses on scientific computing and numerical methods, particularly in the context of cardiac electrophysiology and multiphysics systems. He leads the Scientific Computing group, specializing in domain decomposition methods (BDDC/FETI-DP), isogeometric analysis, and parallel algorithms. His work integrates advanced numerical techniques with biomedical applications, including cardiac electromechanical coupling, drug testing on cardiac tissues, and modeling genetic cardiac disorders like LQT8 syndrome. Key contributions include scalable solvers for nonlinear systems, preconditioners for heterogeneous media, and operator learning for ionic dynamics. Research interests span computational cardiology, numerical analysis, and parallel computing, with applications to biophysics and drug discovery. His projects often involve interdisciplinary collaborations between mathematics, engineering, and medicine. Notable contributions include: Development of BDDC/FETI-DP preconditioners for cardiac models Integration of machine learning with cardiac electrophysiology High-performance computing for multiphysics systems (Biot’s consolidation, protein stability) Labs/Teams: Scientific Computing Group at the University of Pavia’s Department of Mathematics.
Val Tannen is a Professor at the University of Pennsylvania, specializing in database systems, provenance analysis, and programming languages. His research focuses on data management, query languages, and systems like DBSP and ORCHESTRA. Collaborations include work with co-authors such as Zachary Ives, Susan Davidson, and Todd Green. Key research interests include provenance for databases, incremental view maintenance, and data integration. His work bridges theoretical foundations and practical applications in systems like DBSP for stream processing and ORCHESTRA for collaborative data sharing. Publications span provenance frameworks, query optimization, and distributed systems. While no awards are explicitly listed, his contributions to database theory and systems are widely recognized.
Dr. Debajyoti Mondal is an Associate Professor in the Department of Computer Science at the University of Saskatchewan. His research focuses on algorithms, network visualization, computational geometry, and visual analytics. He holds a PhD from the University of Manitoba and has held postdoctoral positions at the University of Waterloo and Microsoft Research. Mondal's work spans interdisciplinary applications, including collaborations with Saskatoon Transit and academic medicine. He has authored over 100 peer-reviewed publications and secured grants such as NSERC Discovery, CFI, and Canada First Research Excellence grants. His awards include the 2023 New Scholar RSAW Award. Education: Ph.D. in Computer Science, University of Manitoba, 2016 MSc in Computer Science, University of Manitoba, 2012 BSc. Engg. in Computer Science, Bangladesh University of Engineering and Technology, 2009 Research Interests : Algorithms, graph drawing, computational geometry, visual analytics, and interdisciplinary applications in software engineering, transportation, and bioinformatics. His lab (VGA Lab) develops visualization systems for big data analysis. Key Contributions : Advanced theoretical foundations in computational geometry and graph drawing, developed practical visualization tools, and contributed to climate-related projects like Global Water Futures. Grants & Awards : NSERC Discovery Grant (2018-2024) CFI Grant (2021-2025) Microsoft Research Internship (2015-2016) New Scholar RSAW Award (2023) Labs/Teams : Leads the VGA Lab, collaborating with interdisciplinary teams on projects like Clone-World (software clone visualization) and SET-STAT-MAP (mixed data visualization).
Greg Ganger is the Jatras Professor of Electrical and Computer Engineering at Carnegie Mellon University and Director of the Parallel Data Lab (PDL). His research focuses on computer systems, including cloud computing, storage systems, distributed systems, and machine learning infrastructure. He holds a Ph.D. in Computer Science and Engineering from the University of Michigan and completed postdoctoral work at MIT. Education: Ph.D., M.S., and B.S. in Computer Science from the University of Michigan (1991–1995). Research Interests: Ganger leads projects in cloud computing, storage/file systems, operating systems, and systems for big data and large-scale machine learning. Recent work includes optimizing cloud resource scheduling, developing sustainable storage solutions, and improving ML cluster efficiency. The PDL explores storage system architecture, file systems, and leveraging new storage technologies like non-volatile memory (NVM). Awards: 2021 OSDI Best Paper, 2021 SOSP Best Paper, 2021 SoCC Test of Time Award, and 2021 R&D 100 Award. His team's work on Kangaroo caching and MACARON cloud caching exemplifies cutting-edge contributions. Advising & Grants: Advises graduate students in ECE and Computer Science. Active in grants related to distributed storage, cloud systems, and ML infrastructure. Collaborates with industry partners like Los Alamos National Lab on storage systems. Labs/Teams: Directs the Parallel Data Lab (PDL), a leading research group in storage and distributed systems. Collaborates with CMU’s CyLab on security aspects of storage systems and ML infrastructure.
Arijit Khan is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark. He leads the Data Engineering, Science and Systems group and is affiliated with the Technical Faculty of IT and Design. His research focuses on Graph Neural Networks , Blockchain , Data Management , and AI interpretability . He is the Principal Investigator (PI) of a major project on Data Management, Fundamental Algorithms, and Machine Learning for Emerging Problems in Large Networks (2022–2027). Research Interests : Graph Data Management & Machine Learning Blockchain Transaction Analysis Large Language Model + Knowledge Graph Synergies Healthcare AI (e.g., ICU glucose prediction) Explainable AI for Graph Neural Networks Research Trends : His publications emphasize neuro-symbolic systems , uncertain graph analysis , and AI-driven blockchain insights . Recent work bridges large language models with knowledge graphs and explores GPU performance optimization via shader code analysis. Awards & Grants : No explicit awards listed, but his active research grants include a 5-year project on large network analysis with interdisciplinary applications in life and health sciences. Funding emphasizes algorithmic innovation and data science integration. Labs/Teams : Head of the Data Engineering, Science and Systems research group, focusing on AI for societal impact ('AI for the People') and scalable graph data systems. Collaborations span blockchain analytics, healthcare informatics, and GPU architecture design.
Xiaojun Ruan is an Associate Professor in the Department of Computer Science at California State University, East Bay. He holds a Ph.D. in Computer Science from Auburn University (2011) and a B.E. in Computer Science and Technology from Shandong University (2005). His primary research focuses on energy-efficient systems, cloud computing optimization, storage systems, and security-aware resource management. He has extensive experience in thermal modeling, parallel I/O performance, and distributed deep learning frameworks. Dr. Ruan’s work emphasizes balancing energy efficiency, reliability, and performance in storage and cloud environments. Notable projects include DuoFS (hybrid storage system), energy-aware VM allocation strategies, and securing cloud infrastructure against co-residence attacks. His research bridges hardware-software co-design principles with practical system optimizations. His publications span topics from NVMe SSD performance optimization to text augmentation for spam detection, reflecting a blend of storage systems and machine learning applications. He has actively contributed to improving Shuffle I/O in big data processing, thermal management in clusters, and secure virtualization techniques. Dr. Ruan collaborates on interdisciplinary projects involving distributed computing, cybersecurity, and real-time systems. His lab focuses on deploying energy-efficient solutions while maintaining robust reliability, evidenced by over 50 peer-reviewed articles and ongoing contributions to academic conferences.
Juan Manuel Cebrian Gonzalez is an Assistant Professor at the Department of Computer Engineering and Technology, Faculty of Informatics, University of Murcia. His work focuses on computer architecture, parallel systems, and energy-efficient computing. Doctorate: University of Murcia (2011), thesis on fine-grain power and thermal management in multicore processors. Research interests: Designing architectural mechanisms for optimizing power consumption and thermal management in multicore systems, cache coherence in parallel architectures, and vectorization techniques for high-performance computing. His work also explores heterogeneous architectures, fault tolerance, and efficient memory systems. Recent article trends: Focus on cache management, speculative execution, lock-free constructs, and performance-energy trade-offs in edge and heterogeneous computing. Key methodologies include gem5 simulation, Arm SVE, and AVX-512 vectorization. Collaboration: Supervised by Dr. Juan Luis Aragón Alcaraz and Dr. Stefanos Kaxiras. Active in the Computer Architecture and Parallel Systems research group.
Dr. Richard Molyet is a Senior Lecturer and Undergraduate Director in the Department of Electrical Engineering and Computer Science at the University of Toledo's College of Engineering. After retiring as Associate Professor in 2002, he returned to academia in 2005 as Visiting Professor and transitioned to Associate Lecturer in 2008. Education: Ph.D. in Engineering Science (1981) from University of Toledo His research spans Automatic Control , Robotics , Smart-Grid Systems , and Biomedical Applications . Recent publications focus on deep learning for medical diagnostics and hybrid power network optimization , while earlier work explored repetitive control algorithms and microprocessor-based motion analysis . Scientific Recognition: IEEE Third Millennium Medal (2000) IEEE-USA Professional Achievement Award (2002) University of Toledo Outstanding Teacher Award (2016) Currently advising 3 PhD students and multiple Master’s candidates, Dr. Molyet has served on numerous academic committees since the 1980s. He maintains an active role in IEEE Toledo Section's executive board for 39 years .
