Find Academic Supervisors

Leonardo QuerzoniView profile

Professor

• Cybersecurity
• Stream Processing
• Dependable Distributed Systems
• +6 more

Leonardo Querzoni is a Professor at the Department of Computer, Automatic and Management Engineering Antonio Ruberti within Sapienza University of Rome . He currently serves as Deputy Rector for Digital Technologies and Cybersecurity (2024-2026) and President of the CYBER 4.0 Competence Center since 2022. His academic career spans over two decades with 120+ publications. PhD in Computer Engineering (2007) with thesis "Techniques for Efficient Event Routing" Coordinates Italy's first joint PhD program in Cybersecurity (Sapienza-Luiss) Co-author of Italy's National Cybersecurity Framework Research spans Cybersecurity , Stream Processing , Dependable Distributed Systems , and Big Data Scalability . Particular focus areas include: Malware analysis and incident forensics Secure distributed systems Binary code analysis using neural networks Security for cyber-physical systems IoT attack triage Stream processing optimization Key awards: 2023 DEBS 10 Years Time Award 2021 ASPLOS Distinguished Paper 2019 DIMVA Best Paper Runner-Up 2017 DEBS 10 Years Time Award Active in European projects EU-GUARDIAN, ATENA, and national initiatives like PRIN TENACE. Industry collaborations include: Presidenza del Consiglio dei Ministri Leonardo-Finmeccanica CISCO Research Banca MPS

• information systems
• knowledge management
• open innovation
• +7 more

Isabel Maria Pinto Ramos is an Associate Professor with Habilitation at the Department of Information Systems, School of Engineering, University of Minho , where she leads the ISTTOS R&D Lab and contributes to the Algoritmi Research Centre . She has held leadership roles including Head of Department of Information Systems and President of the Portuguese Association for Information Systems . Her career spans academic, research, and international collaboration roles, including a Visiting International Professor position at the University of Münster, Germany. Academic Leadership : Doctoral Programme Director (2014-2019), IFIP TC8 Chair (2019-2023), Vice President for Membership Services at Association for Information Systems (2022-2025) Research Themes : Innovation management, knowledge systems, digital transformation, crowdsourcing, organizational memory, gender in STEM, and technology's socio-cultural impacts International Engagement : Collaborations with Carnegie-Mellon, University of Agder, Georgia State, and Federal University of Santa Catarina through research projects and mobilities Scientific Recognition : IFIP Outstanding Service Award (2009) IFIP Silver Core Award (2013) IIAKM Lifetime Academic Achievement (2021) Editorial Contributions : Associate Editor for Communications of the AIS (since 2020), editorial board member for Enterprise Information Systems , International Journal of Knowledge Engineering , and Revista de Administração Mackenzie Project Involvement : ERASMUS+, Horizon 2020, FP7 programs with focus on digital workplaces, knowledge management, and organizational resilience Her scientific output demonstrates consistent exploration at the intersection of Information Systems and Organizational Innovation , with recent works focusing on Digital Transformation (2021-2025), Crowdsourcing Platforms (2009-2016), and Organizational Memory (2005-2014). The 2023-2025 publications particularly emphasize Smart City Resilience , Multigenerational Workforce Challenges , and AI Development Democratization .

Sijing ShenView profile

Associate Professor

• Numerical Simulations of Galaxy Formation and Evolution
• Galaxy-Intergalactic Medium Interactions
• Galactic Winds
• +9 more

Dr. Sijing Shen is an Associate Professor at the Institute of Theoretical Astrophysics, University of Oslo . She specializes in numerical simulations of galaxy formation and evolution, focusing on interactions between galaxies and the intergalactic medium, galactic winds, and the physics of the interstellar medium. PhD in Astrophysics (McMaster University, 2010) Postdoctoral Fellow (University of California, Santa Cruz, 2010-2014) Research Associate (University of Cambridge, 2014-2016) Associate Professor (University of Oslo, 2017-present) Her research employs high-performance computing and numerical magnetohydrodynamics to model the co-evolution of supermassive black holes and galaxies, the origins of galactic magnetic fields, and observational signatures of high-redshift galaxies. She develops and utilizes massively parallel magneto-hydrodynamic codes to create virtual universes for comparison with observational data. Recent publications highlight her work on dwarf galaxies, circumgalactic medium physics, and spectral diagnostics for next-generation telescopes like AtLAST. Key themes include feedback mechanisms, dark matter interactions, and computational methods in astrophysics. Young Research Talents Grant, Research Council of Norway Shen supervises 2 PhD students and 1 postdoctoral fellow. Her work bridges theoretical modeling with observational predictions, particularly in galaxy formation and evolution processes.

Rashmi Korlakai VinayakView profile

Associate Professor

• Distributed Systems
• Cloud Computing
• Fault Tolerance
• +2 more

Rashmi Korlakai Vinayak is an Associate Professor in the Computer Science Department at Carnegie Mellon University, where she leads the TheSys research group and contributes to the Parallel Data Lab (PDL). Her research focuses on robustness and resource efficiency in data systems across storage, computation, and communication. She employs a multidisciplinary approach combining computer systems, information theory, and machine learning to develop next-generation data systems. Research interests span: Fault tolerance and efficiency in storage systems High-performance caching architectures Machine learning systems optimization Coding theory applications in distributed systems Recent publications demonstrate a strong emphasis on distributed computing optimization, GPU utilization, and novel algorithms for web caching and storage management. Her work consistently addresses scalability challenges in large-scale systems. Awards include prestigious fellowships from Sloan Foundation and IEEE, with industry recognition from Meta, VMware, and Google. Current projects involve collaborations with NetApp, Facebook, Cisco, Intel, and Cloudera. She advises PhD and Master's students working on systems optimization projects, and has developed industry-adopted solutions now integrated into platforms like Apache Hadoop and VMware.

Shangping RenView profile

Professor

• Cyber-Physical Systems
• Real-Time Scheduling
• Cloud Computing
• +4 more

Shangping Ren is a Professor and Department Chair at the Department of Computer Science, San Diego State University (SDSU), within the College of Sciences. She earned her Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 1997. Research focus areas: Cyber-Physical Systems, Real-Time Scheduling, Cloud Computing, Medical Cyber-Physical Systems, Software Architecture, and Resource Optimization Key projects: EMBG System for Emergency Care, Medical Best Practice Guideline Modeling, Real-Time Task Scheduling, and Many-Core Virtualization Her recent publications emphasize medical cyber-physical systems (e.g., safety verification, implicit assumption detection) and real-time scheduling (e.g., rejuvenation techniques, resource degradation). She also explores cloud computing for DAG applications and mobile low-power sensing via Bluetooth/RFID. Grants: Funded by NSF, Air Force Research Laboratory, and Fermi National Accelerator Laboratory. Current projects include data science workforce training, medical guidance systems, and water infrastructure analytics. Collaborations: Works with Lui Sha, Chunhui Guo, Zheng Li, and institutions like University of Illinois, Yale University, and National Science Foundation.

Li KeqinView profile

Professor

• Computer Networking
• Heterogeneous Computing Systems
• Computer Architectures and Systems
• +6 more

Professor Keqin Li is a globally recognized academic holding concurrent professorships at Hunan University (China) and State University of New York (USA). He has held distinguished titles including National Distinguished Professor and SUNY Distinguished Professor. His research focuses on computer networking, heterogeneous systems, energy-efficient computing, and distributed systems. He has authored over 800 publications with an h-index of 64 and secured major grants from Chinese National Science Foundation and Ministry of Science and Technology. Education: PhD in Computer Science from University of Houston (1990), B.S. from Tsinghua University (1985). Research Interests: Includes parallel computing architectures, cloud-edge computing integration, energy-efficient algorithms, and task scheduling optimization. His work spans foundational studies to applied solutions in distributed systems, with notable contributions to network function virtualization and secure cloud data sharing. Awards: Ranked top globally in distributed computing by Scopus citation metrics (2020-2021) IEEE Fellow (2015) Recipient of 1000 Plan Chinese Talent Program (2013) Professional Activities: Editorships at ACM Computing Surveys, IEEE Transactions on Parallel and Distributed Systems, and over 190 conference organizing roles. Principal Investigator of 12+ major research projects (2013-2025). Labs/Teams: Leads research groups focused on parallel computing, cloud-edge systems, and energy-efficient architectures across multiple institutions.

• High Performance Computing
• Numerical Algorithms and Software
• Parallel and Distributed Computing
• +3 more

Panruo Wu is an Associate Professor in the Department of Computer Science at the University of Houston (UH). He joined UH in 2018 as an Assistant Professor and holds a Ph.D. in Computer Science from the University of California, Riverside, and a B.S. in Mathematics from the University of Science and Technology of China (USTC). His primary research interests include high performance computing, numerical algorithms, parallel and distributed computing, fault-tolerant systems, and big data analytics. He leads projects such as LATER (Linear Algebra on Tensor Cores) and LibKernel, focusing on accelerating numerical computations and kernel machines. His recent work emphasizes optimizing linear algebra operations on emerging hardware accelerators like GPUs, with a focus on tensor cores and energy-efficient solvers. Key contributions include fault-tolerant matrix decompositions, scalable frameworks for serverless parallel computing (Wukong), and accelerating kernel machines (TensorSVM). Awards include a Best Paper Nomination at HPDC'20. His teaching spans algorithms, parallel computing, and numerical analysis, with courses like COSC 3320 (Algorithms and Data Structures) and COSC 6374 (Parallel Computations). His research is supported by NSF Grant No. 2146509.

• Cybersecurity
• Cloud Security
• Cryptography
• +2 more

Abir Awad serves as Principal Academic in Cyber Security within Bournemouth University's Computing Department, bringing over 18 years of international research experience in cryptographic systems and cloud security. She obtained her PhD in Cryptography from Polytech' Nantes, University of Nantes, France. Dr. Awad develops chaos-based cryptographic systems for secure communications, cloud data protection, and network anomaly detection. Her innovations include chaotic neural network encryption algorithms, secure mobile cloud storage frameworks, and self-configuring network security systems. Recent work explores AI-driven network monitoring and multi-resource optimization in virtualized environments. As co-chair of the International Workshop on Trust in Cloud Computing, Dr. Awad advances industry-academia collaboration. Her research contributes to practical security solutions including DNA-based image encryption, integrity verification protocols, and risk assessment frameworks. Dr. Awad maintains active industry connections through LinkedIn and international research partnerships, enhancing her applied cybersecurity research program.

• Database Systems
• Distributed Computing
• Machine Learning
• +3 more

Carsten Binnig is a researcher affiliated with TU Darmstadt, Germany, focusing on database systems, distributed computing, and machine learning integration. His work spans high-performance data management, query optimization, and hardware-software co-design for modern networks. He holds a PhD from Heidelberg University (2008) and has contributed extensively to conferences like SIGMOD, VLDB, and CIDR. His research emphasizes scalable OLTP/OLAP systems, secure cloud DBMSs, and leveraging GPUs/accelerators for database operations. Notable projects include GOLAP (GPU-accelerated OLAP), ScaleStore (storage engine using NVMe/RDMA), and DBMS Annihilator (workload generator). He co-leads initiatives on testing database systems (DBTest workshop) and benchmarks for shared databases.

Marcin CopikView profile

Researcher

• Parallel Computing
• High-Performance Computing (HPC)
• Serverless Systems
• +3 more

Dr. Marcin Copik is a Researcher in the Professorship for Scalable Parallel Computing at ETH Zürich, focusing on advancing high-performance computing (HPC), serverless systems, and cloud computing. His work emphasizes optimizing resource utilization, improving scalability, and enhancing performance in distributed and parallel environments. His research interests span parallel computing architectures, serverless benchmarking (e.g., SeBS suite), and sustainable HPC practices. He explores techniques like automatic loop scheduling, acceleration-as-a-service models, and fault-tolerant distributed systems (e.g., FaaSKeeper). His contributions address challenges in cloud elasticity, stateful services, and energy-efficient computing. Recent publications highlight innovations in serverless workflows, AI reasoning frameworks, and hardware-accelerated protocols. His work bridges theoretical foundations with practical implementations, such as Cppless for high-performance serverless programming and XaaS for HPC-cloud integration. While no formal awards or grants are listed, his contributions to open-source projects like STEllAR-GROUP/hpx and Extrapeak demonstrate active engagement in the HPC community. He collaborates closely with academic and industry partners to advance scalable computing solutions.

Boris KoldehofeView profile

Professor

• Networked and Distributed Systems
• Middleware
• Event-based Systems
• +7 more

Boris Koldehofe is a Professor of Computer Networks at the University of Groningen's Faculty of Science and Engineering, where he leads research in distributed systems and network technologies. He also maintains significant academic connections as an Adjunct Professor at TU Darmstadt (as part of the DFG Collaborative Research Centre 1053 MAKI) and recently joined TU Ilmenau as Professor heading the Distributed Systems and Operating Systems Group. His educational background includes a Habilitation in Communications and Distributed Systems from Technische Universität Darmstadt (2019), a Ph.D. from Chalmers University of Technology (2005), and a Diplom in Informatik from Universität des Saarlandes (1999). Throughout his career, he has held positions at prestigious institutions including Technische Universität Darmstadt, Universität Stuttgart, EPFL, and Ruprecht-Karls-Universität Heidelberg. Koldehofe's research focuses on networked and distributed systems with particular emphasis on middleware, event-based systems, software-defined networking, and the emerging field of in-network computing. His work has evolved significantly toward energy-efficient cognitive computing, especially through the exploration of memristor-based technologies for network functions. His research addresses critical challenges in distributed systems performance, privacy, and adaptability in increasingly complex network environments. His recent publications demonstrate a clear trajectory toward energy-efficient network computing, with a significant portion of his work in the last three years focusing on memristor applications for cognitive network functions, in-network data stream processing, and privacy-preserving analytics. His research bridges theoretical distributed systems concepts with practical networking implementations, particularly leveraging programmable data planes and novel hardware architectures. 2019 ACM Service Recognition Award for organizing the 13th ACM Conference on Distributed and Event-Based Systems 2017 Best Research Paper at ACM USENIX Middleware Conference 2015 Ausgezeichneter Ort Land der Ideen (Excellent innovation with CRC MAKI) As an academic advisor, Koldehofe has supervised several doctoral students including Manisha Luthra (awarded KUVS Dissertation Award for 'Network-centric Complex Event Processing') and Saad Saleh (who successfully defended his PhD on memristor-based computing architectures). His research has been supported by numerous grants including DFG-funded projects like 'DynSDN', industry collaborations with Deutsche Telekom, and participation in the CogniGron research center focused on cognitive systems and materials. He leads the Distributed Systems Group at University of Groningen and collaborates extensively with research teams at TU Ilmenau and TU Darmstadt through the MAKI research center.

Ron C. ChiangView profile

Associate Professor

• Distributed Systems
• Cloud Computing
• High Performance Computer Architectures
• +4 more

Ron C. Chiang is an Associate Professor at the University of St. Thomas, specializing in software engineering, distributed systems, and cloud computing. His research focuses on optimizing virtualization, resource management, and high-performance architectures. He holds a Ph.D. in Computer Engineering from George Washington University and has contributed to advancements in cloud security, I/O prefetching, and precision agriculture through machine learning. Education: Ph.D. in Computer Engineering (George Washington University). Research Interests: Distributed systems, cloud computing, virtualization systems, and data-intensive applications. He has developed innovative solutions for container placement strategies, interference-aware scheduling, and energy-efficient memory management. Awards include the Best Student Paper Finalist at SC11, a 'Best of HotPower' award, and grants from IBM and Amazon. He teaches courses on cloud computing, database systems, and infrastructure as code. His work integrates theoretical computer science with practical applications, such as sustainable farming through AI and healthcare platforms in cloud environments. Grants: IBM Academic Initiative (2015–2017), Amazon AWS Educator Credits (2015–2016). He has published extensively in journals like IEEE Transactions and at conferences including SC, ICAC, and ICPA.

Martin SchulzView profile

Professor

• Parallel and Distributed Architectures
• Performance Monitoring and Analysis
• Memory System Optimization
• +4 more

Prof. Martin Schulz is a Full Professor and Chair for Computer Architecture and Parallel Systems at Technische Universität München (TUM), joining in 2017. He also serves on the board of directors at the Leibniz Supercomputing Centre. Previously, he worked at Lawrence Livermore National Laboratory (LLNL) and Cornell University. His research focuses on parallel architectures, quantum computing integration, HPC optimization, and fault tolerance. He has published over 250 papers and chairs the MPI Forum, a key standard in HPC. Education: Doctorate in Computer Science (TUM, 2001), Master of Science (UIUC). Research Interests: Parallel systems, performance analysis, quantum computing, memory optimization, and tool support for HPC. Awards: Gordon Bell Award (2006), R&D 100 Award (2011), Best Paper Awards at IPDPS and SC. Grants/Projects: SEANERGYS, PlasmaPEPS, OpenCUBE, and others. Active in academic service, including program chairs for ISC, EuroMPI, and IPDPS.

Thomas LippertView profile

Professor

• Quantum Computing
• High-Performance Computing (HPC)
• Supercomputing Architecture
• +3 more

Prof. Dr. Thomas Lippert is a Professor at Goethe University Frankfurt and the Director of the Jülich Supercomputing Centre (JSC) at Forschungszentrum Jülich. He leads efforts in quantum computing integration, high-performance computing (HPC), and modular supercomputing architectures. His work focuses on hybrid quantum-classical systems, supercomputer development (e.g., JUPITER, JUWELS), and enabling computational science across domains like neuroscience, materials science, and earth systems. Research Interests include quantum algorithms, quantum-classical hybrid systems, supercomputing infrastructure, and applications in quantum simulation, optimization, and AI. He spearheads projects like JUNIQ (quantum computing infrastructure) and contributes to Helmholtz Information Program initiatives. He has been instrumental in developing Europe’s first exascale system (JUPITER) and advancing quantum computing integration into HPC environments. Notable contributions include the JUWELS Booster for AI research, the QSolid quantum computing demonstrator, and interdisciplinary collaborations in neuroscience computing. He oversees JSC’s supercomputers (JUWELS, JUSUF) and storage systems, fostering industry-academia partnerships. His team addresses challenges in quantum benchmarking, error mitigation, and large-scale scientific workflows. Lippert collaborates on EU-funded projects (e.g., HPCQS) and national initiatives, driving innovation in supercomputing and quantum technologies. His work emphasizes sustainable digital infrastructure and Germany’s tech sovereignty through advanced computing.

• Computer Science
• Software Engineering
• Database Systems
• +4 more

Wei Wang is a faculty member at the University of Waterloo's David R. Cheriton School of Computer Science. His primary research areas include database systems, distributed computing, and software engineering, with a focus on performance optimization, testing methodologies, and formal verification techniques. He has contributed to advancements in garbage collection analysis, graph database testing, and distributed system management. His work often bridges theoretical foundations with practical implementations, addressing challenges in cloud computing and big data processing. Affiliations: University of Waterloo (primary), with collaborative ties to Chinese Academy of Sciences and other institutions. Notable Projects: Ripple (cloud service management), GraphFlow (streaming graph processing), and Gremlin-based testing frameworks. Research emphasizes system reliability and efficiency, including cache optimization strategies for Apache Spark and isolation bug detection in transactional systems. His work has been published in top-tier venues such as ICSE, ISSTA, and VLDB.