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Yuanyuan YangView profile

Professor

• Quantum Computing and Networking
• Edge Computing
• Machine-Learning Enabled Computing
• +3 more

Yuanyuan Yang is a distinguished professor at Stony Brook University's Department of Electrical and Computer Engineering and Computer Science, part of the College of Engineering and Applied Sciences. She holds a BEng and MS from Tsinghua University and a PhD from Johns Hopkins University. Her research focuses on quantum computing, edge computing, machine-learning enabled systems, data center networks, wireless networks, and distributed systems. She is an IEEE Fellow and National Academy of Inventors Fellow, with significant contributions to interconnection networks and patents in networking technologies. Yang has led NSF programs, served as Associate Dean, and directed research labs. Her work bridges theoretical advancements and practical applications in high-performance computing and networking. Education: BEng, Tsinghua University (China) MSE, Johns Hopkins University PhD, Johns Hopkins University Research Highlights: Explores cutting-edge topics like quantum networking, edge computing architectures, and machine learning integration. Her work emphasizes scalable, efficient systems with applications in distributed computing and cloud infrastructure. Awards: Fellow, IEEE (2009) Fellow, National Academy of Inventors (2022) Best Paper Awards at IEEE GLOBECOM 2024, IEEE INFOCOM 2020 Outstanding Service and Leadership Awards in IEEE Grants & Sponsorship: Supported by NSF and Army Research Office grants. Her research addresses challenges in edge-cloud continuum, energy-efficient networks, and quantum computing infrastructure.

Kevin SkadronView profile

Professor

• Computer Architecture
• Memory Architecture
• Processing in Memory (PIM/PNM)
• +7 more

Kevin Skadron is the Harry Douglas Forsyth Professor of Computer Science at the University of Virginia's School of Engineering and Applied Science, where he has been faculty since 1999. He previously served as department chair from 2012-2021 and has made significant contributions to computer architecture research. Dr. Skadron received his B.S. in Electrical and Computer Engineering and B.A. in Economics from Rice University in 1994, and his Ph.D. in Computer Science from Princeton University in 1999. He spent the 2007-08 academic year on sabbatical at NVIDIA Research. His research focuses on computer architecture, particularly novel heterogeneous processor organizations, accelerator architecture, processing in memory, and automata processing. He has pioneered work in processing-in-memory (PIM) architectures, automata processing for pattern matching, and heterogeneous computing systems. His work addresses critical challenges in thermal management, power delivery, process variations, and wear-out in modern computing systems. Current projects include Fulcrum, Gearbox, Sieve, and DRAM-CAM architectures, along with the development of the PIMeval simulation framework and PIMbench benchmark suite. Skadron's recent publications reveal a strong focus on processing-in-memory architectures, with numerous papers on PIM design, benchmarking, and applications. His research also emphasizes automata processing for pattern matching, with contributions to FPGA-based implementations and programming models. Many of his recent works address graph processing acceleration, bioinformatics applications, and memory system optimization, demonstrating the practical impact of his theoretical contributions. Dr. Skadron has received numerous accolades including the 2023 SRC/SIA University Research Award for lifetime research contributions to the U.S. semiconductor industry, the 2011 ACM SIGARCH Maurice Wilkes Award, and is a Fellow of both IEEE and ACM. He was also named a University of Virginia Teaching Fellow for 2003-04. Skadron has advised numerous graduate students, with recent PhD graduates now working at leading companies like IBM, AMD, Apple, and Black Sesame. His research has been supported by the National Science Foundation, Semiconductor Research Corporation, DARPA, and industry partners including NVIDIA, Intel, and Micron. He co-founded IEEE Computer Architecture Letters and served as editor-in-chief from 2010-2012. He is actively involved in the UVA Center for Automata Processing (CAP) and has served as director for the SRC JUMP 1.0 Center for Research on Intelligent Storage and Processing in Memory (CRISP). He is currently a member of the SRC JUMP 2.0 Center for Research on Processing in Storage and Memory (PRISM).

Sebastian HackView profile

Professor

• Compiler Construction
• Domain-Specific Languages
• Program Analysis
• +6 more

Sebastian Hack is a Professor of Computer Science at Saarland University since 2010. He previously served as an assistant professor at the same university (2008-2010), a Post-Doc at EPFL in the LAMP lab, and a Post-Doc with INRIA at ENS Lyon. His research focuses on compiler construction, domain-specific languages, program analysis and synthesis, code generation, and vectorization. Recent publications highlight advancements in program synthesis (Sorting Kernels), memory safety instrumentations, automatic differentiation frameworks (MimIrADe), microarchitectural analysis (AnICA), and GPU acceleration for bioinformatics (Anyseq/gpu). His work bridges theoretical compiler design with practical applications in high-performance computing and security. He has held administrative roles as Dean of Study Affairs (2012-2014) and Dean of the Department of Mathematics and Computer Science (2018-2020). His software contributions include libFirm and GrGen , and he maintains active involvement in projects like AnyDSL and UniAna.

Qing-Long HanView profile

Professor

• Control Theory
• Control Engineering
• Networked Control Systems
• +12 more

Professor Qing-Long Han is Pro Vice-Chancellor (Research Quality) and Distinguished Professor at Swinburne University of Technology, Australia. He has previously held academic and leadership roles at Griffith University and Central Queensland University. He is a globally recognized expert in control theory and engineering, particularly in networked and time-delay systems. Pro Vice-Chancellor (Research Quality), Swinburne University of Technology Distinguished Professor, Swinburne University of Technology Editor-in-Chief, IEEE/CAA Journal of Automatica Sinica President-Elect, Asian Control Association His research interests span Control Engineering, Networked Control Systems, Multi-Agent Systems, Smart Grids, Unmanned Surface Vehicles, Neural Networks, and Secure Control . He is deeply involved in resilient and secure control under cyber threats, optimization, and industrial metaverse applications. His work integrates control theory with AI, IoT, and industrial automation. The recent publications highlight a strong trend toward secure, resilient, and distributed control in cyber-physical systems, particularly under attacks (replay, falsification), communication constraints, and delays. There is a clear focus on real-world applications in autonomous vehicles, trains, drilling systems, and chiller networks, using advanced methods such as reinforcement learning, event-triggering, and neurodynamic optimization. Award highlights include: IEEE Dr.-Ing. Eugene Mittelmann Achievement Award (2024) Norbert Wiener Award (2021) M. A. Sargent Medal (2021) IEEE Andrew P. Sage Best Paper Award (2019, 2020, 2022) Highly Cited Researcher (Clarivate, multiple years) Professor Han actively supervises PhD students in cutting-edge control topics and leads major research grants from the Australian Research Council and National Natural Science Foundation of China. He is a Fellow of IEEE, IFAC, Engineers Australia (Honorary), and the Chinese Association of Automation. He also contributes to editorial leadership in top journals and professional societies. He is associated with research teams and editorial boards in control systems, automation, and intelligent systems, fostering international collaboration and innovation in engineering.

Roland LeißaView profile

Assistant Professor

• Intermediate Representations in Compilers
• Program Optimizations
• Automatic Parallelization
• +1 more

Roland Leißa is an Assistant Professor in the School of Business Informatics and Mathematics at the University of Mannheim, Germany. His research focuses on programming languages, compilers, and domain-specific languages (DSLs) for high-performance computing across heterogeneous architectures. He teaches courses on parallel programming, compiler construction, and advanced programming topics. His work emphasizes automatic parallelization, intermediate representations, and program optimizations, particularly through partial evaluation techniques. He has contributed to tools like MimIR, AnyDSL, and FLOWER, which address challenges in GPU programming, FPGA synthesis, and ray tracing. Roland leads research on abstracting industrial and scientific application problems into reusable, theoretically sound compiler solutions. His projects span sequence alignment accelerations, dataflow compilation, and vectorization strategies, targeting modern hardware including GPUs and SIMD architectures. Contact: leissa@uni-mannheim.de | Personal Website | ORCID: 0000-0002-2444-6782

In Kee KimView profile

Associate Professor

• Parallel and Distributed Computing
• Cloud Computing
• Edge Computing
• +4 more

In Kee Kim is an Associate Professor in the School of Computing at the University of Georgia, holding a Ph.D. in Computer Science from the University of Virginia (2018). His research focuses on performance and resource management problems in diverse computing systems, including cloud, high-performance computing (HPC), edge, and IoT environments. Funded by agencies like NSF, DoD, and Army Research Labs, his work bridges theoretical innovation with practical system optimization. Education : Ph.D. in Computer Science (University of Virginia, 2018) University : University of Georgia School : School of Computing Department : Department of Computer Science Current research directions include: Edge AI : System-level optimization for AI inference, training, and compression at the edge Serverless Workflow Management : Cost-performance optimization in edge-cloud collaboration Reproducible Benchmarking : Workload characterization across cloud, HPC, edge, and IoT systems Recent publications highlight advancements in: Edge device scheduling for heterogeneous AI accelerators Model compression techniques for constrained environments Energy scheduling for environmental sensors Serverless computing benchmarking Scientific Awards : Best Paper Award at IEEE EDGE 2024 for 'Characterizing Deep Learning Model Compression...' His Adaptive Computing & Edge Intelligence (ACE) Lab at UGA supports student research through facilities in Boyd Graduate Studies Research Center (Rooms 817 and 819). Current and former advisees have contributed to publications at top venues including IEEE EDGE, CLOUD, and ACM Transactions.

Philipp FrieseView profile

Researcher

• High-Performance Computing (HPC)
• Cloud Computing
• CPU Topologies
• +5 more

Philipp Friese is a Researcher at the Technical University of Munich , affiliated with the Chair of Computer Architecture & Parallel Systems (Department of Computer Architecture & Parallel Systems). His research focuses on the integration of High-Performance Computing (HPC) with Cloud Computing , performance monitoring, and optimization of CPU topologies and high-speed networks . He contributes to projects such as SEANERGYS (EuroHPC) PlasmaPEPS OpenCUBE DaREXA-F ScalNEXT PDexa MUNIQC-ATOMS BB-KI_Chips QuaST Q-DESSI (MQV) SensE (BFS) and previously led work in Time-X (EuroHPC) DEEP SEA (EuroHPC) REGALE (EuroHPC) TurbO - GasTurbinenOptimierung (2017-2020) ENVELOPE - Effizienz und Zuverlässigkeit: Selbstorganisation in HPC-Systemen (2017-2019) Predictive Autoscaling Smart Cloud Operations Parallel in Time Integration with Rational Approximations targeting Weather and Climate Simulations (2020) FDN (Function Delivery Network) - Extending Serverless Computing to Heterogeneous Platforms (2019-2022) BEHAVE - Behavioral Modeling of Application Functions in Serverless Computing (2021-2022) His recent publications address HPC-cloud convergence , network monitoring , and CPU topology exploration , reflecting interdisciplinary expertise in Computer Science and Distributed Systems . Scientific recognitions include Best Presentation Award at ARCS 2024 He actively develops software tools like autopin DBrew DDS-Perf QMPI and works with hardware infrastructures CAPS Cloud and HimMUC . His research aligns with initiatives such as the MPI Forum and Eurolab4HPC .

Carsten TrinitisView profile

Professor

• High Performance Computer Architectures
• Microprocessor Architectures
• Co-design
• +5 more

Prof. Dr. Carsten Trinitis is a full professor at the Chair of Computer Architecture & Parallel Systems within the TUM School of Computation, Information and Technology. Specializing in high-performance computer architecture with a unique focus on spaceflight applications and informatics ethics, he leads the 'Gesellschaft für Informatik und Ethik' (Society for Informatics and Ethics). Ph.D. in Electrical Engineering from TUM (1998) Industry experience before returning to academia Former Assistant Professor in History of Science (2002-2010) at Universität der Bundeswehr München Full Professor of Distributed Computing at University of Bedfordshire (2010-2014) His research spans three major domains: High-Performance Computing: Focused on microprocessor architectures, hardware-oriented optimizations, and co-design approaches. Recent work includes GPU power capping strategies and Data Distribution Service (DDS) middleware enhancements. Space Systems: Pioneering computer architectures for nanosatellites with projects like MOVE-II mission and MicroPython-based satellite control systems. Digital Ethics: Through his 'Gewissensbits' initiative, he explores ethical decision-making frameworks for technology and contributes to the German Informatics Society's ethical guidelines. Key publication trends show interdisciplinary work connecting: HPC systems and edge AI applications FPGA-based verification and RISC-V processors Time series analysis at petascale performance Hardware-software co-design for extreme environments Continued emphasis on ethical computing frameworks Scientific Recognition: TeachInf Award for Outstanding Teaching (2023) Hans Meuer Award for Best Paper (2020) ZARM Master Thesis Awards (2016, 2017) Prof. Trinitis leads multiple research projects including: SEANERGYS (EuroHPC) - Energy-efficient computing systems PlasmaPEPS - Plasma physics simulation environments OpenCUBE - Open computing frameworks for space MUNIQC-ATOMS - Quantum computing integration

• Compilers
• Computer Architecture
• High-Performance Storage
• +5 more

Mahmut Taylan Kandemir is a Professor in the Department of Computer Science at Pennsylvania State University. His research spans optimizing compilers, runtime systems, embedded systems, I/O, high-performance storage, and power-aware computing. He leads the Microsystems Design Lab and collaborates with institutions like Argonne National Lab, Microsoft, and Intel. Education: B.S. Computer Engineering, Istanbul Technical University (1988) M.S. Computer Engineering, Istanbul Technical University (1992) Ph.D. Computer Science, Syracuse University (1999) Research Focus: Dr. Kandemir's work integrates hardware-software co-design to address challenges in multi-core architectures, non-volatile memory systems, and energy efficiency. His recent projects explore 3D NAND flash optimization, near-data computing, and reliability in heterogeneous systems. Key themes include reducing data movement overhead, enhancing storage longevity, and developing compiler-directed solutions for emerging hardware. Publications: His articles frequently appear in premier venues (PLDI, ASPLOS, HPCA) and emphasize memory hierarchy optimization, security, and parallelism. Trends include hardware-assisted resilience, approximate concurrency, and energy-efficient VR streaming. Awards: Premier Research Award, Penn State Engineering Society (2017) IEEE Fellow NSF CAREER Award (2000) Best Paper Awards (IPDPS 2008, ICPADS 2006) Top ACM Digital Library Download (2006) Advising & Grants: He has graduated 32 Ph.D. and 20 master's students, with 15 Ph.D. and 5 master's students currently advised. His research is funded by NSF, DARPA, DOE, and industry partners. Active projects include: NSF/OAC: Re-Engineering Galaxy for Performance (2019-2023) NSF/SHF: 3D NAND Flash Optimization (2019-2022) NSF/SPX: Heterogeneous Intermittent Accelerators (2018-2022) Labs & Teams: Leads the Microsystems Design Lab, focusing on architecture-compiler co-design. Collaborates with the Storage Systems Research Center and heterogeneous computing groups at Penn State.

• Structural Engineering
• Earthquake Engineering
• Structural Reliability and Risk
• +5 more

Yong Li is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Alberta's Faculty of Engineering. His research integrates mechanics-based modeling, probabilistic methods, and machine learning to address structural reliability challenges in civil infrastructure. Dr. Li's research focuses on structural dynamics , earthquake engineering , and AI-assisted structural analysis . He develops computational frameworks for probabilistic performance evaluation of bridges, buildings, pipelines, and wind turbines under seismic, landslide, and fire hazards. His work emphasizes stochastic modeling , structural health monitoring , and nonlinear system identification using field data and finite element simulations. His publication portfolio reveals a strong trend toward machine learning integration in structural engineering since 2020, with increasing focus on neural network applications for hysteretic behavior prediction and real-time thermal analysis. The research spans civil structures (bridges, masonry, steel/concrete buildings) and energy infrastructure (pipelines, wind turbines), consistently applying probabilistic frameworks to quantify uncertainty. Dr. Li leads the Structural Modeling and Reliability Analysis (SM&RA) Research Group and collaborates with The Masonry Centre and The Steel Centre at U of A. He teaches core structural engineering courses including CIVE 372 (Structural Analysis I) and CIVE 779 (Structural Reliability), mentoring graduate students in computational mechanics and risk analysis.

• Operations Research
• Scheduling Optimization
• Optimization Algorithms
• +3 more

A/Prof Yakov Zinder is a Visiting Professor at the School of Mathematical and Physical Sciences, University of Technology Sydney. His research focuses on operations research, scheduling optimization, and applied mathematics. He has extensive experience in developing optimization models for complex scheduling problems, including workforce routing, transportation logistics, and maintenance planning. Dr. Zinder's work integrates algorithms like genetic algorithms, iterated local search, and Lagrangian relaxation to address real-world challenges such as resource allocation, parallel machine scheduling, and conflict management. His funded research includes projects for industries like rail maintenance, retail logistics, and power distribution grid management. He has collaborated on over 78 research outputs since 2003, applying mathematical optimization to solve practical operational problems. Research Interests: Scheduling optimization, operations management, metaheuristics, and algorithm design. Recent Articles: Focus on parallel machine scheduling, maintenance planning, and railway traffic optimization. Funded Projects: Includes optimization for train maintenance scheduling and workforce routing in retail sectors. He has supervised numerous research projects and taught advanced optimization courses. His contributions bridge theoretical scheduling models with practical industrial applications.

• Database Systems
• Query Compilation
• High-Performance Computing
• +3 more

Ruby Tahboub is a Teaching Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign (UIUC), since 2022. She previously served as a Visiting Assistant Professor at Purdue University (2019–2022). She holds an M.S. (2016) and Ph.D. (2019) in Computer Science from Purdue University. Her research focuses on query compilation, spatial databases, and high-performance computing, with notable contributions to Apache Spark optimization and educational tools like LIMO for programming education. She teaches courses such as Introduction to Data Mining (CS 412) and Introduction to Programming for Engineers and Scientists (CS 101). Ruby has received the Raymond Boyce Graduate Teacher Award (Purdue, 2016) and the Best Demo Award at ACM SIGSPATIAL (2015) for her work on LIMO. Her research spans database systems, compiler design, and distributed computing, emphasizing practical applications in both industry and education. Her work bridges theoretical advancements in query processing with real-world scalability challenges, particularly in spatial and heterogeneous workloads. She also advocates for innovative teaching methods to enhance programming literacy through interactive tools like map-based activities.

John TsitsiklisView profile

Professor

• Control Theory
• Optimization
• Network Dynamics
• +3 more

John Tsitsiklis is the Clarence J. Lebel Professor of Electrical Engineering at MIT's Department of Electrical Engineering and Computer Science (EECS), within the School of Engineering. He has held significant administrative roles, including director of the Institute for Data, Systems, and Society (IDSS) and co-director of the Operations Research Center. Born in Thessaloniki, Greece, he earned his BS in mathematics and electrical engineering, followed by MS and PhD in electrical engineering from MIT (1980-1984). His research focuses on control theory, optimization, network dynamics, and queueing systems. He is a National Academy of Engineering member and holds an honorary doctorate from Université catholique de Louvain. Research interests span distributed systems, stochastic networks, and algorithmic approaches to complex systems. His work emphasizes theoretical foundations and practical applications in scheduling, resource allocation, and multi-agent coordination. Notable contributions include studies on max-weight scheduling policies, consensus algorithms, and robust control strategies. He has also contributed to academic governance, serving in Greek institutions like the National Council on Research and Technology and Harokopio University. His scientific awards include the prestigious Clarence J. Lebel Professorship and honorary doctorate. His research outputs address challenges in communication networks, distributed computing, and optimization under uncertainty. Tsitsiklis is affiliated with MIT's Laboratory for Information and Decision Systems (LIDS) and has authored foundational texts in parallel and distributed computation.

• High Performance Computing
• Numerical Methods
• Scientific Computing
• +4 more

Dr. Anne Reinarz is an Associate Professor in the Department of Computer Science at Durham University, specializing in High Performance Computing, Numerical Methods, and Scientific Computing. Her research focuses on developing scalable algorithms and software frameworks for complex systems, including ExaHyPE for wave simulations and dune-composites for composite material modeling. She actively collaborates with interdisciplinary teams to address challenges in fault tolerance, parallel computing, and uncertainty quantification. Her work emphasizes computational efficiency and resilience, with contributions to adaptive mesh refinement, high-order numerical methods, and error detection in task-based systems. Reinarz has supervised multiple postgraduate students in computational mechanics and numerical analysis. Key projects include ExaHyPE—an engine for hyperbolic PDE systems—and UM-Bridge, a tool for uncertainty quantification. Her publications span conferences like SC and journals such as Journal of Computational Physics , reflecting her impact on computational science and engineering. Reinarz’s research bridges theoretical advancements with practical applications in aerospace composites, seismic simulation, and gravitational physics, driven by the need for robust and scalable computational solutions.

• High Performance Heterogeneous Computing
• Energy Efficient Computing
• Parallel Computing
• +4 more

Alexey Lastovetsky is an Associate Professor at the School of Computer Science, University College Dublin (UCD), where he is the founding Director of the Heterogeneous Computing Laboratory (HCL). He holds a PhD from the Moscow Aviation Institute and a Doctor of Science (Habilitation) from the Russian Academy of Sciences. His research focuses on high-performance heterogeneous computing, energy-efficient computing, and parallel algorithms for modern architectures. He has published over 175 peer-reviewed papers and authored influential monographs in the field. Education: PhD (Moscow Aviation Institute), Doctor of Science (Russian Academy of Sciences) Affiliations: UCD since 2001; previously Senior Scientist at Lomonosov Moscow State University (1989–1994) and Principal Scientist at the Russian Academy of Sciences (1995–1999) His research interests span heterogeneous computing , energy-efficient systems , and high-performance parallel algorithms . He has developed novel methodologies for workload distribution optimization, energy modeling, and communication performance analysis. His work on the OpenH programming model and SUARA communication algorithm exemplifies his contributions to scalable parallel computing. He has secured over €4.5M in grants , including four prestigious SFI Investigator awards. His recognition includes being ranked in the Stanford/Elsevier Top 2% Scientists list since 2020 and a ScholarGPS Highly Ranked Scholar distinction. He has organized over 300 international conferences and serves on editorial boards of journals like Journal of Parallel and Distributed Computing . Teaching responsibilities include coordinating modules on High-Performance Computing , Parallel Computing , and UNIX Programming . His lab, HCL, is a global leader in heterogeneous computing research, advancing energy-efficient and scalable solutions for modern HPC platforms.