جست‌وجوی استاد و پژوهشگر

• Safety-critical real-time systems
• Autonomous systems verification
• Robotics
• +۳ مورد دیگر

Professor René Hexel currently serves as Dean (Learning & Teaching) and Pro Vice Chancellor (Sciences) at Griffith University, where he directs the Cyber-Physical Systems Lab. With over 85 peer-reviewed publications, he specializes in safety-critical real-time systems and autonomous system verification. 2022–present: Dean (Learning & Teaching), Griffith Sciences 2016–2022: Deputy Head of School (L&T), Griffith School of ICT 2007–2016: Director for Internationalisation, Griffith School of ICT 1999: PhD from Vienna University of Technology His research focuses on safety-critical systems , time-triggered protocols , and human-in-the-loop AI , with applications in aerospace, automotive, and healthcare robotics. Recent work explores humanoid robots for dementia care and verifiable executable models. Key article trends show expertise in: Formal verification techniques Robotics communication protocols Explainable machine learning Real-time system architectures Embedded system validation Human-robot interaction design Research grants include: Australian Space Manufacturing Network (2023–2025) SPASE STEM program grant (2022) Social media wellness monitoring (2019–2020) Internal Griffith grants (2003–2010) He has supervised 12 PhD candidates across topics including: Autonomous system verification Robotics security VR sickness mitigation Intelligent control systems Distributed real-time architectures Professor Hexel previously held academic positions at Vienna University of Technology and founded an IT consulting firm specializing in safety-critical systems.

Madhusudhan Govindarajuمشاهده پروفایل

استاد

• Cloud Computing
• Big Data Computing
• Distributed Systems
• +۴ مورد دیگر

Madhusudhan Govindaraju is a Professor in the School of Computing at Binghamton University and currently serves as the Vice Provost for International Education and Global Affairs (IEGA). He holds a PhD and MS from Indiana University. His research focuses on cloud computing, big data systems, distributed and high-performance computing, and web service frameworks. He has contributed to optimizing multi-threaded applications for multi-core processors and semantic web technologies for data reduction. Education: PhD in Computer Science, Indiana University MS in Computer Science, Indiana University His research interests span cloud resource management, distributed systems optimization, and energy-efficient computing. Notable contributions include frameworks for Kubernetes and Apache Mesos environments, as well as studies on power-aware scheduling and vertical scaling techniques. Key publications from 2019 to 2024 highlight advancements in fair resource allocation, GPU-accelerated data structures, and multi-cloud orchestration. His work emphasizes practical solutions for scalability, efficiency, and performance in distributed computing systems. Awards include the Chancellor's Award for Excellence in Teaching (2008-2009) and the Chancellor's Award for Excellence in Faculty Service (2016-2017). He has been affiliated with initiatives such as the Center for Technology for Advanced Scientific Component Software (TASCS) and contributed to science gateway integrations like SEAGrid. His advising and grants focus on advancing distributed computing technologies. He is actively involved in labs such as TASCS, exploring component-based software and middleware for scientific applications.

Darshan Sarojiniمشاهده پروفایل

استادیار

• Multidisciplinary Design, Analysis, and Optimization (MDAO)
• Aircraft Design
• Systems Engineering
• +۵ مورد دیگر

Darshan Sarojini is an Assistant Professor in the Aerospace and Ocean Engineering Department at Virginia Tech (College of Engineering). His research focuses on Multidisciplinary Design, Analysis, and Optimization (MDAO) , Certification-Driven Aircraft Design , and Systems Engineering , with applications in Urban Air Mobility (UAM) and electric vertical takeoff and landing (eVTOL) systems. He leads the Intelligent Design Optimization (IDOpt) Lab , which develops advanced methods for complex engineering systems design. Education: Ph.D. & M.S. in Aerospace Engineering, Georgia Institute of Technology M.S. in Computational Science and Engineering, Georgia Tech B.E. in Mechanical Engineering, B.M.S. College of Engineering, India Research expertise includes integrating Model-Based Systems Engineering (MBSE) with MDAO, high-performance computing (HPC) workflows, and structural optimization under dynamic loads. He has served as Technical Discipline Chair for the AIAA Modeling and Simulation Technologies committee and contributed to NASA ULI projects. Recent work emphasizes large-scale MDO of eVTOL concepts, certification-constrained design, and computational frameworks for aircraft systems. He has mentored students from the ENLACE program and collaborated with industry partners like Archer Aviation and Aurora Flight Sciences. Honors include Best Application Paper Awards at IFToMM 2015 and the 2015 National Conference on Machines and Mechanisms. His lab actively engages in student competitions (e.g., IndySCC) and promotes broader participation in HPC through workshops.

• Parallel Computing
• High-Performance Computing
• Computer Architecture
• +۳ مورد دیگر

Prof. Laxmikant V. Kale is a Research Professor and Paul and Cynthia Saylor Professor Emeritus at the University of Illinois at Urbana-Champaign's Department of Computer Science within the College of Engineering. He specializes in parallel computing, high-performance systems, and scientific applications. His research focuses on runtime systems like Charm++, NAMD, and OpenAtom, emphasizing scalability and efficiency in distributed environments. Education: B. Tech., Electrical Engineering, Banaras Hindu University (1977) M.E., Computer Science, Indian Institute of Science (1979) M.S. & Ph.D., Computer Science, SUNY Stony Brook (1985) Research Interests: Parallel programming models, adaptive runtime systems, distributed and GPU computing, and applications in molecular dynamics, computational biology, and astrophysics. His work bridges theory and practice, aiming to optimize performance for exascale systems. Awards: Gordon Bell Award (2002) IEEE Fellow (2010) Sidney Fernbach Award (2012) HPCC Challenge Award (2011) Grants & Projects: NIH-funded NAMD development, NSF projects on ParaTreet and OPEN ATOM, DOE's XPACC Center, and collaborations with NCSA. He advises students on HPC and molecular dynamics, with notable advisees including Maya Taylor and Jessica Williams. Labs & Teams: Director of the Parallel Programming Laboratory, leading Charm++ and NAMD development. Collaborates with teams on OpenAtom for ab initio simulations and EpiSimdemics for disease modeling.

Jörgen Blomvallمشاهده پروفایل

دانشیار

• Financial Engineering
• Stochastic Programming
• Quantitative Finance
• +۳ مورد دیگر

Jörgen Blomvall is an Associate Professor at Linköping University's Department of Management and Engineering (IEI). His research focuses on optimal financial decision-making, accurate financial measurement, and stochastic optimization models applied to financial markets. He develops methods to enhance measurement accuracy for quantities like forward rates, default intensities, and local volatilities, which are critical for equity, interest, credit, and derivative markets. Blomvall's work integrates stochastic programming and dynamic programming to address portfolio optimization, risk management, and transaction cost reduction. He is affiliated with the Operations Management and Finance research group, exploring resource optimization in manufacturing and service industries. His key research areas include financial engineering, stochastic programming, and quantitative risk management. Recent work emphasizes improving dividend estimation from intraday quotes, reducing transaction costs in hedging strategies, and analyzing stage complexity in stochastic programming for portfolio choice. Blomvall's methodologies have advanced the modeling of systematic risks and optimal investment decisions under real-world market constraints. Blomvall has contributed extensively to the development of optimization-based frameworks for performance attribution and yield curve estimation. His articles frequently address practical applications of stochastic models in financial markets, balancing theoretical rigor with computational feasibility. Despite his prolific output, no specific academic awards or student advisees are documented in the provided texts. He is active in the Production Economics (PEK) research group at Linköping University.

• Parallel Computing
• Programming Models
• System Software
• +۳ مورد دیگر

Mats Brorsson is a Professor at the Division of Software and Computer Systems , KTH Royal Institute of Technology. His research spans multiple areas of computer architecture and parallel computing, with a focus on system software, energy-aware architectures, and performance debugging tools. He is actively involved in projects like the PaPP ARTEMIS collaboration and coordinates the KTH-SICS Scalable Computing Systems initiative. Research Interests : Mats Brorsson's work primarily addresses parallel computing , task-based programming models (e.g., OpenMP), and energy-efficient computer architectures . He has made significant contributions to NUMA system optimization , work-stealing schedulers , and runtime systems for high-performance computing. Professional Activities : Mats Brorsson serves as coordinator for the PaPP ARTEMIS project and is a member of the KTH-SICS Collaboration in Scalable Computing Systems. His publications reflect deep engagement with task scheduling , cache coherence protocols , and adaptive resource management for parallel systems.

Francesc Verdugo Rojanoمشاهده پروفایل

استادیار

• Scientific Computing
• Parallel Algorithms
• Partial Differential Equations
• +۳ مورد دیگر

Francesc Verdugo Rojano is an Assistant Professor at the Faculty of Science (Computer Systems) at Vrije Universiteit Amsterdam, with additional affiliations at the Network Institute and Distributed Computer Systems. His research focuses on scientific computing, parallel algorithms, and numerical methods for partial differential equations, particularly leveraging finite element methods. He actively contributes to open-source software like Gridap, an extensible finite element toolbox in Julia. His work spans computational physics, structural engineering, and materials science, emphasizing scalable numerical techniques for complex systems. Notable research includes fusion technology simulations, hydroelastic analysis of floating structures, and optimization of surface-enhanced Raman spectroscopy substrates. Collaborations involve international teams and interdisciplinary applications. Teaching responsibilities include courses on parallel programming and large-scale parallel systems. His research outputs highlight contributions to unfitted finite element methods, geometric discretization, and computational mechanics, with a strong emphasis on practical software implementations.

• High Performance Computing
• Parallel Computing
• Computer Architecture
• +۳ مورد دیگر

Wolfgang E. Nagel is a Professor of Computer Science and Director of the Center for Information Services and High-Performance Computing (ZIH) at Technische Universität Dresden. Since 2001, he has held the Chair for Computer Architecture, leading research in parallel computing and high-performance systems. Previously, he served as Dean of the Computer Science Department (2006–2009) and Director of the High-Performance Computing Center (ZHR) at TU Dresden (1997–2005). His professional experience includes roles at the Research Center Jülich and the California Institute of Technology (CACR). Education : 1985: Diplom-Informatics in Computer Science, RWTH Aachen 1993: Dr. rer. nat. in Computer Science, RWTH Aachen Research Focus : Nagel’s work centers on high-performance computing (HPC), parallel system architectures, and performance analysis tools like VAMPIR. He has contributed to exascale computing roadmaps, cache architecture studies, and scalable application development frameworks. His interdisciplinary projects span computational biology (e.g., endocytosis modeling) and physics simulations. Leadership Roles : Chair of Gauss-Allianz e.V. (since 2008) Member of DFG Commission for Computer Infrastructure (2005–2012) Advisory Board roles for HLRS Stuttgart and FIZ Karlsruhe Grants & Collaboration : His leadership in ZIH and ZHR has driven infrastructure development for German HPC networks. He coordinates national and international initiatives in computational infrastructure and education. Labs/Teams : Directs ZIH, a central hub for Dresden’s HPC services, supporting research across engineering and natural sciences.

• Algorithmic aspects of communication networks
• Time-varying graphs
• Computing with explorable uncertainty
• +۳ مورد دیگر

Professor Thomas Erlebach is a faculty member in the Department of Computer Science at Durham University. Previously, he held roles at the University of Leicester (2004–2021), where he was a Reader and later a Professor. His academic journey includes a PhD from TU München (1999) and postdoctoral research at ETH Zürich (2000–2004). He is affiliated with the NESTiD (Network Engineering, Science, and Theory) and ACiD (Algorithms and Complexity in Durham) research groups. Erlebach's research focuses on algorithmic aspects of communication networks, time-varying graphs, computing with explorable uncertainty, approximation algorithms, and online combinatorial optimization. He has supervised numerous PhD students, including recent advisees like Kunanon Burathep and Jakob T. Spooner. His publications span conferences like ESA, APPROX/RANDOM, and ICALP, with recent work addressing temporal graph exploration, scheduling under uncertainty, and parameterized algorithms. Erlebach actively participates in program committees for theoretical computer science events and holds editorial roles in journals like Algorithmica and The Computer Journal . Grants include EPSRC funding for projects like ACUTE (Algorithms for Computing with Uncertainty) and collaborations on network-related research. His work bridges theoretical foundations and practical applications in distributed systems and algorithm design.

Santiago Narvaezمشاهده پروفایل

پژوهشگر

• High Performance Computing
• Parallel Programming
• C++ Programming
• +۶ مورد دیگر

Santiago Narvaez is a doctoral candidate at Technische Universität München (TUM) and a Research Associate at the Chair of Scientific Computing in Computer Science (SCCS) since 2019. He holds an M.Sc. in Computational Science and Engineering from TUM (2019) and a B.Sc. in Electrical and Telecommunications Engineering from Universidad del Cauca, Colombia. Research Interests: High Performance Computing (HPC) Parallel Programming with C++ and MPI Development of libraries for invasive/elastic/malleable MPI applications Molecular dynamics simulations Dynamic load balancing Teaching Activities: Santiago has supervised multiple seminars and lab courses at TUM, including the Advanced Programming lecture (C++), Bachelor/Master seminars in HPC, and courses like Parallel Numerics and Turbulent Flow Simulation on HPC-Systems since 2019. Students Supervised: He has mentored thesis projects by Huaiwei Zhang, Radu Raicea, Lukas Kirchmair, Chang-Gen Lai, Leonard Evers, Tim Alexander Meyerhoff, and Thomas Beranek. These projects focused on molecular dynamics simulations, library development, and algorithm evaluations for invasive computing.

Alexander L. Wolfمشاهده پروفایل

استاد

• distributed systems
• networking
• software engineering
• +۳ مورد دیگر

Alexander L. Wolf is the Dean of the Baskin School of Engineering and a Distinguished Professor of Computer Science and Engineering at the University of California, Santa Cruz. He holds a B.A. in Geology and Computer Science from Queens College (CUNY), and M.S./Ph.D. degrees from the University of Massachusetts Amherst. His career includes foundational roles at AT&T Bell Laboratories, the University of Colorado (where he held the Charles Victor Schelke Chair), the University of Lugano (founding the Faculty of Informatics), and Imperial College London. Wolf's research bridges distributed systems , networking , and software engineering , with recent projects in cloud computing, data-center networking, and mobile ad hoc networks (MANETs). His work emphasizes scalable architectures, fault tolerance, and adaptive service deployment. Key projects include HARNESS (hardware-enhanced cloud systems) and SERECA (secure cloud enclaves). His publications focus on optimizing networked systems, fault localization, and adaptive resource management. Trends include rigorous simulation-based validation, cross-layer optimization (e.g., integrating GPUs/networking), and practical applications in geosciences (Reverse-Time Migration) and machine learning (Bing's AdPredictor). Awards: ACM/IEEE/AAAS Fellow; Royal Society-Wolfson Research Merit Award; Two-time ACM SIGSOFT Research Impact Award; ACM SIGSOFT Outstanding Research & Distinguished Service Awards. Wolf leads major grants (e.g., EU H2020 SERECA, EPSRC NaaS) and has chaired ACM's Executive Committee. He founded ACM's Europe Council and contributed to seminal tools like the Siena publish/subscribe system. His team develops experimental platforms (e.g., Weevil for distributed testbeds) and collaborates with industry (AT&T, Microsoft Bing).

Jun Huangمشاهده پروفایل

استادیار

• Machine Learning
• Mobile Networks
• 6G Systems
• +۳ مورد دیگر

Jun Huang is an Assistant Professor in the McComish Department of Electrical Engineering and Computer Science at South Dakota State University (SDSU). He holds a Ph.D. in computer science from Beijing University of Posts and Telecom and a joint Ph.D. in telecommunications from Waseda University, Japan. His research focuses on machine learning in mobile networks, space-air-ground integrated networks for 6G systems, and cyber-physical systems. Key professional roles include serving on SDSU’s Graduate Program Committee and Faculty Search Committee, and membership in the IEEE as a senior member. Prior to SDSU, he held academic positions at Baylor University (2021–2023), Northwestern Polytechnical University (2020–2021), and Chongqing University of Posts and Telecom (2012–2020, 2017–2020), alongside industry experience at the National Institute of Standards and Technology (2016–2017). His research explores cutting-edge topics such as federated learning, edge computing, and energy-efficient resource management in wireless networks. Notable contributions include the Best Paper Award at Mobimedia 2019 for work on energy-efficient D2D communications. His publications span prestigious journals like IEEE Transactions on Wireless Communications and ACM Transactions on Internet Technology. Dr. Huang’s work also intersects with smart cities, vehicular networks, and IoT systems, emphasizing practical applications of theoretical advancements. He leads the Wireless Networking and Mobile Computing Laboratory at SDSU, fostering innovation in next-generation communication technologies.

• Computer Architecture
• Mobile Systems
• Robotics and Automation
• +۴ مورد دیگر

Jon Ludlam is a Senior Research Associate at the Department of Computer Science and Technology, University of Cambridge. His primary affiliation is with the Computer Architecture Group, focusing on advanced systems research. Research Interests: Computer Architecture Mobile Systems and Robotics Programming Languages & Verification Security & Systems Networking His work bridges theoretical foundations with practical implementations in distributed computing, cloud infrastructure, and virtualization technologies. Publications reflect a trajectory from early studies in materials physics (e.g., vibrational localization in disordered systems) to modern systems challenges in virtualization, unikernel optimization, and network-aware resource management. Notable contributions include Jitsu: Just-In-Time unikernel summoning and heterogeneous processor pool virtualization frameworks. No scientific awards listed. No advisees documented. Active in collaborative research within the department's Energy and Environment Group and Accelerate Programme for Scientific Discovery.

Sungho Shinمشاهده پروفایل

استادیار

• Mathematical optimization
• Control theory
• Machine learning
• +۳ مورد دیگر

Sungho Shin is an Assistant Professor in the Department of Chemical Engineering at the Massachusetts Institute of Technology (MIT) . He leads the Shin Group, focusing on mathematical optimization, control theory, machine learning, and high-performance computing for applications in process systems engineering and energy systems. Education : PhD in Chemical Engineering (University of Wisconsin-Madison), B.S. in Mathematics and Chemical Engineering (Seoul National University) Postdoc : Argonne National Laboratory (Mathematics and Computer Science Division) His research addresses scalable algorithms for decision-making in complex systems, GPU computing for nonlinear optimization, data-driven control methods, and decarbonization of energy systems through modeling, optimization, and software development. His group has produced award-winning publications, including the 2022 Outstanding Paper Award from Mathematical Programming Computation. Key software contributions include MadNLP.jl (GPU-accelerated nonlinear programming solver) and ExaModels.jl (graph-structured optimization modeling). His team maintains local computing resources for GPU development, including NVIDIA GV100 and AMD Radeon VII systems. Scientific Awards : W. David Smith, Jr. Graduate Publication Award AIChE CAST Directors’ Student Presentation Award IFAC ADCHEM Young Author Award IFAC NMPC Young Author Award Korea Presidential Science Fellowship Kwanjeong Fellowship Grainger Wisconsin Distinguished Graduate Fellowship

• Database Management
• Transaction Management
• Replication
• +۴ مورد دیگر

Alan Fekete is a Professor of Enterprise Software Systems in the School of Computer Science at the University of Sydney. He holds a BSc (Hons) from the University of Sydney and a PhD from Harvard University's mathematics department. With over 30 years at the University of Sydney, he has made significant contributions to database systems, distributed computing, and computing education. His research focuses on improving software systems' state-of-practice, particularly in transaction management, replication, and cloud computing. Education background: BSc(Hons) from the University of Sydney, PhD in Mathematics from Harvard University. Research interests include database management (transaction systems, replication), cloud computing, distributed systems, software engineering, and computing education. His teaching spans enterprise software architecture, data models, and systems analysis. He has supervised 13 doctoral students and contributed to curriculum development, notably in Java programming and cloud workforce training. Recent publications explore transactional frameworks, database benchmarking, and concurrency control. He is a Distinguished Scientist of ACM and a member of IEEE and Australian Computer Society. His work on robust rolling-upgrades and cloud storage benchmarking addresses modern system challenges. Fekete's teaching innovations include data-centric curricula and frameworks for self-assessment in programming education.