جست‌وجوی استاد و پژوهشگر

Fang Liuمشاهده پروفایل

دانشیار مدعو

• High Performance Computing
• Parallel/Distributed Scientific Computing
• Software Interface Design
• +۵ مورد دیگر

Fang Liu is an Adjunct Associate Professor in the School of Computational Science and Engineering (CSE) at Georgia Institute of Technology. She holds a Ph.D. in Computer Science from Indiana University (2009) and serves as a Research Scientist at the Partnership for Advanced Computing Environment (PACE) center. Her primary roles include diagnosing complex technical issues in HPC systems, developing HPC software stacks, teaching courses on Linux and Python, and conducting research on big data analytics. She has also served in leadership roles for HPC conferences since 2014, including program chair for HPC2012 and HPC2013. Education: Ph.D. in Computer Science, Indiana University Bloomington (2009) Dissertation: "Building Sparse Linear Solver Component for Large Scale Scientific Simulation and Multi-physics Coupling" Advisor: Professor Randall Bramley Research Interests: Her work focuses on High Performance Computing (HPC) , parallel/distributed scientific computing , multi-physics coupling , big data infrastructure , and data management systems . She actively explores Hadoop/Spark-based solutions for graph databases and streaming data security, collaborating with Prof. Polo Chau's group. Recent projects include optimizing HPC cluster operations through big data analytics and developing provenance-capturing frameworks. Technical Contributions: She has pioneered tools like ProvBench for performance tracking and Phoenix for cost model modernization at Georgia Tech. Her work on automated storage cleanup and hybrid software deployment workflows has improved research computing efficiency across heterogeneous systems. Professional Activities: Served as program committee member for HPC, ICCS, and ICCSA conferences. Currently chairs the HPC conference steering committee since 2014. Labs & Teams: Works within the PACE center and collaborates closely with CSE faculties on computational science projects. Leads instructional initiatives via the ICE federated cluster environment.

• Mathematical modeling
• Partial Differential Equations (PDE)
• Numerical methods
• +۱۰ مورد دیگر

Assoc. Prof. Anton Stoilov is an Associate Professor at South-West University in Blagoevgrad, Bulgaria. He holds a Ph.D. in Energy Conversion Technology (2006) and a Master's in Physics (2001), both from South-West University. His teaching focuses on personal productivity with IT and website development. His research spans mathematical modeling, PDEs, numerical methods, computer science, engineering, and physics, with notable work in bioinformatics, HPC, embedded systems, and medical instrumentation. Education: Ph.D. in Energy Conversion Technology and Systems, South-West University, 2006 Master of Physics, South-West University, 2001 Research Interests: Mathematical modeling and numerical methods Partial differential equations (PDE) and their applications High Performance Computing (HPC) in bioinformatics Embedded systems and sensor technology Web design and server administration Particle physics and collider experiments (e.g., HERA) Publications Trends: Recent work includes hypermedia communication technologies (2019), MATHDebate teaching methodologies (2018), and bioinformatics algorithms (2018–2016). Earlier contributions focused on particle physics experiments at HERA (2006–2007) and thermal analysis of solar systems (2004–2006). His articles bridge computational science, engineering, and education. Awards: None explicitly listed. Advising/Grants: No student advisees or grants mentioned in the provided text. Labs/Teams: Not specified in available information.

• Computational Sustainability
• Cloud Computing
• Edge Computing
• +۳ مورد دیگر

Ivona Brandic is a Full Professor of High Performance Computing Systems at TU Wien's Institute of Information Systems Engineering, leading the HPC Research Group. She specializes in Computational Sustainability, Cloud/Edge Computing, and Quantum-Classical Systems. Her roles include Head of the Computational Sustainability Research Unit and membership in TU Wien's Faculty Council. She teaches courses such as AI/ML in Climate Change and Hybrid Quantum-Classical Systems. Her research focuses on sustainable IT, energy-efficient systems, and hybrid quantum-classical workflows. Projects include computational sustainability initiatives funded by the Austrian Science Fund (FWF) and industry partnerships like the Virtual Shepherd project. She has contributed to over 50 publications, emphasizing edge computing, quantum algorithms, and HPC optimization. Notable contributions include developing frameworks like RIGOLETTO for hybrid scientific workflows and FRESCO for edge offloading. Her work bridges theoretical advancements with practical applications in environmental monitoring and energy efficiency.

Roberto Espositoمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۴ مورد دیگر

Roberto Esposito is an Associate Professor at the Department of Computer Science, University of Turin. His research focuses on Machine Learning, Deep Learning, and Fairness in AI, with collaborations at Johannes Gutenberg University Mainz and INRAE. He leads the European PILOT project (H2020) and participates in initiatives like NextPerception and HPC4AI. He received the SELEX Innovation Award for territory monitoring systems and achieved notable results in the DREAM Challenge. He teaches courses on Neural Networks and Deep Learning and supervises PhD students in Modeling and Data Science. Education: Details not explicitly provided in text. Research interests include AI ethics, semi-supervised learning, and HPC integration with AI. He has contributed to projects like the European PILOT (30M€), NextPerception (smart sensors), and HPC4AI. His work on gene expression prediction via neural networks has been highly cited (>1300 citations). Awards include the SELEX Innovation Award and top placements in the DREAM Challenge. He advises PhD students and collaborates with industry partners like Intesa San Paolo and Leonardo/SELEX.

Stefan M. Wildمشاهده پروفایل

استاد مدعو

• Numerical Optimization
• Machine Learning
• Scientific Computing
• +۳ مورد دیگر

Stefan M. Wild serves as Director of the Applied Mathematics and Computational Research (AMCR) Division and Senior Scientist at Lawrence Berkeley National Laboratory, while holding an adjunct faculty position in the Industrial Engineering and Management Sciences (IEMS) department at Northwestern University's McCormick School of Engineering. He also serves as a Senior Fellow at NAISE (Northwestern Initiative for AI and Society). Dr. Wild earned his Ph.D. and M.S. in Operations Research from Cornell University (2009, 2007) and his M.S. and B.S. in Applied Mathematics from the University of Colorado, Boulder (2003, 2002). His academic journey includes an Argonne Director's Postdoctoral Fellowship (2008-2010) and the DOE Computational Science Graduate Fellowship (2005-2008). His research program focuses on developing numerical optimization and automated learning algorithms for challenging science and engineering problems at interfaces involving computer simulations, complex data, and physical experiments. Dr. Wild leads multiple community software projects including BAND, parMOO, libEnsemble, deepHyper, NUCLEI, POptUS, and surmise, with applications spanning nuclear physics, materials science, and astrophysics. His work bridges derivative-free optimization, uncertainty quantification, high-performance computing, and scientific machine learning. Dr. Wild has advised numerous postdocs who have established successful careers at national laboratories and academic institutions. His editorial responsibilities include Mathematical Programming Computation, INFORMS Journal on Computing, Data Science in Science, and SIAM Review. U.S. Department of Energy Early Career Research Award (2020) STS Forum Future Leader (2018) IDC HPC Innovation Excellence Award (2015) SIAM SIGEST Award (2014) Strategic Laboratory Leadership Program (UChicago Booth School) (2013) DOE Computational Science Graduate Fellowship (2005-2008) As AMCR Division Director, he leads a diverse team of applied mathematicians, computational scientists, and software engineers addressing some of the world's most challenging computational problems across scientific and engineering disciplines. His leadership emphasizes both technical excellence and commitment to inclusion, diversity, equity, and accountability in scientific research.

Vincent Heuvelineمشاهده پروفایل

استاد

• Uncertainty Quantification
• High Performance Computing
• Data Intensive Computing
• +۳ مورد دیگر

Professor Vincent Heuveline serves as Professor and Head of the Engineering Mathematics and Computing Lab (EMCL) at Heidelberg University's Interdisciplinary Center for Scientific Computing (IWR). He concurrently holds the positions of Chief Information Officer (CIO) and Director of the Computing Centre at the university, while leading the Data Mining and Uncertainty Quantification (DMQ) group at HITS gGmbH. His academic background includes: Studies in Mathematics, Physics, and Computer Science at Caen (France) and Würzburg (Germany) PhD in Computer Science (1997) from Université de Rennes and INRIA Habilitation in Mathematics (2002) at Heidelberg University Research focuses on uncertainty quantification (UQ) , high-performance computing (HPC) , and data-intensive computing with applications in medical engineering. He is deeply engaged in IT security research and teaching, developing practical frameworks for industrial deployment of numerical simulations and secure computing infrastructures. Recent publications reveal converging trends in biomedical image segmentation (e.g., Biomedisa platform), metabolic modeling for newborn diagnostics, and cybersecurity solutions like DGA detection. Key interdisciplinary themes include machine learning for medical applications, HPC algorithm optimization, and network security frameworks addressing real-world industrial challenges. He leads the EMCL and DMQ research groups, driving innovation in computational science. As CIO, he shapes Heidelberg University's digital infrastructure strategy while maintaining active industry collaborations for translating academic research into simulation and security solutions.

• Big Data
• Data Storage Technology
• Spatial Data Management
• +۹ مورد دیگر

Thomas Heinis is a Professor in Computing at Imperial College London. He leads the SCALE Lab and conducts research in DNA data storage and high-performance data analytics. His academic journey includes a Ph.D. and M.Sc. in Computer Science from ETH Zürich, a Postdoctoral fellowship at EPFL’s DIAS Lab, and a Fulbright Scholarship at Purdue University. He specializes in scalable data management techniques for scientific and spatial datasets, novel hardware optimization, and interdisciplinary applications such as medical data analysis. Education: Ph.D. and M.Sc. in Computer Science, Swiss Federal Institute of Technology in Zürich (ETH Zürich) Postdoctoral Fellow, DIAS Lab, EPFL Fulbright Scholarship, Purdue University (2002–2004) Research Interests: Big Data and Distributed Processing Spatial Data Indexing and Visualization High-Performance Computing (HPC) Data Analytics Data Management on Novel Hardware (e.g., neuromorphic systems) Synthetic DNA Storage Technology Interdisciplinary Applications in Medicine and Neuroscience Recent Work Trends: Heinis’s publications emphasize innovative storage solutions like Motif-based DNA encoding, efficient spatial indexing algorithms (e.g., FLAT, SCOUT, TOUCH), and machine learning-driven approaches for healthcare and scientific data analysis. His work bridges computational methods with real-world applications in neuroscience, medicine, and environmental science. Scientific Awards: SystemsX Interdisciplinary Ph.D. Fellowship (2007) Finalist, Venture Leaders Entrepreneurship Competition (2007) Finalist, Purdue Burton D. Morgan Entrepreneurship Competition (2003) Fulbright Scholarship (2002–2004) Advising & Grants: Supervises Ph.D. students in scientific data management, spatial data, and DNA storage. Funding opportunities include Marie-Curie post-doctoral fellowships, CSC Imperial Scholarships, and others. His lab actively collaborates with institutions like the Blue Brain Project and explores scalable tools for data-driven research. Labs & Teams: Leader of the SCALE Lab at Imperial College. Collaborations with the Blue Brain Project (BBP) on neuroscientific data management.

Long Chengمشاهده پروفایل

پژوهشگر

• Cloud Computing
• Big Data Analytics
• Parallel and Distributed Systems
• +۳ مورد دیگر

Dr. Long Cheng was a former Researcher at the International Center for Computational Logic (ICCL) within the Faculty of Computer Science at TU Dresden. His research focused on cloud computing, big data analytics, and distributed systems, particularly in optimizing large-scale data processing, outer join operations, and semantic web technologies. He contributed to projects involving parallel programming models like X10, MapReduce frameworks, and efficient data compression techniques for RDF datasets. Cheng collaborated with the Knowledge-Based Systems research group, addressing challenges in scalable systems, high-throughput indexing, and parallel reasoning under non-monotonic logics. His work emphasized performance optimization in distributed environments, including skew handling, data redistribution strategies, and algorithm scalability for large datasets. Publications span topics such as outer join evaluation in cloud environments, efficient compression of semantic web data, and high-performance query execution over distributed systems. His research bridges theoretical computational logic with practical applications in big data and cloud infrastructure.

• Computer Systems
• Functional Programming
• Hardware Description
• +۳ مورد دیگر

John O'Donnell is an Honorary Lecturer at the University of Glasgow's School of Computing Science. His research spans functional programming, hardware description languages, parallel computing, and computer science education, with notable applications in music technology. Key research areas: Functional approaches to hardware design and simulation Parallel data structures and algorithms for specialized architectures Programming misconception identification and pedagogical tools Computational modeling of musical performance techniques Publications demonstrate consistent innovation in applying functional programming paradigms to diverse domains, from circuit design to music pedagogy. Recent work focuses on educational aspects of computer systems and programming.

• Computer Architecture
• High-Performance Computing (HPC)
• Cloud Computing
• +۴ مورد دیگر

Prof. Weikuan Yu is a Professor in the Department of Computer Science at Florida State University. His research focuses on computer architecture, high-performance computing (HPC), cloud computing, parallel file systems, and deep learning applications. He holds the role of Chair and can be contacted via yuw@cs.fsu.edu or (850) 644-5442. His expertise includes optimizing storage systems and I/O behaviors in scientific workflows, developing scalable distributed systems, and applying machine learning to improve computational efficiency. Notable projects include work on burst buffer systems (e.g., BurstFS, TRIO), persistent memory management (PHAST), and distributed deep learning frameworks (e.g., compression techniques for time-evolutionary data). Recent research trends emphasize enhancing HPC storage efficiency through novel file systems and I/O emulation, as well as leveraging machine learning for fault tolerance and configuration tuning. His work bridges hardware-software co-design to address challenges in exascale computing and big data analytics. Prof. Yu has contributed to multiple open-source projects and frameworks, including OpenSHMEM-based key-value stores and MapReduce optimizations. His publications highlight advancements in parallel processing, distributed algorithms, and energy-efficient memory architectures.

Dylan Keonمشاهده پروفایل

استادیار

• Spatial Analysis
• Spatio-Temporal Data
• Climate Data
• +۵ مورد دیگر

Dylan Keon is an Assistant Professor (Senior Research) at Oregon State University's School of Electrical Engineering and Computer Science. He serves as the Associate Director of the Northwest Alliance for Computational Science & Engineering (NACSE). His research focuses on spatial analysis, spatio-temporal data systems, climate data production, and decision support systems for environmental and disaster management applications. He holds a Ph.D. in Computational Geography and M.S. in Plant Ecology and GIS/Statistics from Oregon State University, along with a B.S. in Botany from Western Michigan University. Dr. Keon has over 20 years of experience in developing geospatial tools for environmental monitoring, including projects funded by USDA, NSF, and other agencies. His work spans topics such as precision agriculture, climate modeling, tsunami engineering, and aquatic pathogen tracking. He currently leads operations on multiple USDA-funded initiatives and has contributed to critical infrastructure resilience frameworks for coastal regions. His research trends emphasize interdisciplinary collaboration, leveraging computational methods to address complex environmental challenges. Recent efforts include advancing solar radiation models, improving tsunami inundation simulations, and creating databases for tracking aquatic pathogens. Keon’s contributions bridge academic research with real-world applications, particularly in disaster preparedness and sustainable environmental practices.

Maria Elena Innocentiمشاهده پروفایل

استاد

• Plasma Physics
• High Performance Computing
• Space Physics
• +۳ مورد دیگر

Prof. Dr. Maria Elena Innocenti is a Professor of Computational Plasma Physics at Ruhr-Universität Bochum, Germany. Her research focuses on multiscale processes in heliospheric plasmas, including magnetic reconnection, turbulence, and instabilities. She specializes in High Performance Computing (HPC) methods and applies machine learning to analyze space plasma data. She leads the AIDA (Artificial Intelligence Data Analysis) work package under the Horizon 2020 project and collaborates with NASA’s HERMES science center. Education: PhD in Applied Mathematics (KU Leuven, Belgium), Master of Science and Bachelor of Science in Physics Engineering (Turin Polytechnic, Italy, both with distinction). Professional Highlights: Developed the multi-level multi-domain method and kinetic Expanding Box Model for plasma simulations. Holds fellowships from NASA and FWO (Research Foundation - Flanders). Extensive postdoctoral experience at KU Leuven and NASA’s Jet Propulsion Laboratory. Research Interests: Combining kinetic plasma modeling, HPC optimization, and data-driven approaches to study solar wind dynamics and magnetic energy storage in space plasmas.

Nikolay Simakovمشاهده پروفایل

پژوهشگر

• High-Performance Computing
• Machine Learning
• Scientific Software Optimization
• +۳ مورد دیگر

Dr. Nikolay Simakov is a Computational Scientist at the University at Buffalo's Center for Computational Research (CCR), joining in 2012. He holds a PhD in Computational Chemistry from Carnegie Mellon University, alongside BA and MA degrees in Applied Physics and Mathematics from Moscow Institute of Physics and Technology. Research Interests: High-Performance Computing (HPC), Machine Learning, Scientific Software Optimization, Data Analytics for Large Datasets, and Simulation of Biomolecular Systems Teaching: Taught graduate courses like EAS509 Statistical Learning and Data Mining-II and CDA501 Introduction to Data Driven Analysis, mentoring undergraduate interns at CCR Key Contributions: Developed Slurm Simulator for HPC scheduling, contributed to XDMoD/ACCESS Metrics for resource auditing, optimized GPU-based Poisson equation solvers, and advanced multi-rheology geophysical flow modeling Technical Focus: Performance monitoring of HPC systems, node sharing analysis, and ARM architecture benchmarking His work bridges computational infrastructure optimization and biological system modeling, with publications spanning HPC resource management, biomolecular simulations, and parallel computing frameworks.

Barton P. Millerمشاهده پروفایل

استاد

• Binary Code Analysis
• Software Security
• Distributed Systems
• +۵ مورد دیگر

Barton P. Miller is a Vilas Distinguished Achievement Professor and the Amar & Balinder Sohi Professor of Computer Sciences at the University of Wisconsin, Madison, where he has made significant contributions to computer science research and education. As a faculty member in the Computer Sciences Department within the College of Engineering, he directs major research initiatives including the Paradyn Tools project and leads cybersecurity efforts as Chief Scientist of the DHS-funded Software Assurance Marketplace (SWAMP) research center. Miller received his B.A. degree from the University of California, San Diego in 1977, and M.S. and Ph.D. degrees in Computer Science from the University of California, Berkeley in 1980 and 1984. His educational background laid the foundation for his pioneering work in software testing and analysis. His research spans multiple critical areas of computer science, with a primary focus on binary code analysis and instrumentation , software security , and distributed and parallel program performance . Miller is particularly renowned for founding the field of Fuzz random software testing in 1988 and dynamic binary code instrumentation in 1992. His work bridges theoretical computer science with practical applications, addressing real-world challenges in high-performance computing systems, cybersecurity, and scalable distributed systems. His research has direct applications in national security through his work with DHS and TrustedCI, the NSF Cybersecurity Center of Excellence. Miller's publications reveal a consistent focus on binary analysis tools, security vulnerability detection, and performance optimization techniques. His recent work shows increasing emphasis on ransomware analysis, GPU performance optimization, and practical security tool development for developers. The trajectory of his research demonstrates how foundational work in binary instrumentation has evolved to address contemporary cybersecurity challenges while maintaining relevance to high-performance computing environments. Among his notable honors, Miller is a Fellow of the ACM and recipient of the prestigious Jean-Claude Laprie Award in Dependable Computing , an R&D 100 Award , and multiple best paper awards including at HPDC 2009 and CCGrid 2018. His contributions to the field have been recognized through distinguished lectureships at institutions including Ben Gurion University and IBM T.J. Watson Research Center. As an educator, Miller has mentored numerous students and taught courses spanning operating systems, software security, and distributed systems. He has received teaching awards and developed educational resources including free and open software security training materials. His work with TrustedCI has focused on cybersecurity for scientific communities, helping researchers secure their computational infrastructure while maintaining research productivity. Miller also co-directs the MIST software vulnerability assessment project in collaboration with colleagues at the Autonomous University of Barcelona. Miller directs the Paradyn Tools project, which investigates program scalability and binary program analysis technologies for use in HPC, systems design, and cyber-security. His work extends to practical applications through collaborations with government agencies including DHS and the U.S. Secret Service Electronic Crimes Task Force. His research group has developed tools like Dyninst, MRNet, and the Wisconsin Safety Analyzer that have become foundational in their respective domains.

Tevfik Kosarمشاهده پروفایل

استاد

• Data Intensive Computing
• Distributed Systems
• Petascale Systems
• +۴ مورد دیگر

Prof. Tevfik Kosar is a Professor in the Department of Computer Science and Engineering at the University at Buffalo (UB), part of the School of Engineering and Applied Sciences. His research focuses on data-intensive computing, distributed systems, petascale storage, and energy-efficient data transfer optimization. He has taught numerous courses on operating systems, distributed systems, and green computing, including CSE 421/521 (Operating Systems), CSE 709 (Green Computing), and CSE 710 (Distributed File Systems). His achievements include the UB Exceptional Scholar Award (2020), SEAS Researcher of the Year (2018), and an NSF CAREER Award (2011). He leads the DIDCLAB , which develops innovative solutions for data-intensive distributed computing. Prof. Kosar holds a PhD from the University of Wisconsin-Madison (2005), MS from Rensselaer Polytechnic Institute (1999), and BS from Bogazici University (1997). His work spans academic contributions, industry collaboration, and sustainability-focused research. Teaching Highlights: Courses: Operating Systems (repeated annually since 2011), Green Computing (since 2013), and specialized seminars on distributed file systems. Grants & Projects: Collaborative NSF grants for GreenSW (2024), CloudScent (2023), and energy-aware data transfer optimization (2018–present). His research emphasizes minimizing energy consumption in cloud and HPC systems while maximizing data transfer efficiency. Recent projects include GreenABR+ for energy-aware video streaming and Greendataflow for zero-carbon data movement. He has also pioneered tools like FlowTracer for AI training cluster analysis and PhoneLab , a smartphone testbed for real-world network studies.