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Andrew GrimshawView profile

Professor

• Grid Computing
• Parallel Processing
• Distributed Systems
• +3 more

Andrew Grimshaw is a Professor of Computer Science at the University of Virginia's School of Engineering and Applied Science. He received his Ph.D. from the University of Illinois at Urbana-Champaign in 1988. His career has focused on advancing distributed computing, grid systems, and high-performance computing. Grimshaw is best known for designing the Legion system, a foundational grid operating system, and co-founding Avaki Corporation (acquired by Sybase), which commercialized grid technologies. Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign, 1988 Research Interests: Grimshaw's work spans distributed systems architecture, parallel processing, and cyberinfrastructure. He pioneered systems like Mentat and ELFS, and contributed to Grid Computing standards through projects such as XSEDE and the Global Grid Forum. His research emphasizes scalable, fault-tolerant systems and interoperability between heterogeneous resources. Publications: His work reflects advancements in grid computing, GPU algorithms, and federated systems. Key themes include global virtual computing, market-based resource allocation, and standards-based job execution. Recent efforts focus on XSEDE's Global Federated File System (GFFS) and service-oriented cloud federations. Awards: Frost & Sullivan Technology Innovation Award (2003) Grants & Roles: Grimshaw has served on NSF advisory committees, the NPACI Executive Committee, and the Global Grid Forum. His leadership in Avaki and XSEDE highlights his commitment to bridging academic and industrial computing challenges. Current projects explore campus bridging and standards-driven cyberinfrastructure integration. Labs/Teams: Associated with the University of Virginia's Computer Science department and XSEDE initiatives, his work drives collaborative research on distributed systems and high-performance computing frameworks.

Miryung KimView profile

Professor

• Software Engineering
• Data-Intensive Computing
• Heterogeneous Computing
• +7 more

Miryung Kim is a Professor and Vice Chair of Graduate Studies in the Computer Science Department at the University of California, Los Angeles . She directs the Software Engineering and Analysis Laboratory and has pioneered research in Software Engineering for Data Intensive Computing (SE4DA) and Software Engineering for Heterogeneous Computing (SE4ML) . Her work spans code clone detection, automated testing/debugging for Apache Spark, and developer tools for heterogeneous systems. Education: BS in Computer Science, Korea Advanced Institute of Science and Technology (KAIST), 2001 MS and PhD in Computer Science and Engineering, University of Washington (2003 & 2008) Research Interests: Her research focuses on software evolution , data-intensive systems , and heterogeneous computing . Key contributions include foundational work on code clone analysis , large-scale studies of data scientists' roles , and fuzz testing frameworks for big data and AI accelerators. She established SE4DA and SE4ML research directions. Article Trends: Recent publications emphasize fuzz testing for MLIR compilers , Apache Spark systems, and quantum software stacks . The work spans automated test generation , heterogeneous platform validation , and code optimization in dataflow environments. Scientific Awards: ACM SIGSOFT Influential Educator Award (2022) ICSME Most Influential Paper Awards (2023 & 2020) NSF CAREER Google Faculty Research Award Humboldt Research Fellowship ACM Distinguished Member Advising & Grants: She has mentored 8 tenure-track faculty members (Columbia, Purdue, Virginia Tech) and leads a $4.9M Office of Naval Research project on synergistic software customization . Her former students (Tianyi Zhang, Muhammad Ali Gulzar) have received NSF CAREER awards.

August ErnstssonView profile

Lecturer

• Parallel Programming
• Heterogeneous Computing Systems
• High-Performance Computing
• +3 more

August Ernstsson is a Lecturer at Linköping University’s Department of Computer and Information Science (IDA). He is affiliated with the Graduate School in Computer Science (CUGS) and research groups Programming Environments (PELAB) and Software and Systems (SAS). His research focuses on parallel programming models, heterogeneous computing systems, and optimizing software for FPGA and GPU architectures. Notable contributions include frameworks for high development productivity (EXA2PRO) and deterministic parallel algorithms. Ernstsson’s work emphasizes performance portability, compiler design, and abstraction layers for heterogeneous systems. He collaborates with international teams on projects like pattern-based programming abstractions and FPGA-accelerated systems.

Phu NguyenView profile

Lecturer

• Computational Mechanics
• Phase-field Fracture
• Multiscale Modeling
• +5 more

Dr. Phu Nguyen is a Lecturer in Structural Engineering at Monash University's Department of Civil and Environmental Engineering. He holds a PhD in Computational Mechanics from Delft University of Technology (2011) and has held academic positions at Johns Hopkins University, Cardiff University, and the University of Adelaide before joining Monash in 2016. His expertise spans computational mechanics, phase-field fracture modeling, and multiscale material analysis. Education: Bachelor of Science in Civil Engineering, Hochiminh University of Technology (1998) Master of Science in Computational Mechanics, University of Liège (2005) PhD in Computational Mechanics, Delft University of Technology (2011) Research Interests: Focuses on computational modeling of failure in quasi-brittle materials (concrete, composites) using advanced numerical methods such as phase-field fracture, material point method (MPM), isogeometric analysis, and multiscale homogenization. Active in developing open-source computational tools like MPMat and Karamelo . Publications: Over 78 peer-reviewed articles, including highly cited works on meshless methods, phase-field fracture, and computational homogenization. Notable contributions to journals like Computer Methods in Applied Mechanics and Engineering and Engineering Fracture Mechanics . Awards: ARC DECRA Fellowship (2016–2019) Best PhD Presentation Award (2010) CIFRE Scholarship Most Cited Article in Advances in Applied Mechanics (2025) Grants & Supervision: Leads research projects on hydraulic fracturing and composite failure. Supervised PhD student Tushar K Mandal. Active in teaching courses like CIV4235 (Advanced Structural Design) and CIV3284 (Concrete and Masonry Structures). Professional Roles: Editorial board member of Engineering Fracture Mechanics , Computers, Materials and Continua , and Computer Modeling in Engineering & Sciences . Reviews for top journals including Computer Methods in Applied Mechanics and Engineering and International Journal of Numerical Methods in Engineering .

Fabio GratlView profile

Researcher

• Molecular Dynamics
• High-Performance Computing
• Node-Level Performance Optimization
• +3 more

Dr. Fabio Gratl is a former Researcher (Wissenschaftlicher Mitarbeiter) at the Chair of Scientific Computing in Computer Science (SCCS) at the Technical University of Munich (TUM). He holds a M.Sc. in Informatics from TUM (2017) and completed his doctoral studies (Dr. rer. nat.) in 2020. His research focuses on high-performance computing, molecular dynamics simulations, and auto-tuning algorithms for particle systems. He contributed to the development of the AutoPas library, which enables node-level auto-tuning for optimal performance in molecular dynamics. Key research interests include parallel computing (shared memory/task-based/mpi), vectorization, and algorithm selection strategies. He has advised numerous theses on topics like GPU acceleration, auto-tuning with machine learning, and space debris modeling. His work integrates software development (C++, Python) with computational engineering challenges. Teaching activities included courses on scientific computing, molecular dynamics, and numerical programming at TUM. He also organized seminars on leadership and future HPC trends. Notable software contributions include AutoPas, ls1 mardyn extensions, and tools for polyhedral gravity modeling. Publications span computational science journals and conferences, emphasizing algorithm optimization and cross-domain applications. Despite leaving the SCCS chair, his research legacy persists through ongoing AutoPas development and collaborations.

Martin SchreiberView profile

Professor

• High-Performance Computing
• Applied Mathematics
• Scientific Computing
• +6 more

Martin Schreiber is a Full Professor at Université Grenoble Alpes (France), focusing on interdisciplinary research at the intersection of High-Performance Computing (HPC) and Applied Mathematics. He is also affiliated with INRIA and the AIRSEA team. Previously, he held roles including Lecturer/Researcher at Technical University of Munich (2018–2021), Proleptic Lecturer at University of Exeter (2015–2018), and doctoral candidate at TUM under Prof. Bungartz (2010–2014). His research spans dynamically adaptive grids, parallel algorithms, GPU/FPGA architectures, and climate/weather simulations. Education: 2010: Diploma in Computer Science (TUM, Germany) 2014: PhD in Scientific Computing (TUM, Germany) 2008–2010: Minor in Theoretical Medical Science (TUM) 2004–2010: Computer Science Studies (TUM) 2007: Semester at Hong Kong University of Science and Technology Research Interests: Parallelization strategies for HPC architectures Dynamically adaptive grids using space-filling curves Real-time visualization and simulation Applications in weather, climate, and ocean modeling Algorithm development for scientific computing His work emphasizes interdisciplinary collaboration to advance computational methods for complex scientific challenges. Teaching & Labs: Current courses at Université Grenoble Alpes: C++, Partial Differential Equations, Numerical Methods Former courses: High-Performance Computing, Time Integration, Computer Architectures Led projects on GPU-based Lattice Boltzmann Methods and parallel-in-time algorithms He is also part of the SWEET and Sierpinski software frameworks for weather/climate simulations.

Peng XuView profile

Adjunct Assistant Professor

• Computational Chemistry
• Quantum Mechanics
• Molecular Modeling
• +2 more

Peng Xu is an Assistant Adjunct Professor at the Ames Laboratory of US DOE, affiliated with Iowa State University. His research develops computational methodologies to model complex interfacial processes across multiple scales, focusing on quantum chemistry and high-performance computing implementations. Research spans quantum mechanical algorithms, molecular fragmentation methods, GPU-accelerated computing, and force field development. Recent publications demonstrate advances in protein-ligand binding calculations, catalytic surface interactions, and exascale computing portability. Contact via pxu@iastate.edu or phone (515-294-6342).

Dimitrios SkarlatosView profile

Assistant Professor

• Computer Architecture
• Security and Privacy
• Operating Systems
• +1 more

Dimitrios Skarlatos is an Assistant Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. His research bridges computer architecture and operating systems with a focus on performance, security, and scalability. His work revolves around two central themes: (1) redesigning abstractions and interfaces between hardware and OS layers to improve performance and scalability, and (2) uncovering security vulnerabilities and building defenses at the hardware-OS boundary. Research Focus: Operating system security and vulnerability prevention Virtual memory systems and page table optimizations Serverless computing architectures Hardware-software co-design for security Datacenter resource management Dr. Skarlatos advises five PhD students and has published extensively on systems research. He received his PhD from the University of Illinois at Urbana-Champaign, where his dissertation received the ACM SIGARCH & IEEE CS TCCA Outstanding Dissertation Award for contributions to redesigning hardware-OS abstractions.

Giuseppe BarcaView profile

Lecturer

• High-Performance Computing
• Artificial Intelligence
• Machine Learning
• +4 more

Giuseppe Barca is a Lecturer of High-Performance Computing (HPC) in the School of Computing at the Australian National University (ANU), part of the ANU College of Systems & Society. He holds leadership roles in multi-institutional collaborations, including PaCER projects and the GAMESS Exascale Computing Project. His research focuses on integrating HPC, AI, and quantum chemistry to revolutionize computational science, enabling transformative digital technologies in materials science, drug discovery, and sustainable manufacturing. Barca’s work has led to world records in computational-driven chemical modeling and the 2024 ACM Gordon Bell Prize for the EXESS software. His research interests span high-performance algorithms, machine learning for digital chemistry, and autonomous discovery protocols. He leads a group advancing quantum chemistry software like EXESS, optimized for exascale supercomputers. The group’s digital workflows automate and accelerate chemical R&D by combining quantum mechanics with AI-driven predictions. Barca’s contributions bridge computational efficiency and scientific impact, addressing challenges in scalable HPC algorithms and AI integration. Awards: 2024 ACM Gordon Bell Prize, World Record in Material Structure Modelling (2020–2021) Leadership Roles: Pawsey Centre for Extreme-scale Computing (PaCER), GAMESS Exascale Project Software: EXESS (winner of Gordon Bell Prize), AliSim-HPC, CONQUEST Barca’s team offers competitive graduate and postdoctoral openings in HPC, AI, and digital chemistry, emphasizing programming skills in C/C++, GPU architectures, and version control systems. Research themes include algorithmic innovation for quantum chemistry, generative AI for material discovery, and autonomous digital workflows for drug design.

Abdullah Al-MamunView profile

Assistant Professor

• High-Performance Computing
• Distributed Systems
• Blockchain Technology
• +5 more

Abdullah Al-Mamun is an Assistant Professor and Director of the High Performance & Secure Data Computing (HPSDC) Lab at Augusta University's School of Computer and Cyber Sciences. His research focuses on resource-efficient distributed systems, high-performance computing, and blockchain-based solutions for secure data management. He holds a Ph.D. from the University of Nevada, Reno, and a double M.Sc. from RWTH Aachen University and the University of Trento under the Erasmus Mundus program. His work has been supported by grants including an NSF CRII award ($165,000) and a Google Cloud Research Grant ($10,000). Education: Ph.D., Computer Science & Engineering (2022), University of Nevada, Reno M.Sc., Computer Science (2013), RWTH Aachen University / University of Trento (Double Degree) B.Sc., Computer Science & Engineering (Summa Cum Laude, 2009), American International University-Bangladesh Research Interests: High-Performance Blockchain Resource-Efficient HPC Extreme-Scale Data Management Secure Data Provenance Edge Computing Selected Awards: NSF CRII Award (2024–2026) Best Paper Award (Cloud'19) Outstanding Graduate Student Scholarship (UNR, 2020–2021) Service & Teaching: ACM Programming SIG Club Advisor (2023–present) Reviewer for IEEE IoT Journal, IEEE Transactions on Consumer Electronics, and others Teaches courses in Low-Level Programming, Computer Architecture, and High-Performance Computing Labs & Teams: Directs the HPSDC Lab, publishing in top-tier venues like SC, ICDE, and IPDPS.

Zhanyang ZhangView profile

Professor

• Wireless Communication and Networks
• Cyber-Physical Systems & IoT/Sensor Networks
• Numerical Modelling and Simulation
• +6 more

Dr. Zhanyang Zhang is a Professor of Computer Science at the City University of New New York, jointly appointed at the College of Staten Island and the CUNY Graduate Center. His expertise bridges wireless networking, cyber-physical systems, IoT, numerical simulation and high-performance computing, with recent emphasis on 5G/edge intelligence, ocean oil-spill modelling, and assistive technologies for the visually impaired. Education: Ph.D. in Computer Science, City University of New York M.S. in Computer Science, College of Staten Island B.E. in Computer Engineering, Jilin University, Changchun, China Research Interests: Dr. Zhang's research integrates communication theory, data science and large-scale simulation. He develops protocols for next-generation wireless networks, designs energy-efficient algorithms for underwater and terrestrial sensor systems, and applies lattice-Boltzmann methods to environmental disasters such as oil-spill propagation and urban flooding. His group also explores deep-learning-driven anomaly detection in 5G and edge-computing offloading strategies using game-theoretic and reinforcement-learning frameworks. Publication Trends: Across the past decade his scholarly output has shifted from foundational sensor-network coverage and localisation toward interdisciplinary applications: assistive smart-city services, GPU-accelerated ocean-surface transport models, and AI-based traffic and flood prediction. A recurrent methodological thread is the fusion of stochastic modelling (Markov, game, queueing) with high-performance simulation to derive scalable, real-time solutions. Grants & Projects: He currently leads or co-leads projects funded by the U.S. National Science Foundation and multiple CUNY schemes, totalling more than USD 1 M since 2020. Topics range from assistive cyber-connections for the visually impaired to next-generation oil-spill forecasting assimilating large sensor datasets. Labs & Teams: Dr. Zhang heads the Wireless & Cyber-Physical Systems Research Group at CSI, maintains affiliations with the CUNY High-Performance Computing Center, and supervises doctoral students within the Ph.D. Program in Computer Science at the Graduate Center.

Weizhen MaoView profile

Professor

• Computer Science
• Algorithms
• Theoretical Computer Science
• +3 more

Weizhen Mao is a Professor in the Department of Computer Science at the College of William and Mary. His research focuses on algorithms, theoretical computer science, parallel computing, distributed systems, and networking. He holds an office in McGlothlin-Street Hall 114 and can be reached at wm@cs.wm.edu. Teaching Responsibilities: Professor Mao instructs a range of courses including Discrete Structures (CSci 243), Algorithms (CSci 303), Theory of Computation (CSci 663), and advanced topics in algorithms and computation at both undergraduate and graduate levels. His courses emphasize foundational concepts in computer science theory and practical applications. Research Interests: His work spans algorithm design, scheduling theory, distributed computing, and network protocols. Recent publications address challenges in sensor networks, RFID systems, vehicular communication, and parallel processing. His research often bridges theoretical guarantees with practical implementations in real-world systems. Professional Contributions: With over 50 publications in top conferences and journals (e.g., IEEE Transactions on Wireless Communications, ACM MobiHoc), his work has impacted areas like energy-efficient networking, optimal storage placement in sensor networks, and GPU cluster optimization. He maintains active collaborations with industry and academic partners to advance computational methodologies.

Mathias BrieuView profile

Professor

• Mechanics of Materials
• Computational Mechanics
• Soft Polymers
• +5 more

Mathias Brieu is a Professor in Mechanical Engineering at California State University, Los Angeles. He specializes in solid mechanics, computational mechanics, and the mechanics of soft polymers and bio-polymers for biomedical applications. His research focuses on material characterization, constitutive modeling, and numerical methods, particularly in pelvic soft tissues and polymer sustainability. Education: Habilitation à Diriger des Recherches (H.D.R.) in Physics, University of Lille (2005) PhD in Mechanical Engineering, Ecole Normale Supérieure Paris-Saclay (1999) - Highest Distinction MS in Mechanical Engineering, University Paris-Sorbonne (1996) - Highest Distinction Agrégation de Mécanique (French National Examination for Higher Education) Research Interests: Experimental and computational mechanics of soft materials Patient-specific simulation of pelvic systems for medical device design Bio-polymers and sustainability in polymer applications Medical imaging integration with biomechanical modeling Publications Trends: Recent work emphasizes pelvic biomechanics, composite material behavior under dynamic loads, and medical device innovation. His articles bridge experimental methods (e.g., MRI analysis) with computational models to address clinical challenges in gynecology and surgical planning. Awards: PhD 'Très honorable avec félicitations du jury' MS 'Mention Très Bien' Agrégation de Mécanique (French Ministry) Grants & Advising: While specific grants are not detailed, his work implies sustained funding for interdisciplinary research. He has advised students through his academic roles, with no named advisees listed. Labs & Teams: Engages in collaborative projects involving medical imaging, material testing, and biomechanical simulation, though specific lab names are not mentioned.

Michael A. ThomasView profile

Professor

• Bacteriophage Genomics
• Evolutionary Biology
• Environmental Toxicology
• +3 more

Michael A. Thomas is a Professor in the Department of Biological Sciences at Idaho State University, where he has served since 2003. His research focuses on bacteriophage biology and genomics with applications in combating antibiotic-resistant infections, alongside investigations into environmental impacts of pharmaceuticals on neurological health. Dr. Thomas' educational background includes: Ph.D. in Entomology from Pennsylvania State University (2000) M.S. in Biology from Kansas State University (1994) B.S. in Biology from the University of Nebraska, Lincoln (1991) Postdoctoral fellowship at the Medical College of Wisconsin (2000-2003) on the Rat genome project His research program spans bacteriophage host range evolution, genome annotation of novel phages (particularly Microbacterium-infecting clusters EG and EE), and environmental neurotoxicology examining psychoactive pharmaceuticals' links to autism and Alzheimer's disease. He employs genomic, proteomic, and bioinformatic approaches to study viral evolution and contaminant impacts on biological systems. Recent publications (2022-2025) reveal dual research trajectories: bacteriophage discovery/annotation (Rowlf, SallyK, KillerTomato) and environmental neurotoxicology, with increasing emphasis on educational innovation through course-based research experiences. No scientific awards or fellowships are documented in the provided materials. Dr. Thomas mentors undergraduate and graduate researchers in his Life Sciences 436 laboratory, developing course-based research experiences including the Bacteriophage Discovery & Bioinformatics Lab (BIOL 1111/1112). His teaching portfolio spans Genetics, Organic Evolution, and Senior Seminar courses. Research funding appears focused on phage genomics and environmental toxicology through NIH and NSF mechanisms, though specific grants aren't detailed. His laboratory maintains active bacteriophage isolation and sequencing capabilities, supporting his inclusive Research and Education Community (iREC) model for undergraduate science education reform, which integrates research and teaching through structured course-based experiences.

Patrick ErdeltView profile

Professor

• Data Science
• Database Management Systems
• Performance Benchmarking
• +4 more

Prof. Patrick Erdelt is a Professor at the Department of Mathematics of Berlin University of Applied Sciences (BHT). He leads the Laboratory for Numerical Mathematics and serves as Deputy Head of Studies for the Data Science program. His roles include membership in the AKO and PA committees of the Data Science + X research network. His research focuses on database systems, performance benchmarking, cloud-native computing, and numerical methods leveraging GPUs. Erdelt is actively involved in developing benchmarking tools like the DBMS-Benchmarker and orchestrating experiments via Kubernetes in cloud environments. He collaborates with industry partners through projects such as OCIDA, addressing customer churn through data-driven strategies. Erdelt is the Central Point of Contact for the Amazon Web Services University Program and contributes to open-source projects including PyPI packages and Docker images. His work emphasizes reproducible experiments and scalable cloud-based benchmarking frameworks, published in venues like TPCTC and the Journal of Open Source Software.