Find Academic Supervisors

Rafael Ferreira da SilvaView profile

Assistant Professor

• Scientific Workflows
• Parallel and Distributed Computing
• High Performance Computing
• +7 more

Rafael Ferreira da Silva is a Research Assistant Professor in the Department of Computer Science at University of Southern California and a Senior Research Scientist at Oak Ridge National Laboratory. He serves as Group Leader for the Workflow and Ecosystem Services group at ORNL's National Center for Computational Sciences and is the Founder and Executive Director of the Workflows Community Initiative. Additionally, he is the Special Content Editor for the Future Generation Computer Systems journal and holds Senior Member status with both IEEE and ACM. Dr. Ferreira da Silva specializes in modeling and simulation of parallel and distributed computing systems, with expertise spanning scientific workflows, hybrid quantum classical systems, and autonomous science. His technical proficiency includes sophisticated scheduling algorithms, high fidelity modeling and simulation, multi-objective optimization, fault tolerant system design, and energy efficient computing across cloud, edge, and HPC environments. His research focuses on creating resilient digital infrastructures that dynamically adapt to changing research demands, accelerating scientific discovery through robust computational foundations. His recent publications demonstrate significant contributions to exascale workflow applications, HPC-quantum convergence, agentic workflow control mechanisms, and terminology standardization for scientific workflow systems. His work bridges multiple disciplines including high-performance computing, artificial intelligence, quantum computing, and autonomous laboratory systems, reflecting a broad research impact across computational science. Professional Recognition: Senior Member of IEEE Senior Member of ACM Special Content Editor for Future Generation Computer Systems journal With 149 scientific publications, 22 chair roles in conferences, 69 PC member roles in conferences, 18 research grants, and involvement in 270+ research projects, Dr. Ferreira da Silva maintains an extensive research portfolio and leadership position in the computational science community. His Workflows Community Initiative has fostered a thriving network of 51 international workflow users, developers, and researchers. As Group Leader at ORNL, he directs research efforts focused on advancing workflow technologies and their applications across scientific domains, with particular emphasis on creating interoperable systems that can operate across multiple computing facilities and environments.

Loïc PottierView profile

Researcher

• scheduling
• performance models
• HPC systems
• +2 more

Loïc Pottier is a Postdoctoral Scholar - Research Associate at the University of Southern California, affiliated with the Science Automation Technologies group at the USC Information Sciences Institute. His research focuses on scheduling and performance models for high-performance computing (HPC) systems, scientific workflows management, and parallel algorithms. Education: PhD in Computer Science from ENS de Lyon, France (2018) Project Support: His work at USC is supported by the U.S. National Science Foundation Office of Advanced Cyberinfrastructure under Grant #2127548.

Carl TapeView profile

Professor

• Seismology
• Computational Seismology
• Earthquake Hazard Assessment
• +4 more

Carl Tape is a Professor at the University of Alaska Fairbanks (UAF), affiliated with the Geophysical Institute (GI) and the Department of Geosciences. His research focuses on seismology, computational modeling of seismic wavefields, and imaging Earth’s internal structure using adjoint tomography and waveform inversion techniques. Current Students: Aakash Gupta, Nealey Sims, Amanda McPherson, Bella Seppi Former Students: Ulrika Cahayani Miller (MS 2014), Celso Alvizuri (PhD 2016), Vipul Silwal (PhD 2018), Kyle Smith (PhD 2020), Cole Richards (MS 2020) His work leverages UAF’s high-performance computing resources to simulate 3D seismic wavefields for earthquakes in Alaska and global subduction zones. These simulations improve ground motion predictions and tectonic interpretations, particularly for regions like the Cook Inlet Basin and the Hikurangi Subduction Margin in New Zealand. Carl also develops open-source tools like adjTomo for automating seismic waveform inversion and adjoint tomography workflows. Collaborations include projects with the Alaska Earthquake Center, the Alaska Volcano Observatory (AVO), and NASA-supported GNSS ground motion studies. He contributes to interdisciplinary initiatives like the NSF-funded Arctic Observing Network, which integrates seismic data with meteorological and geodetic observations to track climate-induced geophysical changes.

• High Performance Computing (HPC)
• Cloud Computing
• Interconnection Networks
• +3 more

Dr. Ernst Gunnar Gran is an Affiliated Researcher at the Simula Research Laboratory , specializing in the Department of High Performance Computing . With a career spanning over a decade, his research focuses on optimizing network performance in large-scale computing environments. PhD in Congestion Management in Lossless Interconnection Networks (2008) Co-developer of the NorNet Core multi-homed research testbed Research Focus Gran’s work addresses critical challenges in: InfiniBand hardware optimization and congestion control Self-adaptive networking for HPC-cloud integration Resource management in multi-tenant clusters Efficient routing algorithms for fat-tree architectures Real-time anomaly detection in time-series data Edge-cloud ecosystem performance modeling Publication Trends His recent research (2020-2022) emphasizes: Distributed traffic prediction using LSTM customization Lightweight anomaly detection frameworks Integration of mobile edge computing with multi-cloud systems Prior work (2015-2018) established foundational advancements in: Fat-tree reconfiguration algorithms Virtualized HPC network architectures Fault-tolerant routing strategies for complex topologies SR-IOV vSwitch implementations Technical Contributions Developed patented network reconfiguration systems Created dynamic cloud architectures with SA query caching Advanced partition-aware routing techniques Optimized load-balancing mechanisms for HPC environments

• Theoretical Physical Chemistry
• Computational Chemistry
• Materials Science
• +5 more

Nikolaos Lembesis is a tenured Assistant Professor in the Department of Chemistry at the University of Ioannina, specializing in Theoretical Physical Chemistry and Computational Chemistry. His research develops multi-scale computational simulation methods to understand structure-property relationships of matter for applications in energy, environment, and quality of life improvement. His educational background includes: PhD in Chemical Engineering, National Technical University of Athens (2013) BSc in Chemical Engineering, Technical University of Munich (2007) BSc and MSc in Chemical Engineering, National Technical University of Athens (2007) Dr. Lembesis's research integrates molecular dynamics, ab initio simulations, and stochastic methods to model materials at atomic-to-macroscopic scales. His group investigates perovskite solar cell interfaces, droplet absorption phenomena, and defect engineering using advanced computational techniques including classical/ab initio molecular dynamics and high-performance computing. Analysis of his 15 most recent publications (2023-2025) reveals dominant focus on perovskite photovoltaics, with recurring themes of interface engineering, strain manipulation, and defect passivation to enhance efficiency and stability. Key subfields include crystal orientation control, wide-bandgap perovskite optimization, and novel monolayer interface designs. His group offers undergraduate and master's thesis opportunities in Computational Chemistry, providing training in molecular simulation techniques, Unix/Linux systems, HPC resources, and software development for materials modeling. The research team utilizes multi-scale simulation approaches to study water-perovskite interactions, organic monolayer protection mechanisms, and thermomechanical properties of advanced materials, with strong emphasis on bridging computational predictions with experimental validation.

Muaaz Gul AwanView profile

Researcher

• High Performance Computing
• Bioinformatics
• GPU Computing
• +7 more

Muaaz Gul Awan is a Computer Systems Engineer 4 at the National Energy Research Scientific Computing Center (NERSC) under the Science Engagement & Workflows Dept. His work focuses on high-performance computing (HPC) with specialization in bioinformatics software development, GPU porting, and performance optimization for large-scale scientific applications.

Jack DeslippeView profile

Researcher

• Computer Software
• Distributed Computing
• Artificial Intelligence and Image Processing
• +4 more

Jack Deslippe serves as the Application Performance Group Lead at the National Energy Research Scientific Computing Center (NERSC), part of Lawrence Berkeley National Laboratory. He has held this position since joining the laboratory in January 2006, currently working as Computer Systems Manager 1 in the HPC Department within Computing Sciences. Dr. Deslippe earned his PhD in Physics from UC Berkeley in 2011, with research focused on materials physics and nano-science, specifically scaling many-body Green's function computational methods for studying optical properties of complex materials. His research spans multiple computational domains: Computer Software and Distributed Computing Artificial Intelligence and Image Processing Numerical and Computational Mathematics Pure Mathematics Materials Physics and Nano-science His publication record demonstrates expertise in high-performance computing optimization, particularly in GPU acceleration, parallel computing strategies, and performance modeling across physics simulations, materials science, and bioinformatics. He has made significant contributions to the BerkeleyGW software package for many-body physics calculations, focusing on scalability and performance optimization for leadership-class computing systems. As leader of NERSC's Application Performance Group, Dr. Deslippe plays a critical role in enabling scientific discovery through high-performance computing. His team works extensively with researchers to optimize computational workflows on NERSC's systems, including the transition to exascale architectures like the Cori system with Intel Knights Landing processors. His work bridges the gap between domain science and computer science, developing computational approaches that enable new scientific discoveries across multiple disciplines.

Rebecca Hartman-BakerView profile

Researcher

• Applied Mathematics
• Numerical and Computational Mathematics
• Computation Theory and Mathematics
• +7 more

Rebecca Hartman-Baker is a computational scientist and the User Engagement Group Lead at the National Energy Research Scientific Computing Center (NERSC) under Lawrence Berkeley National Laboratory. She specializes in scalable parallel algorithms for petascale systems and focuses on inverse problems, numerical optimization, and HPC user training. PhD in Computer Science – University of Illinois at Urbana-Champaign BS in Physics – University of Kentucky Her research spans applied mathematics, distributed computing, and computational physics, with a strong emphasis on software sustainability and checkpointing techniques for large-scale systems. Recent publications highlight her work in HPC education, fault tolerance, and the IDEAS Productivity Project. Scientific awards include the R&D100 Award (Oak Ridge), James Corones Award (2019), and the Director's IDEA Mentorship Award (2023). Professional activities range from leading the Student Cluster Competition to board membership at Griffin Technology Academies.

Eric RomanView profile

Researcher

• High Performance Computing
• Operating Systems
• Checkpoint/Restart Technology
• +3 more

Eric Roman is a Computer Systems Engineer and Manager at the National Energy Research Scientific Computing Center (NERSC), part of Lawrence Berkeley National Laboratory. He has been with Berkeley Lab since 1999 and currently works in the HPC Technology Department within the Computing Sciences division. Dr. Roman earned his PhD in physics from the University of California, Berkeley in 2010, with a dissertation entitled "Orientation Dependence of the Anomalous Hall Effect in 3D Ferromagnets." His doctoral research involved ab initio simulations of nonlinear optical properties of semiconductors, spin transport in metals, and the anomalous Hall effect. Roman's primary research focus is on operating systems for high performance computing, with significant contributions to Berkeley Lab's Checkpoint/Restart (BLCR) technology since 2001. His work spans several key areas: Development of multithreaded checkpoints and restarts Implementation of file and pipe support in BLCR On-the-fly compression of checkpoint files Direct I/O capabilities for HPC systems Integration with batch systems like Torque Optimization of file I/O operations His publication record shows consistent contributions to HPC systems research over 15+ years, with recent work focusing on resilience techniques, failure prediction, and system optimization. Roman has collaborated with researchers from multiple institutions, advancing parallel computing, fault tolerance, and system-level technologies that enable scientific discovery at scale. His highly cited works on live migration and checkpoint/restart frameworks demonstrate significant impact in the field. Roman has been actively involved in the HPC community, leading Linux kernel seminars and organizing projects like "High-End Computing with K42" under the FastOS initiative. He continues to collaborate with the Berkeley ParLab on cutting-edge research in high-performance computing systems.

Torsten HoeflerView profile

Professor

• High-Performance Computing (HPC)
• Machine Learning
• Parallel Computing
• +2 more

Torsten Hoefler is a Professor at ETH Zurich , renowned for pioneering contributions to High-Performance Computing (HPC) and its application to Artificial Intelligence (AI) . His work on scalable network design, parallel algorithms, and Message Passing Interface (MPI) advancements has revolutionized supercomputing and AI infrastructure. Hoefler chairs key MPI working groups and co-developed foundational concepts like "3D parallelism" that underpin modern AI systems. Hoefler's research spans interconnection networks, congestion avoidance protocols (e.g., Slim Fly, PERCS), and performance modeling. His innovations in nonblocking collective operations (Iallreduce, Iallgather) power distributed deep learning frameworks. He also leads benchmarking and reproducibility initiatives that set global HPC research standards. ACM Prize in Computing (2024) : Recognized for enabling AI's computational scale through HPC breakthroughs ACM Gordon Bell Prize (2019) : For quantum transport simulations mapping transistor heat distribution Hoefler's work impacts millions via large-language model training (e.g., ChatGPT) and reduces data center cooling costs through thermal modeling. He leads teams at ETH Zurich's Scalable Parallel Computing Laboratory , translating theoretical advances into industry-adopted technologies.

• Climate Modelling
• High-Performance Computing
• Earth System Models
• +3 more

Professor David E Keyes is a faculty member at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. He is part of a 12-member international team that won the 2024 ACM Gordon Bell Prize for Climate Modelling for pioneering exascale climate emulation techniques. Research Interests: His work focuses on high-performance computing (HPC) solutions for climate science, including Earth System Models (ESMs), data storage optimization, and the application of machine learning (ML) to climate modelling. The team's research leverages exascale supercomputers and mixed-precision algorithms to enhance computational efficiency and spatial resolution (0.034°, ~3.5 km) for more accurate climate predictions. Scientific Awards: 2024 ACM Gordon Bell Prize for Climate Modelling Collaborative Impact: The project, involving supercomputers like Frontier and Leonardo, achieved exascale-level simulations (318 billion hourly observations) while reducing storage needs by orders of magnitude. This work bridges computational science, climate physics, and AI, offering transformative potential for climate policy and future climate forecasting systems.

Bradford L. ChamberlainView profile

Adjunct Professor

• Parallel Computing
• Programming Languages
• High-Performance Computing
• +3 more

Bradford L. Chamberlain is a Distinguished Technologist at Hewlett Packard Enterprise and an Affiliate Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. With over two decades of experience in high-performance computing, he has made significant contributions to parallel programming languages, particularly as the technical lead of the Chapel programming language project since 2006. His educational background includes: Ph.D. in Computer Science & Engineering from the University of Washington (2001) M.S. in Computer Science & Engineering from the University of Washington (1995) B.S. in Computer Science from Stanford University (1992) Chamberlain's research focuses on improving programmer productivity for high-performance computing through innovative language design, compiler techniques, and runtime systems. His work centers around the Chapel programming language, which aims to provide a multiresolution programming model that allows developers to express parallelism at varying levels of abstraction while maintaining performance across diverse architectures from laptops to supercomputers. His research spans parallel language design, compiler optimization, data distribution strategies, locality management, and performance portability. Chapel builds on his earlier work with the ZPL language, where he developed region-based approaches for sparse parallel computing. His publication record over the past fifteen years demonstrates a consistent focus on practical approaches to parallel programming, with recent work emphasizing data locality, heterogeneous architectures, and performance portability. His articles show an evolution from foundational language design concepts to increasingly sophisticated implementations addressing real-world HPC challenges, particularly in the areas of domain mapping, iterator abstractions, and memory management for large-scale systems. As a Distinguished Technologist at HPE, Chamberlain has played a key role in growing the Chapel project from a modest effort to one involving nearly 20 full-time developers. His leadership has positioned Chapel as one of the most promising languages for addressing the challenge of productive parallel programming at scale. He has secured funding, established collaborations with academia and industry, and performed extensive outreach through talks, tutorials, and research visits. At the University of Washington, Chamberlain serves as a liaison between academia and industry, participating in student committees, teaching graduate courses like Parallel Computation, and fostering communication between the department and HPE/Cray. His teaching experience spans from undergraduate data structures to graduate seminars on parallel programming environments. He has also volunteered as a tutor for underrepresented students in computer science.

Tobias GrosserView profile

Associate Professor

• Compilers
• Programming Language Design
• Performance Programming
• +8 more

Tobias Grosser is an Associate Professor in the Department of Computer Science and Technology at the University of Cambridge. His research focuses on compiler technology, programming language design, and performance programming, with applications spanning hardware design, climate science, and quantum computing. He leads a research group developing innovative compiler frameworks and tools that bridge theoretical foundations with practical applications. Dr. Grosser completed his undergraduate studies in Computer Science at the University of Passau in Germany and pursued his PhD at École Normale Supérieure Paris as a Google PhD Fellow. Prior to joining Cambridge, he served as a Reader at the University of Edinburgh and held an Ambizione Fellowship at ETH Zurich. His research program centers on rethinking performance programming by re-connecting developers and compilers. He aims to make compilation more modular, predictable, automatic, and trustworthy while bringing open-source compiler innovation to increasingly diverse targets from GPUs to FPGAs and custom hardware. His work spans multiple domains including polyhedral compilation, constraint solving, quantum computing, and hardware design automation. Dr. Grosser is particularly interested in breaking down barriers between compilers and programmers by enabling their interaction through the programming language environment. His recent publications demonstrate a strong focus on compiler infrastructure development, particularly around the MLIR framework. He has pioneered work on Presburger arithmetic optimization with the FPL library, developed new intermediate representations for hardware description and quantum computing, and created tools for compiler education and prototyping like xDSL. His research shows a consistent theme of creating practical, high-performance compiler technologies that address real-world challenges across multiple domains. HiPEAC Technology Transfer Award 2021 for "Fast linear programming through transprecision computing on small and sparse data" OOPSLA 2021 Distinguished Paper Award for "FPL: Fast Presburger arithmetic through transprecision" Dr. Grosser actively mentors PhD students and postdoctoral researchers, currently supervising a team of over a dozen researchers working on various aspects of compiler technology. His group collaborates with industry partners including ARM and Xilinx, and maintains strong ties with the LLVM and MLIR open-source communities. He has secured funding for multiple research projects including work on verified compilation with Lean-MLIR, quantum compiler development, and hardware design automation. His research group operates at the intersection of multiple projects including Open-Source Electronic Design Automation, Seamless design of Smart Edge Processors, Lean-MLIR for verified compilation, FPL for fast Presburger arithmetic, and compilation frameworks for quantum computers. They maintain strong community engagement through regular Compiler Social events in Cambridge and active participation in LLVM developer meetings.

Mariusz MatuszekView profile

Assistant Professor

• High-Performance Computing
• Parallel Computing
• Energy Efficiency in Computing Systems
• +3 more

Mariusz Matuszek serves as an Assistant Professor at Gdańsk University of Technology within the Department of Computer Systems Architecture, Faculty of Electronics, Telecommunications and Informatics. His research focuses on energy-efficient computing systems and high-performance parallel architectures. His primary research interests span High-Performance Computing , GPU Acceleration , and Energy Efficiency in Computing Systems . Current work examines power-capped optimization for deep neural network training, hardware/software energy measurement methodologies for CPU+GPU systems, and resource-aware problem formulations using ILP, greedy algorithms, and evolutionary approaches. His research bridges theoretical optimization with practical implementation in multi-GPU environments. Analysis of his 2022-2025 publications reveals strong emphasis on measuring and optimizing energy consumption in parallel computing systems, with particular focus on deep learning workloads. Key methodologies include professional hardware metering (Yokogawa WT-310E), Intel RAPL, and NVIDIA NVML interfaces across multi-GPU architectures. His work demonstrates significant performance-energy improvements through strategic power capping. Matuszek actively contributes to computer science education through curriculum development in parallel programming using MPI, OpenMP, and CUDA frameworks. His teaching case studies address the growing demand for HPC expertise in modern machine learning infrastructure development.

Mariusz UchrońskiView profile

Professor

• Discrete Optimization
• High-Performance Computing (HPC)
• Parallel Algorithms
• +3 more

Professor Mariusz Uchroński is affiliated with Wrocław University of Science and Technology (WUST) as a faculty member in the field of discrete optimization and high-performance computing. His research focuses on advanced computational methods including parallel and distributed algorithms, quantum computing, and task scheduling optimization. Research Interests: He specializes in developing efficient algorithms for complex computational problems, with applications in high-performance computing environments and emerging quantum computing paradigms. His work addresses optimization challenges in parallel/distributed systems and task scheduling methodologies. Contact Information: Email: mariusz.uchronski@pwr.edu.pl Phone: +48 71 320 2456 Office: Z. Janiszewskiego Street 11/17, Wrocław, building C-3, room 214 Consultations: Mondays 16:00-18:00