Find Academic Supervisors

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

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

Ryan GrantView profile

Assistant Professor

• Cloud Computing
• High Performance Networks
• Low-level Hardware-Software Interfaces
• +6 more

Dr. Ryan Grant is an Assistant Professor in the Department of Electrical and Computer Engineering at Queen’s University, Canada. He leads the Computing at Extreme Scale Advanced Research (CAESAR) lab and is affiliated with the Ingenuity Labs Research Institute. His expertise spans cloud computing, high-performance networks, low-level hardware-software interfaces, and energy-efficient supercomputing systems. Dr. Grant holds a PhD from Queen’s University (2012) and previously worked at Sandia National Laboratories (2012–2021), where he contributed to critical supercomputer communication protocols now deployed globally. He has authored over 80 peer-reviewed articles and received prestigious awards including the R&D100 Award and Queen’s University’s 125th Engineering Alumni Award. His research emphasizes advancing Canada’s supercomputing infrastructure to support AI, climate science, and national security applications. Education: PhD in Computer Engineering, Queen’s University (2012) MSc in Computer Engineering, Queen’s University (2005) BSc in Computer Engineering, Queen’s University (2004) Research Interests: Dr. Grant’s work focuses on optimizing supercomputing architectures for extreme-scale systems, with an emphasis on: High-performance networking and MPI communication protocols Power/energy management in HPC systems AI-driven network traffic prediction and resource disaggregation GPU-accelerated computing and cloud infrastructure integration National sovereignty in supercomputing for sensitive applications (e.g., defense, healthcare) Awards & Recognition: R&D100 Award (Oscars of Research) U.S. Defense Programs Awards Public Good Innovator Award Queen’s University 125th Engineering Alumni Award Grants & Labs: Dr. Grant directs the CAESAR lab, one of the world’s leading supercomputing architecture research groups. His work is supported by grants from Canadian and international agencies, focusing on sovereign supercomputing and HPC-AI convergence. Labs/Teams: CAESAR Lab (Queen’s University) Ingenuity Labs Research Institute

Dirk PflügerView profile

Professor

• Scientific Computing
• High-Performance Computing (HPC)
• Parallel and Distributed Systems
• +3 more

Dirk Pflüger is a Professor at the University of Stuttgart's Institute of Parallel and Distributed Systems, within the Faculty of Computer Science, Electrical Engineering and Information Technology. His research focuses on high-performance computing (HPC), parallel and distributed systems, and sparse grids. He has led projects in astrophysical simulations, machine learning applications, and uncertainty quantification. Notable contributions include developing scalable algorithms for exascale computing using HPX, Kokkos, and SYCL frameworks. His expertise spans distributed computing architectures, task-based parallel programming, and interdisciplinary applications in astrophysics and medical AI. Recent work includes optimizing hyperparameter tuning, simulating stellar mergers, and enhancing blood glucose prediction models using deep reinforcement learning. Pflüger's research emphasizes performance portability, fault tolerance, and cross-platform collaboration. He has contributed to open-source tools like PLSSVM and hws, which address hardware monitoring and GPU acceleration challenges. His work bridges theoretical advancements with practical implementations for real-world computational problems.

Alan C. CalderView profile

Professor

• Theoretical Astrophysics
• Computational Astrophysics
• High Performance Computing
• +4 more

Alan C. Calder is a Professor in the Department of Physics and Astronomy at Stony Brook University and a core faculty member of The Institute for Advanced Computational Science (IACS). His research focuses on computational astrophysics, particularly simulating explosive phenomena such as supernovae, with an emphasis on high-performance computing and numerical modeling. Prior to Stony Brook, he held research positions at the National Center for Supercomputing Applications (NCSA) and the University of Chicago’s Center for Astrophysical Thermonuclear Flashes. He also taught at the School of the Art Institute of Chicago. Education includes a Ph.D. in Physics from Vanderbilt University (1997), with postdoctoral work at Argonne National Laboratory and NCSA. His research interests span theoretical astrophysics, radiation transport, and verification/validation of astrophysical codes. He has advised numerous graduate and undergraduate students on projects related to supernova modeling and computational methods. Notable awards include the 2017 GPU Hackathon participation, Wendell G. Holladay Lecture (2015), and the NSF’s Gordon Bell Prize (2000). He leads projects like the deployment of the Fujitsu A64FX-based Ookami supercomputer. Calder has authored over 50 refereed papers, advancing understanding of supernova progenitors and explosion mechanisms.

Thomas FahringerView profile

Professor

• Parallel Computing
• Distributed Systems
• High-Performance Computing (HPC)
• +4 more

Thomas Fahringer is a full Professor and Head of the Distributed and Parallel Systems Group at the University of Innsbruck's Institute of Computer Science. His research focuses on parallel computing, distributed systems, and high-performance computing (HPC), with particular emphasis on GPU acceleration, cloud/edge computing, and serverless architectures. He leads interdisciplinary projects involving scientific workflows, resource management systems, and exascale computing solutions. Key research areas include: Design of high-level APIs for accelerator clusters (e.g., Celerity-RSim) Optimization of IoT and LoRa networks using machine learning Scalable key-value stores for geo-distributed systems Workflow scheduling in edge-cloud continuum environments Recent work emphasizes automation of deployments, energy-efficient transmission policies, and fault-tolerant orchestration of serverless functions. He actively participates in community initiatives like the Workflows Community Summit, driving advancements in scientific workflows and HPC ecosystems. His lab develops frameworks such as Apollo and AllScale, targeting exascale computing and distributed runtime systems.

• High Performance Computing (HPC)
• Data Locality
• GPU Computing
• +7 more

Dr. Didem Unat is an Associate Professor at Koç University, College of Engineering, Department of Computer Engineering . Her research focuses on High Performance Computing (HPC) , GPU Computing , and Compiler Design , with particular emphasis on data locality optimization, multi-GPU communication, and adaptive mesh refinement. She leads the Parallel and Multicore Computing Lab , driving innovations in exascale co-design and heterogeneous system optimization. ACM SIGHPC Emerging Woman Leader in Technical Computing Award (2021) ERC Starting Grant (1.5M EUR) for the SparCity Project (2025) EuroHPC Grant for Extreme Scale Technologies and Applications (2025) 2025 BAGEP Award (Science Academy) Her work bridges Computer Architecture and Machine Learning , addressing challenges in data locality , multi-GPU systems , and performance portability . Recent publications highlight trends in sparse tensor optimization , asynchronous AMR execution , and compiler support for heterogeneous memory . She has developed tools like ComScribe for intra-node GPU communication analysis and ReuseTracker for multicore reuse distance analysis. Dr. Unat’s research is supported by grants from ERC and EuroHPC , reflecting her leadership in exascale technologies and GPU-centric systems . She contributes to the evolution of tiling abstractions , dynamic LLM training , and lightweight communication detection for modern architectures.

Estela SuarezView profile

Professor

• High Performance Computing (HPC)
• Heterogeneous HPC Architectures
• Modular Supercomputing Architecture
• +4 more

Estela Suarez is a Professor of High Performance Computing at the University of Bonn's Computer Science Department and holds leadership roles at the Jülich Supercomputing Centre (JSC) in Forschungszentrum Jülich. Her work focuses on advancing modular supercomputing architectures, heterogeneous systems, and application optimization for exascale computing. She currently serves as Chair of the EuroHPC Joint Undertaking's Research and Innovation Advisory Group (RIAG) and leads the Next Generation Architectures and Prototypes research group at JSC. Education includes a PhD in Physics from the University of Geneva (2010) and a Master's in Physics (Astrophysics specialization) from Universidad Complutense de Madrid (2004). She has held senior research positions at JSC since 2010 and has led major EU-funded projects like DEEP, DEEP-ER, and DEEP-SEA. Research interests include HPC system design, hardware-software co-design, and energy-efficient supercomputing architectures. She was awarded the University of Bonn's 2023/2024 teaching award and actively contributes to initiatives like the European Processor Initiative (EPI) and the NUMERIQS consortium. Current projects include optimizing exascale systems for Earth system modeling (IFCES2), AI-driven data analytics (AIDAS), and modular supercomputing software (DEEP-SEA). During her 2024/2025 sabbatical, she is not accepting new students. Her work is published in journals like Geoscientific Model Development and conferences such as ISC and EuroHPC.

• High Performance Computing
• Computational Mechanics
• Materials Science
• +5 more

Luisa Silva is a Researcher and Professor at Centrale Nantes, affiliated with the GeM Research Institute. She holds a mechanical engineering diploma from Portugal, a Master's in Computational Mechanics, and a PhD from École des Mines de Paris. Her research focuses on high-performance computing (HPC) for computational mechanics, spanning materials science (e.g., fibrous suspensions) and biomedical applications (e.g., heart simulations, tumor growth). She collaborates with institutions like IRT Jules Verne and industries such as Michelin, integrating AI with HPC to develop predictive tools. Her work leverages Centrale Nantes' supercomputing platforms and contributes to regional initiatives like the Connect Talent project. Silva is also involved in education, co-advising PhD students and leading a new joint degree in Big Data and Management with Audencia Business School. Research Interests: High-performance computing, computational mechanics, fluid-solid interactions, numerical methods, and industrial/materials science applications. Her work bridges theoretical advancements with practical implementations in health and engineering sectors. Collaborations & Infrastructure: Works with INRIA, CHU Nantes, and the IRT Jules Verne. Manages a supercomputing platform at Centrale Nantes, part of France's top five HPC facilities. Engages in cutting-edge projects like quantum computing integration and mesh adaptation techniques. Future Directions: Expanding access to supercomputing resources, exploring quantum platforms, and advancing training programs in data science and engineering.

Bernd DammannView profile

Associate Professor

• High-Performance Computing
• Scientific GPU Computing
• Parallel Programming
• +3 more

Bernd Dammann is an Associate Professor in Scientific Computing at the Technical University of Denmark (DTU) , Department of Applied Mathematics and Computer Science. In parallel he serves as Scientific Lead & Architect in the DTU Computing Center (DCC) and co-founded DTU’s GPUlab in 2007, giving him dual roles in both academic research and institutional HPC services. Education Dipl.Phys. in Statistical Physics, University of Mainz, 1992 PhD in Physical Chemistry, DTU, 1996 Research interests Over almost three decades Dammann has focused on computational methods for large-scale scientific problems. His core expertise lies in: High-Performance Computing and parallel programming paradigms (OpenMP, MPI, GPU) Scientific GPU computing and many-core architectures Application tuning and performance optimization for heterogeneous systems Uncertainty quantification, Monte-Carlo methods, and real-time control applications in biomedicine (diabetes technology) Ultrasound beamforming and advanced imaging techniques Publication trends His 34 publications (1996-2025) trace a clear evolution from theoretical physics and surface-science simulations to GPU-accelerated engineering and biomedical systems. Recent work clusters around real-time biomedical control (artificial pancreas, virtual clinical trials) and high-resolution ultrasound imaging, all leveraging GPU acceleration and high-performance computing to achieve real-time performance. Scientific awards & recognition Contributor to 1 patent family and widely downloaded technical reports (>10 k downloads for OpenCL tutorial) Consistently high citation counts on key optimization and control papers (e.g., 93 citations for 2014 Management Science article) Teaching, supervision & grants He teaches or co-teaches multiple DTU courses: High-Performance Computing , Large-scale Computations , Python & HPC , Mathematical Software , and HPC with FORTRAN . While individual students are not named, his role in GPUlab and DCC implies ongoing PhD and MSc supervision, and his projects list six active collaborations including EU and national HPC infrastructure grants. Labs & teams Dammann co-founded GPUlab (2007) and presently leads the HPC architecture team in the DTU Computing Center , a central facility supplying cluster resources to the entire university.

Al A Geist IIView profile

Research Fellow

• High-Performance Computing
• Exascale Computing
• Fault Tolerance
• +4 more

Al A Geist II is a Corporate Research Fellow at Oak Ridge National Laboratory (ORNL), serving as Chief Technology Officer of the Leadership Computing Facility, Chief Scientist for the Computer Science and Mathematics Division, and Chief Technology Officer of the DOE Exascale Computing Project. With 35 years of continuous service at ORNL, he leads the ASCR technical Council on Resilience and directs the multi-lab Extreme-Scale Algorithms and Software Institute as Principal Investigator, establishing him as a pivotal figure in national high-performance computing strategy. His research centers on exascale computing resilience, parallel and distributed systems, and numerical linear algebra, with foundational contributions including co-development of the Parallel Virtual Machine (PVM) standard and active involvement in MPI-1/MPI-2 standards. Recent work focuses on fault tolerance mechanisms for extreme-scale systems, heterogeneous computing frameworks, and scientific applications spanning neutron science, biomolecular simulation, and materials science, reflecting a career-long integration of theoretical algorithms with real-world computational infrastructure. Analysis of his publication record reveals consistent innovation in enabling scientific discovery through resilient computing, with recent articles emphasizing cooperative fault management, pre-exascale system deployment, and science gateways for neutron facilities. This trajectory demonstrates evolving expertise from early distributed computing (PVM, MPI) to contemporary exascale challenges, maintaining strong relevance to DOE mission-critical scientific domains while addressing hardware-software resilience gaps. As Principal Investigator for the Extreme-Scale Algorithms and Software Institute and former 20-year leader of ORNL's 25-member Computer Science Research Group, Geist has directed major collaborative projects shaping national computing initiatives. Though student advising details are unmentioned, his development of foundational software standards has broadly influenced the HPC community through widespread adoption of PVM and MPI. His work is operationally embedded within ORNL's Leadership Computing Facility and Spallation Neutron Source, where he develops critical infrastructure like the Neutron Science TeraGrid Gateway. Current leadership roles in the Exascale Computing Project and ASCR Resilience Council position him at the forefront of next-generation computational science infrastructure development.

Martin MorenoView profile

Assistant Professor

• Compiler Optimizations
• High-Performance Computing
• Quantum Computing
• +3 more

Martin Moreno is a tenure-track Assistant Professor in the Department of Computer Science and Engineering at The Ohio State University. His office is located in Dreese Laboratories at 2015 Neil Ave, Columbus, OH. His research focuses on the intersection of compilers, programming languages, high-performance computing, and quantum computing, with goals to enhance program execution speed, reliability, and portability across architectures. Research Interests: Dr. Moreno specializes in polyhedral compilation, GPU optimizations, quantum computing abstractions, distributed-memory algorithms, and energy-aware computing. His work bridges theoretical compiler design with practical performance improvements for modern hardware, including GPUs and quantum processors. Recent Research Trends: Analysis of his 15 most recent publications reveals strong emphasis on: 1) Automated code generation for distributed systems and tensor computations, 2) Quantum program optimization through affine transformations, 3) Energy-efficient GPU computing via polyhedral models, and 4) Novel compiler frameworks for task-based parallelism. His work consistently integrates formal methods (e.g., SMT solvers) with practical runtime systems. Student Advising: Actively seeks motivated students for research projects in compiler optimizations and quantum computing. Applicants to OSU are encouraged to list him as a potential advisor and mention PLSE/HPC interests in statements of purpose.

Olav ZimmermannView profile

Researcher

• Bioinformatics
• Molecular Simulation
• Markov Chain Monte Carlo
• +3 more

Olav Zimmermann serves as Team leader of Simulation & Data Laboratory "Biology" at the Jülich Supercomputing Centre (JSC), which operates within the Institute for Advanced Simulation at Forschungszentrum Jülich. He also functions as a Principal Investigator in Helmholtz Information Program 1, Topic 1, indicating his significant research responsibilities within this major German research institution. His research expertise encompasses multiple computational biology domains: Bioinformatics Molecular Simulation techniques Markov Chain Monte Carlo statistical methods Machine Learning applications for biological problems Protein Folding dynamics Python programming for High-Performance Computing environments Dr. Zimmermann's work sits at the critical intersection of advanced computing infrastructure and biological research, where he likely develops and implements sophisticated computational approaches to address complex problems in molecular biology. His leadership of the Simulation & Data Laboratory suggests responsibility for directing research strategy, managing technical resources, and facilitating collaborations between computational scientists and domain researchers in biology.

Dongkuan XuView profile

Assistant Professor

• Machine Learning
• Natural Language Processing
• Computer Vision
• +7 more

Dongkuan Xu is an Assistant Professor in the Department of Computer Science at North Carolina State University, leading the Generative Intelligent Computing (GIC) Lab. His research spans Artificial Intelligence , focusing on scalable, efficient, and reliable AI systems for large language models, diffusion models, and generative frameworks. Education Ph.D. in Computer Science, Pennsylvania State University (2022) M.S. in Optimization and Data Mining, University of Chinese Academy of Sciences (2017) B.E. in Information Management and System, Renmin University of China (2014) His research explores test-time adaptation, computational efficiency, and trustworthy AI through uncertainty-aware learning and robust generalization. Applications include Education , Robotics , Agriculture , Networking , and Healthcare . Recent work emphasizes hardware-algorithm co-design and domain-specific AI platforms like Synthora and Gentopia. His publications from 2024–2025 highlight advancements in Vision Transformers , Diffusion Models , Edge Computing , and Trustworthy AI , with grants from NVIDIA , Microsoft , and the National Science Foundation . Awards include the AAAI Best Demonstration Award (2025), NVIDIA Academic Grant (2025), and the Carla Savage Award (2024). He actively mentors undergraduate researchers and co-chairs workshops at top venues like DAC , CVPR , and KDD , aiming to democratize AI access and enhance its reliability across real-world domains.

• Dependable Distributed Systems
• Secure Autonomy
• Embedded Wireless Networks
• +5 more

Saurabh Bagchi is a Professor at Purdue University, affiliated with the Elmore Family School of Electrical and Computer Engineering and the Department of Computer Science . He serves as Director of the NSF CHORUS Center (2024-29), the Army A2I2 Institute (2020-25), and ECE Corporate Partnerships. He holds a PhD in Computer Science (2001) from the University of Illinois at Urbana-Champaign. Research Interests: His work spans dependable distributed systems , secure autonomy , embedded wireless networks , and predictable machine learning , with a focus on applying learning theory to systems. He leads a research group of 14 graduate and 4 undergraduate researchers, emphasizing practical system-building and security. Scientific Awards: Alexander von Humboldt Research Award (2018) Adobe Faculty Awards (2021, 2017) AT&T VURI Award (2016) Google Faculty Award (2015) IBM Faculty Award (2014) IET Fellow (2022) IEEE Computer Society Distinguished Contributor (2021) IEEE CS Board of Governors (2022-24) Students: He has mentored 25 PhD and ~50 Master’s graduates, many of whom are now in industry or academia.

Barry LinnertView profile

Researcher

• Resource Management in Cluster and Grid Environments
• High Performance Computing (HPC)
• Operating Systems
• +2 more

Barry Linnert is a researcher at Freie Universität Berlin, affiliated with the Software Engineering research group. His work focuses on resource management in cluster and grid environments, high-performance computing (HPC), and operating systems. He also has additional interests in parallel and distributed simulations. Email: barry.linnert@fu-berlin.de | Office: Takustr. 9, Room 008, Berlin, Germany | Phone: +49 30 838 50953 Research Areas: Resource Management in Cluster/Grid, HPC, Operating Systems, Parallel/Distributed Simulations Teaching: Cluster Computing, Non-sequential Programming, Operating Systems, Software Engineering Publications (Selected): Dynamic resource allocation in HPC, Advance reservations in grid systems, Failure recovery in distributed environments, Workflow management for large-scale grids Barry Linnert actively contributes to the development of resource scheduling algorithms and grid computing architectures. His recent publications (2024-2023) address deadline-oriented job management and dynamic allocation policies in HPC systems, while earlier works (2014-2004) explore advance reservation protocols, Manhattan distance mapping, and distributed recovery mechanisms. Research spans both theoretical frameworks and practical implementations for cluster management. He holds positions in academic advisory bodies: Advisory board member of Berlin University Alliance's cooperation platform Board member of Landesvertretung Akademischer Mittelbau Berlin (LAMB)