Find Academic Supervisors

• High-Performance Computing
• Parallel Computing
• Message Passing Interface (MPI)
• +7 more

William D. Gropp is the W. W. Grainger Chair and Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign. He holds additional professorships in Electrical and Computer Engineering, Coordinated Science Lab, School of Information Sciences, and Center for Global Studies. As Director of the National Center for Supercomputing Applications (NCSA) and the Center for Extreme-Scale Computation, he leads initiatives in high-performance computing (HPC) and computational infrastructure. His research focuses on parallel computing, MPI, quantum-centric supercomputing, and optimizing data movement on heterogeneous architectures. Gropp has contributed to seminal tools like PETSc and MPICH, advancing scalable scientific computing. Research Interests: High-Performance Computing, Parallel Algorithms, MPI, Quantum Computing Integration, Heterogeneous Architectures, and Scientific Software Libraries. His work addresses challenges in scalable systems, communication optimization, and exascale computing. Recent Contributions: Recent articles explore benchmarking tools for hierarchical networks, agent-based models, and quantum-centric supercomputing applications. His work emphasizes practical solutions for real-world HPC challenges, such as optimizing GPU performance and I/O systems. Awards & Honors: AAAS Fellow (2018) ACM-IEEE CS Ken Kennedy Award (2016) IEEE Fellow (2010) NAE Member (2010) Labs & Teams: Directs NCSA and oversees the Center for Extreme-Scale Computation. Collaborates on projects like the Delta Gateway for GPU resource access and the Quantum-centric Supercomputing initiative. Active in community-driven efforts to advance HPC standards and tools.

Corentin CadiouView profile

Research Fellow

• Dark Matter
• Star Formation
• High Resolution Simulations
• +5 more

Corentin Cadiou is a Research Fellow at Lund University's Department of Physics, specializing in Astrophysics. He holds dual positions as a Postdoctoral Fellow in both the Astrophysics division and the eSSENCE: The e-Science Collaboration initiative. His research focuses on computational astrophysics and cosmology, with particular expertise in dark matter, galaxy formation, and cosmic structure evolution. His primary research interests include: Dark matter physics and cosmic web dynamics High-resolution cosmological simulations Galaxy formation and evolution mechanisms Star formation processes in various environments Computational methods in astrophysics Large-scale structure of the universe He employs advanced simulation techniques to study galaxy-scale phenomena and cosmic evolution. Cadiou's recent publications demonstrate a strong focus on developing and optimizing astrophysical simulation codes, analyzing cosmic structures like filaments and dark matter halos, and investigating galaxy formation processes across cosmic time. His work frequently combines theoretical modeling with high-performance computing approaches to address fundamental questions in cosmology. He is currently engaged in the project: eSSENCE@LU 11:4 - Galaxy formation in the exascale era (2025-2026), where he serves as a researcher developing next-generation galaxy formation simulations.

• Distributed Computing
• High-Performance Computing
• Parallel Systems
• +7 more

Tore Brox-Larsen is an Associate Professor in the Department of Informatics at UiT The Arctic University of Norway. He is actively involved in research and teaching, with a focus on distributed computing, high-performance systems, and visualization technologies. His work is centered on scalable systems for large displays, sensor networks, and remote data visualization, often in collaboration with interdisciplinary teams. His research interests include distributed and parallel computing, remote visualization over wide-area networks, large-scale display systems, health informatics, and Arctic monitoring technologies. He has contributed significantly to the development of systems for tiled display walls, genomics visualization, and sensor-based observatories. His work bridges theoretical computer science with practical applications in healthcare, environmental monitoring, and collaborative research environments. The most recent publications reflect a strong trend in applying computing technologies to real-world challenges, particularly in Arctic research and healthcare. His work emphasizes performance optimization, usability, and scalability in distributed systems. Topics span from low-level communication latency analysis to high-level collaborative visualization frameworks. Scientific Awards: No scientific awards mentioned in the provided text. Advising and Grants: There is no explicit information about students advised or research grants received. However, his extensive publication record and collaborative projects suggest active participation in funded research initiatives and potential mentorship of junior researchers and students. Labs and Teams: Tore Brox-Larsen is part of a long-standing research group at UiT involving Otto Anshus, John Markus Bjørndalen, and others, focusing on high-performance distributed systems. He has contributed to the development of the MultiStream system, scalable display walls, and the Arctic observatory sensor network. His work indicates involvement in both software and systems research teams, likely associated with UiT’s informatics infrastructure and high-performance computing initiatives.

Chelsea Maria JohnView profile

Researcher

• Natural Language Processing
• Artificial Intelligence
• High-Performance Computing
• +5 more

Chelsea Maria John is a Researcher at the Accelerating Devices Lab within the NOVAS division of the Jülich Supercomputing Centre (JSC) at Forschungszentrum Jülich. Her work focuses on high-performance computing and artificial intelligence, specializing in efficient machine learning model training, hardware accelerator benchmarking, and optimization strategies for communication and computation. Her research spans: Natural Language Processing and Large Language Models (LLMs) Scientific Machine Learning with Neural Operators High-Performance Computing system optimization Benchmarking frameworks for emerging AI hardware European open-source AI initiatives like OpenGPT-X Analysis of her 15 publications (2021-2025) reveals pioneering work in training LLMs on HPC systems, development of benchmarking suites (JUPITER, CARAML), and European LLM projects (Teuken series). Her research bridges AI and computational science, notably applying Fourier Neural Operators to fluid dynamics simulations. As part of the Accelerating Devices Lab, she contributes to advancing AI acceleration through hardware-software co-design, focusing on communication efficiency and computational optimization for next-generation scientific computing workloads.

Santiago NarvaezView profile

Researcher

• High Performance Computing
• Parallel Programming
• C++ Programming
• +6 more

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

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

Prof. Jesper Larsson Träff is a Full Professor and Head of the Research Unit in Parallel Computing at Technische Universität Wien (TU Wien). His academic role is centered within the Department of Computer Engineering. He holds an MSc and PhD, and has contributed extensively to the field of parallel computing through his research and leadership in international projects. His research focuses on parallel algorithms, scheduling, and optimization of the Message Passing Interface (MPI), with emphasis on high-performance computing (HPC) systems and distributed architectures. Key projects include the Process Mapping initiative (2019–2024), Autotune (2021–2025), and contributions to Exascale Programming Models. He has also led efforts in evaluating MPI performance tools and their reproducibility challenges. Research Areas: Parallel Algorithms, MPI Optimization, HPC, Process Mapping, Collective Communication, and Memory Models. Notable Achievements: Awarded the Best Paper at EuroMPI 2014 and recognized by the Innovation Radar for his work on PGAS-based MPI interoperability in 2018. Teaching: Teaches courses such as Advanced Multiprocessor Programming, Bachelor/Master Thesis supervision, and seminars in Computer Engineering and Theoretical Computer Science. Prof. Träff’s advising includes students researching topics like lock-free data structures, MPI datatype optimization, and task scheduling. His grants span Austrian and EU funding bodies, addressing challenges in exascale computing and reproducible experimental research. He is affiliated with the Vienna Mapping and Sparse Quadratic Assignment (Vienna Mapping) project and actively contributes to international workshops and conferences.

• Human-Computer Interaction
• Music Technology
• Digital Instrument Design
• +7 more

Adam Tindale is an Associate Professor of Human-Computer Interaction at OCAD University’s Faculty of Arts & Science, specializing in electronic musical instruments and innovative interaction design. He is renowned for his work on the E-Drumset, an instrument combining physical modeling, machine learning, and intuitive interfaces. His research bridges music technology, wearable systems, and immersive environments, emphasizing cross-disciplinary collaboration. Education: B.Mus. (Queen's University), M.M. in Music Technology (McGill University), Ph.D. in Interdisciplinary Studies (University of Victoria, focusing on Music, Computer Science, and Electrical Engineering). Research interests include: Design of digital musical instruments Wearable technology for performance VR audio interfaces Gesture-based interaction systems His recent work explores: Generative art through GANs (e.g., calligraphic Arabic scripts) Anisochronous rhythmic interfaces 3D-printed information embedding Key contributions include the JunctionBox toolkit for sound interfaces and frameworks like Flocking for web-based music.

Sandro Luigi FioreView profile

Associate Professor

• Data Science
• Big Data
• Scientific Data Management
• +13 more

Sandro Luigi Fiore is an Associate Professor in the Department of Information Engineering and Computer Science at the University of Trento, Italy. He has held academic positions at the University of Salento and visiting scientist roles at the University of Chicago and Lawrence Livermore National Laboratory. His research integrates data science, big data, and high-performance computing with climate informatics and open science. Education: Ph.D. in Innovative Materials and Technologies, University of Lecce, 2004 Master of Science in Computer Engineering (with honors), University of Lecce, 2001 His research interests span Data Science, Big Data, Scientific Data Management, Artificial Intelligence, and Distributed/Cloud/Parallel Computing , with a strong application focus on Climate Change and Open Science . He develops FAIR-enabled data analytics solutions and contributes to large-scale data infrastructures such as ESGF, EOSC-hub, and INDIGO-DataCloud. His work emphasizes provenance, reproducibility, and interoperability in scientific computing. The articles reflect a consistent trajectory in large-scale scientific data systems , evolving from Grid-DBMS and parallel computing to modern applications in climate informatics, AI, and FAIR data. Key themes include distributed data management, middleware, exascale software, and reproducible workflows in HPC environments. Scientific Awards: Earth System Grid Federation Team Award (2017) UNIDATA Community Equipment Award (2011) Best Student Paper Award, ITCC2003 Fiore has advised on and led multiple national and European projects including EOSC-hub, IS-ENES, and BARRACUDA (RDA-Europe3). He actively supports the adoption of FAIR data principles in scientific repositories, advising on data policies, architecture, provenance, and interoperability. He is a member of the FAIR Champions group and serves on the Advisory Board of FAIRsFAIR. He is involved in key research teams and projects such as the Earth System Grid Federation (ESGF) , Globus Lab (University of Chicago) , and PCMDI/LLNL . His work contributes to climate model intercomparison (CMIP5/6) and the development of next-generation data infrastructure for open science.

Popirlan Claudiu IonutView profile

Lecturer

• Artificial Intelligence
• Mobile Agents
• Multiagent Systems
• +6 more

Dr. Popirlan Claudiu Ionut is a Lecturer in the Computer Science Department at the Faculty of Exact Sciences, University of Craiova, Romania. He has been actively involved in academic and research activities since 2004, progressing from Assistant Lecturer to Assistant Professor and currently serving as a Lecturer. He holds a Ph.D. in Computer Science from the University of Pitesti and has extensive teaching experience in advanced programming, databases, GIS, and software engineering. His educational background includes: Ph.D. in Computer Science, University of Pitesti (2005–2009) Master in Artificial Intelligence, University of Craiova (2003–2004) B.Sc. in Computer Science, University of Craiova (1999–2003) Secondary Education, Fratii Buzesti National College (1995–1999) Dr. Popirlan's research is centered on Artificial Intelligence, with a strong emphasis on Mobile Agents and Multiagent Systems. His work explores knowledge representation, processing, and management using agent-based architectures, with applications in robotics, contact centers, and virtual organizations. He has also contributed to web-based 3D visualization and modeling in mechanical engineering. His technical expertise spans Java technologies, databases, and software engineering. The analysis of his recent publications reveals a consistent focus on mobile agents for knowledge processing, distributed systems, and intelligent control. Themes include agent architectures, knowledge base management, pathfinding algorithms, and simulation systems, indicating a deep and sustained research trajectory in autonomous and intelligent software systems. He has received research grants such as TD CNCSIS and CNCSIS IDEI, where he served as director and team member respectively, focusing on mobile agents and knowledge management. His editorial roles include Scientific Referent for INFO-PRACTIC and Editorial Secretary for the Annals of the University of Craiova. Dr. Popirlan is an active member of the academic community, affiliated with IEEE, IEEE Computer Society, ACM, IBM Academic Initiative, Microsoft Faculty Connection, and the Romanian Mathematical Society. He is also part of the Research Center of Artificial Intelligence in Craiova.

• Programming Languages
• Formal Logic
• Software Verification
• +5 more

Jesper Bengtson is a Lecturer in Theoretical Computer Science specializing in Programming Logic and Semantics at the IT University of Copenhagen. His research primarily focuses on formal methods for programming languages, with particular expertise in session types, separation logic, and proof assistants. Dr. Bengtson's research interests center around programming language theory and formal verification. His work explores how to mathematically verify software correctness through logical frameworks. He specializes in session types for communication protocols, separation logic for reasoning about concurrent programs, and developing tools for interactive theorem proving. His research bridges theoretical computer science with practical applications in software verification. His publication record shows consistent contributions to programming language theory over the past decade, with a clear trend toward applying formal methods to concurrent and distributed systems. Recent work demonstrates increasing focus on mechanized verification of session type systems and integration of session types with separation logic for concurrent program verification. Dr. Bengtson has been involved in several significant research projects including MECHANIST (MECHANIsation of Session typEs, 2021-2025), ToMeSo (Tools and Methods for Scalable Software Verifications, 2009-2013), and a general-purpose framework for software verification (2013-2017). These projects were funded by major Danish research foundations including Danmarks Frie Forskningsfond and Independent Research Foundation of Denmark. His work has been featured in media outlets, including a 2015 article titled 'Danish researcher wants to eradicate software errors' and a 2021 piece about 'Natural or woke? Big shooter introduces controversial soldier,' indicating public engagement with his research.

• Bioinformatics
• High-Performance Computing
• Parallel Algorithms
• +3 more

Lukas Hübner is a Doctoral Researcher at the Karlsruhe Institute of Technology (KIT) and a Visiting Scientist at the Heidelberg Institute for Theoretical Studies (HITS) . He holds a B.Sc. in Molecular Biotechnology from Heidelberg University and an M.Sc. in Computer Science from KIT. His research focuses on integrating computational methods with biological data analysis, particularly in phylogenetic inference, high-performance computing, and parallel algorithm design. He explores fault-tolerant systems, MPI optimizations, and reproducibility in computational workflows. Key research interests include: Parallel algorithms for large-scale phylogenetic analysis Optimization of MPI-based distributed systems Reproducibility in high-performance computing Fault-tolerance mechanisms in parallel algorithms His work often bridges computer science and bioinformatics, addressing challenges in genetic data processing and algorithm scalability. Recent publications emphasize techniques like memoization for genealogical forests and zero-overhead C++ bindings for MPI. He has collaborated on tools like RAxML-NG and ReStore, advancing robust computational methods for phylogenetic studies.

• Computer Engineering
• Network-on-Chip (NoC)
• Fault-Tolerant Systems
• +3 more

Heikki Kariniemi is a Doctoral Researcher at the Faculty of Engineering and Natural Sciences (Tampere University). His work focuses on advanced computer architecture and fault-tolerant systems. Tampere University Research Interests: Network-on-Chip (NoC) design Fault-tolerant communication protocols Multiprocessor Systems-on-Chip (MPSoC) Field-Programmable Gate Arrays (FPGA) Globally Asynchronous Locally Synchronous (GALS) architectures Publication Trends (2001-2010): 21 total outputs, including 18 conference papers and 1 dissertation. Key themes: reconfigurable multiprocessor systems, fat-tree network topologies, and FPGA-based fault-tolerant communication mechanisms. Academic Activities: Session chair at SoC 2009 and SoC 2010 symposia Peer reviewer for journals: IEEE Transactions on Circuits and Systems, IEEE Transactions on Electronic Publishing Referee for conference articles at NORCHIP 2009

Andrej RisteskiView profile

Associate Professor

• Machine Learning
• Statistics
• Theoretical Computer Science
• +5 more

Andrej Risteski is an Associate Professor at the Machine Learning Department of Carnegie Mellon University (CMU) since 2025. He previously held the Norbert Wiener Research Fellow position jointly between the Applied Math Department and IDSS at MIT (2017–2019) after completing his PhD in Computer Science at Princeton University (2012–2017) under Sanjeev Arora . His research focuses on the intersection of machine learning, statistics, and theoretical computer science , emphasizing generative models, representation learning, and out-of-distribution generalization with applications to natural language processing and scientific domains . His recent publications explore edge embeddings in Graph Neural Networks (GNNs) , score matching efficiency , and theoretical foundations of diffusion models . Key contributions include analyzing computational bottlene.com/activities/statistical-and-computational-challenges-in-probabilistic-scientific-machine-learning-sciml/">NSF CAREER Award , DOE Computational Science Graduate Fellowship for Stephen Huan, and co-organizing the COLT workshop on Theory of AI for Scientific Computing . He advises PhD students across Machine Learning, Computer Science, and Mathematics , including Bingbin Liu (Kempner Institute Fellow) and Elan Rosenfeld (Google Research Scientist). Teaching includes Probabilistic Graphical Models and Advanced Deep Learning at CMU, plus Applied Mathematics at MIT. His work is supported by NSF, DoD , and OpenAI Superalignment grants. Education : PhD in Computer Science (Princeton), BSc in Computer Science (Princeton) Current Positions : Associate Professor, CMU Machine Learning Department Former Positions : Norbert Wiener Fellow, MIT IDSS & Applied Mathematics Research Areas : Generative Models (GANs, Diffusion Models) Representation Learning Out-of-Distribution Generalization Neural Language Models AI for Scientific Applications Sampling and Optimization Algorithms Scientific Awards : NSF CAREER Award (2023) Google Research Award (2024) Amazon Research Award (2022) OpenAI Superalignment Grant (2023) Recent Talks (2023–2025): "Architectural Nuances and Benchmark Gaps in Scientific ML" (UC Berkeley, 2025) "The Statistical Cost of Score-Based Losses" (Simons Institute Boot Camp, 2024) "Neural Networks for PDEs" (ETH Zurich, 2024) "Discernible Patterns in Transformers" (Theory of Interpretable AI, 2024) He leads a research group producing work at the interface of computational complexity and graph learning , with empirical validation on topological bottlenecks and hub node dynamics . Current projects include ICML 2025 paper on edge embeddings in GNNs and COLT 2025 workshop on AI for Scientific Computing co-organized with MIT, Duke, and ETH Zurich collaborators.

Lucas C. WilcoxView profile

Professor

• Scientific Computation
• Numerical Solution of Partial Differential Equ
• Wave Propagation
• +3 more

Lucas C. Wilcox is a Professor in the Department of Applied Mathematics at the Naval Postgraduate School. His research focuses on scientific computation, particularly in the numerical solution of partial differential equations with emphasis on wave propagation and uncertainty quantification using high-order methods. He is active in developing scalable algorithms for adaptive mesh refinement and parallel computing.

• High Performance Computing
• Network Simulation
• Parallel Discrete Event Simulation
• +5 more

Kevin A. Brown is an active researcher in the field of High Performance Computing with a strong publication record spanning over a decade. His work primarily focuses on HPC network simulation, performance analysis, and optimization of parallel computing systems. He has collaborated extensively with researchers including Christopher D. Carothers, Robert B. Ross, and Satoshi Matsuoka across multiple institutions. Dr. Brown's research interests center around network simulation techniques, particularly Parallel Discrete Event Simulation (PDES) for modeling HPC networks. His recent work explores multi-fidelity network simulation frameworks, surrogate modeling for performance prediction, and machine learning applications for network traffic forecasting. He has made significant contributions to understanding network congestion, quality-of-service mechanisms, and the interference between different types of traffic in HPC environments. His publication record shows consistent output with 19 publications documented between 2014 and 2025, with increased productivity in recent years. The 2023-2025 period shows particularly strong activity with 12 publications, indicating ongoing research momentum. His work appears primarily in top HPC conferences including SIGSIM-PADS, CLUSTER, and ICPP. Notable recent contributions include the development of MFNetSim for multi-traffic modeling of Dragonfly systems, research on zombie packet techniques for hybrid PDES simulation, and work on steady-state fluid models for HPC networks. His research demonstrates a clear trajectory from fundamental network performance analysis toward more sophisticated simulation frameworks incorporating machine learning techniques.