Zoran Budimlić is an Instructional Associate Professor in the Department of Computer Science and Engineering at Texas A&M University. He also serves as Director of Undergraduate Studies for Galveston. His roles include teaching and academic leadership in computer science education and high-performance computing. He holds a Ph.D. in Computer Science from Rice University (2001) and a B.S. in Computer Science and Engineering from the University of Belgrade (1994). His research interests focus on high-performance and parallel computing, compiler optimizations, programming languages, runtime systems, and high-level programming models. He emphasizes improving educational practices in computer science through innovative methods and curricula. His recent publications span parallel algorithms, task parallelism integration with MPI, and compiler optimizations for performance. Earlier work includes contributions to Java runtime optimization and static analysis techniques. Zoran Budimlić has no explicitly listed scientific awards or grants in the provided text, but his contributions to parallel computing and compiler design are notable. He advises students in these areas but no names are provided in the text.
Dominik Huber is a Ph.D. candidate and researcher at the Technical University of Munich , affiliated with the Chair of Computer Architecture & Parallel Systems . His work focuses on Dynamic Resource Management in High-Performance Computing (HPC) , with expertise in Parallel & Distributed Programming Models and Hardware-aware programming . He has actively contributed to teaching courses like Parallel Programming Systems and Advanced Computer Architecture . His research emphasizes adaptive resource allocation in hybrid HPC clusters, leveraging technologies such as MPI Sessions , PMIx , and frameworks like LAIK and XBraid . Recent projects include the DynRes software suite for dynamic resource management and collaborations on quantum-HPC integration. Huber has advised students on topics ranging from Dynamic Resource Management in Charm++ to CI Systems for HPC Software , and his publications address challenges in malleability, scheduling, and power-constrained environments. Current affiliations include participation in the SEANERGYS (EuroHPC) and PlasmaPEPS projects.
Dr. Grey Ballard is an Associate Professor in the Department of Computer Science at Wake Forest University . He earned a B.S. in Math and Computer Science (2006), M.A. in Math (2008) from Wake Forest, and PhD in Computer Science (2013) from the University of California, Berkeley. He was a Truman Fellow at Sandia National Laboratories before joining Wake Forest. Research Focus: Ballard develops communication-optimal algorithms for high-performance computing , particularly in tensor decompositions , symmetric matrix computations , and nonnegative matrix factorization . His work combines numerical linear algebra with parallel algorithm design to reduce data movement costs in distributed systems. Publications demonstrate expertise in communication lower bounds , randomized tensor rounding , and visualization tools for parallel algorithms. He has contributed software packages such as TuckerMPI , GentenMPI , and PLANC for large-scale data compression and clustering. Scientific Awards: Wake Forest Excellence in Research Award NSF CAREER Award SIAM Linear Algebra Prize Three Conference Best Paper Awards (SPAA, IPDPS, ICDM) C.V. Ramamoorthy Distinguished Research Award (UC Berkeley) ACM Doctoral Dissertation Award – Honorable Mention Teaching: Courses include Introduction to Computer Science , Numerical Linear Algebra , and Parallel Algorithms . He has developed educational tools using the Thread-Safe Graphics Library to visualize parallel dynamic programming and collective communication.
Dr. Michelle Zhu is a Professor and Associate Director for Faculty and Academic Affairs at the School of Computing, Montclair State University. She previously held roles as Associate Professor and Director of Undergraduate Programs at Southern Illinois University Carbondale. Dr. Zhu holds a Ph.D. in Computer Science from Louisiana State University and a B.S. in Biomedical Engineering from Zhejiang University. Her research focuses on parallel/distributed computing, big data analytics, and high-performance networking, supported by grants from NSF, DOE, and NVIDIA. She has authored over 150 peer-reviewed publications. Education: Ph.D., Computer Science, Louisiana State University (2005) M.Sc., Computer Science, Louisiana State University (2002) B.S., Biomedical Engineering, Zhejiang University (1996) Her research interests span parallel computing architectures, cloud workflow scheduling, and cybersecurity. She has led initiatives integrating computational thinking into STEM education and developed robotics-based learning tools. Her work has been funded through NSF grants such as the $1.1M "Assimilating Computational and Mathematical Thinking into Earth and Environmental Science" project (2017–2022). Dr. Zhu’s articles explore topics like blockchain-based cloud security, GPU-accelerated Gibbs sampling, and edge computing deployment strategies. She actively contributes to academic governance, serving on Montclair State’s Middle States accreditation committee and the University Academic Assessment Council. Key Grants: NSF MRI: Multimodal Collaborative Robot System (MCROS), $321,737 (2021–2024) DOE: Scalable Application Support Platform for E-Sciences, $389,398 (2009–2013) Service Roles: Curriculum Committee Chair, Computer Science Department Blue Ribbon Task Force for Gen Ed Redesign (2019–2020) She collaborates on robotics projects like MCROS and leads outreach efforts to engage pre-university communities in AI and robotics education.
Torsten Hoefler is a Full Professor of Computer Science at ETH Zurich, Switzerland, with an adjunct appointment in Electrical Engineering. He previously held roles at the National Center for Supercomputing Applications (University of Illinois at Urbana-Champaign) and Indiana University. Full Professor of Computer Science, ETH Zurich (2020–present) Adjunct Professor of Electrical Engineering, ETH Zurich (2020–present) Member at Large, ACM SIGHPC Executive Committee (2013–present) Leadership roles in the MPI Forum and Blue Waters project His research focuses on performance-centric system design , with emphasis on scalable networking, parallel programming models, and performance modeling. Key contributions include the Slim Fly network topology, Data-Centric Python framework, and innovations in parallel graph computations and RDMA-based systems. Recent publications span topics like LLM training networks , quantization geometry , chiplet interconnects , and AI-driven climate modeling , reflecting his interdisciplinary approach combining HPC, AI, and hardware-software co-design. ACM Gordon Bell Prize (2019) ERC Consolidator Grant (2020) IEEE TCSC Award for Excellence (2019) SIAM SIAG/SC Junior Scientist Prize (2012) Latsis Prize of ETH Zurich (2015) He has received multiple best paper awards at top conferences (SC10, SC13, SC14, SC19, IPDPS'15, HPDC'15, OOPSLA'16) and contributed to MPI-3 standardization.
Amanda Bienz serves as an Assistant Professor in the Department of Computer Science at the University of New Mexico (UNM), where she leads the Scalable Solvers Lab and acts as faculty advisor for Women in Computing. Her academic roles include teaching operating systems and parallel computing courses while spearheading efforts to restructure New Mexico's CS4ALL curriculum for statewide computer science education expansion. Her research centers on overcoming communication bottlenecks in high-performance computing systems, specifically targeting the performance gap between emerging exascale hardware and real-world applications. Key focus areas include developing portable communication optimizations, enhancing MPI collective operations, creating topology-aware message passing extensions, and benchmarking heterogeneous architectures. Her work directly addresses critical challenges in scaling parallel applications through innovations in sparse solvers, neighborhood collectives, and node-aware communication strategies for GPU-accelerated systems. Analysis of her 2022-2024 publications reveals consistent emphasis on communication optimization across diverse HPC domains. Her research demonstrates particular expertise in irregular communication patterns, locality-aware algorithms, and performance modeling for heterogeneous architectures. Significant contributions include novel approaches to sparse dynamic data exchange, compressed linear algebra algorithms, and persistent communication techniques that reduce synchronization overhead in large-scale simulations. Scientific Awards: NSF CAREER Award for "Towards Exascale Performance of Parallel Applications" Dr. Bienz actively mentors students through the Scalable Solvers Lab, welcoming new researchers interested in high-performance computing. Her NSF CAREER grant provides substantial research funding supporting both technical innovation and educational initiatives. The CS4ALL curriculum restructuring project demonstrates her commitment to broadening computer science access throughout New Mexico's K-12 education system. The Scalable Solvers Lab develops open-source tools including the Raptor algebraic multigrid solver and MPI-Advance communication library. Current projects focus on benchmarking heterogeneous architectures (Summit/Lassen supercomputers), optimizing FFT implementations, and creating node-aware communication strategies for conjugate gradient methods. The lab maintains active GitHub repositories with substantial community engagement, including contributions to CUDA-aware MPI implementations and halo exchange libraries for multi-GPU systems.
Maria Jesus Garzaran is an Adjunct Associate Professor at the Siebel School of Computing and Data Science, University of Illinois. Her research focuses on compiler design, computer hardware architecture, parallel computing, and high-performance computing (HPC) systems. Key areas of expertise include GPU utilization, parallelization techniques, and network modeling for next-generation HPC infrastructure. Her work emphasizes optimizing communication protocols in distributed systems, minimizing hardware resource usage, and enhancing performance through innovative compiler and memory management strategies. Recent contributions include advancements in MPI-3 RMA implementations and JavaScript acceleration using hardware transactional memory. No scientific awards are explicitly mentioned. Research collaborations span network design exploration, triggered operations for collective communication, and structural simulation frameworks. Her advising and grant activities are not detailed in the provided text, though her publications suggest active involvement in HPC and parallel computing research projects. No specific lab affiliations are mentioned.
Tao Yang is a Professor in the Department of Computer Science at the University of California, Santa Barbara, where he has been a faculty member since 1993. His research spans web search and mining, database and information systems, machine learning and data mining, parallel and distributed systems, and cloud computing. He serves as an active educator, teaching courses including CS170 Operating Systems (Spring 2024), CS291A Neural Information Retrieval (Fall 2024), and CS140 Parallel Computing (Winter 2025). PhD in Computer Science, Rutgers University ME in Artificial Intelligence, Zhejiang University MS in Computer Science, Rutgers University BS in Computer Science, Zhejiang University Professor Yang's research focuses on advancing the field of information retrieval with particular emphasis on neural approaches to search and ranking. His recent work explores neural document ranking, privacy-aware search systems, and versioned data search. He has led significant projects including Neptune clustering infrastructure, Sorrento self-organizing storage cluster, and TMPI for MPI execution optimization. His research bridges theoretical advances with practical implementations, particularly in scaling search architectures to handle billions of documents while maintaining relevancy, performance, and freshness. His publication record shows a clear evolution from foundational work in parallel and distributed systems toward contemporary research in neural information retrieval. Recent publications demonstrate expertise in optimizing both sparse and dense retrieval methods, with particular focus on efficiency improvements for multi-vector representations. His work consistently addresses real-world challenges in search scalability and privacy preservation. Faculty Research Award, Google Research Research Initiation Award, NSF (1994) UC Regents' Junior Faculty Award (1994) Computer Science Faculty Teacher Award (1995) CAREER Award, NSF (1997) Noble Jeeviant Award, AskJeeves (2002) Professor Yang has supervised numerous graduate students, many of whom have gone on to prominent positions at companies like Google, Apple, and Coursera, or academic positions at universities worldwide. His industry experience as Chief Scientist for Ask.com (2001-2010) and founding Chief Scientist for Teoma (2000-2001) has informed his research direction and provided valuable practical context for his academic work. He has served on program committees for major conferences including WWW, SIGIR, KDD, WSDM, CIKM, ECIR, and EMNLP. His research group maintains active projects in neural information retrieval, privacy-aware search, similarity computing, and parallel computing systems. The group collaborates closely with industry partners, particularly in the search technology space, and has developed systems that power major search engines serving over 100 million users.
Dr. Purushotham V. Bangalore serves as the James R. Cudworth Professor in the Department of Computer Science at the University of Alabama's College of Engineering and holds the position of Associate Director for the Center for Understandable, Performant Exascale Communication Systems (CUP-ECS), a Predictive Science Academic Alliance Program (PSAAP) Focused Investigatory Center. His academic credentials include: B.E. in Computer Science and Engineering from Bangalore University (1991) M.S. in Computer Science from Mississippi State University (1995) Ph.D. in Computational Engineering from Mississippi State University (2003) Dr. Bangalore's research centers on High-Performance Computing (HPC) with emphasis on designing abstraction layers for heterogeneous architectures, predictive performance modeling, and portability. His work extends to fault-tolerant message-passing middleware, exascale storage security, and reliability frameworks. Additional expertise spans data analytics, object-oriented numerical libraries, grid computing environments, and adaptive systems development through three decades of HPC and cloud computing innovation. Analysis of his 2021-2025 publications reveals dominant themes in HPC security architecture, containerization for scientific workloads, and MPI communication advancements. Key application areas include hydrological modeling (NextGen framework), GPU-accelerated communication protocols, and data provenance systems for exascale platforms, reflecting interdisciplinary approaches to computational challenges. Dr. Bangalore has secured approximately $20 million in research funding as PI/Co-PI from NSF, NIH, DoE, and industry partners, resulting in over 90 peer-reviewed publications. His academic service includes editorial roles for IEEE Transactions on Parallel and Distributed Systems, MPI Forum contributions to the MPI-4.0 standard, and organization of DoD-sponsored HPC training workshops. He leads research initiatives through CUP-ECS while maintaining active participation in the MPI Forum. His team develops frameworks for exascale communication systems with focus on security posture analysis, performance portability, and fault tolerance in next-generation computing environments.
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.
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.
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.
Cyril RANDRIAMARO is a Lecturer at the University of Picardie Jules Verne (UPJV), affiliated with the Algorithmic and Complexity (ALCO) research domain. His work focuses on distributed systems and parallel computing paradigms. Research Interests: Dr. RANDRIAMARO's expertise spans distributed storage architectures, peer-to-peer (P2P) network optimization, fault-tolerant systems, and parallel algorithm design. Key areas include dynamic data distribution strategies, cluster I/O performance enhancement, and efficient scheduling methods for block-cyclic array redistribution in high-performance computing environments. Publications: His 15 most recent articles (1996-2010) demonstrate a consistent focus on scalable distributed systems. Major themes include P2P storage resilience, parallel computation scheduling, and cluster I/O optimization, reflecting deep engagement with reliability engineering and performance tuning in networked environments. Affiliations: Historical associations include contributions to the Laboratoire de l’Informatique du Parallélisme (LIP), evidenced in his 1998 publication. No awards, student advisories, or part-time status are documented.
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.
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.
