Desh Ranjan is a Professor at the College of Sciences, Old Dominion University , with a focus on Bioinformatics , High Performance Computing , and Algorithm Design . His work bridges Computational Biology and Parallel Computing . Ph.D., Cornell University (1992) M.S., Cornell University (1990) Other, Indian Institute of Technology Kanpur (1987) His research interests revolve around efficient algorithms for bioinformatics and computational complexity , with applications in protein structure prediction , GPU optimization , and particle accelerator simulations . He has secured over $2 million in federal grants , including a major 2015-2018 $2M award for Hispanic-Serving Institutions . Recent work emphasizes machine learning and real-time simulations in high-fidelity physics and genomic data analysis . 2011: Sage Graduate Fellowship, Cornell University 2011: Outstanding Faculty Member, Iowa State University 2009/2008: NMSU Millionaire Researcher 2006: University Research Council Distinguished Career Award, NMSU 1995: Morrison Award for Best Technical Presentation, Regional ACM Ranjan's publications span 25+ years , with recent trends in GPU-accelerated algorithms , structural biology , and parallel computing . His grants highlight collaborations in bioinformatics , physics simulations , and STEM education projects.
Amit Sawant is a Professor and Vice Chair of Medical Physics in the Department of Radiation Oncology at the University of Maryland School of Medicine. His research focuses on advancing image-guided radiotherapy techniques, particularly for stereotactic body radiotherapy (SBRT) and motion management in cancer treatment. His academic background includes: B.E.(Hons.) in Biomedical Engineering from University of Mumbai (1996) M.S. in Biomedical Engineering from University of Tennessee (1999) Ph.D. in Biomedical Engineering from University of Michigan (2006) Dr. Sawant's research centers on two interconnected domains: Small Animal Image-Guided Radiotherapy (SA-IGRT) and Next-Generation Motion Management. In SA-IGRT, he develops orthotopic tumor models for prostate, lung, and pancreatic cancer using the SARRP platform, investigates thermally-modulated radiotherapy to widen therapeutic windows, and creates advanced imaging techniques for mapping post-radiotherapy inflammation. His motion management research addresses 4D anatomical changes through patient-specific volumetric motion modeling, GPU-accelerated particle swarm optimization for 4D treatment planning, and real-time tumor tracking with dynamic multileaf collimators (MLC) that compensate for translation, rotation, and deformation. His publication record (2008-2016) demonstrates consistent innovation in motion-adaptive radiotherapy, with emphasis on lung SBRT. Key themes include 4D treatment planning optimization, real-time MLC tracking systems, motion phantom development, and MRI-based guidance – all converging toward submillimeter targeting accuracy for moving tumors. His scientific recognition includes: John R. Cameron Young Investigator Award (AAPM 2004) ASTRO Basic Science Research Grant Award (2008) Multiple 'Best in Physics' selections at AAPM/ASTRO meetings (2010, 2014, 2015, 2016) ICCR Young Investigator Award (2007) Dr. Sawant has secured over $4.5 million in research funding as Principal Investigator, including two NIH R01 grants on personalized motion management and radiation injury to pulmonary structures. His industry collaborations with VisionRT and Varian Medical Systems have translated theoretical advances into clinical motion management solutions. He leads active research teams developing orthotopic tumor models and implementing 4D radiotherapy workflows for precision cancer treatment.
Dr. Michael Schlottke-Lakemper is a Professor of High-Performance Scientific Computing at the University of Augsburg, Faculty of Mathematics, Natural Sciences, and Materials Engineering. He previously held positions as an Interim Professor of Computational Mathematics at RWTH Aachen University (2022–2024) and led a research group at the High-Performance Computing Center Stuttgart (HLRS) from 2021 to 2024. His career includes postdoctoral roles at the University of Cologne and RWTH Aachen University/FZ Jülich. Education: Ph.D. in Mechanical Engineering, RWTH Aachen University (2017) Diplom in Aerospace Engineering, University of Stuttgart (2011) His research focuses on adaptive multi-physics simulations, research software engineering for high-performance computing (HPC), and scientific machine learning. Applications span fluid mechanics, aeroacoustics, and astrophysics, with recent work emphasizing robust high-order summation-by-parts methods and Julia-based computational frameworks like Trixi.jl and TrixiParticles.jl. His publications highlight advancements in discontinuous Galerkin methods, entropy stable schemes, and HPC optimization for compressible flows. Scientific contributions include Developing dynamic load balancing algorithms for multiphysics simulations Creating hybrid computational aeroacoustics methods Advancing Julia's adoption in HPC communities Improving error-based step size control in numerical solvers Current teaching activities include graduate seminars on Maschinelles Lernen in Theorie und Praxis and undergraduate courses in Numerische Lineare Algebra . He leads a research team at the University of Augsburg with collaborators across Germany, including Simon Candelaresi, Valentin Churavy, and Niklas Neher.
Dr. Eishi Arima is a researcher at the Chair of Computer Architecture and Parallel Systems within the Department of Informatics at the Technical University of Munich (TUM). His work focuses on cutting-edge computer architecture and high-performance computing systems, with particular expertise in power-aware computing, resource management, and heterogeneous systems. He actively contributes to numerous international conferences and collaborative research projects addressing challenges in modern computing infrastructure. Dr. Arima's research spans multiple critical areas in computer architecture including memory and storage systems, performance modeling and optimization, hardware/software codesign, and processor microarchitectures. His work demonstrates particular strength in addressing energy efficiency challenges in high-performance computing environments, with numerous publications on power capping, resource partitioning, and sustainable computing approaches. His research bridges theoretical concepts with practical implementations, often incorporating machine learning techniques to optimize system performance under various constraints. Analysis of Dr. Arima's publication record reveals a strong focus on addressing the energy efficiency challenges in modern computing systems. His work consistently targets the intersection of hardware architecture and system-level resource management, with particular emphasis on heterogeneous computing platforms combining CPUs, GPUs, and emerging memory technologies. Over time, his research has evolved from traditional cache and memory system optimizations toward more holistic approaches incorporating machine learning for resource management in power-constrained environments. Recent publications demonstrate increasing attention to sustainability aspects of computing, reflecting broader industry trends toward greener computing solutions. Dr. Arima has served in various organizational capacities for major international conferences including as Program Committee member for SC, IPDPS, and Cluster conferences, and as Program Co-Chair for ACM CF'20. His journal review activities span multiple prestigious publications including IEEE TPDS and Elsevier FGCS. This extensive service demonstrates his recognition as a respected member of the international computer architecture research community. Dr. Arima has mentored numerous students through bachelor's theses, master's theses, and guided research projects. His students have produced research on topics including reinforcement learning for resource management, job scheduling optimization, memory system improvements, and power-aware computing techniques. Several student projects have resulted in publications at reputable conferences, indicating the high quality of research conducted under his supervision. His mentoring covers both theoretical aspects of computer architecture and practical implementation challenges in real-world systems. Dr. Arima is actively involved in multiple research projects including SEANERGYS (EuroHPC), PlasmaPEPS, OpenCUBE, DaREXA-F, ScalNEXT, PDexa, MUNIQC-ATOMS, BB-KI_Chips, QuaST, and Q-DESSI. These projects address various aspects of high-performance computing, from energy efficiency to quantum computing integration. His work contributes to the development of next-generation computing infrastructure that balances performance requirements with sustainability concerns.
Yi Ju is a Researcher and Ph.D. candidate at the Technical University of Munich, affiliated with the Department of Informatics (Informatik 10) and the Max Planck Computing and Data Facility (MPCDF) under Prof. Erwin Laure. He works within the Chair of Computer Architecture and Parallel Systems led by Prof. Martin Schulz, focusing on cutting-edge HPC research and teaching advanced computer architecture courses. His primary research areas include In-Situ Techniques for real-time data processing, Performance Modelling of computational workflows, and Dynamic Resource Management in heterogeneous systems. His work bridges theoretical modeling with practical implementation in GPU-accelerated environments, computational fluid dynamics, and quantum-HPC integration, emphasizing efficiency and scalability in exascale computing. Analysis of Yi Ju's publications reveals a consistent trajectory toward optimizing in-situ processing pipelines across diverse architectures. His research demonstrates increasing sophistication in resource allocation strategies, with recent work exploring MPI-session frameworks and GPU-specific optimizations. Emerging trends show growing emphasis on quantum computing integration and precision-aware algorithms for scientific simulations. Yi Ju actively contributes to major HPC initiatives: SEANERGYS (EuroHPC) for energy-efficient computing PlasmaPEPS for plasma physics simulations OpenCUBE for big data analytics DaREXA-F for exascale applications ScalNEXT for scalable computing frameworks He collaborates extensively with MPCDF on performance modeling and resource management solutions for large-scale scientific simulations, while also mentoring students through TUM's advanced computer architecture curriculum.
Pedro Petersen Moura Trancoso is a Full Professor at the Department of Computer Engineering , Chalmers University of Technology, Sweden. His research focuses on deep learning accelerators , heterogeneous computing , energy-efficient architectures , and memory system optimization for IoT and edge devices. Key projects: AutoPIM (autonomous vehicle accelerators), VEDLIoT (efficient AIoT), eProcessor (European processor ecosystem), PRIME (PIM systems) Collaborations: European Commission, Swedish Research Council, Swedish Foundation for Strategic Research Research Trends : Hybrid CNN/GPU/FPGA Acceleration On-Chip/Scratchpad Memory Optimization Adaptive Resource Allocation for Energy Efficiency Hardware-Software Co-Design for AIoT Publications demonstrate leadership in deep learning hardware , heterogeneous memory systems , and edge computing architectures . Key journals: IEEE ISPASS, ACM Computing Frontiers, DATE Conference.
Andrea Marongiu is an Associate Professor at the Department of Physical, Computer and Mathematical Sciences at the University of Modena and Reggio Emilia, specifically affiliated with the Mathematics department. He maintains an active research profile while teaching multiple courses in computer architecture and parallel systems. His research interests span computer architecture, high performance computing, parallel programming, and embedded systems. Marongiu focuses particularly on memory systems, heterogeneous computing architectures, FPGA-based acceleration, and real-time performance analysis. His work bridges theoretical foundations with practical implementation challenges in modern computing systems, with special attention to predictable execution models and quality of service guarantees. Analysis of his recent publications reveals a strong emphasis on memory bandwidth management in heterogeneous systems, particularly focusing on FPGA-based architectures and multicore SoCs. His research trajectory shows consistent work in memory interference analysis, PREM (Predictable Execution Model) scheduling techniques, and fine-grained QoS control mechanisms. The publications demonstrate a progression from general parallel programming concepts toward increasingly specialized techniques for resource-constrained environments like autonomous vehicles and edge computing devices. Marongiu teaches several advanced computer science courses including Computer Architecture I & II, Compilers, High Performance Computing, and Electronic Calculators across multiple degree programs. His teaching approach emphasizes both theoretical foundations and practical implementation, with a focus on RISC-V architecture and modern parallel programming techniques. The course materials indicate he incorporates hands-on laboratory work as an essential component of his pedagogy, particularly in areas like compiler construction and parallel programming.
William D. Gropp serves as the Director of the National Center for Supercomputing Applications (NCSA) at the University of Illinois Urbana-Champaign, where he holds the prestigious Thomas M. Siebel Chair in Computer Science and the Grainger Distinguished Chair in Engineering within the Siebel School of Computing and Data Science. His leadership extends to major NSF-funded initiatives including the Delta and DeltaAI supercomputing systems. Gropp's research centers on high performance computing, with particular expertise in parallel I/O systems, scalable numerical algorithms for partial differential equations, and programming models for massively parallel applications. His work bridges theoretical computer science with practical implementation through widely adopted software frameworks. Gropp has received numerous prestigious honors including election to the National Academy of Engineering (2010) and fellowships from ACM, IEEE, SIAM, and AAAS. In 2024, he was named one of the 35 HPC Legends by HPC Wire, recognizing his foundational contributions to the field. AAAS Fellow (2018) SIAM Fellow (2011) National Academy of Engineering Member (2010) IEEE Fellow (2010) ACM Fellow (2006) 35 HPC Legends by HPC Wire (2024) HPCWire Readers Choice Award for Outstanding Leadership in HPC (2023) Gropp leads multiple major research initiatives including the Midwest Big Data Hub and the MPI Forum, serving on the Computing Research Association board and Computing Community Consortium executive committee. His current projects focus on heterogeneous computing systems, data movement optimizations, and next-generation MPI implementations. As Director of NCSA, Gropp oversees significant cyberinfrastructure resources including the Delta supercomputer, designed to accelerate adoption of GPU computing and non-POSIX file systems by the computational science community, and DeltaAI, an AI/ML optimized supercomputer.
Hai Lin is a Professor at the State Key Lab of CAD&CG (Zhejiang University, China). His research spans computer graphics, scientific visualization, volume rendering, virtual reality, and graphical electromagnetic computing. He earned B.Eng and M.Eng degrees from Xidian University (1987, 1990) and a Ph.D. in Computer Science from Zhejiang University. Current Position: Professor, Zhejiang University (1990–Present) Research Fellow, Medical Visualization, De Montfort University (2000–2003) Visiting Professor, University of Bedfordshire Research Focus includes: Medical Imaging: AI-driven tumor segmentation (colorectal, liver), retinal disease classification (SatFormer), and mandible segmentation via LRVRG. Electromagnetic Computing: Shape deformation optimization, GPU-based wave propagation prediction, and scattering analysis. Scientific Visualization: Graph convolutional networks for volume data, voxel2vec representations, and dynamic network exploration. Collaborations involve PhD students (Huan Liu, Yiming Li, YanKai Jiang, Han Wang) and teams at Zhejiang University. His work integrates AI with electromagnetic and medical imaging domains, emphasizing GPU acceleration and novel algorithm design.
Klaus Mueller is a Professor in the Computer Science Department at Stony Brook University , with additional appointments in Biomedical Engineering and Radiology. He serves as Director of the Visual Analytics and Imaging (VAI) Lab, Liaison for the SUNY Korea CS Program, and Interim Chair of the Department of Technology and Society . His career spans roles at Brookhaven National Lab and leadership positions at SUNY Korea. Dr. Mueller earned his PhD in Computer and Information Science (1998), MS in Computer and Information Science (1996), and MS in Biomedical Engineering (1990) from The Ohio State University , alongside a BS in Electrical Engineering (1987) from the Polytechnic University of Ulm, Germany. His research focuses on visual analytics , explainable AI , algorithmic fairness , computational imaging , and medical imaging . He has pioneered GPU-accelerated CT reconstruction techniques, bias mitigation frameworks (e.g., D-BIAS), and tools like DOMINO for causal reasoning. His work bridges data science , human-computer interaction , and medical applications , often integrating large language models for visualization tasks. Recent publications highlight advances in multivariate volume rendering , LLM-driven bias detection , and mDDPM-based medical image synthesis . His articles span IEEE Transactions , Nature Machine Intelligence , and conferences like IEEE VIS and ACM CHI . Award highlights include NSF CAREER (2000), SUNY Chancellor Award (2011), IEEE Golden Core Award (2016, 2022), induction into the National Academy of Inventors (2018), and elevation to IEEE Fellow (2024). He has chaired major conferences and served as Editor-in-Chief of IEEE Transactions on Visualization and Computer Graphics (2019-2022). He teaches graduate and undergraduate courses in visualization , medical imaging , and GPGPU programming , and leads the Visual Analytics Seminar (CSE 648). His lab ( VAI Lab ) fosters interdisciplinary research in GPU-accelerated analytics and ethical AI.
Prof. Dr. Thomas Rauber is a Professor at the University of Bayreuth in the Faculty of Mathematics, Physics and Computer Science, where he leads the Chair of Applied Computer Science II – Parallel and Distributed Systems. His research spans several decades with continuous scholarly output, demonstrating significant contributions to parallel computing, high-performance systems, and energy-efficient computation. Rauber maintains strong collaborative relationships with researchers including Gudula Rünger and Matthias Korch, with whom he has co-authored numerous publications. His primary research interests include parallel and distributed systems, high-performance computing, task scheduling, energy efficiency in computing, scientific computing with focus on Runge-Kutta methods and ODE solvers, and performance modeling. Rauber's work has evolved from foundational parallel programming concepts to contemporary concerns about energy consumption in computing systems. His research addresses both theoretical aspects of parallel algorithms and practical implementation challenges on modern architectures. Rauber's publication record shows a clear trend toward energy-aware computing, with recent work focusing on the trade-offs between performance, energy consumption, and solution accuracy. His 2023-2025 publications demonstrate continued innovation in task scheduling, software-defined environments for cloud applications, and optimization of numerical methods for modern multicore processors. His textbook "Parallel Programming for Multicore and Cluster Systems" (now in its third edition) has become a standard reference in the field. While specific grant information isn't detailed in the provided text, Rauber's extensive publication record across multiple decades suggests sustained research funding. His work on projects like TGrid (Runtime environment for heterogeneous systems and grid systems) and investigations into task pools for dynamic load balancing indicates involvement in significant research initiatives. Rauber maintains an active research laboratory focused on parallel and distributed systems, with ongoing projects examining communicating multiprocessor tasks, runtime environments for heterogeneous systems, and self-adaptation techniques for time-step-based simulations on heterogeneous HPC systems. His research group continues to produce influential work at the intersection of theoretical computer science and practical high-performance computing applications.
Erin Claire Carson is an Assistant Professor at the Department of Numerical Mathematics, Faculty of Mathematics and Physics, Charles University, Prague. A specialist in numerical linear algebra and high-performance computing, she leads the ERC Starting Grant project InEXASCALE focused on exascale algorithms. Her research explores mixed precision arithmetic, communication-avoiding Krylov subspace methods, and stability analysis in finite precision. Ph.D., University of California, Berkeley (2015) Courant Instructor, New York University (2015-2018) Postdoctoral Researcher and PRIMUS Fellow, Charles University (2018-2022) Dr. Carson's work bridges theoretical analysis with practical implementations for supercomputers. Her recent publications include advancements in low-synchronization orthogonalization, silent error detection, and multilevel sampling techniques. She received the 2025 Wilkinson Prize from SIAM for outstanding contributions to numerical analysis. Current trends in her research involve: Exploiting mixed precision arithmetic for algorithm acceleration Developing stable communication-avoiding Krylov methods Optimizing numerical stability in GPU-based solvers Understanding error propagation in multistage refinements Scientific honors: 2025 SIAM Wilkinson Prize in Numerical Analysis and Scientific Computing 2023 ERC Starting Grant recipient 2019-2022 PRIMUS Research Fellow She supervises PhD and Master’s students while teaching advanced courses in numerical linear algebra and high-performance computing. Her work has been featured in WIRED and Forbes Czech Republic for improving supercomputer algorithms.
Dagmara Kulig, PhD, Eng., serves as a Lecturer at the Department of Particle Interactions and Detection within the Faculty of Physics and Applied Computer Science at AGH University of Science and Technology in Kraków, Poland. Her research focuses on advancing radiation measurement technologies for medical applications, particularly in radiotherapy quality assurance and dosimetry innovation. Her primary research domains include Radiation Dosimetry, Medical Physics, and Radiotherapy, with specialized expertise in Optically Stimulated Luminescence (OSL) and Thermoluminescence (TL) phenomena. Kulig investigates novel luminescent materials—especially LiMgPO 4 -based compounds—and develops 3D-printed scintillators for precise dose measurement. Her work bridges experimental physics with clinical oncology through computational modeling and deep learning applications for treatment planning optimization. Analysis of her 15 most recent publications (2016-2025) reveals a consistent trajectory in radiation monitoring systems, with increasing emphasis on modular detector architectures and AI-driven medical imaging. The Dose-3D project represents a significant computational contribution, while her material science work on LiMgPO 4 dosimeters addresses critical challenges in signal stability and sensitivity. Recent publications demonstrate growing integration of 3D printing and deep learning for personalized radiotherapy solutions. Scientific awards: No awards documented in provided information Advising and grants: No student supervision details available No external funding sources specified Laboratory engagement: Core contributor to Dose-3D project developing Monte Carlo simulation platforms Experimental work on radiation detector systems at Department of Particle Interactions and Detection Material synthesis and characterization for luminescent dosimeters
Bingcong Li is a postdoctoral researcher at ETH Zurich collaborating with Prof. Niao He and the ODI group. Previously, they completed doctoral studies at the University of Minnesota under Prof. Georgios B. Giannakis, followed by industry experience focused on large language models (LLMs). Education includes a PhD from the University of Minnesota under Prof. Georgios B. Giannakis. Research centers on making computation efficient, accessible, and affordable across heterogeneous resources—from GPU clusters to consumer hardware—through interdisciplinary approaches combining deep learning, optimization, and signal processing. Key research areas address foundational computing architectures, large-scale system sustainability, and personalized AI access. Their work develops theoretically grounded methods for explainable systems, with recent focus on LLM fine-tuning efficiency. Publication trends show consistent contributions to top conferences (NeurIPS, ICML, ICLR) with emphasis on optimization techniques for resource-constrained LLM deployment. Their advising and grant activities aren't explicitly detailed, though they actively participate in academic service through conference talks (EUROPT 2025, ICASSP 2025) and co-organizing events like the Efficient LLMs Fine-tuning Track at AI+X Summit. Lab affiliation centers on ETH Zurich's ODI group under Prof. Niao He, focusing on optimization-driven AI solutions.
Dr. Deniz Bezgin is a Researcher at the Department of Aerodynamics and Fluid Mechanics of the Technische Universität München (TUM) . Her work focuses on computational fluid dynamics (CFD), machine learning integration in numerical methods, and high-order differentiable solvers for compressible flows. Research specialties include shock-capturing methods, multi-phase flow modeling, and data-driven shape optimization. Developed JAX-Fluids, a fully-differentiable framework for compressible two-phase flows. Key contributions to ENO/WENO schemes and thermodynamically consistent interface models. Current projects explore machine-learned discretizations and GPU-based high-performance computing. Her recent publications address differentiable simulations, data assimilation, and turbulence modeling. She has not received any explicitly listed scientific awards.
