HPC Software Support Professors and Supervisors | ApplySharif

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Justin M. WozniakView profile

Researcher

University of TrierGermany

Justin M. Wozniak is a computational scientist at Argonne National Laboratory’s Mathematics and Computer Science Division within the Computing, Environment and Life Sciences directorate. He is a key contributor to advanced scientific workflow systems such as Swift/T and Parsl, enabling scalable, distributed, and many-task computing for data-intensive science. His work supports major initiatives in cancer research (CANDLE), epidemiological modeling, and exascale computing (ExaWorks). He collaborates extensively with leading researchers including Ian T. Foster, Michael Wilde, and Kyle Chard. His research focuses on high-performance computing, scientific workflows, distributed systems, and machine learning applications in science. He has pioneered techniques in workflow automation, fault tolerance, in-situ data analysis, and performance optimization. His work enables robust, scalable execution of complex computational pipelines across heterogeneous environments, from supercomputers to cloud platforms. His recent publications (2021–2025) emphasize workflow interoperability, resilience, benchmarking, and applications in cancer and epidemic modeling. Themes include automated model comparison, job management portability (PSI/J), adaptive workflow steering, and exascale-ready workflow toolkits. These works reflect a strong trend toward reproducibility, scalability, and real-world scientific impact. Justin M. Wozniak has no listed scientific awards in the provided text. However, his leadership in major DOE-funded projects and high-impact publications in top venues (SC, HPDC, e-Science) underscores his significant contributions to computational science. He has mentored or collaborated with numerous researchers, though specific advisees are not listed. His work is supported by large-scale computing grants and initiatives such as the ExaWorks project and CANDLE, which aim to accelerate scientific discovery through advanced computing infrastructure. He contributes to open science through tools like Parsl and Swift/T, which are widely used in the scientific community. He is a core developer in the ExaWorks ecosystem and contributes to workflow frameworks that integrate with AI/ML pipelines, containerization, and real-time data analysis. His work on Braid-DB and provenance tracking supports AI-driven science with full reproducibility. These efforts are central to modern computational laboratories aiming for autonomous, data-intensive discovery.

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Alexander NolteView profile

Assistant Professor

Eindhoven University of TechnologyNetherlands

Alexander Nolte is an Assistant Professor at the Mathematics and Computer Science school of Eindhoven University of Technology and an Adjunct Associate Professor at Carnegie Mellon University since 2020. His work bridges software engineering , human-computer interaction , and collaborative innovation , with a focus on hackathons , open source , and business process management . Key research themes include: Hackathon Impact : Analyzing collaboration patterns, creativity, and project sustainability in software startups and open source. Process Analytics : Developing tools for prescriptive monitoring and explainable AI systems. Acoustic Software : Building open-source platforms for room acoustics research. Diversity Initiatives : Creating inclusive environments in technical teams. His 15 most recent publications highlight trends in large language models , collaborative event design , and software diversity . Awards include multiple best paper and demo awards from 2021 to 2024. Nolte actively contributes to scientific datasets and collaborates on interdisciplinary projects, particularly in open-source room acoustics software with TU/e and Zenodo. He teaches Human-Computer Interaction and Software Development for Engineers , emphasizing practical, event-driven learning.

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Markus GeimerView profile

Researcher

Jülich Research CenterGermany

Markus Geimer serves as a Researcher and Head of the ATML Parallel Performance group at the Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich, a position he has held since 2025. He joined JSC in 2006 following completion of his PhD. He earned his PhD in Computer Science from the University of Koblenz-Landau, establishing his foundation in computational research methodologies. Geimer's research centers on developing scalable software tools for parallel performance analysis of high-performance computing (HPC) applications. As lead developer of the Scalasca Trace Tools package and key contributor to the Score-P instrumentation and measurement system, he enables optimization of large-scale scientific computing workloads. His innovations address critical challenges in profiling distributed-memory applications across exascale architectures, significantly advancing HPC performance engineering capabilities. Leading the ATML Parallel Performance team, Geimer directs research efforts toward next-generation performance analysis frameworks that support emerging supercomputing technologies and complex scientific simulations.

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Jan Andre ReuterView profile

Research Fellow

Jülich Research CenterGermany

Jan Andre Reuter serves as a Research Fellow and Software Developer at the Jülich Supercomputing Centre (JSC), part of Forschungszentrum Jülich in Germany. His role centers on developing high-performance computing solutions for scientific research infrastructure. His core research interests include: High Performance Computing (HPC) architectures GPU acceleration techniques Parallel programming models Scientific software engineering Specializing in C++, Python, CUDA, OpenACC, and OpenMP, he contributes to optimizing computational workflows for Europe's leading supercomputing facilities. His work enables efficient execution of complex scientific simulations across diverse research domains requiring massive computational power. As a key member of JSC's technical team in Building 16.3, he supports advanced computing resources that drive innovation in climate modeling, materials science, and computational neuroscience through cutting-edge software development.

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Benedikt FeldottoView profile

Researcher

Technical University of MunichGermany

Benedikt Feldotto is a Researcher at the Technical University of Munich, affiliated with the Department of Computer Science, Chair of Robotics, Artificial Intelligence and Real-Time Systems led by Prof. Knoll. He works on the Neurorobotics Platform as part of the European Human Brain Project and serves as lead developer of the NRP Robot Designer since 2017. His educational background includes a Bachelor of Engineering in Mechatronics from Baden-Wuerttemberg Cooperative State University (DHBW) with industry experience in automation pre-development, including a development stay in the USA. He further specialized with a Master of Science in "Robotics, Cognition, Intelligence" at Technical University of Munich. Feldotto's research focuses on biomimetic learning in neurorobotic systems, with particular emphasis on spiking neural networks for embodied cognition. His work bridges cognitive neuroscience and robotics, exploring how robots can interact naturally with humans and environments through biologically-inspired learning mechanisms. Key areas include musculoskeletal modeling, human-robot interaction, and the ethical implications of learning robots in society. He actively develops simulation tools to enable large-scale neurorobotic experiments and has contributed significantly to the Human Brain Project's Neurorobotics Platform infrastructure. His publication record shows a clear progression from foundational platform development toward increasingly sophisticated embodied simulations using high-performance computing. Recent work emphasizes scaling spiking neural networks for robotic control, analyzing neural network architectures for specific motor tasks, and validating biomechanical models against human movement data. The research consistently connects theoretical neuroscience with practical robotics applications. His scientific recognition includes: Best Poster Award from the Graduate School of Bioengineering (2018) As an educator, Feldotto has taught Cognitive Systems course exercises from Spring Semester 2018 through Spring Semester 2022. He regularly offers thesis and project opportunities in biomimetic robotics and neural network learning, though no current openings are listed. His teaching and research are supported through the European Human Brain Project funding framework. Feldotto leads development of the NRP Robot Designer, a critical tool within the Neurorobotics Platform ecosystem. He has presented this work internationally through workshops at major conferences including the European Robotics Forum and Japanese Neural Network Society meetings. His outreach extends to public exhibitions at the Deutsches Museum and participation in ethics discussions on robot stereotypes, demonstrating commitment to both technical advancement and societal implications of neurorobotics research.

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Jérémie GaidamourView profile

Researcher

University of LorraineFrance

Jérémie Gaidamour is a CNRS Research Engineer at the Institut Élie Cartan de Lorraine (University of Lorraine), specializing in high-order numerical methods and high-performance computing (HPC) for quantum mechanics simulations. His work focuses on parallel solvers for large sparse linear systems, including hybrid direct-iterative methods and algebraic multigrid (AMG). PhD Thesis: "Conception d'un solveur linéaire creux parallèle hybride direct-itératif" (2009, Université de Bordeaux I) His research interests span Quantum Mechanics (Bose-Einstein condensates via Gross-Pitaevskii equations), Numerical Methods (pseudo-spectral techniques, domain decomposition), and High-Performance Computing (MPI, parallel algorithms). Recent publications highlight advancements in fractional gradient flows and energy-minimizing AMG preconditioners for nonlinear Schrödinger equations. Software development projects include BEC2HPC (parallel spectral methods for BECs), MueLu (AMG solver in Trilinos), and HIPS (hybrid direct-iterative sparse solver). He has contributed tutorials on HPC tools (MPI, OpenMP, GPU/Xeon Phi) and participated in Grid'5000/IDRIS support teams.

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Steffen HeberView profile

Professor

North Carolina State UniversityUnited States

Steffen Heber is a Professor in the Department of Computer Science at NC State University with a joint appointment in the College of Sciences to support the Bioinformatics Research Center. His work bridges computational methods with biological discovery, focusing on algorithm development for biological data analysis and translation regulation in plants. Ph.D. in Mathematics (2001) Staatsexamen in Mathematics and Biology (1998) Diploma in Mathematics (1995) His research interests span Bioinformatics , Computational Biology , Alternative Splicing , and Translation Regulation . He develops novel algorithms for analyzing genomic data and investigates translational control mechanisms in plants using ribosome footprinting technologies. Recent publications focus on ChIP-seq processing , Ribo-seq analysis , and translational regulatory elements in Arabidopsis. Key themes include data quality control, ribosome positioning algorithms, and plant hormone signaling networks. Key Scientific Awards: IBM Faculty Award (2008) Carol Miller Graduate Lecturer Award (2018, 2013) Thank a Teacher Recognition (multiple years) Best Paper Award, ACMSE (2007) Heber has secured significant funding from the National Science Foundation, including grants for RiboPI technology development and translational hormone-response networks . His work also emphasizes undergraduate research training and K-12 science outreach programs.

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Celso Luiz MendesView profile

Researcher

University of Illinois Urbana-ChampaignUnited States

Celso Luiz Mendes is a Senior Research Programmer at the National Center for Supercomputing Applications (NCSA) at the University of Illinois. His work focuses on High-Performance Computing (HPC), supercomputer reliability, and performance optimization of computational models. Institution: University of Illinois Department: National Center for Supercomputing Applications (NCSA) Research Interests: Celso's research spans GPU architecture, hydrological modeling, and parallel systems. He contributes to optimizing computational performance and scalability in HPC environments. Publications: His recent work on the Blue Waters supercomputer analyzes system reliability and user-support challenges. He also explores performance tuning for environmental models like MGB, targeting multi-core and GPU architectures. Collaborations: Celso works with teams at NCSA and institutions like IEEE, focusing on HPC tools, grid applications, and load balancing solutions.

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Robert ChisholmView profile

Researcher

University of SheffieldUnited Kingdom

Dr. Robert Chisholm is a Research Software Engineer at the University of Sheffield's School of Computer Science, specializing in GPU acceleration and parallel algorithms. He leads the module COM4521 on GPU computing and supports researchers via the University's Research Software Engineering (RSE) team. He is a core contributor to the FLAMEGPU2 framework, which enables GPU-accelerated agent-based modeling. His work focuses on optimizing large-scale simulations in domains like cancer research and traffic systems. Research Interests: GPU CUDA and High-Performance Computing (HPC) Agent-Based Modeling and Multiscale Simulation Software Performance Optimization Notable Projects: PRIMAGE: Developed GPU-accelerated models for neuroblastoma research. FLAMEGPU2: Enhanced scalability of agent-based simulations, enabling millions of agents. Python Profiling Course: Educates researchers on performance optimization techniques. Labs/Teams: Member of the Computer Vision research group and Research Software Engineering team.

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Ralph Müller-PfefferkornView profile

Researcher

Dresden University of TechnologyGermany

Ralph Müller-Pfefferkorn is Head of the Department of Distributed and Data Intensive Computing at the Center for Information Services and High Performance Computing, Technische Universität Dresden. His work focuses on data management, metadata systems, and high-performance computing infrastructure. He holds a PhD in Physics from TU Dresden (2001) and has extensive experience in research data infrastructure development, spanning roles from 1996 to present across physics and computational science domains. Education: 2001: Dr. rer. nat., Physics, TU Dresden (Thesis on BABAR detector calibration) 1996: Diploma in Physics, TU Dresden 1992–1996: Studies in Physics at TU Dresden 1990–1992: Studies at TU Chemnitz Research interests emphasize distributed data management, long-term archiving, and FAIR data principles. He contributes to initiatives like the Research Data Alliance (IG Co-Chair) and UNICORE Forum board. His publications address challenges in big data workflows, metadata standards, and interdisciplinary infrastructure integration. Labs/Teams: Leads the Distributed and Data Intensive Computing team within TU Dresden's HPC center, collaborating on projects like MoSGrid and MASi repository systems.

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Eiko YonekiView profile

Researcher

University of CambridgeUnited Kingdom

Eiko Yoneki is a Senior Researcher and Affiliated Lecturer at the University of Cambridge's Department of Computer Science and Technology. She holds a Ph.D. in Computer Science from Cambridge (2007) and a Postgraduate Diploma (2003). Her research focuses on distributed systems, networking, databases, and large-scale graph processing, with a recent emphasis on auto-tuning frameworks using machine learning techniques like Bayesian Optimization and Reinforcement Learning. She has extensive industry experience, including roles at IBM across multiple countries. Affiliations: Department of Computer Science and Technology, University of Cambridge Education: Ph.D. (2007), Postgraduate Diploma (2003) in Computer Science, University of Cambridge Her research interests span system autotuning, probabilistic performance modeling, and scalable graph analytics. Notable projects include hierarchical Bayesian optimization for high-dimensional parameter spaces and reinforcement learning for GPU schedule optimization. She has contributed to frameworks like RLFlow and HiBO, advancing efficient resource management in compute systems. Labs/Teams: Leads research groups focused on machine learning-driven system optimization and large-scale graph processing. Collaborates with industry partners on cloud computing efficiency and hardware-aware algorithms. Grants/Advising: No specific grants listed, but her work is supported by academic and industrial collaborations.

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Bernd MohrView profile

Researcher

Saarland UniversityGermany

Bernd Mohr is a senior scientist and Division Head of the 'Application Support' division at the Jülich Supercomputing Centre (JSC) , part of the Institute for Advanced Simulation (IAS) at Forschungszentrum Jülich. He leads the Scalasca performance tools project in collaboration with Prof. Felix Wolf (TU Darmstadt) and oversees user support, performance analysis, and training at JSC. Previously, he served as deputy head of the division for 15 years and held a postdoctoral position at the University of Oregon, where he developed the foundational TAU performance analysis framework . Education: Diploma thesis and Ph.D. at the University of Erlangen (Germany), focusing on performance analysis tools for parallel computing. Research Expertise: Specializes in parallel programming, HPC performance optimization, and benchmarking. His work supports exascale computing initiatives, including leadership roles in the International Exascale Software Project (IESP/BDEC), European Exascale Software Initiative 2 (EESI2), and Jülich’s EIC and ECL projects. He has authored dozens of articles on parallel program performance analysis and tuning. Professional Involvement: Served on steering committees for SC and ISC conference series. Team leader of the 'Programming Environments and Performance Analysis' group from 2000 to 2024. Labs/Teams: Heads the ATML Parallel Performance team and contributes to the Scalasca collaboration. His work bridges tool development, user support, and large-scale computational challenges.

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Bartosz BaliśView profile

Associate Professor

AGH University of KrakowPoland

Bartosz Baliś serves as Associate Professor at the Institute of Computer Science, AGH University of Science and Technology, and holds a Senior Postdoctoral position in Extreme-scale Data and Computing at the Sano Centre for Computational Medicine. He actively contributes to the CERN ALICE experiment as a core team member. His educational foundation includes dual degrees from AGH University and Jagiellonian University, culminating in both PhD and DSc (habilitation) qualifications in Computer Science from AGH University. These advanced credentials underpin his specialized expertise in computational methodologies. Dr. Baliś pioneers research in scientific workflows, data science, and distributed computing systems , with particular emphasis on cloud-native architectures and e-Science infrastructures. His innovative work bridges theoretical computer science with practical applications in large-scale data processing, advancing methodologies for scientific computing environments through novel workflow management solutions. Recent publications demonstrate a clear trajectory toward serverless computing paradigms and comprehensive observability frameworks for scientific applications, reflecting his commitment to solving scalability challenges in modern computational research through cutting-edge cloud technologies. He has secured substantial research funding through participation in major European initiatives including CrossGrid, CoreGRID, K-Wf Grid, ViroLab, Gredia, UrbanFlood, PaaSage, and WATERLINE across FP5-FP7 and Horizon 2020 programs. His leadership extends to conference organization as Euro-Par 2020 General Co-chair, HPCS 2018-19 Tutorials Co-chair, and IEEE/ACM SC committee roles. Within institutional frameworks, Dr. Baliś contributes to the Extreme-scale Data and Computing research group at Sano Centre while maintaining active collaboration with the international CERN ALICE collaboration, where his computational expertise supports high-energy physics research infrastructure.

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Sohan LalView profile

Researcher

Technical University of Berlin (TU Berlin)Germany

Sohan Lal is a postdoctoral researcher at the Technical University of Berlin (TU Berlin), focusing on advanced modeling and runtime support for large-scale HPC clusters under a DFG-funded project. His PhD in Computer Engineering from TU Berlin (2019) explored power modeling and architectural techniques for energy-efficient GPUs. He contributed to EU-funded LPGPU projects on low-power GPU computing, leading tasks and collaborating across consortium members. Previously, he lectured at Shri Mata Vaishno Devi University and worked as an IT specialist in the Government of India. Education: PhD in Computer Engineering, TU Berlin (2019) Masters in Computer Science, IIT Delhi (2011) Bachelor in Computer Science and Engineering, GCET Jammu (2003) His research interests span GPU architecture, power/performance modeling, memory systems, and applied machine learning. Notable contributions include techniques like Selective Lossy Compression (SLC) for GPUs and entropy encoding-based memory compression (E²MC). He received HiPEAC travel/grants and was an ACM SRC semifinalist (2018). Grants & Collaborations: HiPEAC Collaboration Grant for joint work with TU/e DFG-funded postdoctoral research He actively teaches advanced computer architectures and multicore systems at TU Berlin, reflecting his passion for education developed during his early teaching career.

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Fang LiuView profile

Adjunct Associate Professor

Georgia Institute of TechnologyUnited States

Fang Liu is an Adjunct Associate Professor in the School of Computational Science and Engineering (CSE) at Georgia Institute of Technology. She holds a Ph.D. in Computer Science from Indiana University (2009) and serves as a Research Scientist at the Partnership for Advanced Computing Environment (PACE) center. Her primary roles include diagnosing complex technical issues in HPC systems, developing HPC software stacks, teaching courses on Linux and Python, and conducting research on big data analytics. She has also served in leadership roles for HPC conferences since 2014, including program chair for HPC2012 and HPC2013. Education: Ph.D. in Computer Science, Indiana University Bloomington (2009) Dissertation: "Building Sparse Linear Solver Component for Large Scale Scientific Simulation and Multi-physics Coupling" Advisor: Professor Randall Bramley Research Interests: Her work focuses on High Performance Computing (HPC) , parallel/distributed scientific computing , multi-physics coupling , big data infrastructure , and data management systems . She actively explores Hadoop/Spark-based solutions for graph databases and streaming data security, collaborating with Prof. Polo Chau's group. Recent projects include optimizing HPC cluster operations through big data analytics and developing provenance-capturing frameworks. Technical Contributions: She has pioneered tools like ProvBench for performance tracking and Phoenix for cost model modernization at Georgia Tech. Her work on automated storage cleanup and hybrid software deployment workflows has improved research computing efficiency across heterogeneous systems. Professional Activities: Served as program committee member for HPC, ICCS, and ICCSA conferences. Currently chairs the HPC conference steering committee since 2014. Labs & Teams: Works within the PACE center and collaborates closely with CSE faculties on computational science projects. Leads instructional initiatives via the ICE federated cluster environment.

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