Dylan Keon is an Assistant Professor (Senior Research) at Oregon State University's School of Electrical Engineering and Computer Science. He serves as the Associate Director of the Northwest Alliance for Computational Science & Engineering (NACSE). His research focuses on spatial analysis, spatio-temporal data systems, climate data production, and decision support systems for environmental and disaster management applications. He holds a Ph.D. in Computational Geography and M.S. in Plant Ecology and GIS/Statistics from Oregon State University, along with a B.S. in Botany from Western Michigan University. Dr. Keon has over 20 years of experience in developing geospatial tools for environmental monitoring, including projects funded by USDA, NSF, and other agencies. His work spans topics such as precision agriculture, climate modeling, tsunami engineering, and aquatic pathogen tracking. He currently leads operations on multiple USDA-funded initiatives and has contributed to critical infrastructure resilience frameworks for coastal regions. His research trends emphasize interdisciplinary collaboration, leveraging computational methods to address complex environmental challenges. Recent efforts include advancing solar radiation models, improving tsunami inundation simulations, and creating databases for tracking aquatic pathogens. Keon’s contributions bridge academic research with real-world applications, particularly in disaster preparedness and sustainable environmental practices.
Dr. Nikolay Simakov is a Computational Scientist at the University at Buffalo's Center for Computational Research (CCR), joining in 2012. He holds a PhD in Computational Chemistry from Carnegie Mellon University, alongside BA and MA degrees in Applied Physics and Mathematics from Moscow Institute of Physics and Technology. Research Interests: High-Performance Computing (HPC), Machine Learning, Scientific Software Optimization, Data Analytics for Large Datasets, and Simulation of Biomolecular Systems Teaching: Taught graduate courses like EAS509 Statistical Learning and Data Mining-II and CDA501 Introduction to Data Driven Analysis, mentoring undergraduate interns at CCR Key Contributions: Developed Slurm Simulator for HPC scheduling, contributed to XDMoD/ACCESS Metrics for resource auditing, optimized GPU-based Poisson equation solvers, and advanced multi-rheology geophysical flow modeling Technical Focus: Performance monitoring of HPC systems, node sharing analysis, and ARM architecture benchmarking His work bridges computational infrastructure optimization and biological system modeling, with publications spanning HPC resource management, biomolecular simulations, and parallel computing frameworks.
Barton P. Miller is a Vilas Distinguished Achievement Professor and the Amar & Balinder Sohi Professor of Computer Sciences at the University of Wisconsin, Madison, where he has made significant contributions to computer science research and education. As a faculty member in the Computer Sciences Department within the College of Engineering, he directs major research initiatives including the Paradyn Tools project and leads cybersecurity efforts as Chief Scientist of the DHS-funded Software Assurance Marketplace (SWAMP) research center. Miller received his B.A. degree from the University of California, San Diego in 1977, and M.S. and Ph.D. degrees in Computer Science from the University of California, Berkeley in 1980 and 1984. His educational background laid the foundation for his pioneering work in software testing and analysis. His research spans multiple critical areas of computer science, with a primary focus on binary code analysis and instrumentation , software security , and distributed and parallel program performance . Miller is particularly renowned for founding the field of Fuzz random software testing in 1988 and dynamic binary code instrumentation in 1992. His work bridges theoretical computer science with practical applications, addressing real-world challenges in high-performance computing systems, cybersecurity, and scalable distributed systems. His research has direct applications in national security through his work with DHS and TrustedCI, the NSF Cybersecurity Center of Excellence. Miller's publications reveal a consistent focus on binary analysis tools, security vulnerability detection, and performance optimization techniques. His recent work shows increasing emphasis on ransomware analysis, GPU performance optimization, and practical security tool development for developers. The trajectory of his research demonstrates how foundational work in binary instrumentation has evolved to address contemporary cybersecurity challenges while maintaining relevance to high-performance computing environments. Among his notable honors, Miller is a Fellow of the ACM and recipient of the prestigious Jean-Claude Laprie Award in Dependable Computing , an R&D 100 Award , and multiple best paper awards including at HPDC 2009 and CCGrid 2018. His contributions to the field have been recognized through distinguished lectureships at institutions including Ben Gurion University and IBM T.J. Watson Research Center. As an educator, Miller has mentored numerous students and taught courses spanning operating systems, software security, and distributed systems. He has received teaching awards and developed educational resources including free and open software security training materials. His work with TrustedCI has focused on cybersecurity for scientific communities, helping researchers secure their computational infrastructure while maintaining research productivity. Miller also co-directs the MIST software vulnerability assessment project in collaboration with colleagues at the Autonomous University of Barcelona. Miller directs the Paradyn Tools project, which investigates program scalability and binary program analysis technologies for use in HPC, systems design, and cyber-security. His work extends to practical applications through collaborations with government agencies including DHS and the U.S. Secret Service Electronic Crimes Task Force. His research group has developed tools like Dyninst, MRNet, and the Wisconsin Safety Analyzer that have become foundational in their respective domains.
Prof. Tevfik Kosar is a Professor in the Department of Computer Science and Engineering at the University at Buffalo (UB), part of the School of Engineering and Applied Sciences. His research focuses on data-intensive computing, distributed systems, petascale storage, and energy-efficient data transfer optimization. He has taught numerous courses on operating systems, distributed systems, and green computing, including CSE 421/521 (Operating Systems), CSE 709 (Green Computing), and CSE 710 (Distributed File Systems). His achievements include the UB Exceptional Scholar Award (2020), SEAS Researcher of the Year (2018), and an NSF CAREER Award (2011). He leads the DIDCLAB , which develops innovative solutions for data-intensive distributed computing. Prof. Kosar holds a PhD from the University of Wisconsin-Madison (2005), MS from Rensselaer Polytechnic Institute (1999), and BS from Bogazici University (1997). His work spans academic contributions, industry collaboration, and sustainability-focused research. Teaching Highlights: Courses: Operating Systems (repeated annually since 2011), Green Computing (since 2013), and specialized seminars on distributed file systems. Grants & Projects: Collaborative NSF grants for GreenSW (2024), CloudScent (2023), and energy-aware data transfer optimization (2018–present). His research emphasizes minimizing energy consumption in cloud and HPC systems while maximizing data transfer efficiency. Recent projects include GreenABR+ for energy-aware video streaming and Greendataflow for zero-carbon data movement. He has also pioneered tools like FlowTracer for AI training cluster analysis and PhoneLab , a smartphone testbed for real-world network studies.
Dr. Charles James Gillan is a Senior Lecturer at Queen's University Belfast's School of Electronics, Electrical Engineering and Computer Science, part of the Faculty of Engineering and Physical Sciences. His research focuses on high-performance computing (HPC), heterogeneous accelerators (FPGAs and GPUs), and their applications in diverse fields such as clinical physiology, electromagnetic fields, and image analysis. He collaborates extensively with the QUB Medical School on data analytics and machine learning for ICU patient monitoring. Gillan has led multiple projects, including an InterTrade Ireland-recognized Fusion project with CreVinn Ltd, which transferred FPGA programming knowledge to industry. His work spans academic and industrial partnerships, emphasizing innovation in computing systems and real-world problem-solving. His research interests include KTP projects, EPSRC funding, and H2020 initiatives. He has contributed to high-impact projects like the HPC-NI center and handheld olfactory detection systems. Awards include an InterTrade Ireland award for his Fusion project. Gillan has also engaged in outreach, training graduates in OpenCL programming and fostering industry-academia collaboration. Publications highlight advancements in neural networks for blood pressure prediction, exascale computing algorithms, and AI-driven healthcare solutions. His work bridges theoretical computing and practical applications, with a focus on edge computing architectures and transparency in food systems.
Dr. Antonios Antoniadis is a Lecturer in Computational Engineering Science and Director of the Computational Fluid Dynamics (CFD) Masters program at Cranfield University. He leads the VR/AR Laboratory within the Aircraft Integration Research Center and a research group focused on computational methods for fluid dynamics. His expertise spans aeronautical systems, turbulence modeling, and renewable energy technologies. Education: BEng in Automotive Engineering, University of Sussex MSc in Mechanical Engineering, University College London PhD in Aerospace CFD, Cranfield University (2013) Research Interests: Dr. Antoniadis develops cutting-edge computational frameworks for engineering challenges. His work includes high-order numerical methods on unstructured grids, data-driven turbulence models, and multi-physics simulations. Applications range from helicopter aerodynamics and wind-farm optimization to supersonic flow analysis and fluid-structure interactions in morphing UAVs. He integrates AI/ML techniques for turbulence modeling and design automation, advancing sustainable aerospace solutions. Publication Trends: Recent articles highlight advancements in rotorcraft CFD, shock absorber fluid dynamics, and high-performance solver development. Predominant themes include validation of high-order schemes for transonic flows, vortex dynamics in hovering rotors, and scalable algorithms for renewable energy systems. His work consistently bridges theoretical computational methods with industrial aerospace applications. Awards: Fellowship of the Higher Education Academy (FHEA) Labs & Teams: Dr. Antoniadis directs a VR/AR laboratory pioneering human-computer interaction for CAE applications. His research group collaborates with industry partners (e.g., BAE Systems, Airbus, Red Bull Racing) on projects involving aerodynamic prediction, multi-phase flows, and HPC parallelization.
Dr. Thomas Zeiser serves as Head of Systems & Services and Chief Operating Officer HPC at NHR@FAU (Center for National High Performance Computing Erlangen) at Friedrich Alexander University Erlangen-Nuremberg. He leads the Systems & Services group of NHR@FAU and HPC4FAU since the end of 2020 and serves as deputy for NHR@FAU in the NHR Betreiberausschuss. His work focuses on transitioning from serving FAU only to national center operations, managing HPC systems, procuring new infrastructure, financial controlling of NHR@FAU's budgets, and supporting planning for a new data center building. Dr. Zeiser's research interests center around High-Performance Computing with specific expertise in Lattice Boltzmann Methods , large-scale simulations, evaluation of HPC hardware and software, and efficient operation of HPC systems. His work bridges the gap between theoretical computational methods and practical implementation in high-performance environments. He has implemented the first job-based job and performance monitoring for RRZE's HPC systems and has extensive experience in procurement of HPC infrastructure. Analysis of Dr. Zeiser's publication record reveals a consistent focus on practical applications of computational methods in HPC environments. His research demonstrates a progression from fundamental lattice Boltzmann method development toward increasingly complex system-level concerns including fault tolerance, performance monitoring, energy efficiency, and scalable implementations. The publications show strong collaboration patterns with researchers at FAU and other German institutions, particularly in the areas of computational fluid dynamics and parallel computing. Dr. Zeiser regularly serves as a reviewer for various journals and compute time commissions of different HPC centers. He is also one of the local organizers for the Ferienakademie of TUM, FAU, and Universität Stuttgart held in Sarntal. His work with NHR@FAU positions him at the forefront of national high-performance computing infrastructure in Germany. At NHR@FAU and RRZE (Regional Computing Center Erlangen), Dr. Zeiser leads teams responsible for operating some of Germany's most powerful academic supercomputing resources. His group plays a critical role in supporting computational research across multiple disciplines at FAU and increasingly at the national level through the NHR initiative.
Mariusz Fraś is a researcher affiliated with the Faculty of Information and Communication Technology at Wrocław University of Science and Technology. He works within the Department of Computer Science and Systems Engineering , focusing on computational systems' performance and cloud-based architectures. His research spans database optimization, parallel processing, and mobile application frameworks. Research interests include: Computational cloud scalability and cost optimization Database application performance analysis Parallel processing frameworks Mobile sensor data processing Web content delivery efficiency His publications demonstrate expertise in cloud computing trends, with 10 peer-reviewed works since 2014. Notable collaborations include researchers like Piotr Karwaczyński and Bogdan Marczuk. Current research likely continues exploring hybrid cloud environments and software performance metrics. Contact: mariusz.fras@pwr.edu.pl , Room 301, Building C-3, Wrocław University of Science and Technology.
Zbigniew T. Kalbarczyk is a Research Professor at the Coordinated Science Laboratory and the Department of Electrical and Computer Engineering (ECE) at the University of Illinois at Urbana-Champaign (UIUC), holding this position since 2004. He holds M.S. degrees in Mechanical Engineering (Technical University of Warsaw, 1981) and Electronic Engineering (Technical University of Sofia, 1984), as well as a Ph.D. in Computer Science from the Bulgarian Academy of Sciences (1992). His research focuses on reliable and secure computing systems , including fault tolerance, cybersecurity, and cyberphysical systems. He leads the DEPEND Group, which investigates trustworthy systems, health analytics, and big-data applications. Key projects include securing supercomputing infrastructure against environmental attacks, improving IoT security, and advancing healthcare systems through AI-driven solutions. Recent collaborations involve industry partners like IBM, Mayo Clinic, and the National Science Foundation (NSF), with notable funding such as a $500K NSF grant for cybersecurity research. His work bridges theoretical concepts and practical implementations, emphasizing real-world testing on systems like Blue Waters supercomputers and smart grids. Notable students include Krishnakant Saboo (Ph.D. 2023), Homa Alemzadeh (William Carter Award recipient), and Arjun Athreya (best paper award winner). The DEPEND Group also collaborates internationally, including with Singapore’s VinUniversity and the National University Hospital.
Anthony Goodacre is a Professor of Computer Architectures at the School of Computer Science, holding a part-time role while serving as Director of Technology and Systems at ARM Ltd. in Cambridge. He leads research spanning nanotechnology, hardware design, operating systems, and heterogeneous runtimes, with a focus on scalable, power-efficient systems for embedded, enterprise, and HPC applications. Research interests include: Exascale computing through EU-funded projects like EUROSERVER and ExaNoDe 3D silicon integration and system virtualization for heterogeneous architectures Quantum-classical programming languages (e.g., Quff) Power management techniques like cyclic power-gating Memory models and runtime systems for ARM-based data centers His 15 most recent publications highlight trends in FPGA acceleration, radiation-hardened MPSoC systems, and hybrid quantum-classical programming. Contributions to EU FP7/H2020 projects (e.g., EUROSERVER, ExaNEST) emphasize commercialization of ARM technology for exascale computation. Current collaborations include: KALEAO Limited (CSO/CTO since 2015) ARM Ltd. (Director since 2002) Technology Strategy Board (member since 2015) Academic partnerships in EU H2020 projects He co-supervises two PhD students and contributes to policy discussions, including parliamentary select committee evidence. His work aligns with UN SDGs for energy efficiency and sustainable computing.
Dong Dai is an Associate Professor at the University of Delaware in the Department of Computer and Information Sciences under the College of Engineering. He directs the Data Intelligence Research Lab (DIRLab) , focusing on optimizing intelligent infrastructure for high-performance, data-intensive systems using machine learning techniques. Research Interests : Artificial Intelligence, Machine Learning, High-Performance Computing, Storage Systems, Metadata Management, Graph Storage, Job Scheduling Education : PhD and BSc from the University of Science and Technology of China Grants : CCF-EAGER, CNS, CCF-Hybrid NVM, CCF-Parallel Graph-Based Paradigm, OAC-Provenance Collection Teaching : Courses include CISC 361 Operating Systems , CISC 360 Computer Architecture , ITCS 5145 Parallel Computing , and ITSC 3050 Undergraduate Research .
Shantenu Jha is a Professor of Computer Engineering at Rutgers University and Chair of the Department (Center) for Data Driven Discovery at Brookhaven National Laboratory. He leads the RADICAL Lab and the RADICAL-Cybertools project, focusing on middleware for large-scale scientific applications. His research bridges high-performance computing, data-driven science, and health informatics, collaborating with diverse fields like molecular sciences and high-energy physics. Education: Ph.D., Syracuse University (2004); M.Sc., IIT Delhi (1995). Research Interests : High-Performance and Distributed Computing Data-Intensive Science & Engineering Cyberinfrastructure for Science Health Computing (Personalized Medicine) Awards : NSF CAREER Award (2013) Chancellor’s Excellence in Research (2016) Rutgers Board of Trustees Fellowship (2014) Multiple best paper awards at SC/ISC conferences Grants & Funding : Supported by NSF, U.S. DOE, NIH, and UK EPSRC. Current projects include ExaWorks and quantum-HPC middleware development. Labs/Teams: PI of the RADICAL Lab, leading initiatives in FAIR principles, quantum computing integration, and autonomous laboratories.
Jose Francisco Llosa Espuny is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Faculty of Computer Science of Barcelona (FIB) and the Department of Computer Architecture. He is a key member of the PM - Programming Models research group and has been involved in numerous competitive R&D projects related to high-performance computing and computer architecture. University: Universitat Politècnica de Catalunya School: Faculty of Computer Science of Barcelona Department: Department of Computer Architecture Research Group: PM - Programming Models Email: josepll@ac.upc.edu ORCID: 0000-0001-7740-3148 His research focuses on advanced computer architecture topics, particularly VLIW processors, software pipelining, register allocation, and compiler optimizations for high-performance and low-power systems. He has also made significant contributions to computer science education, exploring active learning, problem-based learning, and the use of interactive response systems for formative assessment. His recent publications and projects, including the European Master for HPC Curriculum and various HPC education initiatives, highlight his ongoing engagement in both technical research and pedagogical innovation. The articles reflect a strong trend in optimizing processor architectures for performance and energy efficiency, as well as improving teaching methodologies in computer science. He has been involved in significant research projects funded by the Spanish government and the European Union, such as those under the HORIZON 2020 program and various national R&D plans. Llosa Espuny has advised doctoral students, including Manoj Gupta and Francisco Zalamea, indicating his role in mentoring the next generation of researchers. He has collaborated extensively with leading figures in the field, such as Mateo Valero and Eduard Ayguadé, on numerous publications and projects. His work is associated with the Barcelona Supercomputing Center, a key entity in national and European high-performance computing efforts.
Chris Fietkiewicz is an Associate Professor in the Department of Mathematics & Computer Science at Hobart and William Smith Colleges. He joined the faculty in 2019 and is based in Lansing Hall, with research interests in computational neuroscience and high-performance computing. Research Interests: His work focuses on Computational Neuroscience , particularly the development of neural simulators for educational and clinical applications. He leads the Applied Neural Control Toolkit (ANC Toolkit) , which enables simulation of neural and axonal responses for neuroprosthetic development. His research also spans High Performance Computing , including software optimization, parallel computing, and cluster management tools. Collaborations: He collaborates with Dr. Thomas Mortimer (Case Western Reserve University) on neural stimulation and with Dr. Chen Liu (Tianjin University) on Parkinson’s disease modeling and deep brain stimulation. His publications reflect a strong interdisciplinary focus on biomedical signal processing, control systems, and neuromorphic computing. Publication Trends: His recent work (2016–2022) centers on closed-loop control of pathological neural oscillations, computational models of Parkinson’s disease, and real-time neural simulation. These studies appear in top journals like IEEE Transactions and Biomedical Signal Processing and Control , emphasizing algorithmic innovation and clinical relevance. Scientific Contributions: Co-developer of the ANC Toolkit for neuroscience education. Researcher in neural control systems and HPC optimization. Active contributor to computational models of movement disorders. Advising and Grants: While no formal students are listed, his collaborative projects suggest mentorship of undergraduate and graduate researchers. Grant details are not provided, but his research cluster and web tools indicate funded infrastructure development. Labs and Teams: He is building a research computing cluster and developing web-based batch programming tools, indicating a focus on scalable, accessible computational neuroscience platforms.
Jorge Lobo is an Assistant Professor at the Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Coimbra. His research focuses on computer vision, sensor fusion for mobile robotics, and low power computing, with recent emphasis on quantum computing and bio-inspired computational approaches. B.Sc/M.Sc in Electrical Engineering (University of Coimbra) Ph.D. in Electrical and Computer Engineering (2007, thesis: 'Integration of Vision and Inertial Sensing') Research Interests: Specializes in artificial perception systems combining computer vision and inertial sensing for robotics. Develops probabilistic models for sensor fusion and explores unconventional computing architectures like stochastic circuits and quantum computing for efficient Bayesian inference. Key applications include autonomous robotic grasping and disaster response systems. Scientific Leadership: Principal Investigator for Q-Bet: Bridging classical/quantum computing via HPC and bio-inspired methods Founding member of Quantum@UC interest group Coordinator of quantum computing specialization courses (collaboration with Physics & Computer Science departments) Key Projects: Contributed to European initiatives BACS (Bayesian Cognitive Systems), HANDLE (robotic dexterity), BAMBI (Bayesian inference hardware), and ECOBOTICS.SEA (marine ecosystem robotics). Academic Recognition: IEEE Senior Member and former president of the Portuguese IEEE RAS chapter. Developed innovative remote labs for stochastic computing education.
