Nicholas Wright serves as the NERSC Chief Architect and Advanced Technologies Group Lead at Lawrence Berkeley National Laboratory's National Energy Research Scientific Computing Center (NERSC) since 2009. He holds a PhD in Chemistry from the University of Durham, United Kingdom. Role: Focuses on evaluating emerging technologies for scientific computing Key Contributions: Chief architect for NERSC-10 procurement (2026), optimized Perlmutter machine architecture His research explores performance analysis of HPC applications and architectural evaluation for future technologies. Recent publications address: GPU frequency optimization using DNN-based models FPGA acceleration for HPC workloads Quantum computing cost scaling Disaggregated memory system evaluation Scientific workflow characterization Scientific awards include: Co-investigator on SDCI HPC Improvement grant (2007-2012) His work bridges computer architecture and energy-efficient computing through rigorous performance modeling and technology evaluation for NERSC's diverse scientific users.
Luke Olson is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), part of the College of Engineering. He holds an affiliate appointment in the Department of Mechanical Science and Engineering. His research focuses on numerical methods, high-performance computing, and parallel algorithms, particularly algebraic multigrid (AMG) solvers and sparse matrix computations. He leads the development of open-source libraries like PyAMG and RAPtor, advancing computational tools for scientific and engineering applications. Education: Ph.D. in Applied Mathematics from the University of Colorado Boulder (2003), M.S. in Mathematics from the University of Iowa (1999), and B.A. in Mathematics and Physics from Luther College (1997). Research Interests: Algebraic multigrid methods and preconditioners High-performance computing and parallel algorithms Numerical solutions to partial differential equations Scientific computing and software development Machine learning integration in numerical simulations Recent Articles Trends: Recent work merges machine learning with traditional numerical methods, such as neural network closures for turbulent combustion and reduced basis approximations using neural networks. Ongoing contributions include optimizing multigrid methods for exascale architectures and enhancing communication efficiency in distributed systems. Awards: Recognized with the NSF CAREER Award (2007), UIUC Campus Award for Excellence in Teaching (2024), and the Donald Biggar Willett Faculty Scholar distinction (2016). Active in conference organization, including the Copper Mountain Conference on Multigrid Methods. Teaching & Grants: Teaches courses like Numerical Methods for PDEs and Scientific Machine Learning. Leads projects funded by NSF and industry collaborations, emphasizing education innovation through the AE3 fellowship (2014–2016). Labs/Teams: Directs the Scientific Computing Group at UIUC and contributes to interdisciplinary initiatives like the Center for Exascale-enabled Scramjet Design (CEESD).
Jeffrey K. Hollingsworth is a Professor in the Computer Science Department at the University of Maryland and serves as Vice President for Information Technology and Chief Information Officer (CIO) for the university. He holds appointments in CS, UMIACS (University of Maryland Institute for Advanced Computer Studies), and ECE (Electrical and Computer Engineering). His research focuses on High Performance Computing (HPC), parallel programming environments, and system architecture. He received a Ph.D. from the University of Wisconsin at Madison (1994) and a B.S. in Electrical Engineering from UC Berkeley. Hollingsworth leads the university’s IT infrastructure, overseeing critical services like networking, cybersecurity, and support for research and teaching. He has held leadership roles in professional organizations, including past chair of the ACM Special Interest Group on HPC (SIGARCH) and board positions with Internet2 and the Computing Research Association. His awards include IBM Faculty Partnership Awards (2016, 2001), an NSF CAREER Award (1997), and IEEE Senior Member status (2003). Key contributions include advancing HPC education through programs like the SC Student Cluster Competition and developing tools for performance analysis (e.g., PIPER, Chapel profilers). He has authored over 150 papers and has been cited for innovations in auto-tuning, parallel algorithms, and system optimization.
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.
Yang Li is an Assistant Professor of Computer Science at Iowa State University, specializing in computer architecture, machine learning, and their intersection. He holds a Ph.D. and M.S. from Carnegie Mellon University (2020), an M.S.E. from the University of Texas at Austin (2013), and a B.E. from Tsinghua University (2011). Prior to academia, he worked as a Senior Research Scientist at Meta, a Research Scientist at Meta, and a Researcher at Microsoft. His research focuses on large language models (LLM) acceleration, on-device AI, cloud infrastructure optimization, and spatiotemporal forecasting. He has contributed to over 20 peer-reviewed publications at top venues like ASPLOS, EMNLP, and ICASSP. Research Interests: Algorithmic and systems-level acceleration of LLMs On-device AI co-design and privacy Cloud memory/power management Graph-based spatiotemporal forecasting Teaching: Taught COMS 6730 (Advanced Topics in ML), COM S 321 (Computer Architecture), and guest-lectured on graph signal processing. Recent teaching scores include 4.75/5.0 (Fall 2024) and 4.67/5.0 (Spring 2025). Awards: IBM Patent Application Award (2021) and multiple patents on power management systems for data centers. Service: Program committee member for DAC 2024, NeurIPS 2024, and ICLR 2025. Reviewer for ACM TACO, IEEE TPAMI, TPDS, and others.
Julian Hall is a Professor in the Department of Mathematics at the University of Edinburgh, specializing in optimization and operational research. He is a key developer of HiGHS, an open-source linear optimization software, and previously developed a solver used in global animal feed formulation and oil reservoir management. His research focuses on enhancing the simplex method for linear programming efficiency. Julian holds a PhD from the University of Dundee under Roger Fletcher after studying at Oxford and working at ICI/AstraZeneca. He has received four best paper awards, including COAP recognitions in 2005, 2013, 2015, and 2018. His work bridges academia and industry, with funding from Google and Huawei. Julian emphasizes applied mathematics’ real-world impact, particularly in agriculture and computational sustainability. Education: PhD in Numerical Analysis and Optimization (Dundee), MSc (Dundee), BA (Oxford), King’s School (Macclesfield). Research Interests: Linear programming algorithms, parallel computing, optimization in agriculture and environmental systems. His software HiGHS is deployed in multi-billion-dollar industries, solving problems like optimal animal feed blends and sustainable livestock production. Julian collaborates with industrial partners and mentors PhD students advancing optimization techniques. His recent work includes parallel simplex methods and GPU-accelerated algorithms, reflecting his commitment to scalable computational solutions.
Professor Sergey Karabasov is a leading academic in computational modeling and aeroacoustics at Queen Mary University of London’s School of Engineering and Materials Science . As Director of the Centre for Intelligent Transport , he bridges aerospace engineering with environmental technology and bioengineering. Education: PhD (1999, Moscow State University), DSc (2010, Keldysh Institute) Affiliations: Fellow of the Royal Aeronautical Society (FRAeS), Fellow of the Higher Education Academy (FHEA), Associate Fellow of AIAA (AFAIAA) Research Interests span multiscale fluid dynamics, computational aeroacoustics, and high-performance computing. His work addresses: Future Mobility: Noise reduction in urban air mobility and conventional aircraft Environmental Technologies: Turbulence modeling for renewable energy and climate systems Digital Twins: Physics-based and data-driven simulations for aerospace and bioengineering Article Trends focus on: Hybrid LES-acoustic models for jet noise Multiscale methods in nanofluidics and molecular systems GPU-accelerated algorithms (e.g., CABARET) for complex flows Climate dynamics (Southern Ocean jets, Chandler wobble) Scientific Awards include: Fellowships at Royal Aeronautical Society and Higher Education Academy Associate Fellowship at AIAA Guest Editor for Royal Society Phil.Trans. A theme issues (2014, 2019) Advising includes current PhD student Hussain Ali Abid and alumni working on: Jet noise optimization Graphene suspension rheology Hybrid molecular-continuum simulations Labs & Teams involve the Centre for Intelligent Transport , GPU-Prime.Ltd consultancy, and collaborations with institutions like Cambridge University and Keldysh Institute.
Masoumeh Ebrahimi is an Associate Professor at KTH Royal Institute of Technology, Division of Electronics and Embedded Systems, and holds an Adjunct Professor position at the University of Turku, Finland. She leads research in hardware acceleration, neural architecture search, and fault-tolerant systems. Her work bridges machine learning, embedded systems, and network-on-chip (NoC) design. Research Interests: Hardware-Accelerated Machine Learning 6G Network Architectures Fault-Tolerant Computing High-Performance GPU Systems Network-on-Chip (NoC) Design Federated Learning Key Projects: Co-supervisor of Hui Chen’s postdoc project Generalizing hardware acceleration for nonlinear functions . Active in Digital Futures, a cross-disciplinary center focusing on societal challenges using digital tech. Collaborates on edge computing, 6G networks, and resilient embedded systems. Labs & Teams: Core member of KTH’s Digital Futures initiative, advancing AI accelerators and next-gen communication systems. Engaged in EU-funded projects on NoC reliability and federated learning frameworks.
Jean Decaix is a Researcher at the University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis - Haute Ecole d'Ingénierie ) within the Department of Energy and Environmental Techniques . His work focuses on hydropower systems , computational fluid dynamics (CFD) , and cavitation modeling for hydraulic turbines. Decaix contributes to projects like SCCER-SoE (Supply of Electricity center) and XFlex Hydro , aiming to enhance grid stability through advanced turbine operation. Decaix's research spans Francis and Pelton turbines , with a focus on flow topology , unsteady cavitating flows , and hydraulic short-circuit modes . He develops freely distributable CFD tools for building airflow and turbine efficiency, validated through experimental measurements and numerical simulations . Notable projects : SCCER-SoE (2017-2020): Innovation roadmaps for geothermal and hydropower Solution de transfert d'énergie par pompage-turbinage à petite échelle (2015-2017): Economic model development for small-scale hydropower Decaix's 15 most recent publications (2015-2024) cover topics like cavitation suppression , vortex rope dynamics , Pelton turbine efficiency , and CFD validation for building energy systems. His work emphasizes renewable energy integration and mechanical stress reduction in hydropower plants.
Michael Shah is a Senior Lecturer of Computer Science at Yale University's School of Engineering & Applied Science. His research focuses on software visualization tools, performance analysis frameworks, and innovative computing education methodologies. He has contributed to fields such as parallel computing, GPU execution visualization, and asynchronous programming patterns. Dr. Shah's work bridges technical software development with educational applications, including curriculum design for introductory graphics courses and teaching assistant training programs. His research emphasizes practical tool development for debugging and performance optimization in both academic and industry contexts. Notable projects include the Daisen GPU visualization framework, DrAsync for JavaScript anti-pattern detection, and the Iceberg static analysis tool for Java concurrency issues. His publications span topics from procedural content generation in games to runtime performance bug mitigation strategies.
Dr. Enyue (Annie) Lu is a Professor of Computer Science at Salisbury University, affiliated with the Department of Computer Science within the Richard A. Henson School of Science & Technology. She holds a Ph.D. from the University of Texas at Dallas. Her research focuses on High Performance Computing, including parallel and distributed processing, cloud computing, and GPU computing. She also explores Computer and Communication Networks, such as network security and wireless sensor networks, alongside Algorithm Design and Graph Theory applications in bioinformatics and medical imaging. Dr. Lu has led and contributed to numerous research projects, including the REU (Research Experience for Undergraduates) program, which emphasizes emerging computing in science and engineering. Her work spans interdisciplinary areas like medical imaging algorithms and stable matching problems, leveraging GPU acceleration and parallel computing techniques. She has advised over 20 students in these areas and collaborates with institutions like the University of Maryland Eastern Shore and Johns Hopkins University. Her publications emphasize practical applications of theoretical computer science, such as improving tomographic imaging and network intrusion detection through machine learning and distributed systems. Despite not listing explicit awards, her extensive contributions to undergraduate research and algorithm development highlight her scholarly impact.
Rosa Maria Badia Sala is a Research Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Departament d'Arquitectura de Computadors and the Barcelona Supercomputing Center (BSC-CNS). She specializes in distributed computing, heterogeneous systems, and task-based programming models. Her work focuses on advancing high-performance computing (HPC), cloud computing, and workflow management for large-scale scientific applications. She holds a Doctorat en Informàtica and has been actively involved in numerous research projects, including contributions to the COMPSs programming framework and the optimization of HPC workflows. Her collaborations span institutions like BSC-CNS and international initiatives such as JLESC. Recent research includes GPU-accelerated computing, quantum optimization algorithms, and digital twins for power networks. Badia's publications span journals like Future Generation Computer Systems and IEEE Transactions on Parallel and Distributed Systems, reflecting her expertise in parallel computing, distributed systems, and real-time data analysis. She has supervised multiple doctoral theses and contributes to research grants focused on HPC and AI integration.
Stefanos Papadakis serves as a Research Staff Scientist at the Telecommunications and Networks Laboratory (TNL) of the Institute of Computer Science at Foundation for Research and Technology-Hellas (FORTH) and holds an Adjunct Lecturer position in the Department of Computer Science at the University of Crete. Since 2001, he has pioneered hardware and software prototyping at TNL-FORTH, currently leading the Software Defined Radio (SDR) group he established to drive vertical integration from physical layer design to application development. His educational foundation includes a Physics degree (2001) and M.Sc. (2004) and Ph.D. (2009) in Computer Science, all earned at the University of Crete. Teaching responsibilities encompass core courses CS-330: Introduction to Telecommunication Systems Theory and CS-435: Network Technology & Programming. Papadakis' research spans wireless innovation frontiers including software-defined/cognitive radios, spectrum sharing, heterogeneous networking, position location, radio propagation modeling, and emergency communications. His work emphasizes practical implementation, yielding functional prototypes across the entire communications stack. Notable contributions include GPU-accelerated SDR frameworks, robust spectrum virtualization techniques, and emergency response communication systems validated through international competitions. Analysis of his 15 most recent publications (2010-2016) reveals dominant themes in SDR optimization for IoT and critical communications, with significant focus on GPU parallelization, interference management in dense networks, and real-time spectrum sharing mechanisms. His experimental approach consistently bridges theoretical models with hardware validation, particularly in emergency response and heterogeneous network scenarios. Key recognitions include: Ericsson Award of Excellence in Telecommunications for position location research First place in PENED doctoral proposal competition Fourth place in IEEE DySPAN 2015 5G Spectrum Challenge Second place in Virginia Tech ShaRC 2016 with the 'Skynet' cognitive radio system Mentorship spans 16 undergraduate projects (11 completed), 8 M.Sc. students (3 completed theses), and 1 Ph.D. candidate. His research is sustained through major EU and national projects including REDComm (emergency communications), EU-MESH (metropolitan networks), RERUM (IoT security), and Heraklion smart city initiatives. The SDR group maintains critical infrastructure like the Heraklion metropolitan wireless network, FORTH campus network, and specialized mobile emergency nodes equipped with multi-radio SDR platforms, satellite transceivers, and high-performance computing resources.
Vijay Narayanan is the Robert Noll Chair Professor in Computer Science & Engineering and Electrical Engineering at Pennsylvania State University. He co-directs the Microsystems Design Lab and leads research in embedded visual analytics, self-powered processors, and system design using emerging devices. Education: B.E in Computer Science and Engineering (1993) from University of Madras, India Ph.D. in Computer Science and Engineering (1998) from University of South Florida, USA His research spans Power Aware Computing , Computer Architecture , and Embedded Systems , with emphasis on Visual Cortex on Silicon and Self-Powered Processors . Current work includes Non-Volatile Processors and Design Automation under unreliable power conditions via NSF ERC ASSIST. Recent publications focus on GPU architecture (Tensor Cores, ACE), Memory Consistency Verification (QED), and Neural Radiance Fields (Disorf, Distwar) for robotics and rendering. Key collaborations include Tsinghua University and DARPA/SRC LEAST Center . Scientific Awards: IEEE Fellow ACM Fellow He leads the Architecture, Benchmarking and Circuits Thrust in the DARPA/SRC LEAST Center and contributes to NSF ERC ASSIST for self-powered systems. Grants and projects emphasize cross-layer optimizations and hardware-software co-design.
Guido Reina is a Senior Academic Councillor at the Visualization Institute of the University of Stuttgart (VISUS), affiliated with the Ertl Working Group. His research focuses on scientific visualization, computer graphics, and virtual reality, with a particular emphasis on rendering techniques for particle data, foveated visualization, and reproducibility in visualization workflows. His work spans interdisciplinary applications, including porous media analysis, energy-efficient rendering, and integration of visualizations into gaming consoles. He has contributed to frameworks like MegaMol and explored adaptive resolution scaling for high-performance 2D visualization. Guido is also a co-author of the EGPGV 2023 Best Paper Award. EGPGV 2023 Best Paper Award Guido collaborates with teams at VISUS and the University of Stuttgart, advancing immersive analytics and in situ visualization methodologies. His publications highlight the evolution of visualization research toward handling large-scale datasets, optimizing rendering pipelines, and enhancing user interaction in augmented/virtual reality environments.
