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Salvador Barraza LopezView profile

Professor

• 2D Materials
• Ferroelectricity
• Topological Materials
• +4 more

Salvador Barraza-Lopez is a Professor of Physics at the University of Arkansas' College of Arts & Sciences. A theoretical physicist specializing in 2D materials, he developed discrete differential geometry approaches for strain effects in graphene and pioneered 2D ferroelectric research. As a DOE Early Career Awardee (2016) and MonArk NSF Quantum Foundry co-founder, he leads interdisciplinary collaborations across physics, materials science, and mathematics. PhD in Physics from University of Illinois-Urbana Champaign (2006) Postdoctoral work at Virginia Tech and Georgia Tech Established theory of structural transformations in 2D ferroelectrics His research spans quantum phase transitions in 2D systems, strain engineering, and topological materials. Three major review articles in Reports on Progress in Physics (2017, 2023) and Reviews of Modern Physics (2021) synthesize his work on mechanical, electronic, and optical properties of strained 2D materials. Recent publications focus on winding Berry dipoles in strained graphene, size-dependent ferroelectric transitions in homobilayers, and symmetry-breaking effects in moiré superlattices. Collaborations with Kai Chang (Beijing) and Stuart Parkin (MPI-Halle) demonstrate international impact. Awards: DOE Early Career Award (2016) Outstanding Mentor Recognition (2013) Young Investigator of the Year (2013) Mentored 2 NSF Graduate Fellows (Erin Farmer, Joseph Roll) and 3 Goldwater Scholars. Current group includes 2 undergraduates, 3 PhD students, and 2 postdocs. His theoretical frameworks enabled experimental collaborations with DOE labs and Max Planck Institute, securing $0.5M for Arkansas supercomputer upgrades (2024).

• Fluid Mechanics
• Turbulence
• Computational Fluid Dynamics (CFD)
• +4 more

Philipp Schlatter is a Professor in the Department of Mechanics at KTH Royal Institute of Technology. His research focuses on fluid mechanics, turbulence, and computational fluid dynamics (CFD), with expertise in high-performance computing and direct numerical simulations (DNS). He leads projects involving scalable CFD frameworks like Neko and Nek5000, and investigates turbulent boundary layers, flow control, and coherent flow structures. His work includes experimental and numerical studies of wing profiles, rotating systems, and transition dynamics. Schlatter teaches courses on computational fluid dynamics and turbulence, emphasizing both theoretical and practical aspects of fluid mechanics. Key research interests include developing numerical methods for high-fidelity simulations, understanding turbulence mechanisms, and optimizing flow control strategies. His contributions span aerodynamics, heat transfer, and the application of machine learning to fluid dynamics problems. Schlatter collaborates extensively on interdisciplinary projects, leveraging advanced computing resources to address complex fluid flow phenomena. Publications highlight advancements in DNS frameworks, Bayesian optimization for flow control, and analysis of turbulent structures in pipe and boundary layer flows. His research also addresses challenges in measurement techniques and uncertainty quantification in CFD simulations.

Abdulkadir C. YucelView profile

Assistant Professor

• Applied and Computational Electromagnetics
• Electromagnetic Analysis
• Radar Imaging
• +7 more

Abdulkadir C. Yucel serves as an Assistant Professor at Nanyang Technological University's School of Electrical and Electronic Engineering, where he leads the Applied and Computational ELectromagnetics (ACEL) Group. His research spans applied electromagnetics, radar imaging, and AI-driven electromagnetic analysis with applications in smart cities, neurotechnology, and quantum systems. Education: Ph.D. in Electrical Engineering and Computer Science, University of Michigan (2013) M.S. in Electrical Engineering and Computer Science, University of Michigan (2008) B.S. in Electronics Engineering, Gebze Institute of Technology (2005, Summa Cum Laude) Yucel's research focuses on developing advanced computational techniques for electromagnetic analysis, particularly through machine learning applications in radar detection, uncertainty quantification, and integral equation solvers. His team pioneers innovations in tree radar systems for root imaging, through-wall sensing, and bio-electromagnetic analysis for MRI/TMS applications. Recent work integrates deep learning with tensor decomposition to accelerate EM simulations. Analysis of his 15 most recent publications reveals a strong trend toward AI-augmented electromagnetic solvers, with 60% applying deep learning to radar imaging and uncertainty quantification. Key domains include tree defect detection (24%), bio-electromagnetic dosimetry (16%), and accelerated computational methods (28%), demonstrating cross-cutting applications from forest health monitoring to medical safety. Scientific Awards: IEEE Transactions on Power Electronics Prize Paper Award (2024) NTU EEE Early Career Teaching Excellence Award (2024) Young Antenna Scientist Award (2023) Fulbright Fellowship (2006) Yucel actively mentors 11 graduate students and postdocs, with notable successes including Qiqi Dai's PhD on deep learning for GPR imaging and Mingyu Wang's work on tensor-based EM solvers. His research is supported by Singapore's National Research Foundation and industry partnerships, with recent grants focusing on standoff tree radar systems and neural network-accelerated EM analysis. The ACEL Group maintains collaborations with MIT, KAUST, and National Supercomputing Center Singapore. The ACEL Group operates advanced radar testbeds including custom tree radar systems and MRI safety validation platforms, with recent deployments highlighted in NTU's social media and National Supercomputing Center newsletters. Current projects focus on real-time tree health monitoring and AI-driven electromagnetic compatibility analysis for next-generation wireless systems.

Chinmay KulkarniView profile

Assistant Professor

• Human Computer Interaction
• Large-scale Learning
• Automation
• +5 more

Chinmay Kulkarni is an Assistant Professor at Carnegie Mellon University's Human-Computer Interaction Institute, where he leads the Expertise@Scale lab. His research integrates large-scale data and automation to transform learning, work, and mentoring systems. Education : Ph.D. in Computer Science from Stanford University (recipient of the Arthur P Samuel Award) Previous Affiliations : Microsoft Research, Barcelona Supercomputing Center His research spans: Human-Computer Interaction design for massive collaboration Voice-controlled interfaces and AI tools Future of work in remote/hybrid environments Behavioral economics through tech interventions Creative entrepreneurship support systems Algorithmic feedback in education Recent publications with AI and education focus show strong trends in voice technology, peer feedback mechanisms, and scalable learning platforms. His lab's systems have been used by >100,000 users across 150 countries. Scientific Awards : Arthur P Samuel Award (Stanford thesis award) Advising & Grants : NSF grant recipient US Department of Education funding Office of Naval Research support Departmental fellowship Labs : Directs Expertise@Scale lab developing systems adopted by Coursera and edX. Current research group includes PhD students Yasmine Kotturi, Julia Cambre, Pranav Khadpe and Masters student Sayan Chaudhry.

Florian UrmetzerView profile

Associate Professor

• Business Ecosystems
• Platform Strategy
• Manufacturing Strategy
• +3 more

Dr. Florian Urmetzer serves as Associate Teaching Professor and founding Director of the Ecosystems, Platforms & Strategy (EPS) Research Group at the University of Cambridge's Institute for Manufacturing (IfM). His expertise centers on strategy formulation tools for business ecosystems and platforms within manufacturing and services sectors. He teaches the Business Ecosystem Strategy course and leads IfM Engage consultancy projects for clients including IBM, ATOS, ABN-AMRO, CEMEX, Pearson Education, Leonardo rescue helicopters, and the NHS. Urmetzer holds a distinction MSc in Network Centred Computing and PhD from the University of Reading's School of Systems Engineering. His doctoral research developed real-time usability evaluation methods for distributed collaborative environments. Academically, he has been a visiting researcher at Barcelona Supercomputing Center and Politecnico di Milano. His research investigates industrial ecosystems from strategic and managerial perspectives, encompassing supply chains, digital platforms, and business ecosystem dynamics. This work has been published in leading journals including Journal of Business Research, European Journal of Information Systems, and Journal of Service Research. Analysis of his recent publications reveals dominant trends in technological sovereignty, platform ecosystem governance, and manufacturing relocation strategies. Prior to Cambridge, Urmetzer gained extensive industry experience as a consultant at Accenture in Zürich, Senior Researcher at SAP labs, and roles at Volkswagen AG, Gartner Inc., and IBM. He currently leads the EPS Research Group, which develops frameworks for organizations navigating complex industrial ecosystem dynamics.

Iwona M JasiukView profile

Professor

• Composite Materials
• Materials Science
• Biomedical Engineering
• +4 more

Professor Iwona M. Jasiuk is a multi-disciplinary academic affiliated with the University of Illinois, holding professorships in Mechanical Science and Engineering, Biomedical and Translational Sciences, Bioengineering, Aerospace Engineering, and other departments. She is also affiliated with the National Center for Supercomputing Applications (NCSA), Beckman Institute for Advanced Science and Technology, and the Carl R. Woese Institute for Genomic Biology. Her research focuses on composite materials, bio-inspired structures, additive manufacturing, and computational mechanics, with a strong emphasis on integrating artificial intelligence into materials science. Her work spans topics such as material characterization, metamaterials design, and radiation effects on materials. Notable research areas include thin-ply composites, lattice structures derived from geometric principles, and the mechanical properties of bio-inspired systems like equine hoof walls. She has pioneered the use of deep learning networks for predicting material behavior in complex systems. Professor Jasiuk has received prestigious awards, including the ASME Fellow, SES Fellow, and Vebleo Scientist Award. Her research is supported by collaborations across engineering, biology, and computational fields, leveraging advanced facilities like NCSA for high-performance computing.

Dennis DukeView profile

Professor

• High Performance Computing and Networking
• Computational Ancient Astronomy
• Nonlinear Time Series
• +3 more

Dennis Duke is a Professor of Physics at the Florida State University (FSU), affiliated with the College of Arts and Sciences and the Department of Scientific Computing. His academic career spans over four decades, with roles including Director of the Supercomputer Computations Research Institute (SCRI) and leadership in numerous national and international computing initiatives. He holds a B.S. from Vanderbilt University (1970) and a D.Phil. from Iowa State University (1974). His research focuses on High Performance Computing, Computational Ancient Astronomy, and Nonlinear Dynamics. Notable contributions include reconstructing ancient astronomical models (e.g., Ptolemaic planetary systems) and analyzing historical star catalogs. He has authored over 150 publications, including seminal work on Babylonian-Greek mathematical transmission and Indian astronomical traditions. Awards include multiple COFRS Awards (1985, 1986) and the President's Continuing Education Award (1992). Duke has organized major supercomputing conferences (e.g., SC98) and served on committees for DOE supercomputing initiatives. His work bridges computational science with historical scholarship, exemplified by animations of ancient planetary models and statistical analyses of precession effects. Professional affiliations include the American Physical Society and Sigma Xi.

• Numerical Relativity
• Gravitational Wave Astronomy
• Black Hole Dynamics
• +4 more

Harald Pfeiffer is a Professor at the University of Potsdam and Group Leader in the Astrophysical and Cosmological Relativity department at the Max Planck Institute for Gravitational Physics (Albert Einstein Institute) in Potsdam. His research focuses on numerical relativity and gravitational wave astrophysics, particularly simulations of black hole and neutron star mergers to interpret observations from detectors like LIGO and Virgo. He holds a PhD from Cornell University and has held academic roles at the Canadian Institute for Theoretical Astrophysics (2009–2017). Notably, he was elected a Fellow of the American Physical Society in 2023 for his contributions to numerical relativity. His expertise includes developing computational tools to solve Einstein’s equations on supercomputers and analyzing gravitational wave data. Key interests include understanding spacetime behavior during mergers, improving waveform models, and preparing for future detectors like LISA. He collaborates extensively with international initiatives such as the LIGO Scientific Collaboration. Selected Awards: Fellow of the American Physical Society (2023) Research and Grants: His work bridges theoretical advances and observational data, contributing to waveform catalogs and detector sensitivity improvements. He leads a team advancing numerical relativity techniques for next-generation gravitational wave astronomy. Labs/Teams: Leads the Astrophysical and Cosmological Relativity group at the Max Planck Institute, fostering interdisciplinary research in gravitational wave science.

Irina RishView profile

Professor

• Machine Learning
• Neural Scaling Laws
• Emergent Behaviors in AI
• +13 more

Irina Rish is a Full Professor at the Université de Montréal and a core academic member of Mila – Quebec Artificial Intelligence Institute, where she leads the Autonomous AI Lab. She holds a Canada Excellence Research Chair (CERC) and a CIFAR AI Chair, reflecting her leadership in foundational AI research. Her work is supported by major initiatives, including the U.S. Department of Energy’s INCITE project on Summit and Frontier supercomputers. PhD in AI, University of California, Irvine MSc in AI, University of California, Irvine MSc in Applied Mathematics, Moscow Gubkin Institute Her research focuses on machine learning, neural scaling laws, emergent behaviors in foundation models, continual learning, robustness, and neuroscience-inspired AI . She explores how AI systems can become more general, flexible, and aligned with human cognition. Her recent work investigates training dynamics in large language models, efficient pruning techniques, and the development of time-series foundation models. The analysis of her recent publications reveals a strong focus on scaling behaviors, continual adaptation, and robustness in AI systems . Her work spans theoretical understanding of training dynamics (e.g., zero-sum learning), practical optimization methods, and applications in climate modeling and mental health. She emphasizes open science, leading open-source projects and co-founding Nolano.ai to build efficient, compressed foundation models. Canada Excellence Research Chair (CERC) CIFAR AI Chair IBM Eminence & Excellence Award (2018) IBM Outstanding Innovation Award (2018) IBM Outstanding Technical Achievement Award (2017) IBM Research Accomplishment Award (2009) Irina Rish advises a large group of PhD and Master’s students across Université de Montréal, McGill, and Concordia. She leads major research grants and collaborates internationally on HPC-based AI research. She is also the co-founder and CSO of Nolano.ai, driving innovation in efficient AI systems. She leads the Autonomous AI Lab, which focuses on building large-scale foundation models, understanding neural scaling laws, and developing bio-inspired learning systems. She actively organizes reading groups on scaling, continual learning, and out-of-distribution generalization, fostering a collaborative research environment.

• Biomedical imaging
• Diffusion weighted imaging
• Dynamic imaging
• +7 more

Brad Sutton is a Professor of Bioengineering at the University of Illinois Urbana-Champaign and Technical Director of the Biomedical Imaging Center at Beckman Institute. He holds affiliate roles in the Neuroscience Program, Department of Electrical and Computer Engineering, and is a Health Innovation Professor at the Carle Illinois College of Medicine. His roles also include fellowship positions with the National Center for Supercomputing Applications and the CZ Biohub Chicago. Education: Ph.D. in Biomedical Engineering from the University of Michigan (2003). Research Interests: Focus on advanced MRI techniques for structural and functional brain imaging, including diffusion-weighted imaging, dynamic imaging, and neuromuscular coupling studies. His work emphasizes multi-scale bioimaging to understand brain function across interventions, aging, and disease. Publications: Over 180 peer-reviewed articles in 2025-2024 highlight innovations in MRI technology and applications in neuroscience, including breakthroughs in laminar fMRI specificity, myelin development modeling, and Alzheimer’s biomarker studies. Recent work extends to clinical applications like aortic imaging automation and mixed reality training tools. Awards: AIMBE and ISMRM Fellowships (2017/2024), Abel Bliss Scholar (2014-), and over 9 patents in imaging techniques. Labs & Teams: Leads the Magnetic Resonance Functional Imaging Lab. Collaborates with interdisciplinary teams across engineering, medicine, and computational science to advance imaging technologies and their clinical translation.

Ruoqing ZhuView profile

Associate Professor

• Biostatistics
• Machine Learning
• Personalized Medicine
• +5 more

Ruoqing Zhu is an Associate Professor in the Department of Statistics at the University of Illinois at Urbana-Champaign, with a primary appointment in the College of Liberal Arts & Sciences. He also serves as an inaugural member of the Carle Illinois College of Medicine, a Faculty Fellow at the National Center for Supercomputing Applications, and an affiliated researcher with the Carl R. Woese Institute for Genomic Biology and the Center for Genomic Diagnostics. His roles include PhD Program Director and Advisory Board member of Prenosis Inc. Dr. Zhu holds a Ph.D. in Biostatistics from the University of North Carolina at Chapel Hill (2013), an MA in Statistics from Bowling Green State University (2008), and dual B.S. degrees in Mathematics and Financial Engineering from Nanjing University (2006, 2005). His postdoctoral training was at Yale University’s Department of Biostatistics (2013–2015). His research focuses on developing statistical methods for decision-making in personalized medicine and reinforcement learning, addressing challenges such as model interpretability, high-dimensional data, and distributional shifts. Key areas include uncertainty quantification, causal inference, and applications in bioinformatics, nutrition, and infectious diseases. He co-teaches courses at Carle Illinois, including Data Science Project and Foundations: Molecules to Populations , and contributes to interdisciplinary initiatives like the Personalized Nutrition Initiative. His recent work emphasizes trustworthy AI in healthcare, including sepsis prediction tools, metabolomic analysis, and biomarker discovery. He is actively involved in translational research, bridging computational methods with clinical and public health applications.

Bo WuView profile

Associate Professor

• Compilers and Programming Systems
• Heterogeneous Computing
• Machine Learning Systems
• +4 more

Bo Wu is an Associate Professor in the Department of Computer Science at Colorado School of Mines. His research focuses on compilers and programming systems, particularly program optimizations for heterogeneous computing and emerging architectures, with applications in machine learning and graph processing. He joined Mines in 2014 after earning a Ph.D. from The College of William and Mary and earlier degrees from Central South University in China. Education : B.S. in Computational Science and Technology (Central South University, 2005) M.S. in Computer Science (Central South University, 2008) Ph.D. in Computer Science (The College of William and Mary, 2014) Research Interests : Wu's work emphasizes enhancing data locality in heterogeneous systems, GPU scheduling, and optimizing applications for emerging architectures. His contributions include frameworks like GraphZero for efficient graph mining and FLEP for GPU preemption. Awards & Grants : NSF SPX Award (2018) NSF CAREER Award (2018) Supercomputing Best Paper Award (2015) Multiple NSF grants for GPU-related research Advising & Grants : Wu has led several NSF-funded projects and actively participates in conference program committees (e.g., PPoPP, SC, ICS). His research spans compiler optimizations, parallel computing, and high-performance systems. Labs & Teams : While specific labs aren’t named, his work involves collaborations on GPU-based systems, graph processing frameworks, and compiler toolchains.

Estela SuarezView profile

Professor

• high performance computing (HPC)
• heterogeneous HPC system architectures
• modular supercomputing architecture (MSA)
• +4 more

Prof. Dr. Estela Suarez is a Professor of High Performance Computing at the Institute for Computer Science, University of Bonn (W2 in the Jülich Model) and Joint Lead of the Division "Novel System Architecture Design" at the Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH. She also leads the Research Group "Next Generation Architectures and Prototypes" at JSC and serves as Spokesperson of Helmholtz Information Program 1, Topic 2. Currently on sabbatical during the 2024/2025 and 2025 academic years, she remains active in research leadership roles. 2010: PhD in Physics from University of Geneva, Switzerland 2004: Master in Physics, Specialization in Astrophysics, University Complutense of Madrid, Spain Professor Suarez specializes in high performance computing with particular expertise in heterogeneous HPC system architectures and modular supercomputing architecture (MSA). Her research spans hardware prototyping and evaluation, system software development, operational data analysis, and co-design methodologies. She has pioneered approaches to address hardware heterogeneity through system-wide orchestration of diverse computing resources, enabling more efficient scientific computing across multiple domains. Her work bridges theoretical computer architecture with practical implementation challenges in exascale computing environments, focusing on real-world applications that require specialized hardware configurations. Professor Suarez's publication record shows a clear evolution from foundational work on the DEEP project (2016) through the development of modular supercomputing concepts (2019-2021) to current applications across diverse scientific domains (2022-2024). Her recent publications demonstrate how modular architectures can be effectively applied to climate modeling, neuroscience simulations, quantum chemistry calculations, and other computationally intensive fields. This trend highlights her focus on practical implementation challenges and the growing importance of adaptable computing architectures in modern scientific research. 2023/2024 Lehrpreis der Universität Bonn: UniBonn teaching award Professor Suarez has secured significant research funding through major projects including NUMERIQS (Projects A05, B02, and Z02), European Processor Initiative (EPI), DEEP-SEA (Software for Exascale Architectures), IFCES2 (optimization of simulation algorithms for exascale supercomputers), and AIDAS (virtual laboratory between Forschungszentrum Jülich and CEA on AI and data analytics). While currently not accepting new students due to sabbatical, she has previously mentored graduate students in high performance computing techniques and has delivered numerous invited lectures at international conferences. Professor Suarez leads the "Next Generation Architectures and Prototypes" research group at JSC and serves as Joint Lead of the "Novel System Architecture Design" division. She chairs the Research and Innovation Advisory Group (RIAG) from EuroHPC Joint Undertaking since 2024. Her work involves close collaboration with international research teams on advancing supercomputing architectures, including contributions to the University of Bonn's new HPC system "Marvin" which ranks on both the TOP500 and GREEN500 lists.

Johan MeyersView profile

Professor

• Wind Energy
• Turbulent Flow Simulation
• Flow Control & Optimization
• +5 more

Johan Meyers is a full Professor at KU Leuven's Faculty of Engineering Science, Department of Mechanical Engineering, where he heads the Applied Mechanics and Energy conversion (TME) research unit. He serves as a contact person for TME and is an active member of the KIES – KU Leuven Institute for Energy and Society. His administrative roles include membership on the Council of the Faculty of Engineering Science, the Mechanical Engineering Department Council and Board, and chairing the HPC Steering Committee. Professor Meyers' research focuses on turbulent flow simulation and optimization, with particular emphasis on wind energy applications, atmospheric pollutant dispersion, and computational methods. His work spans Direct Numerical Simulation (DNS), Large-Eddy Simulation (LES), and model reduction techniques for applications in energy engineering. Current research categories include flow control & optimization, wind farm engineering, and atmospheric pollutant dispersion modeling, with specific applications in radioactive release scenarios and wind turbine system optimization. His recent publications demonstrate a strong trend toward wind energy applications, particularly in optimizing wind farm layouts and operations through advanced computational methods. The research shows significant emphasis on Large-Eddy Simulation techniques to study atmospheric boundary layer interactions with wind farms, with growing interest in hybrid wind-solar energy systems and the effects of surface temperature heterogeneity on flow patterns. His work increasingly integrates machine learning approaches to enhance computational efficiency in wind farm modeling. Professor Meyers actively supervises numerous PhD students including Bon, T., Janssens, N., Jamaer, S., and ALREWENY, A., among others. His research is supported by multiple ongoing projects through 2028, including 'Wind-farm co-design in the North-Sea basin given climate and market uncertainty' and 'Reconstruction of turbulence from partial observations,' primarily funded by research councils and industry partnerships. He leads the Turbulent Flow Simulation and Optimization (TFSO) research group, which develops efficient supercomputing simulation tools for turbulent flow applications in energy engineering. The group specializes in wind farm optimization, atmospheric pollutant dispersion modeling, and airborne wind energy systems, with a particular focus on LES studies of wind farm interactions with the atmospheric boundary layer.

• Cloud Computing
• Green Computing
• High-Performance Computing
• +7 more

Jordi Guitart Fernández is a Professor at the Department of Computer Architecture, Barcelona School of Informatics (FIB), Universitat Politècnica de Catalunya (UPC). He is also affiliated with the Barcelona Supercomputing Center (BSC-CNS), a leading national supercomputing facility. He leads the CROMAI research group, focusing on Computing Resources Orchestration and Management for AI. His work bridges high-performance computing, cloud systems, and artificial intelligence. Research Interests: Cloud Computing and Edge Computing Green and Energy-Efficient Computing Containerization and Virtualization for HPC Resource Orchestration and Management Autonomic and Self-Adaptive Systems Machine Learning Workflow Management AI-Driven System Optimization His recent publications reveal a strong focus on intelligent management of computing resources across cloud, edge, and HPC environments using machine learning and agent-based frameworks. He investigates performance, efficiency, and reliability in containerized AI and HPC workloads, particularly within Kubernetes and distributed infrastructures. His work increasingly integrates human-in-the-loop and trustworthiness aspects into AI systems. Scientific Awards: CLOUD Conference 2025 Best Paper Award VISIGRAPP 2025 Best Student Paper Award Premi Extraordinari de Doctorat 2025 - Àmbit d'Enginyeria de les TIC Test of Time Award Honorable Mention (e-Energy) Reconeixement als Mèrits Docents d'Especial Qualitat Top reviewers for Polytechnic University of Catalonia (Computer Science) - September 2017 Advising and Grants: He has advised doctoral students, including Peini Liu. He leads and participates in numerous competitive R+D+i projects, such as CROMAI and DALEST, funded by national and European programs like HORIZON 2020 and the Spanish State Research Plans. His work is supported by grants focused on knowledge generation and industrial leadership in computing technologies. Labs and Teams: He is the leader of the CROMAI - Computing Resources Orchestration and Management for AI research group at UPC. He also collaborates closely with the Barcelona Supercomputing Center (BSC-CNS), contributing to large-scale computing initiatives and strategic research agendas in Europe.