Professor Anders C. Hansen is a mathematician at the University of Cambridge and University of Oslo, leading the Applied Functional and Harmonic Analysis group. His work bridges functional analysis, artificial intelligence, and computational mathematics, focusing on the Solvability Complexity Index (SCI) hierarchy and stability issues in deep learning. He has held prestigious fellowships, including a Royal Society University Research Fellowship and Peterhouse Bye-Fellowship. Educated at the University of Cambridge, UC Berkeley, and the Norwegian University of Science and Technology Developed groundbreaking theories in compressed sensing and deep learning, revealing algorithmic instability paradoxes Organized workshops on computational mathematics and AI interpretability His research explores the SCI hierarchy , exposing computational barriers in AI, quantum mechanics, and inverse problems. Key projects include Smale’s 18th problem and analyzing neural network stability. His work has transformed understanding of compressed sensing, particularly in medical imaging. Recent scientific awards include the Whitehead Prize (2019), IMA Prize (2018), and Leverhulme Prize (2017). Collaborations span institutions like Caltech, MIT, and the University of Vienna. As an educator, he teaches NST Part IA Mathematical Methods , Part II Numerical Analysis , and a Part III course on Compressed Sensing . His group has mentored 17 PhD and postdoctoral researchers since 2012.
Michael O'Boyle is a Professor at the University of Edinburgh's School of Informatics, where he serves as Director of the ARM Research Centre of Excellence and the EPSRC Centre for Doctoral Training in Pervasive Parallelism. Holding an EPSRC Established Career Research Fellowship, he leads pioneering work in compiler technology for heterogeneous architectures, bridging theoretical advances with practical high-performance computing applications. Professor O'Boyle's research spans multiple cutting-edge areas including heterogeneous code discovery and optimization, neural machine translation for program synthesis, deep neural network system stack optimization, software-defined hardware, and compiler/architecture co-design. His approach integrates constraint analysis, program synthesis, and machine learning to address complex challenges in high-performance computing across diverse hardware platforms. His recent publications reveal a strong trend toward integrating machine learning with traditional compiler techniques, particularly in neural program synthesis, tensor optimization, and architecture-aware compilation. This work represents a paradigm shift in compiler design, moving from rule-based systems to learning-based approaches that can automatically adapt to diverse hardware targets. IEEE/ACM CGO 2025 Distinguished Paper Award for 'Tensorize: Fast Synthesis of Tensor Programs from Legacy Code' IEEE/ACM CGO 2024 Test of Time Award ACM GPCE 2023 Best Paper Award for 'C2TACO: Lifting Tensor Code to TACOM' ACM ASPLOS 2021 Distinguished Paper Award IEEE HPCA 2021 Best Paper Award for 'Prodigy: Improving the Memory Latency of Data-Indirect Irregular Workloads' Professor O'Boyle has successfully mentored numerous PhD students who have secured prominent positions in academia (including at Cambridge, Edinburgh, Leeds, and McGill) and industry (including Meta, NVIDIA, Qualcomm, Huawei, and Microsoft). His research is supported by significant funding from EPSRC, ARM, and European projects including Bonseyes and Transmuter, demonstrating strong international recognition and industry impact. He leads the influential Compiler and Architecture Design (CArD) Group at the University of Edinburgh and is a founder of the HiPEAC Network of Excellence, which has grown into a major European initiative connecting researchers and practitioners in high-performance and embedded computing.
Raphael Hauser is an Associate Professor in Numerical Mathematics at the University of Oxford's Mathematical Institute, Director of Graduate Studies - Teaching, and Tanaka Fellow in Applied Mathematics at Pembroke College. His affiliations include membership in the Data Science, Numerical Analysis, and Mathematical and Computational Finance research groups, as well as a fellowship at the Alan Turing Institute. Education: PhD in Operations Research, Cornell University, Ithaca, USA Dipl. Math. ETH, Swiss Federal Institute of Technology (ETH Zurich), Switzerland Research interests span data science, numerical optimisation, medical imaging, distributed computing, and applied probability/statistics. His work integrates mathematical rigor with practical applications, particularly in optimization algorithms, machine learning theory, and medical imaging technology. Publications focus on optimization theory, stochastic processes, medical imaging systems, and computational finance, with recurring themes in non-convex optimization guarantees, PCA variants, and X-ray tomography innovations. Awards: Oxford University Teaching Award (2007) SIAM Optimization Prize (2005) SIAM Student Paper Prize (2000) Advising includes 15+ DPhil students and 40+ MSc students, with projects in optimization, finance, imaging, and machine learning. Current postdocs and students are affiliated with the Alan Turing Institute and industrial partners like Siemens and Macquarie Group. He leads teams in the Mathematical Institute's research groups and collaborates with the Alan Turing Institute on large-scale data science initiatives.
Quoc Thong Le Gia is an Associate Professor in the School of Mathematics & Statistics at the University of New South Wales (UNSW), Sydney. He holds a PhD in Mathematics from Texas A&M University (2003), an MS in Mathematics from Texas A&M University (2000), and a BSc in Mathematics and Computer Science from UNSW (1998). His research focuses on Numerical Analysis , Approximation Theory , Partial Differential Equations , and Stochastic Processes , with particular expertise in problems on spherical domains. His work bridges theoretical mathematics with practical applications in computational science, data science, and machine learning. Le Gia's recent publications demonstrate a strong focus on numerical methods for PDEs on spheres, stochastic analysis, and machine learning applications. His work shows consistent progression from theoretical foundations to practical implementations, with increasing interdisciplinary applications in recent years. L. F. Guseman Prize in Mathematics, Texas A&M University (2003) As a dedicated academic mentor, Le Gia has supervised numerous PhD, Master's, and Honours students across computational mathematics and data science topics. He has secured significant research funding through ARC Discovery Projects including DP220101811 (2022-2024) and DP180100506 (2018-2020). Professionally, he serves as External Associate Editor for Frontiers in Applied Mathematics and Statistics , Secretary for ANZIAM's Computational Mathematics Group, and Co-chair of Mathematics of Computation and Optimisation (AustMS Special Interest Group).
Professor Paul Goulart is a full Professor of Engineering Science at the University of Oxford and Tutorial Fellow at St Edmund Hall, positions he has held since 2014. He leads research and teaching in robust optimization, control systems, and high-speed numerical methods, with applications spanning fluid flows, traffic networks, and economics. Education SB & MSc, Aeronautics and Astronautics – Massachusetts Institute of Technology (MIT) PhD, Control Engineering – University of Cambridge (Gates Scholar, 2007) Research Interests Professor Goulart’s work lies at the intersection of control engineering and optimization . His core expertise includes: Robust and high-speed convex optimization Model predictive control (MPC) and control barrier functions Neural-network-based control and system identification Optimization over traffic and economic networks Real-time and embedded optimization solvers These interests are reflected in prolific publication output and active supervision of doctoral researchers. Publications & Trends From 2020 to 2025 Professor Goulart has co-authored more than thirty papers. A dominant theme is the development of fast, reliable algorithms for conic optimization and robust control , often leveraging machine-learning techniques to enhance scalability and real-time performance. Recent works emphasize safety certificates, GPU-accelerated solvers, and neural-network controllers for uncertain systems. Awards & Honors Gates Cambridge Scholar (2003) Advising & Grants Professor Goulart actively seeks DPhil students in control engineering and optimization . He leads the Control Group within the Department of Engineering Science and has been involved in multiple industrially funded projects, although specific grant identifiers are not provided in the supplied text. Laboratory & Teams He is a member of the Control Group , Department of Engineering Science, University of Oxford, and serves as Secretary to the Governing Body of St Edmund Hall (Michaelmas Term 2024).
Dr. Susana Castro-Kemp is an Associate Professor in Psychology and Human Development at University College London's Institute of Education (IOE), where she serves as Director of the Centre for Inclusive Education (CIE) since September 2023. Previously, she was a Reader in Education at the University of Roehampton for eight years. Her work focuses on inclusive education policy and practice globally, with particular expertise in special educational needs and disabilities (SEND), early childhood intervention, and mental health in schools. The IOE has been ranked number one in the world for Education for several consecutive years, providing an ideal environment for her impactful research. Dr. Castro-Kemp holds a PhD in Psychology jointly awarded in 2012 by the University of Porto (Portugal) and the University of North Carolina at Chapel Hill (USA). Her doctoral research was fully funded by the Foundation for Science and Technology/European Commission and the Global Education and Development Studies scholarships. She is a Chartered Psychologist with the British Psychological Society, a Senior Fellow of Advance HE, and a Recognised Research Supervisor by the UK Council for Graduate Education. Dr. Castro-Kemp's research centers on policy regulating education, health, and welfare services for children, particularly those with support needs. She examines inclusive education practices from users' perspectives, policy models leading to effective inclusive pedagogy, and country-level policy impacts on outcomes for children with special educational needs. Her work moves away from diagnostic labels toward engagement and civic participation as educational outcomes. She also investigates inclusion and early intervention in low- and middle-income countries, mental health in schools, and quality of early childhood education. Her recent publications reveal several key trends in inclusive education research. There's growing emphasis on understanding educational experiences through the voices of children and families rather than diagnostic labels. Much of her work examines the implementation gap between inclusive education policy and classroom practice. She has conducted significant research on how children with special educational needs experienced the pandemic, revealing both challenges and unexpected "silver linings" for some groups. Her corpus analysis of Ofsted reports shows narrow focus in early childhood education inspections, while her work on SEND policy increasingly takes an international comparative approach. Dr. Castro-Kemp has received recognition for her impactful work, including: Best Digital Humanities Project for Community Engagement Prize (2023) from the National Institute of Humanities and Social Sciences of South Africa for her project "Optimising collaborations and reducing inequalities of Early Childhood Intervention in post-Covid-19 South Africa" Dr. Castro-Kemp has secured approximately £1.3 million in research funding from various sources including ESRC, British Academy/Leverhulme Trust, European Commission, and private sponsors. Her current major project is 'ScopeSEND' (2024-2026), funded by the Nuffield Foundation (£250,000), examining international SEND policies. She has led numerous projects including "Transdisciplinary provision for children with disabilities in the Global South" (£3,500), "Optimising collaborations in South Africa" (£47,200), and "Froebel meets Ofsted" (£19,185). As a supervisor, she mentors MA/MSc and PhD students in education policy, early childhood, and inclusive education, having been nominated for student choice awards for excellent feedback and outstanding research supervision. As Director of the UCL Centre for Inclusive Education since 2023, Dr. Castro-Kemp leads a team focused on advancing knowledge exchange and research in inclusion and special needs. The Centre works with schools, educators, parents, and early years settings to translate research into practice. Her international collaborations include work with the World Health Organization as a technical advisor on the use of the International Classification of Functioning, Disability and Health (ICF) system in educational contexts. She also serves on the SEN Policy Research Forum and has provided oral evidence to the House of Commons Education Select Committee.
Xiaocheng Shang is an Associate Professor in Mathematical Optimisation and Data Science at the University of Birmingham's School of Mathematics. His research focuses on numerical methods for stochastic differential equations, with applications in computational mathematics, statistics, physics, and data science. He is affiliated with the Optimisation and Numerical Analysis Group, Statistics and Data Science Group, and the Institute for Data and AI. Shang holds a PhD in Applied and Computational Mathematics from the University of Edinburgh (2016) and completed postdocs at the University of Edinburgh, Brown University, and ETH Zurich before joining Birmingham in 2019. His academic achievements include fellowships from The Alan Turing Institute, the LMS Emmy Noether Fellowship, and the EUniWell Leadership Fellowship. He has secured funding from EPSRC, the Royal Society, and the Isaac Newton Institute. Shang is actively involved in supervising PhD students and co-organizing research initiatives such as the Data Science and Computational Statistics Seminar. Research interests include structure-preserving integrators, Bayesian sampling techniques, and machine learning applications in dynamical systems. His work bridges numerical analysis, probability theory, and multiscale modeling in materials science. Recent projects involve neural networks for complex dynamical systems and numerical algorithms for deterministic/stochastic systems.
KHOO Siau Cheng is an Associate Professor in the Department of Computer Science at the National University of Singapore (NUS) School of Computing. He also serves as Co-Director of the NUS Business Analytics Centre, established in 2013 in collaboration with the Economic Development Board of Singapore and IBM. His academic journey began with a Ph.D. in Computer Science from Yale University in 1992. Professor Khoo's research focuses on improving software developer productivity through advanced programming language theories and software engineering techniques. His work spans multiple areas including static program analysis, dynamic program optimization, code analytics, and specification mining. He has applied his expertise to develop domain-specific languages for financial data analysis and has pioneered techniques for discovering dynamic program behaviors via data-mining approaches. Research Interests: Programming Languages and Software Engineering Code Analytics and Program Analysis Specification Mining and Bug Signature Discovery Static and Dynamic Program Analysis Program Transformation and Optimization Domain-Specific Languages Professor Khoo's publications demonstrate a consistent focus on improving software quality and developer productivity. His recent work emphasizes scalable approaches to refactoring detection, bug signature mining, and specification inference, showing an evolution from theoretical foundations to practical applications in software maintenance and quality assurance. As an educator and mentor, Professor Khoo has supervised numerous graduate students to completion of their M.Sc. and Ph.D. degrees. His research projects have provided valuable training opportunities for students while addressing important challenges in software development. He has also contributed significantly to academic administration, having served as Vice Dean (Undergraduate Studies) in the School of Computing from 2005 to 2011 and currently as Co-Director of the Master of Science (Business Analytics) Programme.
Thomas Rüde is Universitätsprofessor for Hydrogeology at RWTH Aachen University , Germany, where he leads the Hydrogeology group within the Faculty of Georesources and Materials Engineering. Holding the chair since 2005, he also serves as Managing Director of the Vereinigung Aachener Geowissenschaftler e.V. and has previously been Vice-President (2008-2014) and Executive Council member (2000-2008) of the International Mine Water Association (IMWA). Education 2004 – Privatdozent (Dr. rer. nat. habil.), University of Munich 1995 – Dr. rer. nat., University of Karlsruhe 1991 – Diplom-Geologe, University of Karlsruhe Research focus Professor Rüde’s work centres on understanding and modelling flow and reactive transport in complex aquifer systems . Key themes include: Contaminant hydrogeology – behaviour of geogenic arsenic and uranium in groundwater Groundwater protection and remediation – risk assessment and mitigation strategies Mine-water management – acid mine drainage, dewatering-well clogging, post-mining landscapes Tracer and hydraulic testing – field experiments to quantify subsurface heterogeneity Numerical modelling – high-performance simulation of multi-aquifer systems and karst His research spans Europe (Germany, Austria, Netherlands), Latin America (Mexico, Indonesia) and South-East Asia, frequently in close collaboration with local universities and industry partners. Publication trends Since 2010, Rüde has published extensively on geogenic contamination (As, U, F) in sedimentary and volcanic aquifers, mine-water impacts , and karst hydraulics . Recent work (2022-24) highlights advanced environmental tracers (gadolinium), transboundary groundwater issues, and the sustainable management of post-mining landscapes under climate change. Numerical models range from site-scale dewatering optimisation to catchment-scale coupled flow-transport simulations. Scientific awards & recognition Best Teaching Award 2010 – RWTH Aachen University Best Teaching Award 2012 – RWTH Aachen University Best Teaching Award 2014 – RWTH Aachen University Supervision & academic service Since 1998 he has taught hydrogeology through lectures, seminars, laboratory and field courses, and computer-based modelling labs. To date he has supervised: 13 PhD candidates 34 Diploma students 57 MSc students 63 BSc students He is Chairman of the Study Commission for the BSc programme in Georesources Management at RWTH Aachen, ensuring curriculum development and quality assurance. Laboratory & field infrastructure His group operates modern hydrochemical laboratories for trace-element analyses and maintains field stations for tracer experiments in Germany, Mexico and Indonesia. High-performance computing resources (in collaboration with the Jülich Supercomputing Centre) enable large-scale groundwater modelling and Monte-Carlo uncertainty assessments.
Masayuki Goto is a Professor in the Department of Industrial Systems Engineering, School of Creative Science and Engineering at Waseda University, Japan, where he has served since 2011. He earned his Doctor of Engineering from Waseda University and leads research integrating statistical science, machine learning, information theory and management engineering to solve business-analytics, marketing, AI ethics and industrial optimisation problems. Education: Doctor of Engineering, Waseda University Research Interests: His work spans data science, machine learning, business analytics, statistical learning theory, generative AI, deep neural networks, natural language processing, network analysis and information theory, with recent emphasis on trustworthy AI and synthetic data generation. Publication Trends: Over 2024-2025 his group has published extensively on deep learning for tabular data, vision-language models, recommender systems, causal inference and ethical AI, demonstrating a shift toward generative-AI-driven business analytics and interpretable models. Scientific Awards: Best Paper Award, CIE51 2024 Outstanding Paper Award, APIEMS 2023 Best Paper Award, APIEMS 2022 Best Paper Award, 20th ANQ Congress 2022 2022 PC Conference Best Paper Award Best Paper Award, JASMIN 2021 Best Paper Award, 19th ANQ Congress 2021 Encouragement Award, AAMSA 2021 IDR User Forum 2020 Enterprise & DBSJ Special Awards Best Paper Award, APIEMS 2019 Best Paper Award, ANQ Congress 2018 World CIST'18 Best Paper Award Best Paper Award, ANQ Congress 2017 JSPS Grant Review Commendation 2016 JIMA Distinguished Research Award 2015 Best Paper Award, Journal of JIMA 2015 IPSJ National Convention Best Paper Awards (2015 & 2012) Advising & Grants: He has mentored a large cohort of graduate students evidenced by co-authorship on over 100 recent papers. He has served as PI on numerous JSPS KAKENHI grants and industry projects focused on data-driven management, AI marketing and ethical AI frameworks. Labs & Teams: He heads the Goto Laboratory within the Waseda Institute for Advanced Study, leading interdisciplinary projects on business AI, data-ethics education and industrial optimisation.
Prof. David Ham is a Professor of Computational Mathematics at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His research focuses on high-level abstractions for scientific computation, particularly in geophysical fluids and numerical software. He leads the Firedrake project and co-developed the dolfin-adjoint framework, which received the 2015 Wilkinson Prize for Numerical Software. Ham holds a BSc (Mathematics) and LLB from The Australian National University, and a PhD from TU Delft. His career includes roles as a NERC Independent Research Fellow and Grantham Research Fellow at Imperial College. He is affiliated with the Grantham Institute, Mathematics of Planet Earth, and Software Performance Optimisation groups. His research spans computational science, including finite element methods, adjoint-based inversion, and parallel computing. Recent work emphasizes differentiable programming integration with machine learning and geophysical modeling. Ham has contributed to numerous grants and projects, including EPSRC and NERC-funded initiatives. He leads development of software tools like Firedrake and Thetis, advancing computational methods for oceanography and geodynamics.
Professor Will Wenmiao Shu is the Hay Chair in Biomedical Engineering and Director of Research at the Department of Biomedical Engineering, University of Strathclyde. He has held positions at prestigious institutions including University of Cambridge (PhD), Heriot-Watt University (Lecturer/Reader), and Stanford University (Visiting Position). His work focuses on 3D biofabrication , bioprinting , and regenerative medicine . PhD in Electrical Engineering & Nanoscience, University of Cambridge His research pioneered the first bioprinting of human embryonic stem cells and induced pluripotent stem cells, enabling animal-free drug testing and 3D printed organs . Current projects include 3D Bioprinting of Vascularised Hepatic Cancer Models and BIOME: Bioplastic Injection-moulding Optimisation . Recent publications address nanometrology and soft tissue resection simulations , reflecting his interdisciplinary approach. Awards include dual Best Presentation Prizes in 2024 for work on microvascular networks and animal-free research. Editorial Board Member, IOP Biofabrication Journal Board Director, International Society for Biofabrication (ISBF) Founding Member (emeritus), Royal Society of Edinburgh’s Young Academy
Matteo Brunelli is Associate Professor of “Mathematical Methods of Economics and Actuarial and Financial Sciences” at the University of Trento , Department of Industrial Engineering, and Adjunct Professor (docent) at Lappeenranta University of Technology , Finland. He is nationally habilitated as Full Professor in Italy and has held long-term visiting positions at Berkeley, Turku, Auckland, JAIST and Binghamton. Education: Ph.D. (Doctor of Science) in Information Technologies, Åbo Akademi University, Finland, 2011 – graded Eximia cum laude approbatur M.Sc. in Economics, University of Trento, 2007 – grade 110/110 cum laude B.Sc. in Economics, University of Trento, 2005 Research focus: Brunelli’s work sits at the intersection of multi-criteria decision analysis , operations research and computational optimisation . He develops axiomatic foundations and algorithms for pairwise comparison matrices , consistency indices , the best-worst method and fuzzy preference relations , and applies them to energy planning, sustainable inventory, maintenance scheduling, 3-D printer selection, and blockchain governance. His 2023-2025 articles reveal intensified interest in uncertainty modelling (Dempster-Shafer theory), bi-objective optimisation of inventory and maintenance, and group decision protocols that integrate probabilistic or active-learning components, demonstrating both methodological depth and practical relevance. Scientific awards & grants: Academy of Finland Postdoctoral Researcher grant (€254 670, 2014-2017) Claudio Dematté Research Grant (€19 000, 2008) Teacher of the Year Award, Aalto University (2013 – both Spring & Autumn semesters) Bernard Roy Award 2021 for outstanding contribution to Multiple Criteria Decision Aiding (under-40 category) Supervision & funding: While specific doctoral students are not listed, Brunelli currently supervises graduate theses at Trento and has continuously held competitive national grants. His Academy of Finland project “Consistency of valued preference relations for decision analytics methods” financed three years of full-time research and international collaboration. Editorial & community roles: He serves on the editorial boards of International Journal of General Systems and Mathematical and Computational Applications , and acts as area editor for Journal of Multi-Criteria Decision Analysis , positioning him among the key gatekeepers of the MCDA community.
Professor Michael Pollitt is a faculty member at the University of Cambridge , holding the title of Professor of Business Economics . He serves as Director of the MPhil in Technology Policy Programme and is affiliated with the Centre for Business Research (CBR) . Additionally, he holds advisory roles on Ofwat's Water 2020 board and the National Infrastructure Commission's Regulatory Challenge Panel , and is a Joint Academic Director at CERRE Centre on Regulation in Europe . His research focuses on industrial economics , privatisation and regulation of utilities (particularly electricity), productive efficiency measurement , and the intersection of Christian ethics with business practices . He has advised numerous governmental and international bodies, including the UK Competition Commission , World Bank , and European Commission , and has consulted for major energy firms like National Grid and Eneco . His publications address critical energy and regulatory issues, with recent work exploring electricity market design , carbon pricing , net zero transitions , and infrastructure productivity . He contributes to academic governance as a member of editorial boards for journals such as The Energy Journal and Utilities Policy , and leads the Association of Christian Economists, UK .
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.
