جست‌وجوی استاد و پژوهشگر

Phil Hasnipمشاهده پروفایل

پژوهشگر ارشد

• Research Software Engineering
• Materials Modelling
• Computational Materials Science
• +۳ مورد دیگر

Dr. Phil Hasnip is an EPSRC Research Software Engineering Fellow at the University of York's School of Physics, Engineering and Technology. His work focuses on developing robust software tools for materials modeling, particularly through the CASTEP program, and promoting Research Software Engineering (RSE) skills globally. He holds a PhD from the University of Cambridge and has extensive postdoctoral experience. Research Interests: Combines computational methods with materials science to solve complex problems in energy, electronics, and advanced manufacturing. Specializes in quantum mechanical simulations, high-performance computing (HPC), and software engineering for scientific research. Key projects include optimizing CASTEP for emerging architectures and enhancing exascale readiness through the Particles at Exascale (PAX) initiative. Grants & Awards: EPSRC Fellowship (2018–2023) supports software improvements for materials modeling and RSE advocacy. Collaborates with NVIDIA, ARM, and national facilities like Diamond Light Source. Teaching: Advanced computational methods, Linux, LaTeX, and scientific communication. Labs/Teams: Leads the Research Software Engineering Group at York, collaborating with interdisciplinary teams on software frameworks like BOUT++ for fluid simulations. Future Work: Expanding software accessibility for non-specialists and advancing exascale-ready computational tools.

Dylan Keonمشاهده پروفایل

استادیار

• Spatial Analysis
• Spatio-Temporal Data
• Climate Data
• +۵ مورد دیگر

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.

• High Performance Computing
• Heterogeneous Accelerators
• FPGAs
• +۷ مورد دیگر

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.

Thomas Zeiserمشاهده پروفایل

پژوهشگر

• High-Performance Computing
• Lattice Boltzmann Methods
• Computational Fluid Dynamics
• +۳ مورد دیگر

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.

Shantenu Jhaمشاهده پروفایل

استاد

• High-Performance Computing
• Distributed Computing
• Computational Science
• +۳ مورد دیگر

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.

Ramon Canal Corretgerمشاهده پروفایل

استاد

• Microarchitecture
• Processor Design
• Memory Design
• +۱۵ مورد دیگر

Ramon Canal Corretger is a Full Professor in the Department of Computer Architecture at the Faculty of Informatics of Barcelona (FIB), Universitat Politècnica de Catalunya (UPC). He previously served as Vice Dean of Postgraduate Studies at FIB and leads the VirtuOS (Virtualization and Operating Systems) research group. His work bridges computer architecture, hardware security, and system-level reliability. Doctorate from UPC, co-supervised at the University of Wisconsin-Madison Sabbaticals at Harvard University (2006–2007) and the University of Cyprus (2019–2020) Active leadership in EU-funded projects such as Vitamin-V (Horizon Europe) His research focuses on microarchitecture, processor and memory design, reliability under variability, and security at the hardware-software interface. He explores low-power multicore architectures, virtualization optimizations, and secure RISC-V-based systems. His recent work integrates AI for intrusion detection and privacy-preserving federated learning in fog computing environments. The most recent publications demonstrate a strong trend toward security, reliability, and trustworthy computing , particularly in RISC-V ecosystems and cloud/edge infrastructures. There is increasing emphasis on hardware-software co-design , attack detection via performance monitoring , and energy-efficient secure accelerators using emerging technologies like neuromorphic and photonic computing. Scientific Awards: HiPEAC Paper Awards (2017, 2010) IEEE Senior Member (2016) Fulbright Award (2006) IBM Faculty Award (2000) Best Student Paper at HPCA-6 (2000) Multiple teaching excellence recognitions from UPC and AQU Catalunya First Prize in Epson Foundation Rosina Ribalta Award (2001) He has advised several PhD students including Manish Rana, Zoran Jaksic, and Shrikanth Ganapathy, many of whom received honors such as the Intel Doctoral Student Programme recognition. His research is supported by competitive grants from the Spanish government, EU Horizon programs, and industry collaborations. He is actively involved in the design of secure, reliable, and efficient computing systems for future cloud and embedded applications. He leads the VirtuOS research group, which focuses on virtualization, operating systems, and hardware-software interface optimization. The group contributes to open-source RISC-V initiatives and participates in large-scale European R&D projects targeting trustworthy computing infrastructures.

Bin Renمشاهده پروفایل

استادیار

• Parallel Computing
• High-Performance Computing
• Compiler Techniques
• +۷ مورد دیگر

Bin Ren is an Assistant Professor in the Department of Computer Science at the College of William & Mary, where he has been a faculty member since Fall 2016. He holds a Ph.D. in Computer Science and Engineering from The Ohio State University (2014) and was a postdoctoral research associate at Pacific Northwest National Laboratory from 2014 to 2016. Research Interests: His work centers on high-performance computing, compiler techniques, and machine learning systems, with a focus on enabling real-time and energy-efficient deep neural network execution on mobile and edge devices. He explores compiler optimizations, DNN pruning, neural architecture search, and GPU memory management to improve system performance and efficiency. Publication Trends: His recent publications (2023–2025) reveal a strong focus on compiler-aware deep learning systems, mobile and edge AI, and performance optimization across heterogeneous platforms. Key themes include DNN acceleration, memory efficiency, real-time inference, and hardware-software co-design. His work frequently appears in top-tier venues such as ASPLOS, SC, CVPR, and PLDI. Scientific Awards: NSF CAREER Award, 2021 Best Paper Award, SC 2020 Best Student Paper Nomination, SC 2020 Jeffress Trust Award, 2020 ISLPED Design Contest First Place, 2020 Student Cluster Reproducibility Challenge Paper, SC 2019 Best Paper Award, CGO 2013 SIGPLAN Research Highlights, 2013 Advising and Grants: Bin Ren has advised numerous Ph.D. and master’s students, many of whom have co-authored influential papers. His research has been supported by competitive grants, including the NSF CAREER Award. He actively mentors students in areas of parallel computing, compiler design, and machine learning systems. He has also received funding from the Jeffress Trust Awards and other sources to support interdisciplinary research. Professional Service: He has served in leadership roles such as Program Co-Chair for PPoPP'25 and HIPS'21, Track Co-Chair for ICPP'24 and HiPC'24, and Artifact Evaluation Co-Chair for PPoPP'24 and ALENEX'25. He is a frequent reviewer for top journals and conferences including TPDS, TACO, NeurIPS, and SC. Teaching: He teaches courses such as CS304 (Computer Organization) and CS642 (Compiler Techniques for High Performance Computing), contributing to both undergraduate and graduate education in systems and programming. Lab and Team: His research group focuses on system-software co-design for efficient AI deployment. Collaborators include researchers from institutions like Pacific Northwest National Laboratory and The Ohio State University. His team works on real-world applications in healthcare, autonomous systems, and scientific computing.

Kinshuk Pandaمشاهده پروفایل

پژوهشگر

• Clean energy systems modeling at scale
• Uncertainty quantification
• Multidisciplinary design analysis and optimization
• +۳ مورد دیگر

Kinshuk Panda is a Researcher II in the Computational Sciences division at the National Renewable Energy Laboratory (NREL) . His work focuses on applying computational methods to clean energy systems, particularly in grid operations, wind farm design, and water treatment technologies under uncertainty. He utilizes high-performance computing (HPC) systems, including NREL’s Eagle supercomputer, to develop scalable software solutions. Education: Bachelor of Mechanical Engineering, Manipal Institute of Technology PhD in Mechanical Engineering, Rensselaer Polytechnic Institute Panda’s research spans uncertainty quantification , multi-fidelity optimization , and resilient grid operations . He contributes to projects like the Exascale Computing Project , enhancing modeling under extreme weather events, and develops tools for water treatment performance analysis using HPC. His recent publications highlight advancements in co-simulation frameworks , emergency asset positioning , and parameter sweep analyses , emphasizing scalability and efficiency. Collaborations include work with the IEEE Power and Energy Society and SIAM , focusing on energy systems and computational methods. Panda’s professional engagement includes memberships in IEEE and Society for Industrial and Applied Mathematics , supporting interdisciplinary approaches to energy and environmental challenges.

• Computer Science
• Artificial Intelligence
• Smart Cities
• +۵ مورد دیگر

Sebastian Ernst is a Lecturer at the Department of Applied Informatics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology in Kraków. His research focuses on Computer Science , Artificial Intelligence , and Smart Cities , with a strong emphasis on Geographic Information Systems (GIS) and Energy Efficiency in urban environments. University: AGH University of Science and Technology Faculty: Electrical Engineering, Automatics, Computer Science and Biomedical Engineering Department: Applied Informatics Position: Lecturer Contact: ernst@agh.edu.pl Office: C-2, 4th floor, room 424 Phone: +48 12 617 51 95 His recent publications highlight trends in Graph-Based Modeling , Smart Lighting Systems , and Robust Algorithm Design for urban infrastructure. He explores Pairwise Comparisons , GIS Applications , and Energy Conservation through computational methods.

Neil Drummondمشاهده پروفایل

مدرس ارشد

• Quantum Monte Carlo
• Two-dimensional materials
• Graphene
• +۵ مورد دیگر

Neil Drummond is a Senior Lecturer in the Physics Department at Lancaster University, where he conducts research in computational condensed matter physics. He is affiliated with both the Quantum Technology Centre and the Condensed Matter Theory group, focusing on advanced computational methods for studying quantum systems. Dr. Drummond's research interests center on the development and application of quantum Monte Carlo methods for calculating material properties from first principles. His work spans several key areas including two-dimensional materials (particularly graphene, silicene, and transition metal dichalcogenides), materials at high pressure, and electron(-hole) gases. His computational approach enables precise modeling of quantum effects in condensed matter systems that are challenging to study with conventional methods. Analysis of Dr. Drummond's recent publications reveals a strong focus on quantum Monte Carlo techniques applied to two-dimensional electron systems and novel materials. His work consistently addresses fundamental questions about electron correlation, phase transitions, and quasiparticle properties in low-dimensional systems. The research demonstrates increasing computational sophistication, with recent papers exploring GPU acceleration of quantum Monte Carlo codes and reproducibility of computational methods. Dr. Drummond currently supervises two postgraduate research students, James Doughty and Clio Johnson, guiding them in the development and application of quantum Monte Carlo methods. He serves as Principal Investigator for the PAX-HPC (Particles At eXascale On high Performance Computers) project, funded by the Engineering and Physical Sciences Research Council, which runs from December 2021 to March 2025. This project represents significant research funding supporting advanced computational physics research at Lancaster University. Within the Lancaster University research ecosystem, Dr. Drummond contributes to the Quantum Technology Centre, where his computational expertise complements experimental work on quantum materials and devices. His research forms part of the broader condensed matter physics efforts at the university, which spans both fundamental theoretical investigations and potential applications in next-generation electronic materials.

Juan Carlos Pichel Camposمشاهده پروفایل

استاد

• parallel and distributed computing
• Big Data technologies
• programming models
• +۷ مورد دیگر

Juan Carlos Pichel Campos is a Full Professor at the University of Santiago de Compostela (USC) specializing in high performance computing and language technologies. His research spans quantum computing, distributed systems, and Big Data technologies with a focus on practical applications in health informatics and computational physics. He received his B.Sc. and M.Sc. in Physics from University of Santiago de Compostela (Spain) and completed his Ph.D. there in 2006. He conducted postdoctoral research at University Carlos III de Madrid and University of Illinois at Urbana-Champaign, and worked as a researcher and project manager at Galicia Supercomputing Center. Professor Pichel's research interests include parallel and distributed computing, Big Data technologies, programming models, and software optimization techniques for emerging architectures. His recent work has focused on bridging quantum computing with classical high performance computing systems, developing efficient algorithms for processing massive biological datasets, and creating tools for health-related information retrieval and misinformation detection. His interdisciplinary approach combines techniques from computer science, physics, and biomedical informatics to solve complex computational problems. Analysis of his recent publications reveals a strong trend toward quantum computing applications and integration with classical HPC systems (accounting for approximately 40% of his recent work), followed by health informatics and natural language processing (about 30%), and bioinformatics and computational physics (about 30%). His research demonstrates a consistent focus on developing practical tools and frameworks that address real-world computational challenges across multiple domains. rePowerSiC: High-Efficiency High-Power Laser Beaming In-Space Systems Based On Sic (2024-2028) C3HS: Content curation for consumer health search - Search and misinformation detection (2023-2026) Big-eRisk: Early Prediction of Personal Risks on Massive Data (2021-2024) eRISK: Technologies for the early prediction of signs related with psychological disorders (2019-2021) BigNLP: Approaching High Performance Computing to Big Data Technologies: Natural Language Processing as Case Study (2015-2018) Professor Pichel has established strong collaborations with research groups across Europe and the United States, particularly in the fields of quantum computing and biomedical informatics. He is actively involved with CiTIUS (Centro singular de investigación en tecnoloxías da información e da comunicación de USC), contributing to its mission of advancing information and communication technologies through interdisciplinary research.

Enrico Tronciمشاهده پروفایل

استاد

• Model Checking
• Cyber-Physical Systems
• Software Engineering
• +۷ مورد دیگر

Enrico Tronci is a Full Professor in the Department of Computer Science at Università degli Studi di Roma La Sapienza , Italy. His research focuses on model checking, formal verification, and synthesis of cyber-physical systems, with applications to mission-critical and safety-critical domains such as space systems, smart grids, and healthcare. He leads the Model Checking Lab (MCLab) and has coordinated numerous national and international research projects funded by organizations including the European Community (EC), European Space Agency (ESA), and Italian Ministry of University and Research (MUR). Research Highlights : Automatic control software synthesis from closed-loop specifications Model checking algorithms for hybrid and stochastic systems Technology transfer in sectors like energy, transportation, and aerospace Teaching : Undergraduate: Software Engineering (Fall 2024) Graduate: Automatic Verification of Intelligent Systems (Fall 2024), Verification and Validation of Intelligent Systems (Spring 2025) Scientific Awards : Recipient of the IBM-Italia 1987 prize for best thesis in Artificial Intelligence Publications Trends : 2024: Scaling up model checking for cyber-physical systems via HPC 2023: Hormonal impact on behavior and fault-tolerant sensor deployments 2021-2022: In silico clinical trials, smart grid management, and scenario enumeration 2020: AI-guided diabetes patient modeling and forensic psychiatry applications Software Tools : QKS (Quantized Kontrol Synthesizer) NashMV (MAD systems verification) CMurphi (Hybrid systems model checker) FHP-Murphi (Probabilistic verification) BSP (Boolean symbolic programming)

Swarnendu Biswasمشاهده پروفایل

استادیار

• Programming Languages
• Compilers
• Runtime Systems
• +۷ مورد دیگر

Swarnendu Biswas is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. He teaches courses including Programming for Performance (CS 610), Analysis of Concurrent Programs (CS 636), and Compiler Design (CS 335), demonstrating his expertise across multiple areas of computer systems. His research interests center on Programming Languages, Compilers, Runtime Systems, and Parallel Software Systems. He leads the PROSPAR (Programming Languages and PARallel Systems) research group, which focuses on developing techniques to build efficient and correct parallel software through program analysis, compiler optimizations, and runtime systems. His recent publications reveal a strong trend in addressing fundamental challenges in parallel computing, with work spanning cache coherence, false sharing detection, data race analysis for GPUs, verification of neural networks, and thermal-aware management of heterogeneous systems. His research bridges theory and practice with significant contributions to both hardware and software aspects of parallel systems. His scientific achievements have been recognized through multiple prestigious awards: Google India Research Award 2021 Google Explore CSR 2022 Research Grant from Intel Corporation SERB Start-up Research Grant 2019 Google Cloud Platform Research Credits (2019, 2020) IITK Initiation Grant 2019 As an advisor, he mentors several PhD and MTech students working on cutting-edge research in parallel systems. His PROSPAR group has secured significant funding from industry and government sources, supporting innovative research in programming languages and parallel systems. The group actively collaborates with industry partners including Google and Intel, addressing real-world challenges in parallel computing. He leads the PROSPAR research group at IIT Kanpur, which brings together faculty, PhD students, and MTech researchers to tackle challenging problems at the intersection of programming languages, compilers, and parallel systems. The group maintains strong industry connections and focuses on practical solutions that can be deployed in real systems.

Arjun Guhaمشاهده پروفایل

دانشیار

• Programming Languages
• Program Synthesis
• Low-Resource Programming Languages
• +۶ مورد دیگر

Arjun Guha is an Associate Professor at Northeastern University's Khoury College of Computer Sciences, where he also serves as the Area Chair for Software. He conducts research in programming languages with a focus on program synthesis for low-resource programming languages and understanding how computer science education is impacted by large language models. His work spans multiple domains including WebAssembly, software-defined networking, and serverless computing. Guha's research interests center around programming language design, implementation, and application. He has made significant contributions to understanding JavaScript through formal semantics (LambdaJS), developing functional reactive programming for web applications (Flapjax), and creating tools for software-defined networks (NetKAT, Frenetic). His recent work focuses on leveraging large language models for code generation in specialized programming languages and understanding their impact on programming education. His publications reveal a strong trend toward applying AI and machine learning techniques to programming language problems, particularly in code generation and understanding. The research spans from foundational programming language theory to practical applications in education and software development tools, with increasing focus on the intersection of programming languages and large language models. Guha has received several prestigious awards including the OOPSLA Most Influential Paper Award in 2019 for his work on Flapjax, an ACM SIGPLAN Research Highlight for his work on NetKAT, and a Best Student Paper Award. His research has been recognized for its foundational contributions to programming language theory and practical impact on software development. OOPSLA Most Influential Paper Award (2019) for Flapjax ACM SIGPLAN Research Highlight for A Fast Compiler for NetKAT Best Student Paper Award for Flapjax paper Guha advises numerous PhD, MS, and undergraduate students, with several alumni now at leading tech companies and academic institutions. His research has been supported by the National Science Foundation, the Department of Energy, the Office of Naval Research, and industry partners including Google, JPMorgan Chase, MathWorks, Meta, Oracle, and Roblox. He leads the Programming Research Laboratory at Northeastern and is actively involved in major research collaborations like the BigCode Project. Guha is a member of the Programming Research Laboratory at Northeastern and leads several major research initiatives including the BigCode Project's evaluation working group. His lab develops practical software systems like MultiPL-E (a polyglot benchmark for Code LLMs) and WasmFX (bringing effect handlers to WebAssembly), with applications in education, software development, and high-performance computing.

Stefano Markidisمشاهده پروفایل

استاد

• High-Performance Computing
• Heterogeneous Systems
• Quantum Computing
• +۳ مورد دیگر

Stefano Markidis is a Professor of Computer Science specializing in high-performance computing systems at KTH Royal Institute of Technology in Sweden. He works in the Division of Computational Science and Technology, focusing on supercomputers, quantum computers, and computational methods for scientific simulations. His research spans multiple domains including plasma physics, computational fluid dynamics, and quantum computing. Markidis holds an MS degree from Politecnico di Torino and a PhD in Nuclear Engineering from the University of Illinois at Urbana-Champaign. Prior to joining KTH, he was a graduate research assistant at Los Alamos National Laboratory and Lawrence Berkeley National Laboratory, followed by a postdoc at KU Leuven. His academic journey reflects a strong foundation in both engineering and computational science. His primary research interests include High-Performance Computing , Heterogeneous Systems , and Quantum Computing . Markidis develops computational methods for plasma physics, particle-in-cell simulations, and fluid dynamics. His work bridges theoretical physics and practical computing, with applications in space physics, fusion energy, and materials science. He is particularly known for contributions to parallel computing, GPU acceleration, and the development of scalable simulation frameworks like Neko for computational fluid dynamics. His research increasingly integrates machine learning techniques with traditional numerical methods. Analysis of Markidis' recent publications reveals a strong focus on quantum-classical hybrid computing, advanced particle-in-cell methods, and high-fidelity computational fluid dynamics. His work demonstrates expertise in programming models for heterogeneous architectures including GPUs and quantum processors, with growing emphasis on AI-enhanced scientific computing. R&D100 award (2005) for the CartaBlanca project R&D100 award (2017) for the SHIELDS project Markidis teaches multiple courses at KTH including Applied GPU Programming, Quantum Computing for Computer Scientists, and High-performance Computing for Computational Scientists. He has supervised numerous degree projects across various specializations in computer science and electrical engineering. His research has been supported by various grants related to high-performance computing and quantum technologies, with applications spanning from space physics to medical treatments. Markidis leads research in computational science with a focus on developing frameworks like Neko for extreme-scale computational fluid dynamics. His team works on integrating traditional HPC methods with emerging technologies including quantum computing and AI, contributing to advancements in scientific simulation across multiple disciplines.