KC Sivaramakrishnan is an Assistant Professor at Indian Institute of Technology Madras and concurrently serves as CTO of Tarides. He works at the intersection of programming languages and systems, focusing on concurrency, distributed systems, and OCaml runtime development. Primary Affiliation: Indian Institute of Technology Madras Co-founder: Tarides His research explores: Concurrency and parallelism in OCaml Effect handlers for modular programming Mergeable Replicated Data Types for distributed systems Weak memory models and consistency guarantees Lock-free algorithms and safe multicore programming Recent publications focus on OCaml 5.0's concurrency features, effect handler integration, and verified CRDT implementations. He contributes to compiler design, runtime optimization, and testing frameworks for multicore systems. Service roles include committee memberships in SPLASH, ICFP, OCaml, and PROPL conferences. He actively bridges functional programming with systems research through practical implementations.
Professor Willy Zwaenepoel is a distinguished academic and Dean of the Faculty of Engineering at the University of Sydney. He holds Fellowships from ACM, IEEE, and ATSE. His research focuses on distributed systems, operating systems, and experimental computer science. Previously, he spent two decades at Rice University and nine years as Dean of EPFL's School of Computer and Communication Sciences before joining Sydney in 2018. Education: BS/MS, Ghent University (1979) MS/PhD, Stanford University (1980/1984) Research Interests: His work emphasizes distributed systems and operating systems, with contributions to key-value stores, transactional systems, and large-scale graph processing. Recent projects include optimizing geo-replicated systems and improving datacenter scheduling efficiency. Articles Trends: Recent publications address OS scheduling (Nest), distributed graph mining (Tesseract), and transactional systems' performance limits. He explores hardware-software co-design for multicore systems and energy-efficient data centers. Awards: Fellow of ACM (2021) Fellow of IEEE (2020) Fellow of ATSE (2019) Grants & Advising: Active grants include adaptive key-value store research (2021) and large-graph processing systems (2018). He advises PhD students and postdocs on distributed systems and storage challenges. Labs/Teams: Leads the Sydney systems research group focusing on scalable distributed systems and cloud infrastructure.
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.
Steven Bell is an Associate Teaching Professor in the Department of Electrical and Computer Engineering at Tufts University's School of Engineering. He has held roles including Assistant Teaching Professor (2019–2024) and Lecturer (2018–2019). His research focuses on engineering education, embedded systems, camera systems, and computational photography. He actively develops educational tools like VHDLweb , an online platform for learning VHDL, and advocates for affordable lab solutions using low-cost FPGAs. Bell has received the Teaching with Technology Awards Honorable Mention (2022) and the Tufts Teaching with Technology Award (2019) for his contributions to pedagogy. He teaches courses such as Embedded Systems, Intro to Computing in Engineering, and Special Topics in Advanced Embedded Systems. His professional activities include roles on the SoE Academic Standing Committee and First-Year Experience planning subcommittee. He has also secured grants, including the Affordable Course Materials grant (2022–2023). Bell's GitHub repositories showcase projects like VHDLweb , UPduino , and FPGA tools for educational and research use. His work emphasizes bridging theoretical concepts with hands-on, accessible engineering practices.
Tobias Grosser is an Associate Professor in the Department of Computer Science and Technology at the University of Cambridge. His research focuses on rethinking performance programming by bridging the gap between developers and compilers. He holds a PhD from École Normale Supérieure Paris and has held positions including Reader at the University of Edinburgh and Ambizione Fellow at ETH Zurich. His research interests span compilers, programming language design, static/dynamic analysis, and the integration of machine learning into compiler development. He emphasizes making compilation more modular, automatic, and trustworthy, with applications in quantum computing, climate science, and open-source hardware. Key projects include xDSL (a Python-native compiler framework), LoopOpt, and the Open Earth Compiler for climate simulations. Recent publications highlight advancements in multi-level intermediate representations (IR), formal verification in MLIR, and performance optimization for GPUs and FPGAs. His work often addresses barriers between programmers and compilers, aiming for intuitive collaboration between developers and automated systems. Tobias mentors a dynamic team of PhD students, postdocs, and researchers, including notable contributors like Siddharth Bhat, Arjun Pitchanathan, and Mathieu Fehr. His lab focuses on compiler toolchains for domain-specific hardware accelerators, quantum computing ecosystems, and verified compilation techniques.
Rob H. Bisseling is a Full Professor in Scientific Computing at Utrecht University's Mathematical Institute and a visiting professor at ENS de Lyon's LIP laboratory (March–May 2024). He holds a BSc/MSc in Mathematics (cum laude) from the Catholic University of Nijmegen and a PhD in Theoretical Chemistry from the Hebrew University of Jerusalem. His research focuses on parallel algorithms, sparse matrix/tensor computations, and hypergraph partitioning, with applications in high-performance computing and numerical methods. He has authored a seminal textbook on parallel scientific computing and contributes to pedagogical resources like video lectures. During his visit to LIP, Bisseling collaborates with the ROMA team under Bora Uçar to advance parallel algorithms for large-scale irregular applications. His work includes developing tools like PMondriaan for sparse matrix partitioning and promoting knowledge transfer through lectures on BSP programming. He engages with researchers, PhD students, and engineers at LIP, extending collaborations to Lyon's Institut Camille Jordan and LabPhys for tomographic reconstruction and statistical physics modeling. His academic career includes roles at Royal Dutch Shell and as Utrecht University's Director of Education (2012–2015). He advocates interdisciplinary approaches, bridging computational methods with applied sciences and engineering challenges.
Manuel Alejandro Pajuelo González is a researcher at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture and the School of Computer Science. His research focuses on Performance measurement Operating systems Virtualization Thread assignment in multithreaded processors Recent publications show strong trends in RISC-V architectures, cybersecurity, and performance optimization. Key themes include Hardware virtualization Intrusion detection frameworks Statistical thread assignment approaches Spin-lock overhead analysis Scientific awards include BDigital Global Congress 15ª Edició Computación de Altas Prestaciones VI HiPEAC Paper Award He participated in multiple competitive R&D projects, including the DRAC project focused on RISC-V accelerators for next-generation computing.
Resit Sendag is a Professor and Director of Graduate Studies in the Department of Electrical, Computer and Biomedical Engineering at the University of Rhode Island. He serves as Director of both the URI Computer Architecture Laboratory and the URI Generative AI Development Group, leading cutting-edge research in computer architecture and high-performance computing. His academic credentials include: Ph.D. in Computer Engineering from the University of Minnesota (2003) B.Sc. in Electrical Engineering from Hacettepe University, Ankara (1994) Professor Sendag specializes in computer architecture with research interests spanning processor design, memory systems, parallel computing, and hardware acceleration. His work focuses on improving computational performance through innovative techniques in cache management, prefetching, branch prediction, and specialized hardware implementations using FPGAs and GPUs. Recent research has expanded into applying these architectural principles to solve complex optimization problems like vehicle routing. His publication record demonstrates a consistent evolution from fundamental computer architecture research toward practical applications of architectural techniques. The most recent work shows strong emphasis on implementing genetic algorithms for vehicle routing problems using specialized hardware platforms (FPGAs and GPUs), while maintaining his foundational research on memory access optimization through sophisticated prefetching techniques. Professor Sendag has secured research funding from the Office of Naval Research through collaborative projects with the University of Connecticut focused on advanced manufacturing, shipbuilding processes, and material tracking systems. He actively mentors graduate students, with current advisees working on challenging computer architecture projects. His former students have achieved notable success at leading technology institutions including ETH-Zurich, Intel, NVIDIA, AMD, and various research laboratories. Professor Sendag leads key research initiatives including the URI Computer Architecture Laboratory, the Generative AI Development Group, and the PatternFinder project (an NSF-funded open-source tool for program behavior analysis).
Dai Liu is a researcher at the Chair of Computer Architecture and Parallel Systems at the Technical University of Munich . His work focuses on Dataset Distillation , Deep Learning on Heterogeneous Systems , Network Compression , and Hardware Acceleration for AI , with a particular emphasis on AI optimization for edge devices and specialized hardware like Cerebras. Education : Master of Informatics in Computational Science and Engineering (Technical University of Munich), Bachelor of Engineering in Electrical and Electronic Engineering (Tel Aviv University) His teaching activities include contributions to courses on Parallel Programming Systems , Advanced Computer Architecture , and Efficient Programming of Multicore Processors and Supercomputers .
Panagiotis E. Hadjidoukas is an Adjunct Assistant Professor at the Department of Computer Science, University of Ioannina, Greece. He currently holds a Visiting Scientist position at IBM Research Zurich. His work bridges parallel computing with applications in medical physics, computational biology, and high-performance numerical optimization. His research focuses on parallel and distributed computing, runtime support for parallel programming models, and thread libraries. He has contributed to hybrid programming frameworks and task-parallelism deployment in multicore environments. The articles in his portfolio highlight a strong trend in Monte Carlo simulations applied to radiation biology, parallelization techniques for hierarchical data clustering, and performance optimization in multicore systems. His work often integrates OpenMP and hybrid MPI/OpenMP models for distributed computing. He has developed notable software projects like PSthreads (a runtime library for lightweight threads) and UthLib (a portable non-preemptive user-level threads package).
José García Rodríguez is a full professor at the University of Alicante, affiliated with the Higher Polytechnic School and the Department of Informatics and Computing Technology. He earned a BS in Computer Engineering (1994) and PhD in Artificial Vision (2009) from the same institution. Current roles: Director of the PhD Program in Computer Science Vice Dean of International Relations Editor-in-Chief of the International Journal of Computer Vision and Image Processing Associate Editor of Expert Systems Journal Senior member of IEEE, INNS, and Eucog networks His research spans computer vision, deep learning, robotics, and ambient intelligence, with applications in improving autonomy for individuals with acquired brain damage. He has led 20+ regional/national/international projects, including three consecutive national projects funded by Spain's Ministry of Economy and Competitiveness (DPI2013-40534-R, TIN2016-76515-R, PID2019-104818RB-I00). International collaborations include research stays at the University of Westminster, Queen Mary University of London, and Griffith University (Australia). He has organized special sessions at WCCI conferences (2010–2018) and special issues in JCR journals like Neural Processing Letters and Complexity. Professional memberships include European networks Eucog, HIPEAC, ELLIS, and COST Action IC1307.
Dr. hab. Beata Bylina is a Professor at the Faculty of Mathematics, Physics and Computer Science , Maria Curie-Skłodowska University (UMCS) , affiliated with the Department of Information Systems Software . She specializes in high-performance computing, numerical methods, and energy-efficient parallel programming. ORCID ID : 0000-0002-1327-9747 Contact : beata.bylina@umcs.pl / beata.bylina@mail.umcs.pl Office : Room D-522 (Institute of Computer Science) or Room B-2, Akademicka Street 9, Lublin Consultations : Wednesdays 10:00–12:00 (in-person or remote via Microsoft Teams) Her research focuses on: Parallelization and vectorization techniques for multicore architectures Energy consumption optimization in numerical algorithms Matrix factorization methods (WZ, LU, QR) for CPU/GPU hybrid systems Markov chain modeling for network performance analysis Compiler optimization impact on performance and energy metrics Recent publication trends show emphasis on: Time-energy correlations in multithreaded algorithms OpenMP/OpenACC for hybrid CPU-GPU implementations Efficient sparse matrix storage schemes for GPUs Impact of hardware-specific optimizations (Xeon Phi, frequency scaling) Comparative studies of parallelization strategies
Dr. Jarosław Marcin Bylina serves as a Professor at Maria Curie-Skłodowska University in Lublin, Poland, where he leads the Department of Cybersecurity and Computational Linguistics within the Faculty of Mathematics, Physics and Computer Science. His academic profile shows active engagement in research and teaching activities with current office hours available for student consultations. His research spans critical areas in modern computing, with primary focus on High Performance Computing and Parallel Computing architectures. Specialized interests include optimization of multicore systems, Markov chain applications, sparse matrix algorithms, and energy efficiency in computational systems. His work frequently addresses challenges in Intel Xeon Phi architectures, task-based parallelism using Intel Cilk Plus, and performance analysis of distributed computing environments. Based on bibliometric analysis of his 40 publications, his research demonstrates significant impact in information technology fields with h-index values of 8 (Google Scholar), 7 (Scopus), and 4 (Web of Science). His work contributes to the broader discipline of Information and Communication Technology with particular emphasis on practical implementations in high-performance computing environments. Professional recognition includes a Total CiteScore of 20.8, Total SNIP of 7.874, and a ministerial score of 1,031 in the Polish academic evaluation system. His research output demonstrates consistent contribution to computational science with documented expertise across multiple technical domains. Available for academic consultations at Akademicka Street 9, room D-521, Lublin, with remote consultation options available through university Teams platform (code: jpq0hw9).
Bartłomiej Kotyra serves as an Assistant Professor in the Department of Information Systems Software at the Institute of Computer Science and Mathematics, Faculty of Mathematics, Physics and Computer Science, Maria Curie-Skłodowska University in Lublin, Poland. His research spans interdisciplinary domains with emphasis on: Parallel algorithms implementation using OpenMP and GPU architectures Hydrological modeling including watershed delineation and flow accumulation analysis Financial market applications focusing on forex indicators and technical analysis Dr. Kotyra has published 5 scholarly articles with a Scopus/Web of Science h-index of 3. His computational research demonstrates expertise in applying high-performance computing techniques to geospatial problems and financial market analysis, particularly in longest flow path calculations and multicore processor optimizations. His scholarly impact is reflected in a total impact factor of 14.768, SNIP of 4.92, and CiteScore of 26.3, accumulating 485 points in the Polish ministerial scoring system. Dr. Kotyra maintains regular consultation hours on Mondays from 12:15-14:15 and can be reached at room 306 (phone: 81 537 29 35) or via email.
Ioannis Venetis is an Assistant Professor at the University of Piraeus, School of Information and Communication Technologies, Department of Informatics, specializing in Operating Systems and Parallel Computing. He earned his PhD from the Department of Computer Engineering and Informatics at the University of Patras. His research spans programming models for parallel systems, scheduling optimization, and applications in computational neuroscience and seismology. Research Interests Operating Systems Parallel Computing Scheduling Algorithms High-Performance Computing Computational Neuroscience Seismology Projects Participation in European and national research programs Development of Gisola (GPU-accelerated seismic inversion tool) His teaching portfolio includes courses like Operating Systems, Parallel Processing, and Symbolic Programming. Articles highlight expertise in GPU acceleration, tridiagonal solvers, sensor networks, and many-core architectures. Notable contributions include work on Chimera states in neuronal dynamics and hierarchical workload scheduling frameworks.
