Dwight Makaroff is a Professor in the Department of Computer Science at the University of Saskatchewan . He leads the DISCUS research group , focusing on distributed systems, networking, and performance analysis. Makaroff holds a Ph.D. from the University of British Columbia (1998), an M.Sc. (1988), and a B.Comm. (1985) from the University of Saskatchewan. Research Interests: Distributed Data Processing & Hadoop Network Support for Multiplayer Games Information-Centric Networking Energy Efficiency in Mobile Devices Multicore Architectures Wireless Network Security Sensor Networks & Data Aggregation Teaching: Courses include Operating Systems Principles , Topics in Parallel & Distributed Systems , and advanced systems courses. He coordinated the ACM ICPC programming contest teams for over a decade. Committees: Graduate Committee Chair (2013-2015) University Council Member (2006-2014) Program Committee roles at IEEE/ACM conferences (IPCCC, CASCON, etc.) Recent Research Highlights: IoT security via blockchain Wearable device communication challenges Caching strategies for information-centric networks
Donald Yeung is a Professor and Associate Chair for Undergraduate Education in the Department of Electrical and Computer Engineering at the University of Maryland , with an additional appointment as Affiliate Professor in the Department of Computer Science . His research focuses on Computer Architecture , particularly in memory systems , 3D integration , energy-efficient processors , and parallel processing . He leads projects like Monolithic 3D Integration of CPU and Main Memory and Approximate Computing . Recent work emphasizes ReRAM-based memory architectures , extreme-scale processor design , and micro-fluidic cooling solutions for 3D CPUs. His teaching includes courses like ENE 646: Computer Architecture and ENE 150: Intermediate Programming Concepts . Key achievements include the Best Paper Award at MULTIPROG-2017 and contributions to IEEE Micro and ACM Transactions . His research spans cache optimization , reuse distance analysis , and directory coherence protocols . Current grants include funding for heterogeneous microprocessor parallelism and low-power system design . Advises graduate students Yinuo Wang and Hung-Yu Yeh, and collaborates with teams like the UMIACS Technical Report Group . His lab focuses on memory-centric computing and scalable multicore systems .
Miquel Moreto Planas is a Senior Lecturer in the Department of Computer Architecture at the Barcelona School of Informatics, Universitat Politècnica de Catalunya (UPC). He is also affiliated with the Barcelona Supercomputing Center (BSC-CNS), a leading institution in high-performance computing. His academic profile is deeply rooted in computer architecture and high-performance computing, with a strong emphasis on practical and theoretical advancements in multicore systems, memory management, and hardware acceleration. His research interests span a wide range of topics including computer architecture, high-performance computing, multicore and manycore systems, cache and memory management, hardware acceleration for genomics and AI, RISC-V processor design, processing-in-memory, interconnection networks, and real-time systems. These interests are reflected in his extensive publication record and collaborative projects. The most recent articles highlight a significant trend toward interdisciplinary research, particularly the application of advanced computer architecture techniques to bioinformatics and healthcare. Key themes include the acceleration of genomic sequence alignment using novel hardware such as processing-in-memory, the development of benchmarks for ARM-based HPC systems in genomics, and the creation of AI-based 3D decision support tools for neurosurgical applications. His work also continues to advance core computer architecture topics like cache management, power-aware resource allocation in heterogeneous systems, and the design of secure, post-quantum cryptographic hardware based on RISC-V. Fulbright Award 2011 HiPEAC Paper Award HiPEAC Paper Award 2024 HiPEAC Paper Award Moreto has been a principal investigator or key contributor to multiple competitive R&D+i projects, such as the STRATUM project for neurosurgical tools, REDIOH for open hardware, and the Laboratorio Zettaescala de Barcelona. He has advised several doctoral students, including López, G., Kostalampros, I., and Haghi, A., and is a core member of the CAP (High Performance Computing) research group at UPC. His work is characterized by strong collaborations with leading researchers like Mateo Valero, Eduard Ayguadé, and Jesús Labarta, often bridging the gap between UPC and BSC-CNS. His laboratory and team affiliations are centered around the CAP group and the Barcelona Supercomputing Center, where he contributes to cutting-edge research in high-performance and embedded computer architectures. His recent work on the BIMSA accelerator and the STRATUM project demonstrates a clear future direction toward applying high-performance computing solutions to critical problems in genomics and medicine.
Alan D. Fekete is a Professor at the University of Sydney's Department of Computer Science, specializing in database systems, distributed data management, and consistency models. His work spans transaction processing, cloud computing, and query optimization, with recent focus on enhancing database concurrency and serializable execution. Key Research Areas: Database Concurrency & Transaction Isolation Multicore Scalability & Distributed Systems Cloud Data Consistency & Replication Query Optimization & NoSQL Performance Recent publications (2023-2025) explore transactional frameworks for analytical interfaces, DB-OS co-design for data ingestion, and mixed isolation levels for serializable execution. Earlier works (2018-2014) address scalable lock managers, coordination avoidance in databases, and consistency properties in cloud storage. He has contributed to educational initiatives, including a data-centric computing curriculum (2021) and teaching threading concepts (2008). Collaborations include co-authors like Nancy Lynch, Uwe Röhm, and Joseph Hellerstein.
Keiji Kimura is a Professor in the Department of Computer Science and Engineering at Waseda University's Faculty of Science and Engineering, School of Fundamental Science and Engineering. He earned his Doctor of Engineering from Waseda University and has held academic positions at the university since 1999, progressing from Research Associate to Assistant Professor (2004-2005), Associate Professor (2005-2012), and Professor (2012-present). He is affiliated with multiple professional organizations including ACM, IEEE Computer Society, The Institute of Electronics, Information and Communication Engineers, and Information Processing Society of Japan. His research focuses on computer architecture, particularly parallel computing systems and compiler technology. Kimura has made significant contributions to the development of the OSCAR (Optimally Scheduled Advanced Multiprocessor) automatic parallelizing compiler framework. His work spans multiple areas including multicore processor architecture, power reduction techniques for embedded systems, non-volatile memory systems, and parallelization methods for heterogeneous architectures. His research interests specifically include Multiprocessor Architecture and Parallelizing Compiler development, with applications in real-time systems and energy-efficient computing. Analysis of his recent publications reveals a strong focus on practical implementations of parallel computing technologies across diverse hardware platforms including RISC-V, ARM, and heterogeneous multicore systems. His work demonstrates a consistent trajectory from theoretical compiler development toward practical applications in embedded systems, security, and non-volatile memory technologies. The publications show increasing emphasis on RISC-V architecture, persistent memory programming, and power-efficient computing solutions. MEXT Award for Science and Technology (Research category), 2014.04 Ministry of Education, Culture, Sports, Science and Technology (MEXT) Kimura has served on numerous prestigious conference program committees including PACT, IPDPS, HPCA, and LCPC. His research has been supported through collaborations with major technology companies and government initiatives such as the METI/NEDO project entitled "Multicore Technology for Realtime Consumer Electronics." His work with the OSCAR compiler framework has demonstrated significant performance improvements and power reductions in real-world applications. He leads research in the APAL laboratory (http://www.apal.cs.waseda.ac.jp/) at Waseda University, focusing on advanced parallel processing technologies. His team works on compiler-directed approaches to solve challenges in heterogeneous multicore architectures, with particular emphasis on making parallel programming more accessible while optimizing for both performance and power efficiency. Current research directions include RISC-V secure boot verification, non-volatile memory systems, and GPU-based persistent memory solutions.
Olaf Schenk is a Professor at the Institute of Computing within the Faculty of Informatics at Università della Svizzera italiana (USI), Switzerland. He serves as Director of the Institute of Computing and Co-Director of the Master in Computational Science. He is also an adjunct member of the Computer Systems Institute at USI. PhD in Information Technology and Electrical Engineering, ETH Zurich (2001) Venia Legendi in Mathematics and Computer Science, University of Basel (2009) Applied Mathematics, Karlsruhe Institute of Technology (KIT), Germany His research focuses on high-performance computing , computational science and engineering , and applied algorithms for extreme-scale simulations. He bridges computer science with scientific computing needs, particularly in parallel algorithms , sparse solvers , graph analytics , and manycore architectures . His work emphasizes scalable software tools and programming models for emerging HPC systems. The 15 most recent publications reflect a consistent focus on sparse matrix computations , parallel and task-based algorithms , graph partitioning , and performance optimization for heterogeneous and manycore systems. Keywords span high-performance computing, numerical linear algebra, and large-scale data analysis, showing strong integration of theoretical algorithm design with practical implementation. Olaf Schenk has received several prestigious honors: Elected Fellow, Society for Industrial and Applied Mathematics (SIAM) Senior Member, IEEE and ACM SIAM Supercomputing Prize 2023 IBM Faculty Award Two Leadership Computing Awards from the U.S. Department of Energy He has held leadership roles as Chair, Vice Chair, and Program Director of the SIAM Activity Group on Supercomputing. He serves as Associate Editor for ACM Transactions on Mathematical Software and on the editorial board of SIAM Journal on Scientific Computing . He has participated in over 60 international program committees, including top-tier conferences such as SC, IPDPS, and IEEE CSE. He advises PhD and Master’s students in computational science and leads research projects funded by national and international agencies. He is also the Founder & Director of Panua Technologies Sagl, focusing on high-end software for simulation and optimization. His research group at USI works on next-generation computing tools for extreme-scale scientific simulations, with ongoing work in adaptive algorithms, resilience, and hybrid CPU-GPU computing. He leads collaborative projects with institutions in Europe and the U.S., aiming to develop scalable, robust, and efficient software for future exascale systems.
Roberto Palmieri is an Associate Professor in the Department of Computer Science & Engineering at Lehigh University, where he co-leads the Scalable Systems Software (SSS) Research Group. He holds a Ph.D., M.S., and B.S. in Computer Engineering from Sapienza University of Rome (2012, 2008, 2006). His research focuses on concurrency, synchronization, distributed computing, and heterogeneous systems, emphasizing protocols optimized for multicore, cluster-scale, and geo-distributed infrastructures. His work spans theoretical and practical advancements in distributed systems, including atomic registers, consensus algorithms, and blockchain concurrency control. He has received an NSF CAREER Award for research on RDMA-optimized distributed protocols. Palmieri advises students in the Rossin College of Engineering and collaborates on high-performance computing projects, contributing to frameworks like HyFlow and Shield. Education: Ph.D., Computer Engineering, Sapienza University of Rome (2012) M.S., Computer Engineering, Sapienza University of Rome (2008) B.S., Computer Engineering, Sapienza University of Rome (2006) His research emphasizes scalability, fault tolerance, and performance in distributed systems. Recent work includes protocols for RDMA systems (e.g., ALock, Rome), blockchain transaction management (OCToPus), and hardware-accelerated synchronization (HATS). He has published extensively at top venues like OPODIS, DISC, and IEEE conferences. Awards: NSF CAREER Award (2021) Grants: CAREER: Distributed Protocols and Primitives Optimized for RDMA (2021) Palmieri leads the SSS Group, which develops open-source tools for distributed systems and collaborates on industry-relevant projects. His lab investigates cutting-edge topics like NUMA-aware concurrency, transactional memory, and cloud-optimized data grids.
Jeffrey K. Hollingsworth is a Professor in the Computer Science Department at the University of Maryland and serves as Vice President for Information Technology and Chief Information Officer (CIO) for the university. He holds appointments in CS, UMIACS (University of Maryland Institute for Advanced Computer Studies), and ECE (Electrical and Computer Engineering). His research focuses on High Performance Computing (HPC), parallel programming environments, and system architecture. He received a Ph.D. from the University of Wisconsin at Madison (1994) and a B.S. in Electrical Engineering from UC Berkeley. Hollingsworth leads the university’s IT infrastructure, overseeing critical services like networking, cybersecurity, and support for research and teaching. He has held leadership roles in professional organizations, including past chair of the ACM Special Interest Group on HPC (SIGARCH) and board positions with Internet2 and the Computing Research Association. His awards include IBM Faculty Partnership Awards (2016, 2001), an NSF CAREER Award (1997), and IEEE Senior Member status (2003). Key contributions include advancing HPC education through programs like the SC Student Cluster Competition and developing tools for performance analysis (e.g., PIPER, Chapel profilers). He has authored over 150 papers and has been cited for innovations in auto-tuning, parallel algorithms, and system optimization.
Ravi Reddy Manumachu is an Assistant Professor in the School of Computer Science at University College Dublin (UCD), Ireland. He holds a B.Tech from IIT Madras (1997) and a PhD in Computer Science from UCD (2005), specializing in high-performance heterogeneous computing and energy-efficient systems. His research focuses on optimizing performance and energy efficiency in modern heterogeneous platforms like clouds, grids, and supercomputers through novel models and algorithms. Key contributions include functional performance/energy models, energy-prediction frameworks, and extensions like Heterogeneous MPI and ScaLAPACK for heterogeneous clusters. He has published over 69 articles in top journals/conferences, with recent works addressing data transfer energy measurement, scalable allreduce algorithms (SUARA), and portable programming models (OpenH). Professional roles include Assistant Professor at UCD (2023–present), SEAI Research Fellow (2022–2023), and prior industrial experience at Ansys, Siemens, and IONA Technologies. He has certifications in university teaching, GDPR, and research integrity. Languages include English (fluent), Telugu, and Hindi. Research trends emphasize bi-objective optimization (performance-energy), hardware heterogeneity challenges, and scalable communication algorithms for deep learning. His work addresses energy non-proportionality in CPUs and GPU-CPU interactions, with practical solutions for real-world applications like matrix operations and gene sequencing.
Sean Chester is an Assistant Professor in the Department of Computer Science at the University of Victoria, Canada. He is affiliated with the Faculty of Engineering and Computer Science and specializes in scalable data analytics, with a focus on data management, parallel computing, and algorithm engineering. His research interests include GPU-native algorithms, multicore optimization, spatio-temporal data processing, and graph-based analysis. He actively contributes to open-source projects and course materials on platforms like GitHub, emphasizing open science and education. Recent work highlights include advancements in skyline computation, GPU-accelerated algorithms, and efficient processing of large-scale datasets. His publications span topics such as kNN optimization, social network anonymization, and vectorized k-core decomposition. Sean is involved in teaching courses like CSC 485C/586C on data management on modern hardware and CSC 370 on database systems. No scientific awards or grants are explicitly mentioned in the provided texts. He collaborates with students and researchers through platforms like GitHub, where he maintains repositories related to algorithm engineering and educational materials.
Dhrubajyoti Goswami is an Associate Professor and Graduate Program Director in the Department of Computer Science and Software Engineering at Concordia University, Montreal. He holds a PhD from the University of Waterloo, with prior degrees from McGill University and the Indian Institute of Science. His research focuses on high-performance computing, parallel algorithms, and distributed systems, with recent work emphasizing blockchain sharding and fault tolerance in distributed environments. Dr. Goswami teaches courses such as Operating Systems, Parallel Programming, and Distributed Systems, reflecting his expertise in system software and parallel computing. His publications span conferences like IEEE SBAC-PAD, IEEE ICBC, and IEEE ISPDC, addressing challenges in GPU computing, scalable algorithms, and blockchain optimization. He has secured grants including NSERC Discovery and CFI funding, supporting research in high-performance systems. Dr. Goswami’s advisory work includes supervising over 20 graduate and undergraduate students, with notable contributions in areas like efficient matrix multiplication on GPUs, hierarchical blockchain architectures, and fault-tolerant distributed systems. His professional memberships include IEEE Senior Member status.
Dr. Shengquan Wang is an Associate Professor in the Department of Computer and Information Science at the University of Michigan-Dearborn , affiliated with the College of Engineering and Computer Science . His career spans over a decade, with prior academic experience at Texas A&M University as a Research/Teaching Assistant. He received his Ph.D. in Computer Science from Texas A&M University in 2006, preceded by M.S. degrees in Mathematics (Texas A&M, 2000) and Applied Mathematics (Shanghai Jiao Tong University, 1998), and a B.S. in Mathematics (Anhui Normal University, 1995). Research Interests Real-Time Systems Sustainable Computing (Power/Energy/Thermal Management) Networks and Distributed Systems Security and Privacy Optimization and Machine Learning Publication Trends His work focuses on real-time systems under thermal constraints , secure overlay architectures , and energy-efficient server farms . Recent research explores statistical delay guarantees in wireless networks and nonmonotone optimization techniques . Collaborations span institutions like Texas A&M University and Karlsruhe Institute of Technology. Awards and Grants NSF CAREER Award (CNS 0746906) Rackham Faculty Research Grant Best Paper Award at ECRTS 2006 Advising and Leadership Dr. Wang advises Ph.D. and Master's students like Jun Liu and Nan Wang, fostering innovation in sustainable systems. He leads the Research Laboratory for Sustainable Systems (RLSS) , focusing on thermally constrained real-time systems and secure computing.
Associate Professor Uwe Roehm is a faculty member at the University of Sydney's School of Computer Science, specializing in database systems and big data analytics. He holds a PhD from ETH Zurich and joined the University of Sydney in 2004. His research focuses on distributed data management, integrating machine learning with databases, and scalable data processing for bioinformatics. Education: PhD in Computer Science, ETH Zurich (2002) Diplom-Informatics (MSc equivalent), University of Passau Research Interests: Database Systems and Transaction Processing Human-Centred Data Management Freshness-Aware Scheduling (FAS) Big Data Analytics and Cloud Computing Key Projects and Contributions: Developed Serialisable Snapshot Isolation (SSI), implemented in PostgreSQL Created the Master of Data Science program at the University of Sydney Recipient of the 2018 ACM SIGMOD Test of Time Award Awards: 2018 ACM SIGMOD Test of Time Award 2013 Dean's Award for Outstanding Teaching 2008 ACM SIGMOD Best Paper Award Teaching and Grants: Teaches courses on database systems, data science platforms, and cloud computing Principal Investigator on ARC-funded projects, including Linkage and Discovery grants Labs and Teams: Leads the Database Research Group and contributes to the Human-Centred Technology Research Cluster at the University of Sydney.
Ali José Mashtizadeh is an Associate Professor at the Cheriton School of Computer Science, University of Waterloo. His research focuses on operating systems, distributed systems, and storage, with expertise in system reliability, network optimization, and concurrent programming. Education: Ph.D., Computer Science, Stanford University (2017) M.S., Computer Science, Stanford University (2017) M.Eng., Electrical Engineering and Computer Science, MIT (2007) B.S., Electrical Engineering, MIT (2006) His research centers on designing scalable and reliable systems, with recent publications exploring TCP network frameworks, in-memory data persistence, and microsecond-scale scheduling. Key themes include optimizing tail latency, neutralization-based memory reclamation, and fault-tolerant distributed services. His articles consistently demonstrate innovations in low-latency networking, operating system architecture, and cloud infrastructure, with recent emphasis on serverless benchmarks and processor customization. No scientific awards or advising relationships are detailed in the provided materials.
Jing Li is an Assistant Professor at the Department of Computer Science in the Ying Wu College of Computing at New Jersey Institute of Technology. She holds a Ph.D. in Computer Science from Washington University in St. Louis (2017), advised by Chenyang Lu and Kunal Agrawal. Ph.D. in Computer Science, Washington University in St. Louis (2017) M.S. in Computer Science, Washington University in St. Louis (2014) B.S. in Computer Science, Harbin Institute of Technology, China (2011) Her research spans Real-Time Systems , Parallel Computing , Reinforcement Learning for System Design , and Scheduling Theory . Current projects include NSF-funded work on real-time systems with parallel resources and ARPA-E/IBM collaborations on reinforcement learning for converter design. Recent publications focus on AI-driven scheduling , parallel task optimization , and reinforcement learning applications in traffic control and circuit design. Key venues include AAAI, RTSS, PPoPP, and RTAS. Outstanding Achievement in Research (2022) Outstanding Paper Awards at RTSS (2018), RTAS (2016), ECRTS (2013) Turner Dissertation Award (2017) She advises graduate students in real-time systems and parallel computing, mentors NSF/ARPA-E projects, and leads professional services as TPC member and workshop organizer.
