جست‌وجوی استاد و پژوهشگر

• Heterogeneous computing systems
• High-level synthesis (HLS)
• Hardware specialization for machine learning
• +۳ مورد دیگر

Zhiru Zhang is a Professor at Cornell University in the School of Electrical and Computer Engineering , leading research at the Computer Systems Laboratory . His work focuses on algorithms, methodologies, and design automation tools for heterogeneous computing systems. Recent publications emphasize high-level synthesis (HLS), hardware specialization for machine learning, and programming models for software-defined FPGAs. Education: Ph.D. in Computer Science, UCLA B.S. in Computer Science, Peking University M.S. in Computer Science, UCLA Research Interests: Heterogeneous computing systems High-level synthesis (HLS) optimization FPGA-based hardware acceleration Sparse data format compilers Machine learning for electronic design automation Scientific Awards: IEEE Fellow Intel Outstanding Researcher Award NSF CAREER Award DARPA Young Faculty Award Multiple Best Paper Awards at ASPLOS, ISPD, FPGA, AutoML, FCCM Research Group: Mentors 12 current students including Jordan Dotzel, Jie Liu, and Grace Dinh, with 14 alumni now at institutions like AWS AI, NVIDIA, Google DeepMind, and Microsoft. His lab develops tools like UniSparse for sparse format customization, presented at OOPSLA'24 and IEEE CAL.

Cédric Killianمشاهده پروفایل

استاد

• Electronic Systems
• Computer Science
• Hardware Security
• +۶ مورد دیگر

Cédric Killian is a Full Professor at the University Jean-Monnet of Saint-Etienne since September 2024, affiliated with the SESAM (Secure Embedded Systems & Hardware Architectures) team of the Hubert-Curien Laboratory. Previously, he was a Tenured Associate Professor at the University of Rennes 1 (2013-2023) and a Research Fellow at the LCOMS Laboratory of the University of Lorraine (2012-2013). BSc in Electronic, Electrical Engineering, and Automatic from the University of Nancy (2007) Advanced technician’s certificate (BTS) in electronic systems from Schoeneck, France (2006) Master in Radio-communication and Embedded Systems from the University of Metz (2009) PhD in Electronic System from the University of Lorraine (2012) HDR (Habilitation à Diriger les Recherches) in Computer Science from the University of Rennes 1 (2022) His research focuses on secure embedded systems, hardware architectures, energy efficiency, fault tolerance, and emerging on-chip interconnects for manycore architectures. He has contributed to advancements in reliable reconfigurable Network-on-Chips (NoC) and secure hardware design. While no specific scientific awards are listed in the provided text, Killian has been actively involved in competitive programs and collaborative projects, including work with Inria and IRISA. He has supervised PhD students and mentored junior researchers, though no advisees are explicitly named here. Currently, Killian leads research within the SESAM team at the Hubert-Curien Laboratory, emphasizing secure and efficient hardware architectures. Previously, he was associated with the Taran project-team (formerly Cairn) at IRISA, Inria, and the LICM Laboratory at the University of Lorraine.

Mark C. Jeffreyمشاهده پروفایل

استادیار

• Computer Architecture
• Parallel Computing
• Computer Systems
• +۳ مورد دیگر

Mark C. Jeffrey is an Assistant Professor in the Edward S. Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto, cross-appointed to the Department of Computer Science. His research focuses on computer architecture and systems, aiming to simplify parallel programming for software engineers through novel compilers and large-scale multicore architectures. Previously, he was a Research Scientist at Facebook AI Research and earned his PhD from MIT under Daniel Sanchez. He holds a MASc and BASc from the University of Toronto. Education: PhD in Computer Science, MIT (2019) MASc in Computer Engineering, University of Toronto (2011) BASc in Engineering Science, University of Toronto (2009) Research interests include parallel computing, compilers, multicore architectures, and energy-efficient systems. His work bridges hardware-software co-design to enable scalable parallelism in modern systems. Notable awards include the George M. Sprowls PhD Thesis Award (2nd place, 2019), Best Paper at NoCArc 2023, and a Best Paper Award at ARC 2010. He teaches advanced courses in computer architecture and parallel programming (ECE1755, ECE552). His GitHub repository bf-intersect explores Bloom filter configurations for address-set disambiguation, tied to his SPAA 2011 and MASc thesis work.

Christopher Battenمشاهده پروفایل

استاد

• Computer Architecture
• Electronic Design Automation (EDA)
• VLSI Design
• +۷ مورد دیگر

Christopher Batten is a Professor of Electrical and Computer Engineering at Cornell University, affiliated with the Computer Science department. He leads the Batten Research Group within the Computer Systems Laboratory (CSL), focusing on computer architecture, electronic design automation, and VLSI systems. His work spans programmable accelerators, interconnection networks, and agile chip design methodologies. Batten holds a PhD from MIT, an M.Phil. from the University of Cambridge, and a B.S. from the University of Virginia. He has held visiting roles at UC Berkeley and NVIDIA. Research Interests: Computer Architecture: Accelerators, interconnection networks, and emerging technologies VLSI Design: Agile methodologies, chip prototyping, and physical design Hardware-Software Co-Design: Parallel programming frameworks and productivity tools Recent Work: Focuses on optical interconnects, 3D integration, and AI-driven hardware design. Led projects like Cornell Custom Silicon Systems (C2S2), which taped out multiple chips in SkyWater 130nm. Collaborates with industry partners like NVIDIA and Meta. Awards: Recognized with the ACM/IEEE MICRO Hall of Fame, NSF CAREER Award, and multiple teaching accolades. His group emphasizes student-led chip projects and open-source frameworks like PyMTL3. Grants & Sponsors: Supported by NSF, DARPA, AFOSR, and industrial partners including Intel, NVIDIA, and Xilinx. Current efforts include NSF CSSI projects improving gem5 and NSF Panorama for pangenomics.

JOSE CARLOS CABALEIRO DOMINGUEZمشاهده پروفایل

استاد

• High Performance Computing
• Parallel Systems Architecture
• Parallel Algorithms
• +۷ مورد دیگر

Prof. José Carlos Cabaleiro Domínguez is a Full Professor at the Department of Electronics and Computing, University of Santiago de Compostela. He holds a BS (1989) and PhD (1994) in Physics from the same university. His academic journey includes roles as an associate professor at the University of A Coruña (1990–1994) and associate professor at US圣地亚哥 de Compostela until 2022. He is affiliated with the Center for Research in Intelligent Technologies (CiTIUS) and leads the ARQCOMP research group in Computer Architecture. His research focuses on high-performance computing, parallel systems architecture, LiDAR data processing, and cloud computing. Notable contributions include optimizing parallel algorithms for sparse matrices, developing frameworks like BigOPERA for big data, and advancing LiDAR-based applications in infrastructure mapping and autonomous systems. He has led over 20 national/international projects, including HiPerHC2DA (2023–2026) and TLIX2 (2017–2018). Prof. Cabaleiro’s work spans 150+ peer-reviewed publications, with recent emphases on digital forensics (e.g., Android private browsing analysis), graph-based LiDAR segmentation, and energy-efficient thread migration strategies for NUMA systems. His research integrates hardware performance analysis, parallel algorithm design, and real-world applications in geomatics, bioinformatics, and cybersecurity.

Harini Ramaprasadمشاهده پروفایل

استاد آموزشی

• Real-time Embedded Systems
• Cyber-Physical Systems
• Operating Systems
• +۳ مورد دیگر

Harini Ramaprasad is a Teaching Professor and Associate Dean for Undergraduate Programs and Student Success at the University of North Carolina at Charlotte's College of Computing and Informatics. She holds a Ph.D. in Computer Science from North Carolina State University (2008). Her research focuses on real-time embedded systems, cyber-physical systems, and computer science education, with recent work addressing predictable execution on multi-core architectures and cybersecurity education through interactive modules. Dr. Ramaprasad previously served as Director of Undergraduate Programs at the College and was an Assistant Professor at Southern Illinois University Carbondale. She is an active member of IEEE, ACM, and SWE. Her teaching expertise spans operating systems, real-time systems, software design, and database systems. Her recent publications emphasize practical educational strategies (e.g., using classroom games to develop soft skills) and technical advancements in real-time scheduling, embedded system security, and multi-core architecture optimizations. Despite no listed scientific awards, her contributions to pedagogy and real-time systems highlight impactful interdisciplinary work.

Dustin Richmondمشاهده پروفایل

استادیار

• Computer Architecture
• FPGA
• RISC-V
• +۴ مورد دیگر

Dustin Richmond is an Assistant Professor in the Department of Computer Science and Engineering at the Baskin School of Engineering, University of California, Santa Cruz. His work focuses on secure, usable hardware systems with applications in FPGA acceleration, RISC-V architectures, and side-channel analysis. Email: drichmond@ucsc Office: Engineering 2, Room 221 Research Interests: Secure hardware systems FPGA-based computing Manycore processors High-level synthesis Side-channel vulnerabilities Notable Article Trends: Recent publications emphasize cloud FPGA security, manycore design optimization, and hardware security. Earlier works focus on RISC-V acceleration, OpenCL compiler enhancements, and heterogeneous computing systems. GitHub Contributions: Maintains open-source projects like RISC-V-On-PYNQ and PYNQ-HLS, addressing FPGA programming challenges and RISC-V integration. Active in resolving community issues related to toolchain compatibility and hardware-software interfaces.

Tomás Fernández Penaمشاهده پروفایل

استاد

• High Performance Computing (HPC)
• Parallel and Distributed Systems
• Quantum Computing
• +۵ مورد دیگر

Tomás Fernández Pena is a Full Professor at the University of Santiago de Compostela (USC) and Senior Researcher at the Research Center in Intelligent Technologies (CiTIUS) . With a career spanning over three decades, he has held academic positions since 1990 and contributed extensively to High Performance Computing (HPC), Big Data, and emerging quantum computing fields. Ph.D. in Physics from USC (1994) Senior Member of IEEE Associate Editor for IEEE Transactions on Computers and IEEE Access Research Contributions : His work focuses on parallel systems architecture, cloud computing middleware, and quantum simulation optimization. He has pioneered methods for NUMA systems, LiDAR data processing, and Big Data applications in bioinformatics/cheminformatics. His recent articles show increasing emphasis on quantum computing frameworks and distributed quantum processing. Scientific Recognition : Holds four Spanish Ministry of Education six-year research excellence periods (sexenios de investigación) and has served as Principal Investigator in 3 public projects and co-investigator in 31 EU/Xunta de Galicia funded initiatives. Supervised 7 Ph.D. theses and published 43+ international journal papers. International Collaborations : Maintains academic connections through funded research stays at Loughborough University, University of Tennessee, and University of Illinois Urbana-Champaign. Active in IEEE and participates in global conferences like Euro-Par and CHEP.

• Operating Systems
• Parallel Computing
• Scheduling in Parallel Systems
• +۳ مورد دیگر

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.

Jaejin Leeمشاهده پروفایل

استاد

• Programming systems of heterogeneous machines
• Parallelization and optimization of deep learning frameworks
• Deep learning compilers/architectures
• +۱ مورد دیگر

Jaejin Lee is a Professor in the Department of Computer Science and Engineering at Seoul National University (SNU) and serves as the Director of the Center for Manycore Programming and Multicore Computing Research Laboratory. He holds a BS in Physics from SNU (1991), an MS in Computer Science from Stanford University (1995), and a PhD in Computer Science from the University of Illinois at Urbana-Champaign (1999), where his research was supported by IBM and Korea Foundation for Advanced Studies fellowships. Research Focus: His work centers on heterogeneous computing systems with expertise in GPU/FPGA programming, deep learning compiler architectures, PyTorch/TensorFlow optimization, and quantum computing simulation environments. Key areas include parallelization techniques and performance enhancement for machine learning frameworks. Publications: His research output demonstrates consistent focus on GPU efficiency, compiler-directed optimizations, and distributed computing, with recent emphasis on deep learning acceleration and error resilience in heterogeneous architectures. Awards & Honors: IEEE Fellow IBM Graduate Fellowship Korea Foundation for Advanced Studies Graduate Fellowship Leadership: Directs the Multicore Computing Research Laboratory and Center for Manycore Programming, focusing on next-generation parallel computing architectures.

• Bioinformatics
• Genomics
• Algorithm Design
• +۵ مورد دیگر

Jean-Stéphane VARRÉ is a Professor in the Computer Science Department at the University of Lille and a core member of the BONSAI bioinformatics research team. His administrative leadership includes: Vice-head of the Computer Science Department (since 2018) In charge of the B.Sc. in Computer Science (since 2015) Former head of the MOCAD master's degree in Computer Science (2010-2015) His research centers on algorithmic solutions for genomic challenges, specializing in genomic rearrangements with duplicated markers, transcription prediction (splicing/orthology), third-generation sequencing assembly, and GPU-accelerated bioinformatics. Key contributions include ProCARs for ancestral genome reconstruction and the TFM suite for sequence comparison using position weight matrices. Recent publications (2019) demonstrate dual expertise in microbial genomics (bacterial assembly graph analysis) and clinical virology (coronavirus OC43 sequencing protocols), reflecting his team's capacity to bridge theoretical algorithm design with high-impact biomedical applications through scalable computational frameworks. Professor VARRÉ has co-supervised six doctoral candidates across 17 years: Pierre Marijon: Assembly graph analysis (current) Antoine Thomas: Genomic rearrangements with duplicates Tuan Tu Tran (2009-2012): Manycore architecture data structures Aude Darracq (2006-2010): Plant mitochondrial genome rearrangements Aude Liefooghe (2004-2008): Transcription factor binding site algorithms Martin Figeac (2002-2005): Constrained genomic rearrangement scenarios The BONSAI team maintains an active software repository for genomic analysis tools, including GPU-optimized string matching implementations and breakpoint region analyzers, supporting both academic research and clinical applications through open-source computational pipelines.