Find Academic Supervisors

• Integrated Circuits
• Wireless Communications
• CMOS
• +7 more

Jussi Ryynänen is a Professor and Dean at Aalto University's School of Electrical Engineering, leading the Electronic Circuit Design (ECD) unit within the Department of Micro and Nanosciences. His research focuses on integrated circuits, wireless transceivers, and millimeter-wave systems, with a strong emphasis on RF, analog, and digital IC design. The ECD unit is internationally recognized for its work in nanoscale IC technologies and advanced measurement infrastructure. Key research areas include time-based integrated circuits, beam-steering/MIMO systems, antenna-IC co-design, and RF DAC/ADC development. His work combines cutting-edge semiconductor technologies with innovative circuit architectures for high-speed communication and sensor applications. The group maintains extensive collaborations and funding networks supporting its interdisciplinary projects. Ryynänen's research has been honored with awards such as the ECCTD Best Student Paper (2015), Crowncom Student Award (2011), and a best paper at the Advances in Cognitive Radio Conference (2012). His group actively publishes in top-tier venues like IEEE Transactions on VLSI Systems and IEEE Access, addressing challenges in mmWave circuits, neural network accelerators, and reconfigurable DSP hardware. Current projects span automated design frameworks for analog circuits, passive CMOS circulators for nonreciprocal systems, and energy-efficient signal processing architectures for 5G/6G transmitters. His work bridges fundamental circuit theory with practical implementations for next-generation communication systems.

• Integrated Circuit Design
• Digital Electronics
• Biomedical Engineering
• +3 more

Wim Dehaene is a Full Professor at KU Leuven's Department of Electrical Engineering (ESAT), Faculty of Engineering Science. His research focuses on advanced digital circuit design, in-memory computing, and biomedical applications. He leads projects like Variability Resilient Design of In-Memory Compute Architectures and DECIBUS: Communication in the Body by Ultrasound . Member of MICAS (Micro Electronics and Sensors) division Head of Electronic Circuits and Systems (ECS) group Active in interdisciplinary education and curriculum development His work bridges flexible electronics, low-voltage design, and AI accelerators, with recent publications on neural network hardware and biomedical wearables. Collaborations span institutions like IEEE and projects involving RISC-V controllers and IGZO TFT-based SRAM. Teaching roles include courses on digital integrated circuits, biomedical measurements, and interdisciplinary design methodologies. He contributes to research training in biomedical technology and participates in IEEE conferences as organizer and speaker.

Samuel ThibaultView profile

Professor

• Parallel Computing
• High Performance Computing
• Task-Based Runtime Systems
• +3 more

Samuel Thibault is a Professor at University of Bordeaux, affiliated with Laboratoire Bordelais de Recherche en Informatique (LaBRI) and Inria Bordeaux -- Sud-Ouest. He is a member of the SATANAS team at LaBRI (theme: High Performance Runtime Systems for Parallel Architectures) and the STORM research team at Inria Bordeaux (previously RunTime). His work bridges academic research and practical implementation of high-performance computing systems. Thibault's research focuses on task-based runtime systems, particularly StarPU, for heterogeneous and parallel computing architectures. His work addresses critical challenges in scheduling algorithms, memory management under constraints, data locality optimization, and performance modeling for complex NUMA architectures. He has made significant contributions to the field of parallel computing through the development and analysis of runtime systems that efficiently manage tasks across diverse hardware resources including CPUs, GPUs, and other accelerators. His research has practical applications in scientific computing, deep learning inference, and large-scale simulations requiring extreme computing power. His recent publication trends show a strong emphasis on optimizing task-based runtime systems for heterogeneous architectures with particular attention to memory constraints and data locality. There's a clear progression toward applying these techniques to deep learning inference workloads, as evidenced by his StarONNX project. His work consistently addresses the challenge of balancing throughput and latency in complex computing environments, with increasing focus on recursive task graphs and dynamic adaptation strategies. Thibault is actively involved in European research initiatives including TEXTAROSSA (focusing on exascale technologies) and EXA2PRO (high development productivity on heterogeneous systems). His work has been published consistently in top-tier conferences and journals in parallel and distributed computing. As part of the STORM team at Inria Bordeaux, Thibault contributes to advancing the state of the art in runtime systems for high-performance computing. His team's work on StarPU has become a reference implementation in the field, enabling researchers and practitioners to develop applications that can efficiently utilize heterogeneous computing resources without needing to manage the complexity of different hardware architectures directly.

• Computer Architecture
• High-Performance Computing
• Multicore and Manycore Systems
• +7 more

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.

Daler N. RakhmatovView profile

Associate Professor

• Reconfigurable architectures
• Energy-efficient computing
• Electronic design automation
• +9 more

Daler N. Rakhmatov is an Associate Professor in the Electrical and Computer Engineering Department at the University of Victoria . His research focuses on energy-efficient computing architectures for biomedical imaging applications, including ultrasound systems and embedded systems optimization. He is also involved in hardware/software co-design and battery management systems. Education: Bachelor of Science (BSc) in Engineering, University of Rochester Master of Science (MSc) in Engineering, University of Arizona Doctor of Philosophy (PhD) in Engineering, University of Arizona Professional Engineer (PEng) designation Research Interests: Rakhmatov's work emphasizes four core areas: 1. Low-complexity biomedical ultrasound imaging techniques for energy efficiency. 2. Configurable hardware/software architectures for computation-intensive tasks. 3. Analytical models for battery dynamics in embedded systems. 4. Model-driven configuration algorithms for energy-constrained applications. Teaching: Rakhmatov teaches courses such as ECE 242 (Discrete Structures) , ECE 355 (Microprocessor Systems) , and advanced topics like ECE 568 (System-on-Chip Engineering) . Students & Alumni: Rakhmatov supervises PhD and MASc/MEng students in biomedical imaging and embedded systems. Notable alumni include: - Hamad Alrimeih (PhD, Cryptographic Hardware Acceleration) - Mohammed Albulayli (PhD, Ultrasound Imaging Reconstruction) - Solmaz Khezerloo (MASc, Gradient-Driven Beamforming) Labs & Collaborations: His research integrates with the University of Victoria's biomedical engineering and embedded systems communities. He is a member of the Cadence University Program .

• Computer Architecture
• High-Performance Computing
• Distributed Systems
• +12 more

Yolanda Becerra Fontal is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture within the Barcelona School of Informatics (FIB). She is actively involved in research projects and collaborations, notably with the Barcelona Supercomputing Center, and is a member of prominent research groups such as the High Performance Computing Group (CAP) and CROMAI (Computing Resources Orchestration and Management for AI). Research Interests: Her research spans a broad spectrum of computer systems, with a consistent focus on performance, efficiency, and scalability. Key areas include Computer Architecture , High-Performance Computing (HPC) , Distributed and Cloud Systems , Resource and Energy Management in virtualized environments, and Data-Intensive Computing . More recently, her work has centered on innovative time-series database systems and data management for edge and cloud analytics. Publication Trends: Her recent scholarly output (2020-2022) shows a strong emphasis on time-series data management, proposing novel database architectures like NagareDB and strategies for polyglot persistence. Earlier work (2009-2013) was pivotal in MapReduce workload management, energy accounting for virtualized systems, and optical data center networks, demonstrating a long-standing contribution to foundational distributed computing challenges. Scientific Contributions: Her work has been published in top-tier journals and conferences such as Future Generation Computer Systems , IEEE Transactions , and Nucleic Acids Research . She has also contributed to significant competitive R&D projects and holds patents related to data flow management and distributed indexing. Advising and Grants: Dr. Becerra Fontal has served as a thesis advisor for doctoral students. Her research has been funded through competitive grants from national and regional programs, including Spanish State Research Plans (Plan Estatal de Investigación) and Catalonia's RIS3CAT strategy, supporting projects on high-performance computing and data management. Research Groups and Labs: She is a core member of the CAP - High Performance Computing Group and the CROMAI - Computing Resources Orchestration and Management for AI group at UPC. Her work is closely associated with the Barcelona Supercomputing Center (BSC) , one of Europe's leading supercomputing facilities, indicating access to advanced computational infrastructure.

Yuanchao XuView profile

Assistant Professor

• Computer Architecture
• Security
• Machine Learning Systems
• +3 more

Yuanchao Xu is an Assistant Professor in the Department of Computer Science and Engineering at the University of California Santa Cruz. He earned his Ph.D. from North Carolina State University, advised by Dr. Xipeng Shen and Dr. Yan Solihin, and is a student researcher at SystemResearch@Google since 2021. His research spans computer architecture, security, and ML systems.

• Systems Biology
• Computational Modeling
• Knowledge Representation
• +3 more

Natasa Miskov-Zivanov is an Assistant Professor at the University of Pittsburgh where she leads the MeLoDy Lab (Mechanistic, Logical, and Dynamic Modeling). She holds a PhD in Electrical and Computer Engineering from Carnegie Mellon University and conducts interdisciplinary research at the intersection of computational methods and biological systems. Her education includes: PhD in Electrical and Computer Engineering, Carnegie Mellon University (2009) MS in Electrical and Computer Engineering, Carnegie Mellon University (2005) BS in Electrical Engineering and Computer Science, University of Novi Sad (2003) Her research focuses on developing computational frameworks and tools for biological systems modeling. Primary interests include: Automated knowledge extraction from biomedical literature Dynamic network modeling of cellular signaling pathways Development of standardized knowledge representation formats (BioRECIPE) Hybrid modeling approaches for complex biological systems Applications in cancer systems biology and immunology Her publications demonstrate consistent focus on computational biology methods development, with recent work emphasizing: Context-aware knowledge selection systems Automated model assembly from literature Biomedical text mining frameworks Hybrid multi-resolution modeling Standards for executable biological models She leads several funded research initiatives including DARPA's Big Mechanism program (AIMCancer W911NF-17-1-0135) and University of Pittsburgh-supported projects. Her lab develops open-source tools like CLARINET, ACCORDION, and VIOLIN that facilitate biological network modeling and knowledge extraction.

• Computer Engineering
• Machine Learning
• Neural Networks
• +3 more

Ning Li is an Associate Professor in Electrical Engineering , with a focus on in-memory computing , phase-change memory , and deep learning acceleration . Their research spans hardware-aware training, analog memory device reliability, and energy-efficient AI systems. Key Research Areas : Phase-change memory, In-memory computing, Neural networks, Analog computing, Memory cell design, Resistance drift mitigation. Recent Publications highlight innovations in: Optimizing phase-change memory devices for low-resistance drift Hardware-aware training to maintain accuracy in analog compute-in-memory systems Energy efficiency improvements via drift-aware mapping of neural network weights Collaborations include institutions in the United States and Singapore, contributing to UN Sustainable Development Goals related to industry innovation and affordable clean energy .

Noah LindenView profile

Professor

• Quantum Information Theory
• Mathematical Physics
• Quantum Computing
• +3 more

Professor Noah Linden is a faculty member in the School of Mathematics at the University of Bristol , holding the title of Professor of Theoretical Physics. His research focuses on Quantum Information Theory , Mathematical Physics , and related areas in quantum computing and thermodynamics. He has contributed to 105 research outputs and leads projects such as Reliable and Robust Quantum Computing and Compilation and Verification of Quantum Software . Key research themes include quantum scrambling, entanglement dynamics, and applications in biophysics. His work has been cited in studies on quantum dots, qubit manipulation, and nonlocality limits. He has also contributed datasets on exciton dynamics in purple bacteria and collaborated on projects analyzing decoherence and disorder effects in photosynthetic systems. Professor Linden serves as an editor for the Journal of Physics A: Mathematical and General and maintains active collaborations across quantum information, quantum computing, and interdisciplinary physics. His research output includes foundational studies on quantum error correction, measurement theory, and computational advantages.

• Embedded Systems
• Internet of Things
• Wireless Sensor Networks
• +5 more

João Luís Marques Pereira Monteiro is a Full Professor at the Department of Industrial Electronics, School of Engineering, University of Minho, Portugal. He has been with the university since 1980, progressing from Assistant Trainee to his current position. He has held significant leadership roles including Pro-rector (2005-2009), Director of the Algoritmi Center (1998-2006, 2010-2013), and Dean of the School of Engineering (2013-2019). He currently coordinates the Embedded Systems Research Group (ESRG) and is a Senior Researcher at the Algoritmi Research Center. His educational background includes: Bachelor's degree in Electrical Engineering and Computers from the University of Porto (1980) PhD in Informatics and Systems Engineering - specialization in Computer Engineering - from the University of Minho (1991) Habilitation (Dr habil) from the University of Minho (2003) Professor Monteiro's research spans multiple domains in engineering and computer science, with a strong focus on practical applications. His work in embedded systems has led to innovations in real-time processing, sensor networks, and hardware-software co-design. In medical applications, he has developed textile-based sensors for vital sign monitoring. His recent work focuses on computer vision for autonomous vehicles, particularly 3D object detection from point clouds. He also contributes to educational initiatives in data science and AI, particularly in developing countries. His recent publications show a clear progression toward applications requiring real-time processing on resource-constrained devices. There's a strong emphasis on autonomous systems, particularly self-driving vehicles, with multiple papers on 3D object detection from point clouds. His work bridges embedded systems, computer vision, and wireless communications, often focusing on practical implementations for real-world applications like emergency responder localization and medical monitoring. Professor Monteiro has supervised more than a dozen doctoral students, many of whom have gone on to become faculty members at national and international universities. His research has been supported by various funding agencies including FCT (Portuguese), ADI (Portuguese), and FP7 (European funding), as well as industry partners. He leads the Embedded Systems Research Group (ESRG), which is part of the Algoritmi Research Center. The ESRG focuses on developing embedded solutions for various applications including autonomous vehicles, medical devices, and industrial monitoring systems. The group works on both hardware and software aspects of embedded systems, with particular expertise in real-time processing and resource-constrained environments.

• Computer Architecture
• Parallel Computing
• Compiler Technology
• +3 more

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.

Israat HaqueView profile

Associate Professor

• Software-defined Networking (SDN)
• Cyber-physical Systems (CPS)
• Internet of Things (IoT)
• +12 more

Dr. Israat Haque is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Halifax, where she leads the Programmable and Intelligent Networking (PINet) research group. She also holds an adjunct professorship in the Department of Computing Science at the University of Alberta. PhD in Computer Science, University of Alberta NSERC Postdoctoral Fellow, University of California, Riverside MSc in Computer Science, Concordia University Her research focuses on developing high-performance, secure, and dependable distributed and emerging networking systems, with key interests in Software-Defined Networking (SDN), Cyber-physical Systems (CPS), Internet of Things (IoT), 5G/6G technologies, and AI/ML applications in networking. She applies data-driven approaches to solve real-world problems in network programmability, stream processing, and edge/cloud computing. The 15 most recent publications highlight a strong trend in in-network computing, IoT security, AI-driven network reliability, and stream processing. Her work spans from theoretical surveys to practical system implementations, often leveraging programmable data planes (P4), machine learning, and hardware acceleration to address challenges in performance, security, and fairness in distributed systems. Dr. Haque has received numerous prestigious recognitions: IEEE/ACM N2Women Rising Star Award (2021) ACM FAccT Best Paper Award (2023) IEEE WICE Outstanding Mentoring Award (2025) University of Alberta Alumni Honour Award (2024) Digital Nova Scotia Thinking Forward Award (2022) Intel Fast Forward Initiative Winner (2022) President’s Research Excellence Award, Dalhousie (2021) She actively mentors PhD and MSc students and leads externally funded research projects, including a Canada First Research Excellence Fund (CFREF)-supported initiative on Securing Smart Environments. She has served on editorial boards of IEEE Transactions on Vehicular Technology and IEEE Communications Magazine, and on program committees of top conferences such as IEEE ICNP, IEEE NetSoft, and ACM SIGCOMM. Her lab, PINet, fosters innovation in networking systems and produces high-impact research with real-world applicability. Dr. Haque leads the PINet research group, which includes current PhD and MSc students working on cutting-edge topics like post-quantum cryptography, network programmability for security, and large ML model security. The group collaborates with institutions such as the University of Alberta, Laval University, and Concordia, and has strong industry ties with Intel, Meta, Amazon, and Facebook.

Zahra EbrahimiView profile

Researcher

• Approximate Computing
• Reconfigurable Accelerator Design
• Embedded Systems
• +2 more

Zahra Ebrahimi is a researcher in the field of approximate computing, reconfigurable accelerator design, and embedded systems. She joined the Chair of Embedded Systems at Ruhr University Bochum in April 2024, following her PhD research associate role at the Center for Advancing Electronics Dresden (Cfaed) from 2018 to 2024. Her work focuses on energy-efficient hardware/software co-design for edge-to-cloud computing, with applications in neural networks and bio-signal processing. She leads the BMBF-funded project X-DNet , collaborating with Huawei Research Center Munich. Education : B.Sc. and M.Sc. in Electrical Engineering from Sharif University of Technology, Iran Key Projects : ReAp (DFG), Re-Learning (ESF), X-ReAp (DFG), X-DNet (BMBF) Research Interests : Approximate computing, reconfigurable accelerators, embedded systems, SW/HW co-design, energy-efficient edge/cloud computing. Recent Trends : Zahra’s research emphasizes applying approximation techniques to neural networks for 5G/6G applications and designing specialized hardware like CGRAs for bio-signal processing and distributed computing. Collaborations : Academic-industry partnership with Huawei Research Center Munich.

Yipeng HuangView profile

Assistant Professor

• Quantum Computing
• Computer Architecture
• Analog Computing
• +5 more

Yipeng Huang is an Assistant Professor in the Department of Computer Science at Rutgers University, School of Arts and Sciences. His research focuses on building and helping programmers use quantum, analog, and other emerging computer architectures for the post-Moore's Law era of computing. He teaches undergraduate and graduate courses including Computer Architecture (CS 211) and Quantum Computing: Programs and Systems (CS 558/443). Huang actively mentors graduate students and recruits undergraduates for research in quantum computing and computer architecture. His PhD advisees include Zirui Li, Jonathan Garcia-Mallen, Adeeb Kabir, Haoyan Luo, Enhyeok Jang, and Seungwoo Choi, with undergraduate researchers including Pooja Kedia and Winston Li. Huang's research spans quantum computing, computer architecture, and analog computing. His work examines quantum error correction, quantum software development, and hybrid analog-digital systems for scientific computation. He has published extensively on quantum error correction, quantum compilation frameworks, and analog accelerators for solving differential equations and linear algebra problems. Huang's recent publications show a strong focus on practical quantum computing systems, with several 2025 papers on high-dimensional quantum error correction, qudit simulation, and optimized quantum program generation. His work bridges theoretical quantum computing with practical implementation challenges. 2024 ISCA Distinguished Artifact Award for Tetris: A Compilation Framework for VQA Applications in Quantum Computing 2021 MICRO Top Picks honorable mention 2017 MICRO Top Picks honorable mention 2016 MICRO Top Picks Huang serves on program committees for major computer architecture conferences including ISCA, ASPLOS, HPCA, and MICRO. He is actively involved in science education outreach, organizing the Workshop on Broadly Accessible Quantum Computing and serving on committees for the Summer Science Program and FIRST Robotics Competition.