جست‌وجوی استاد و پژوهشگر

Phillip Gibbonsمشاهده پروفایل

استاد

• Systems

Phillip Gibbons is a Professor in the Department of Computer Science at Carnegie Mellon University, affiliated with the School of Computer Science. His research focuses on Systems , particularly in robotics computing, heterogeneous hardware, and architectural optimization. Recent research trends include hybrid convolution-transformer models for AR/VR systems, robotics performance benchmarking, and neural architecture search for NPU-CIM heterogeneous devices. His work bridges machine learning and computer architecture for modern hardware platforms. Publications (2024-2025) highlight collaborations in robotics computing and hybrid AI-hardware systems. Notable contributions include the RobotPerf benchmarking suite and studies on autonomous systems hardware integration. Students : Siyuan Chen, Valerie Choung, Hyoungjoo Kim.

Máire O'Neillمشاهده پروفایل

استاد

• Hardware Security
• Data Security Architectures
• Computer Arithmetic
• +۵ مورد دیگر

Prof. Máire O'Neill is a Professor of Information Security at Queen's University Belfast, leading the Centre for Secure Information Technologies and the UK Research Institute in Secure Hardware and Embedded Systems (RISE). She holds a MEng and PhD from Queen's University Belfast under Prof. Sir John McCanny. Her work focuses on hardware security, including attack-resilient computer hardware, PUFs, post-quantum cryptography, and secure embedded systems. Notable achievements include a six-fold improvement in AES hardware efficiency and patented PUF techniques addressing counterfeiting. Key roles include EPSRC Leadership Fellowship and Royal Academy of Engineering Fellowship. Research interests span hardware security architectures, nanotechnology-based security, and quantum-resistant cryptography. Awards include Royal Irish Academy membership, Irish Academy of Engineering Fellowship, and British Female Inventor of the Year. Recent projects include the EU H2020 SAFEcrypto initiative for post-quantum encryption and TruDetect for hardware Trojan detection. She advises on cybersecurity for IoT and connected devices, emphasizing secure manufacturing and long-term system resilience against quantum threats. Labs/Teams: Principal Investigator at the Centre for Secure Information Technologies (CSIT) and RISE. Active in multi-national collaborations like the EU's SAFEcrypto. Supervised over 8 PhD students (details in full description). Grants include funding from EU H2020, EPSRC, and industry partnerships.

• High-Performance Computing
• Energy-Efficient Computing
• Parallel and Distributed Systems
• +۵ مورد دیگر

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.

Sherief Redaمشاهده پروفایل

استاد

• Machine Learning
• Computer Architecture
• Energy Efficiency
• +۵ مورد دیگر

Sherief Reda is a Professor of Engineering and Computer Science at Brown University's School of Engineering. He leads the SCALE lab, focusing on energy-efficient computing, digital chip design, embedded systems, and machine learning applications. His research bridges hardware design and combinatorial optimization, with over 130 publications and five US patents. Reda has secured $21M+ in research funding from NSF, DoD, DARPA, and industry partners like Samsung and Intel. Education: PhD in Computer Science & Engineering, UC San Diego (2006) MS in Computer Science, Ain Shams University (2000) BS in Computer Science, Ain Shams University (1998) Research Interests: His work spans resource-efficient AI, approximate computing, and interdisciplinary applications in social sciences. Recent projects include chemical-based computing and thermal management for high-performance chips. Reda's lab explores machine learning for combinatorial optimization, with practical applications in logistics and hardware design. Publications & Awards: Over 130 articles in top venues like IEEE Transactions and Nature Communications. Recipient of the NSF CAREER Award (2021) and IEEE Fellow (2020). His work on energy-efficient computing earned him the AAIA Fellowship. Grants & Collaborations: Principal Investigator on NSF, DoD, and industry-funded projects. Collaborators include Prof. Kim (Chemistry) and Prof. Rose (Engineering). Reda also serves as an Amazon Scholar and expert witness in patent litigation. Labs & Teams: Directs the SCALE lab, which develops open-source EDA tools and novel thermal simulation frameworks like PACT. His team pioneered techniques like ABACUS for approximate circuit synthesis and LoCool for energy efficiency in servers.

Bingzhe Liمشاهده پروفایل

استادیار

• DNA Storage Systems
• Machine Learning Systems
• Low-Power Computing
• +۳ مورد دیگر

Dr. Bingzhe Li is an Assistant Professor in Computer Science at UT Dallas' Erik Jonsson School of Engineering. His research at the Lab for Intelligent Storage and Computing (Lab4ISC) focuses on DNA storage systems, machine learning infrastructure, and energy-efficient computing architectures. Awarded the NSF CAREER Award (2025) and recognized for Best Paper nominations at leading conferences. Research spans DNA storage capacity optimization, reinforcement learning for hybrid SSDs, Kubernetes storage optimization, and stochastic computing architectures. Recent publications demonstrate innovations in out-of-core graph processing and blockchain storage systems. Leads multiple NSF/NASA-funded projects on DNA storage and convertible SSDs. Teaches Digital Logic and Computer Architecture courses. Supervises 8 PhD students and 3 master's candidates in storage systems and low-power computing research.

Vasilios Kelefourasمشاهده پروفایل

مدرس

• Compiler Optimizations
• Deep Neural Networks
• Matrix/Tensor Computations
• +۲ مورد دیگر

Dr. Vasilios Kelefouras is Lecturer in Computer Science at the University of Plymouth's School of Engineering, Computing and Mathematics. His research optimizes software applications for execution time, energy consumption, and memory footprint across CPUs, GPUs and FPGAs. Notable contributions include analytical methodologies accelerating convolution layers in Deep Neural Networks (x1.1-7.2 speedup over Intel oneDNN) and optimization techniques for image processing algorithms (x2.8-40 speedup over Intel IPP). His compression work for Deep Neural Networks includes Tensor Train Decomposition methodologies for edge devices. Honors include HiPEAC Technology Transfer Award (2022) and Best Paper at SAMOS XXII. Teaches Parallel Computing, Computer Systems, and Computing Practice. Current PhD supervision focuses on accelerating ML algorithms on heterogeneous architectures.

Marco Grangettoمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Artificial Intelligence
• +۵ مورد دیگر

Marco Grangetto serves as Full Professor in the Department of Computer Science at the University of Turin, coordinating research in image processing and computer vision. His expertise spans wavelets, image/video coding, data compression, error resilient video coding, and biomedical image processing, with significant contributions to ISO JPEG2000 standardization and editorial roles in IEEE Transactions on Multimedia. His educational background includes a PhD in Electrical and Communications Engineering (2003) and MSc in Telecommunications Engineering (1999), both from Politecnico di Torino. His research integrates Artificial Intelligence and Deep Learning with medical imaging and fundamental compression theory , producing innovations in neural network pruning, capsule networks, and entropy-based models. Recent work focuses on Covid-19 diagnosis from chest X-rays and efficient 3D scene modeling. His publication trends reveal dual trajectories: applied medical AI (Covid-19 diagnostics, lung cancer segmentation) and theoretical advances (learned compression, contrastive learning, bias mitigation). This bridges clinical validation with information-theoretic foundations, particularly in resource-constrained environments. Scientific recognition includes: Premio Optime by Unione Industriale di Torino (2000) Fulbright Grant for research at UC San Diego (2001) He maintains leadership through IEEE editorial positions, ISO standardization participation, and MPAI membership. His research group develops medical datasets (UniToChest, UniToPatho) while advancing neural network efficiency for clinical deployment. Current projects focus on entropy minimization techniques and unbiased representation learning for healthcare applications.

Rasha Karakchiمشاهده پروفایل

مدرس

• High Performance Computing
• Reconfigurable Computing
• Embedded Systems
• +۳ مورد دیگر

Rasha Karakchi serves as a Lecturer in the Department of Computer Science and Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing, where she teaches diverse courses while maintaining an active research program in hardware acceleration and embedded systems. Her academic foundation includes: Ph.D. in Computer Science and Engineering, University of South Carolina (2020) M.E. in Computer Engineering, University of South Carolina (2016) Dr. Karakchi's research centers on high-performance reconfigurable embedded systems, with particular focus on hardware acceleration for automata processing, spiking neural networks, and genomic sequence alignment. Her work bridges theoretical computer science with practical hardware implementation, emphasizing energy efficiency and real-time performance in security-critical applications. Analysis of her 2023-2025 publications reveals a dominant research trajectory applying machine learning to optimize hardware configurations for domain-specific tasks. Key thematic clusters include ML-enhanced automata processors for pattern matching, lightweight encryption engines for embedded security, and specialized architectures for spiking neural network acceleration—all demonstrating consistent innovation in hardware-software co-design for computationally intensive workloads. Her research excellence has been recognized through: SPARC Award (South Carolina's Program to Advance Research and Creativity)

Nasser Kehtarnavazمشاهده پروفایل

استاد

• Signal and Image Processing
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Nasser Kehtarnavaz is an Erik Jonsson Distinguished Professor and Director of the Embedded Machine Learning (EML) Laboratory at the University of Texas at Dallas's Erik Jonsson School of Engineering and Computer Science. He holds a PhD from Rice University (1987) and has over 40 years of academic and industrial experience. His research focuses on signal/image processing, machine learning, biomedical applications, and real-time embedded systems. He has authored/co-authored 11 books and over 450 publications, and supervised 37 PhD and 35 MS students. Leadership roles: Editor-in-Chief of Journal of Real-Time Image Processing , SPIE Conference Chair, IEEE Fellow (2013), AAIA Fellow Industrial collaborations: Texas Instruments, AT&T Bell Labs, US Army TACOM Research Lab Key contributions: Smartphone-based signal processing labs, hearing aid amplification algorithms, and FPGA-based embedded systems Research interests include real-time implementation on embedded processors, biomedical signal analysis, and smartphone-based laboratories. He has pioneered educational tools like anywhere-anytime labs using smartphones for signal processing education. Recipient of numerous awards including IEEE Region 5 Professional Leadership Award (2015), Outstanding Engineering Educator Award (2013), and community recognition from SPIE (2020-2021). His funded projects (62 as PI/Co-PI) span healthcare tech, energy systems, and semiconductor manufacturing. Director of the EML Lab and previously the Signal and Image Processing Lab. Teaches graduate courses like Machine Learning and Pattern Recognition and undergraduate courses in signals and systems.

André Brinkmannمشاهده پروفایل

استاد

• Data Center Management
• Cloud Computing
• Storage Systems
• +۵ مورد دیگر

André Brinkmann is a full professor at the Department of Computer Science, Johannes Gutenberg University Mainz, leading the Efficient Computing and Storage Group. He previously served as head of the university's data center (2011–2021) and was an assistant professor at Paderborn University (2008–2011). He holds a Ph.D. in Electrical Engineering from Paderborn University (2004) and managed the Paderborn Centre for Parallel Computing (PC²). His research focuses on algorithm engineering for data center management, cloud computing, storage systems, and high-performance computing (HPC). Notable projects include: Development of ad hoc file systems like GekkoFS and IO-SEA for exascale architectures Optimization of storage systems (e.g., hybrid RAID, SSD garbage collection) Quantum computing compiler research for trapped-ion architectures Leadership in initiatives like the I/O Trace Initiative and BINARY (Big Data in Atmospheric Physics) He serves as Senior Associate Editor of ACM Transactions on Storage and co-chairs major conferences like FAST 2026 and ARCS 2026.

Sean Chesterمشاهده پروفایل

استادیار

• Data Management
• Parallel Computing
• Algorithm Engineering
• +۵ مورد دیگر

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.

Yidi Wangمشاهده پروفایل

استادیار

• Real-Time Systems
• Embedded Systems
• Cyber-Physical Systems
• +۵ مورد دیگر

Yidi Wang is an Assistant Professor in the Department of Computer Science and Engineering at Santa Clara University (SCU). She holds a Ph.D. in Electrical and Computer Engineering from the University of California, Riverside (2023), and previously worked as a Postdoctoral Scholar at UCR (2023–2024). Her research focuses on real-time, embedded, and cyber-physical systems, particularly addressing challenges in GPU scheduling, energy efficiency, and batteryless device operation. She teaches courses such as Introduction to Embedded Systems and Operating Systems at SCU. Education: Ph.D. in Electrical and Computer Engineering, UC Riverside (2023) M.S. in Electrical and Computer Engineering, UC Riverside (2019) B.S. in Electrical Engineering, Huazhong University of Science and Technology (2018) Research Interests: Real-time scheduling for GPU-accelerated applications, energy-efficient computing, heterogeneous platforms, and reliable systems for intermittently powered devices. Her work spans system-level implementations and theoretical analysis, including novel scheduling algorithms and mathematical models for performance optimization. Awards: None explicitly listed in the provided texts. Advising & Grants: Currently mentoring 3 students (2 MS, 1 undergraduate) and recruiting PhD/MS candidates. She serves on technical committees for RTSS and RTAS conferences and has reviewed for journals like Transactions on Computers and Real-Time Systems. Labs/Teams: Leads research on GPU scheduling and batteryless systems, collaborating with industry and academic partners.

Hualin Zhanمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning for Science
• Solar Energy Materials
• Energy Storage Materials
• +۱ مورد دیگر

Dr. Hualin Zhan is a Research Fellow at the School of Engineering, Australian National University, leading the physics-machine learning nexus team within the ANU Perovskite Photovoltaics group. His research integrates physics and machine learning to advance solar energy and energy storage materials, focusing on quantitative analysis of photovoltaic devices at the ion/electron level to enable precision-driven breakthroughs. Research interests include: Machine Learning for scientific discovery in energy materials Theoretical physics of ion/electron transport Perovskite solar cell optimization Nanoscale energy storage systems His publications demonstrate strong focus on machine-learning applications for energy materials, with 60% of recent works involving perovskite optimization and 40% exploring fundamental ion transport mechanisms. Key trends include Bayesian optimization for material parameter extraction and nanocircuitry design for energy storage. Awards and leadership: ACAP Fellowship (2023) and Best Oral Presentation at PVSEC-35 (2024) Lead CI for ACAP project on autonomous PV materials discovery (2025) Founder of nexSAS research group accelerating next-gen energy materials

Eddie Ballمشاهده پروفایل

استاد

• RF Engineering
• Wireless Systems Design
• Millimeter-Wave Communications
• +۵ مورد دیگر

Professor Eddie Ball is a Professor of Radio Frequency Engineering at the University of Sheffield's School of Electrical and Electronic Engineering, and a UKRI Future Leaders Fellow (2021-2028). He leads the Electromagnetics, Wireless Hardware & RF Devices research theme and directs the EPSRC Millimetre Wave Measurement Laboratory. His expertise spans RF circuit/system design, SDR, and millimeter-wave technologies, with a focus on IoT applications and hardware manufacturing. Qualifications: Ph.D., University of Sheffield (2024) M.Eng (1st class), University of York (1996) Chartered Engineer Research Interests: Novel RF circuit/system design Millimeter-wave transceivers and antennas RF-MMIC and SiGe design IoT radio systems and blockchain integration Low-cost, high-performance wireless protocols Teaching: Creator and instructor for EEE6239: Radio Transceiver System & Circuit Design 2nd-year course leader for VHF Synthesiser for Wireless Communications Labs/Teams: EPSRC Millimetre Wave Measurement Laboratory Future Millimetre Wave RF Transceiver Architectures Project

Sandip Kunduمشاهده پروفایل

استاد

• Hardware Security
• VLSI Design
• CAD Algorithms
• +۷ مورد دیگر

Sandip Kundu is a Professor of Electrical and Computer Engineering at the University of Massachusetts Amherst since 2005. Prior roles include Senior Researcher at Intel (1997–2005) and IBM Research (1988–1997). He holds a Ph.D. from the University of Iowa (1988) and a B.Tech from IIT Kharagpur (1984). Research Interests: Focuses on hardware security, VLSI design, CAD algorithms, microarchitecture, and neural processing. Notable contributions include work on physically unclonable functions (PUF), blockchain-based IC traceability, and securing embedded systems against adversarial attacks. Key Activities: Served as Associate Editor for multiple IEEE journals and held visiting professorships at institutions like University of Freiburg (Germany), Kyushu Institute of Technology (Japan), and Tsinghua University (China). Awarded IEEE Fellow and JSPS Invitational Fellowships. Awards: Includes five best paper awards, Intel’s Development Leadership Pioneer Award, IBM’s Outstanding Technical Achievement Award, and recognition as a Distinguished Visitor by IEEE Computer Society. Grants & Labs: Active in NSF-funded research and leads the Advanced VLSI Design and Test Group, exploring secure hardware design, neuromorphic computing, and resilient architectures for edge devices.