جست‌وجوی استاد و پژوهشگر

Gang Quanمشاهده پروفایل

استاد

• Real-time computing systems
• Power/thermal-aware design
• Electronic design automation
• +۷ مورد دیگر

Gang Quan is a Professor in the Department of Electrical & Computer Engineering at Florida International University (FIU). He holds a Ph.D. from the University of Notre Dame (2002), an M.S. from the Chinese Academy of Sciences (1994), and a B.S. from Tsinghua University (1991). His research focuses on real-time computing systems, power/thermal-aware design, electronic design automation, advanced computing architectures, and reconfigurable computing. He has contributed extensively to neuromorphic computing, homomorphic encryption for secure computation, and energy-efficient scheduling in multi-core and embedded systems. Key areas of expertise include thermal-aware system design for automotive and embedded applications, fault-tolerant neural networks, and resource management in data centers and cloud environments. His work integrates hardware-software co-design principles to address challenges in reliability, energy efficiency, and real-time performance. He has authored numerous peer-reviewed articles in top-tier conferences and journals. Dr. Quan's research has practical applications in improving the resilience and efficiency of computing systems across domains like automotive electronics, cloud infrastructure, and neuromorphic hardware. He maintains an active role in academic leadership at FIU, contributing to both educational initiatives and cutting-edge research projects in electrical and computer engineering.

Di Wuمشاهده پروفایل

استادیار

• Computer architecture
• Domain-specific acceleration
• Emerging computing paradigms
• +۳ مورد دیگر

Di Wu is an Assistant Professor at the University of Central Florida in the Department of Electrical and Computer Engineering. Their research focuses on next-generation computing systems spanning computer architecture , neuromorphic computing , and quantum computing . PhD in Electrical and Computer Engineering from University of Wisconsin, Madison (2023) Research highlights include innovations in domain-specific acceleration and heterogeneous systems . Recent publications demonstrate expertise in value-level parallelism , stochastic computing , and quantum error correction . Current projects target deep learning systems , neuromorphic brain-computer interfaces , and quantum architecture optimization . Professional recognition includes: 2024 ASPLOS Distinguished Artifact Evaluation Award 2024 Harold Peterson Outstanding Dissertation Award 2023 ML & Systems Rising Star Di Wu contributes to academic leadership as a program committee member for top conferences (ASPLOS, HPCA, ISCA, MICRO) and organizing chair roles for WUC @ ASPLOS 2024 and ARTEQC @ HPCA 2025.

Georgiy Krylovمشاهده پروفایل

استادیار

• High Performance Computing
• Compilers and Runtime Environments
• FPGA CAD
• +۲ مورد دیگر

Georgiy Krylov is an Assistant Professor at the Faculty of Computer Science, University of New Brunswick. He holds a PhD from UNB (2024), and master's and bachelor's degrees from Nazarbayev University (2018 and 2016). His research focuses on high-performance computing, compilers/runtime environments, FPGA CAD, and quantum/reversible computing. Education: PhD in Computer Science, University of New Brunswick (2024) MSc in Computer Science, Nazarbayev University (2018) BSc in Computer Science, Nazarbayev University (2016) Research Interests: His work spans compiler optimization, quantum circuit design, FPGA applications, and ahead-of-time (AOT) compilation techniques. He explores geometric refactoring for quantum circuits, heterogeneous logic implementations, and GPU acceleration in genetic algorithms. Publications Trends: Recent work emphasizes AOT compilation frameworks (e.g., Eclipse OMR), quantum computing optimizations, and FPGA-based CAD tools. Earlier studies focused on GPU-accelerated algorithms for quantum circuit synthesis. Grants/Advising: No specific grants or advisees listed. Active in open-source compiler toolchains and hardware-software co-design projects.

Steve Woodheadمشاهده پروفایل

عضو هیئت علمی

• Computer Systems
• Computer Networks
• Malware Analysis
• +۳ مورد دیگر

Dr. Steve Woodhead is a Reader in Computer Systems and Networks at the University of Greenwich's School of Computing and Mathematical Sciences, holding fellowships from IET, BCS, and HEA. His research specializes in malware analysis and network security. Key research areas include banking malware detection using machine learning, epidemiological modeling of network worms, and parallel computing applications in cybersecurity. Recent work focuses on feature selection techniques for Zeus malware identification and worm containment strategies. Publications demonstrate consistent focus on malware analysis methodologies and defensive cybersecurity frameworks. His team develops computational tools including network testbeds and internet-scale simulators. Honors: Fellow of the Institution of Engineering and Technology (FIET) Fellow of the British Computer Society (FBCS) Senior Fellow of the Higher Education Academy (SFHEA)

Nikolaos M. Missirlisمشاهده پروفایل

استاد

• Scientific Computing
• Parallel Computing
• Numerical Analysis
• +۲ مورد دیگر

Nikolaos M. Missirlis is a Professor at the Department of Informatics and Telecommunications at the National and Kapodistrian University of Athens (NKUA) since 1995. He earned his BSc in Applied Mathematics from the University of Ioannina, Greece in 1974 and his PhD in Numerical Analysis from Loughborough University of Technology, UK in 1978. He has held visiting positions at Rutgers University (1986-1987), Cyprus University (2006-2008), and SUNY Stony Brook (2013). His research focuses on Scientific Computing , particularly parallel solution of Partial Differential Equations and load balancing algorithms. His work bridges theoretical mathematics with high-performance computing applications in meteorology and fluid dynamics. His publications demonstrate consistent output in parallel algorithms and numerical methods, with recent work emphasizing GPU acceleration and diffusion-based load balancing techniques. Research trends show evolution from foundational iterative methods to applications in heterogeneous computing environments. Teaching activities include undergraduate courses: Numerical Analysis Numerical Linear Algebra Algorithms and Complexity Introduction to Programming Data Structures Graduate courses: Scientific Computing (Numerical Solution of PDEs) Parallel Algorithms He has advised five PhD students and led major projects including: PYTHAGORAS (Distributed iterative methods for weather prediction) SKIRON (Parallelization of Eta model for Hellenic Meteorology Service) Princeton Ocean Model parallelization Contact: nmis@di.uoa.gr | +30 210 7275103 | Department of Informatics and Telecommunications, NKUA, Panepistimiopolis 157 84, Athens, Greece.

Eric Brouzesمشاهده پروفایل

دانشیار

• Microfluidics
• Single-Cell Analysis
• Cancer Biology
• +۳ مورد دیگر

Eric Brouzes is an Associate Professor in the Department of Biomedical Engineering at Stony Brook University, part of the School of Medicine. His research focuses on droplet microfluidics for high-throughput single-cell genomic analysis, with applications in cancer heterogeneity studies and disease diagnostics. He holds a Ph.D. in Interface Physics-Biology from Université Denis Diderot-Paris7 and completed postdoctoral training at Harvard Medical School. Key research areas include developing microfluidic technologies to analyze cellular heterogeneity in cancer tissues, leveraging droplet-based systems for precise genomic profiling. His work bridges engineering and biology, emphasizing automation and scalability in single-cell assays. Notable contributions include patents on droplet microfluidic devices and methods, such as magnetic connectors for microfluidic systems and techniques to reduce interdroplet contamination. Brouzes teaches courses in nanofabrication, contemporary biotechnology, and biomedical engineering seminars. He has secured grants from the NIH, NSF, and the Simons Foundation. His lab, the Lab for Microfluidics for Quantitative and Genomics Biology, collaborates with industry partners like Raindance Technologies and Metagenomix to advance diagnostic tools and genomic analysis platforms.

• High Performance Computing (HPC)
• Data Locality
• GPU Computing
• +۷ مورد دیگر

Dr. Didem Unat is an Associate Professor at Koç University, College of Engineering, Department of Computer Engineering . Her research focuses on High Performance Computing (HPC) , GPU Computing , and Compiler Design , with particular emphasis on data locality optimization, multi-GPU communication, and adaptive mesh refinement. She leads the Parallel and Multicore Computing Lab , driving innovations in exascale co-design and heterogeneous system optimization. ACM SIGHPC Emerging Woman Leader in Technical Computing Award (2021) ERC Starting Grant (1.5M EUR) for the SparCity Project (2025) EuroHPC Grant for Extreme Scale Technologies and Applications (2025) 2025 BAGEP Award (Science Academy) Her work bridges Computer Architecture and Machine Learning , addressing challenges in data locality , multi-GPU systems , and performance portability . Recent publications highlight trends in sparse tensor optimization , asynchronous AMR execution , and compiler support for heterogeneous memory . She has developed tools like ComScribe for intra-node GPU communication analysis and ReuseTracker for multicore reuse distance analysis. Dr. Unat’s research is supported by grants from ERC and EuroHPC , reflecting her leadership in exascale technologies and GPU-centric systems . She contributes to the evolution of tiling abstractions , dynamic LLM training , and lightweight communication detection for modern architectures.

• Machine Learning
• Data Science
• Computer Vision
• +۳ مورد دیگر

Siddiqi Shafaq is a Researcher at TU Graz's Institute of Human-Centred Computing, holding a Dr.techn. (technical doctorate), BSc, and M.Sc. Her work bridges computer science and human-centric applications, focusing on machine learning, data science, and education technology. She contributes to interdisciplinary research, including smart surveillance systems, cloud computing security, and IoT applications. Her educational background includes advanced technical degrees, though specific institutions are not detailed. Research interests span machine learning interpretability, data cleaning frameworks, outlier detection in non-IID datasets, and educational paradigms like U-Learning. Her publications highlight innovation in declarative ML systems (SystemDS), ontology-driven opinion mining, and CPU-GPU optimization. Her articles collectively explore trends in scalable data science pipelines, ethical AI practices, and real-world deployment challenges. Notable work includes analyzing internet addiction's academic impacts and designing trusted cloud platforms. No awards or grants are explicitly listed, though her active publication history suggests ongoing research engagement. Shafaq is affiliated with TU Graz's Institute of Human-Centred Computing, contributing to both research and teaching activities (via unspecified courses). No student advisees or lab affiliations are mentioned in the provided data.

Mahadevan Ganeshمشاهده پروفایل

استاد

• Computational Electromagnetics
• High-Performance Computing
• Wave Propagation
• +۴ مورد دیگر

Mahadevan Ganesh is a Professor in the Department of Applied Mathematics and Statistics and the Department of Computer Science at the Colorado School of Mines. He also serves as an Affiliate Professor in Electrical Engineering. His primary research interests include stochastic algorithms, model reduction, multiscale methods, uncertainty quantification, computational electromagnetics, and high-performance computing. His work addresses challenges in wave propagation, scattering, and numerical solutions for partial differential equations on complex domains. He has developed advanced algorithms for electromagnetic and acoustic scattering, including T-matrix methods, finite element approaches, and high-order numerical schemes. His research often integrates parallel computing and uncertainty quantification to address real-world applications such as ground motion forecasting and seismic risk modeling. Dr. Ganesh has published extensively in top journals, focusing on topics like scattering theory, integral equations, and computational methods for heterogeneous media. His recent work includes advancements in neural network-driven inverse scattering problems and efficient algorithms for large-scale particle configurations. He teaches courses such as Parallel Scientific Computing, Mathematical Biology, and Numerical Methods for Partial Differential Equations, reflecting his expertise in computational science and engineering. His contributions span theoretical analysis, algorithm development, and high-performance computing applications, with a strong emphasis on interdisciplinary research. He collaborates on projects involving ground motion modeling, stochastic wave propagation, and data-driven uncertainty quantification frameworks.

Zhenhua Liuمشاهده پروفایل

استادیار

• Online Algorithm Design
• Convex Optimization
• Game Theory
• +۷ مورد دیگر

Zhenhua Liu is an Assistant Professor in the Department of Applied Mathematics and Statistics at Stony Brook University, with affiliations to the Department of Computer Science and the Smart Energy Technology Cluster. He joined Stony Brook in 2014 and was on leave in 2014-2015 for the ITRI-Rosenfeld Fellowship at Lawrence Berkeley National Laboratory. His research focuses on sustainable computing systems, cloud platforms for big data, energy management, and renewable energy integration. He holds a Ph.D. in Computer Science from the California Institute of Technology (Caltech), advised by Prof. Adam Wierman and Prof. Steven Low, and prior degrees from Tsinghua University and Peking University. His research interests span distributed algorithms, convex optimization, game theory, quantum networks, and energy-efficient computing. He develops theoretical frameworks and practical systems to address challenges in data centers, edge computing, and renewable energy integration. Notably, his work emphasizes rigorous analysis combined with system design to achieve real-world impact. His publications from 2023-2024 highlight advancements in distributed deep learning optimization, quantum entanglement routing, energy-aware edge computing, and sustainability metrics for data centers. These contributions reflect his interdisciplinary approach to solving complex problems at the intersection of computing, networking, and energy systems. Awards & Honors : Computerworld Honors Laureate, Best Paper Awards in International Green Computing Conference and ACM GreenMetrics, ITRI-Rosenfeld Fellowship, and multiple academic recognitions. Education : Ph.D. in Computer Science, Caltech (2014) M.S. in Computer Science & Technology, Tsinghua University (2009) B.E. in Measurement & Control, Tsinghua University (2006) B.S. in Economics, Peking University (2009)

• Systems Biology
• Computational Biology
• Mathematical Modeling
• +۴ مورد دیگر

James R. Faeder is an Associate Professor and Vice Chair for Educational Programs/Initiatives at the University of Pittsburgh. He holds a PhD in Chemical Physics from the University of Colorado at Boulder. His research focuses on developing mathematical and computational models to understand biological regulatory processes, particularly signal transduction mechanisms. Faeder specializes in stochastic simulation algorithms, model reduction techniques, and automated model construction from protein interaction data. His work integrates spatial and molecular complexity to advance systems biology. Key research areas include rule-based modeling frameworks (e.g., BioNetGen), spatial stochastic simulations (e.g., MCell), and interdisciplinary collaborations to address challenges in cancer, virology, and immunology. He emphasizes reproducibility, open science standards (FAIR/CURE principles), and computational tool development for the scientific community. His publications highlight advancements in weighted ensemble sampling, model integration, and software tools like BioSimulators and WESTPA. Faeder’s contributions bridge theoretical modeling, experimental validation, and translational applications in systems pharmacology and precision medicine. Lab activities involve designing algorithms to analyze heterogeneous biological systems, such as bacterial spore germination and T cell signaling dynamics. His educational initiatives aim to foster interdisciplinary training in computational and systems biology.

Richard Membarthمشاهده پروفایل

پژوهشگر

• High-Performance Computing
• Domain-Specific Compilers
• GPU Acceleration
• +۴ مورد دیگر

Richard Membarth is an academic researcher at the Friedrich-Alexander-Universität Erlangen-Nürnberg, Department of Computer Science. His work focuses on high-performance computing, domain-specific compilers, and GPU acceleration. He has contributed to projects like FLOWER (dataflow compiler), Hipacc (image processing DSL), and XEngine (neural network optimization). Membarth's research bridges compiler design, parallel algorithms, and heterogeneous hardware, with applications in medical imaging, bioinformatics, and autonomous systems. Co-developer of AnyDSL framework for partial evaluation Lead in GPU acceleration for molecular dynamics (tinyMD) Specialized in compiler techniques for FPGAs and GPUs Key areas include: - Domain-specific languages (DSLs) - Parallel algorithm optimization - Medical computing pipelines - Real-time graphics rendering

• Biomedical Engineering
• Microvascular Hemodynamics
• Computational Fluid Dynamics
• +۳ مورد دیگر

Peter Balogh is an Assistant Professor in the Department of Mechanical and Industrial Engineering at New Jersey Institute of Technology (NJIT). His research focuses on computational modeling of blood flow in microvascular systems, particularly investigating hemodynamic characteristics, red blood cell dynamics, and their implications in angiogenesis and cancer cell transport. Balogh leads the Integrated Multiphase Blood Flow Modeling and Experiments project funded by the National Science Foundation (NSF), aiming to predict microvascular growth through advanced simulations. Education: Not explicitly stated in provided texts. His research interests include microvascular hemodynamics, computational fluid dynamics (CFD), and the biomechanical interactions between blood cells and vessel walls. Key topics involve tortuous microvessel simulations, wall shear stress patterns in angiogenic networks, and the role of red blood cell stiffness in blood flow dynamics. Balogh's recent work highlights advancements in high-fidelity blood cell resolved simulations, adaptive physics refinement techniques, and their applications in understanding cancer cell trajectory tracking. His studies often bridge experimental and computational approaches to model complex biological systems. Grants & Projects: NSF Grant (2023-2026): Investigating microvascular growth through multiphase blood flow modeling and experiments. Teaching: Courses include ME 618 (High Performance Computing & BiopInspired CFD) and ME 407 (Heat Transfer). Labs/Teams: Collaborates with interdisciplinary teams on projects involving microvascular networks, cancer cell transport, and GPU-accelerated CFD simulations. Key collaborators include Prof. William Murfee (NJIT), Dr. Aaron Randles (Johns Hopkins), and others in computational biomedicine.

• Compiler Optimization
• High Performance Computing
• Computer Architecture
• +۲ مورد دیگر

Dr. J Nelson Amaral is a Professor in the Department of Computing Science within the Faculty of Science at the University of Alberta. His research focuses on compiler optimization techniques that enhance performance in modern computing architectures through clever code transformations. Educational background includes B.Sc. in Electrical Engineering (PUCRS), M.Sc. in Electrical Engineering (ITA), and Ph.D. in Electrical and Computer Engineering (University of Texas at Austin). Research interests span compiler optimizations for resource utilization, learning technology applications in compilation processes, and feedback-directed optimization efficiency improvements. Current investigations include instruction-level parallelism exploitation, code transformations for heterogeneous architectures, and machine learning-enhanced compilation. Publication analysis reveals strong focus on compiler techniques for performance optimization, particularly for matrix operations, convolution algorithms, and memory hierarchy management. Recent work emphasizes hardware-software co-design for specialized processors and auto-vectorization methods. University of Alberta Faculty of Science Excellence in Teaching Award (2015) Distinguished Engineer, Association for Computing Machinery (2014) Distinguished Speaker, Association for Computing Machinery (2012-2014) Interdepartmental Science Students' Society Award for Excellence in Teaching (2014) IBM Center for Advanced Studies Research Faculty Fellow of the Year (2012) Research includes industry collaborations through the IBM-CAS partnership. Contributes to computing education through curriculum development and student mentoring. Leads compiler optimization research at the IBM Center for Advanced Studies, focusing on performance improvements for enterprise workloads and specialized hardware.

Elliott Forbesمشاهده پروفایل

استادیار

• Computer Architecture
• High-Performance Processors
• Energy Efficiency
• +۳ مورد دیگر

Elliott Forbes is an Assistant Professor and Director of the Computer Engineering program at the University of Wisconsin-La Crosse's Computer Science & Computer Engineering Department. He holds a PhD in Computer Engineering from North Carolina State University (2016), with a focus on computer architecture. His research emphasizes heterogeneous multi-core processors, thread migration latency, and energy efficiency. He has developed tools like the RISC-V assembler 'dt' and the pipeline analysis tool 'dptv'. Education: BS (Michigan Tech, 2005), MS (NC State, 2008), PhD (NC State, 2016). Industry experience includes roles at Intel, Unisys, and Smiths Aerospace. Awards: IEEE Senior Member (2020), Best Paper Award at ICCD-34 (2016), MIT Lincoln Labs Fellowship (2007-2008). He teaches courses ranging from introductory computer systems to advanced topics in architecture and compiler design.