جست‌وجوی استاد و پژوهشگر

• Computer Architecture
• Embedded Systems
• High-Performance Computing
• +۳ مورد دیگر

Zhiqiang Que is a Research Associate in the Department of Computing at Imperial College London, affiliated with the Faculty of Engineering and the Centre for High-Throughput Digital Electronics and Machine Learning. His research focuses on computer architecture, embedded systems, high-performance computing, and CAD tools for hardware design optimization. His research interests include FPGA-based acceleration of machine learning models, hardware-software co-design, and real-time signal processing for scientific applications such as particle physics and gravitational wave experiments. He has contributed to projects involving low-latency graph neural networks (GNNs), Bayesian neural networks, and efficient stream processing on FPGAs. Recent work includes advancements in trustworthy design flows for deep learning acceleration, reconfigurable architectures for recurrent neural networks, and optimizing FPGA-based systems for high-energy physics experiments at the HL-LHC.

Shengwang Duمشاهده پروفایل

استاد

• Atomic, Molecular, and Optical (AMO) Physics
• Quantum Optics
• Quantum Networks
• +۵ مورد دیگر

Shengwang Du is a Professor of Physics and Astronomy and Professor of Electrical and Computer Engineering at Purdue University, serving as Deputy Director of the Purdue Quantum Science and Engineering Institute (PQSEI). He holds adjunct and visiting roles at institutions globally, including co-founding Light Innovation Technology (LiT) companies. His research focuses on quantum optics, quantum computing, optical neural networks, and bioimaging. Du earned his PhD in Physics from the University of Colorado Boulder in 2005, with prior degrees from Peking University and Nanjing University. Education: PhD, Physics (Atomic, Molecular and Optical Physics), University of Colorado Boulder, 2005 MS, Electrical Engineering, University of Colorado Boulder, 2002 MS, Physics, Peking University, 1999 BS, Electrical Engineering, Nanjing University, 1996 Research Interests: AMO Physics, Quantum Optics, Quantum Networks, Quantum Computing, Optical Neural Networks/AI, Optical Microscopy, Bioimaging. Professional Contributions: Associate Editor of Optics Express , Optica Fellow (2019), APS Fellow (2019), and key organizer of international quantum conferences. His publications span quantum optics, bioimaging, and photonics, with recent work emphasizing quantum networks and AI-driven microscopy. Awards: APS and Optica Fellowships (2019), Hong Kong Young Academy of Sciences membership (2020–2025), and the 2011 HKUST Research Award. Grants & Labs: Co-founded Light Innovation Technology (LiT) and NanoBioImaging (NBI) Ltd. Active in quantum engineering, optical instrumentation, and quantum sensing initiatives.

• Microbial Genetics
• Evolutionary Biology
• Genetic Drift and Selection
• +۵ مورد دیگر

Dr. Wei-Chin Ho is an Assistant Professor specializing in microbial genetics and evolutionary biology. His research integrates experimental, computational, and theoretical approaches to study how microorganisms adapt to environmental changes. Key focuses include mutation dynamics, phenotypic plasticity, genetic drift vs. selection, and the molecular mechanisms underlying traits like antibiotic resistance and biofilm formation. His research investigates topics such as mutation rate variation, metabolic adaptation, and evolutionary trade-offs in response to fluctuating resources. Recent work explores microbial survival strategies in feast-famine cycles and the reversibility of phenotypic changes during environmental adaptation. Dr. Ho’s studies emphasize understanding the interplay between genetic and environmental factors in shaping microbial evolution. His publications reflect a focus on experimental evolution, mutation analysis, and the genetic basis of microbial adaptation. Notable themes include hypermutator evolution, ecotype dynamics, and the limits of population genetic parameter estimation using temporal data. No scientific awards or grants are explicitly mentioned in the text. Advising and student mentorship details are not provided. Dr. Ho’s work is centered in microbial systems biology, with implications for understanding antibiotic resistance and environmental microbial responses.

Zhen Xieمشاهده پروفایل

استادیار

• High-Performance Computing (HPC)
• Machine Learning
• Parallel Computing
• +۳ مورد دیگر

Zhen Xie is an Assistant Professor in the Department of Computer Science at Binghamton University (SUNY), serving as Director of the Parallel Computing and Intelligent System (PCIS) Lab. He holds a PhD from the Chinese Academy of Sciences and a BA from Wuhan University of Technology. His research focuses on high-performance computing (HPC), machine learning, and their intersections, particularly optimizing performance for HPC and AI/DL applications across heterogeneous architectures. Research Highlights: Dr. Xie’s work emphasizes system-level performance optimization for ML and HPC, including GPU acceleration, memory optimization, and AI accelerator selection. His team has won the ACM Gordon Bell Special Prize (2022) for their GenSLMs project predicting SARS-CoV-2 evolution. Recent grants include a 2024 gift from OpenAI for AI testbed initiatives. Awards: ACM Gordon Bell Special Prize (2022), Impact Argonne Awards (2023) Lab: PCIS Lab explores middleware for parallel computing, targeting scientific simulations and big data analytics. Collaborations include Argonne National Lab and Lawrence Berkeley National Lab. Teaching: Teaches Distributed Systems (CS 457/557) and oversees independent studies. Previously trained researchers at Argonne’s ATPESC program. Grants & Collaborations: Subcontract with Lawrence Berkeley Lab (HEVI-LOAD), Argonne testbed expeditions, and OpenAI-funded projects. Active in DOE labs like Summit and Aurora supercomputers.

Victorita Dolean-Mainiمشاهده پروفایل

استاد

• Domain Decomposition Methods
• High Performance Computing
• Wave Propagation
• +۲ مورد دیگر

Victorita Dolean-Maini is Professor at University of Strathclyde and Visiting Professor at University of Strathclyde (2023-2026). She specializes in domain decomposition methods for PDEs, high-performance computing, and scientific machine learning. Her work develops fast algorithms for wave propagation problems and complex systems. She co-authored the reference work 'Domain Decomposition Methods' (SIAM 2015) and received the Bull-Joseph Fourier Prize for stroke simulation using HPC. Current research explores physics-informed neural networks with domain decomposition architectures and their application to medical digital twins. She serves on the editorial board of SIAM Journal on Scientific Computing and collaborates with industrial partners on scientific machine learning projects. Recent publications focus on neural network architectures combining domain decomposition with physics-informed learning, multilevel methods, and efficient coarse spaces for partial differential equations.

Marjan Firouzniaمشاهده پروفایل

پژوهشگر

• Medical Imaging
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Marjan Firouznia is a Principal Research Engineer at Linköping University , affiliated with the Division of Diagnostics and Specialist Medicine (DISP) under the Faculty of Medicine and Health Sciences . With a PhD in Electrical Engineering from Amirkabir University of Technology and postdoctoral experience at institutions like Case Western Reserve University, she specializes in advancing machine learning models for precise segmentation of cardiac structures including the left atrium , epicardial fat , and fibrosis using CT and MRI scans. Her work aims to improve diagnostic accuracy and treatment planning in cardiovascular care. Marjan's research focuses on medical imaging , deep learning , and computational anatomy , with recent publications on FractalRG , FK-means , and Poincare-guided UNet for cardiac structure segmentation. Her academic contributions span 15 recent publications , emphasizing fractal geometry , chaos theory , and optimization algorithms in biomedical applications. She actively develops open-source datasets and tools, such as the FK-means codebase , to support reproducibility in medical AI research.

Claudia Sinatraمشاهده پروفایل

مدرس

• Urban Design
• Spatial Planning
• Socio-Spatial Practices
• +۴ مورد دیگر

Claudia Sinatra is a Lecturer at ETH Zürich’s Department of Civil, Environmental and Geomatic Engineering, affiliated with the Institute of Spatial and Landscape Development. She holds an MSc in City Design and Social Science from LSE and an MSc in Architecture from the University of Ferrara (Italy), with additional studies at the Technical University of Munich. Her work bridges urban design, sociology, and visual storytelling, focusing on socio-spatial justice, care-oriented urbanism, and intersectional feminist spatial practices. Since 2022, she has contributed to ETH’s Master’s program in Spatial Development and Infrastructure Systems, teaching the Urban Design Studio for Planners. Her professional experience includes roles at multidisciplinary firms in London and Zurich, leading urban analyses, masterplans, and public realm projects. She actively engages with gender equity in planning through memberships in organizations like Frau + SIA and LARES. Parallel to academia, she practices as an independent graphic designer, integrating visual narratives with urban research. Her research and teaching emphasize collaborative design processes and critical dialogue in complex urban transformations. Notable engagements include moderating panels on planetary urbanization and postcolonial climate discourse, as well as exhibiting projects like “Activism. Feminism. Care” at ETH Zürich.

Divakar Viswanathمشاهده پروفایل

استاد

• Numerical Analysis
• Nonlinear Dynamics
• Population Genetics
• +۳ مورد دیگر

Divakar Viswanath is a Professor of Mathematics at the University of Michigan , with research expertise in numerical analysis, nonlinear dynamics, and mathematical genetics. His work bridges computational mathematics and scientific programming, focusing on problems like coalescent theory in population genetics and fluid dynamics. Education: BTech in Computer Science and Engineering, IIT Bombay (1992) PhD in Computer Science, Cornell University (1998) Research interests include: Numerical analysis of differential equations Nonlinear dynamics and chaotic systems Coalescent theory in population genetics with applications to mutation rates and inference algorithms Connections between dynamical systems and computational mathematics Recent publications span computational fluid dynamics, numerical methods, population biology, and symbolic dynamics, reflecting interdisciplinary applications of mathematics. He authored the book Scientific Programming and Computer Architecture (MIT Press, 2017), which explores program performance, memory optimization, and parallel computing models.

Kristopher Micinskiمشاهده پروفایل

استادیار

• Programming Languages
• Static Analysis
• Formal Methods
• +۴ مورد دیگر

Kristopher Micinski is an Assistant Professor in the Electrical Engineering and Computer Science Department at Syracuse University. His research focuses on scalable program analysis, static analysis, and formal methods applied to computer security and privacy. He holds a PhD in Computer Science from the University of Maryland and a BS in Computer Engineering from Michigan State University. Education: PhD, Computer Science, University of Maryland at College Park BS, Computer Engineering, Michigan State University Research Interests: His work bridges theory and application of program analyses, emphasizing scalable static analysis frameworks, Datalog optimization for distributed systems, and security applications. Recent efforts include GPU-accelerated Datalog engines and large-scale malware analysis pipelines like Assemblage. Grants & Projects: NSF PPoSS Large: $1M grant for declarative analytics DARPA V-SPELLS: $400K for legacy software optimization Assemblage: $350K DoD grant for malware classification Teaching: He teaches undergraduate and graduate courses on programming languages (CIS352) and formal methods (CIS700), with materials publicly available on YouTube.

Armando Foxمشاهده پروفایل

استاد

• Software as a Service
• Cloud Computing
• Parallel Programming
• +۳ مورد دیگر

Armando Fox is a Professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley, where he co-leads the ASPIRE Lab and directs the Berkeley MOOCLab. His work focuses on integrating online learning research into educational frameworks. He holds a PhD, MS and BS from Berkeley, Illinois, and MIT respectively. Fox's research spans applied statistical machine learning, Software as a Service (SaaS), cloud computing, parallel programming, and innovative online education methods. He pioneered Berkeley's first MOOC on software engineering and co-authored the textbook Engineering Software as a Service . His recent publications demonstrate strong focus on: AI-enhanced education tools and assessment systems Cloud-based learning management architectures Generative AI for collaborative programming education Automated testing frameworks for computer science Significant awards include: NSF CAREER Award ACM Distinguished Member Scientific American 50 top researcher recognition Fox previously contributed to Intel Pentium Pro microprocessor design and founded a mobile computing company based on his dissertation research.

• Pervasive Computing
• Wireless Sensor Networks
• Healthcare Informatics
• +۳ مورد دیگر

John Herbert is a Senior Lecturer in Computer Science at University College Cork (UCC), Ireland. His research focuses on pervasive computing, wireless sensor networks, and healthcare informatics, with notable projects like the CARA framework for falls assessment in the elderly. He holds a PhD in Computer Science from Cambridge University and has held visiting roles at institutions including SRI International and the University of Cambridge. Education: BSc (Experimental Physics), MSc (Experimental Physics), Postgraduate Diploma (Computer Science) from UCC; PhD (Computer Science) from Cambridge University. Research Grants: Led projects funded by Science Foundation Ireland (€163,726) and the Irish Research Council (€71,250), focusing on cloud computing and healthcare applications. His work emphasizes context-aware systems, data quality in medical environments, and real-time analysis. Selected awards include an International Fellowship from Digital Systems Research Center (1989) and visiting fellowships at Cambridge (2008-2009). Teaching: Courses include Advanced Software Engineering, Formal Methods, and Model-Based Software Development. Outreach: Advisor to award-winning student teams (e.g., IEEE mobile app contest) and collaborator with industry partners in China and Europe.

Marc Snirمشاهده پروفایل

استاد

• Parallel Computing
• Computer Architecture
• High Performance Computing
• +۳ مورد دیگر

Marc Snir is an Israeli-American Professor of Computer Science at the University of Illinois at Urbana-Champaign (UIUC). He holds a Doctor Honoris Causa from École Normale Supérieure de Lyon (2018). His career includes leadership roles such as Head of the Computer Science Department at UIUC (2001–2007) and Director of the Mathematics and Computer Science Division at Argonne National Laboratory (2011–2016). He is a Fellow of AAAS, ACM, and IEEE, and recipient of the IEEE Seymour Cray Award. Education: PhD in Mathematics from the Hebrew University of Jerusalem (1979). Research focuses on parallel algorithms, high-performance computing (HPC), distributed systems, and fault tolerance. He co-developed the MPI standard for parallel programming and contributed to IBM's Blue Gene supercomputer architecture. His Erdős number is 2, reflecting collaborative links to mathematician Paul Erdős. Awards include the IEEE Award for Excellence in Scalable Computing and leadership in advancing supercomputing through publications like The Future of Supercomputing (2004).

Donatella Sciutoمشاهده پروفایل

استاد

• Electronic Design Automation
• Embedded Systems
• Multiprocessor Systems
• +۵ مورد دیگر

Donatella Sciuto is a Full Professor of Computer Science and Engineering at Politecnico di Milano, serving as Executive Vice Rector overseeing research strategies. She holds a PhD from the University of Colorado, Boulder and an MBA from Bocconi University. Her research focuses on embedded systems design, low-power electronics, and cyber-physical systems, with contributions to smart cities and ICT infrastructure. Education: Bachelor's in Electronic Engineering, Politecnico di Milano (1984) PhD in Electrical and Computer Engineering, University of Colorado, Boulder MBA, SDA Bocconi School of Management Research Interests: Embedded systems, multiprocessor architectures, hardware/software co-design, power-efficient computing, and building automation via IoT technologies. She leads the Embedded Systems Design research group at Politecnico di Milano and coordinates EU-funded projects in smart cities and reconfigurable systems. Awards: IBM Women Leaders in AI (2021) IEEE Fellow (2011) EDAA Fellow (2010) Outstanding Contribution Award, IEEE Computer Society (2009) Professional Roles: Board Member: Bank of Italy, Istituto Italiano di Tecnologia, STM, Avio Former President, IEEE Council of Electronic Design Automation (2011-2013) Executive Committee Member, Design Automation and Test in Europe (DATE) conference Labs/Teams: Leads the Embedded Systems Design and Design Methodologies group at Politecnico di Milano, collaborating with CEFRIEL on executive education programs in embedded systems and IoT.

• Edge Computing
• Fog Computing
• Cloud Computing
• +۴ مورد دیگر

Dragi Kimovski is a Habilitated Assistant Professor in Distributed Systems at Klagenfurt University, Austria, focusing on Edge Computing and AI. He previously held roles at the University of Innsbruck and the University of Information Science and Technology in Macedonia. His research spans Edge/Fog/Cloud computing, multi-objective optimization, and high-performance computing. He has coordinated major projects like 6GContinuum and KärtnerFog, and led initiatives such as DataCloud and ASPIDE. His teaching includes courses on Distributed Computing, Cloud Computing, and IoT. He is the co-creator of the Carinthian Computing Continuum and maintains a blog on Edge AI World. His work emphasizes sustainable and efficient computing solutions for emerging technologies. Education: Not explicitly listed in the provided text. Research Interests: Edge Computing, Fog Computing, Cloud Computing, Multi-objective Optimization, High-Performance Computing, AI in Distributed Systems. His work addresses challenges in resource management, latency reduction, and scalability across heterogeneous environments, with applications in healthcare, IoT, and 6G networks. Projects: 6GContinuum (Coordinator): Focuses on AI services over 6G networks. KärtnerFog (Scientific Coordinator): Develops adaptive Fog infrastructures over 5G. DataCloud (WP5 Leader): Manages Big Data pipelines on the Computing Continuum. ASPIDE (Scientific Coordinator): Advances exascale programming models for data processing. Teaching: Klagenfurt University: Courses include Distributed Computing, IoT, Cloud Computing, and Advanced Programming. University of Innsbruck: Taught Advanced Parallel and Distributed Systems. University of Information Science and Technology: Courses in High-Performance Computing and Network Architectures. Labs/Teams: Co-created the Carinthian Computing Continuum, an automated SDN testbed for Edge computing research. Active in interdisciplinary teams addressing extreme data processing and sustainable computing.

Zhuang Zhengمشاهده پروفایل

مدرس

• Smart Grids
• Building Energy Management
• Renewable Energy Systems
• +۳ مورد دیگر

Dr. Zhuang Zheng is a Lecturer (Assistant Professor) at the School of Computing, Engineering & Digital Technologies, Teesside University since April 2024. Previously, he served as a Postdoc Fellow at the Hong Kong Polytechnic University’s Department of Building Environment and Energy Engineering. He holds a PhD in Architecture and Civil Engineering from City University of Hong Kong (2021). His research focuses on smart energy systems, particularly next-generation residential energy management, grid-building interactions, urban-scale energy modeling, and cyber-physical-social frameworks for low-carbon systems. Education: PhD (2021) from City University of Hong Kong; Postdoc (2021–2024) at Hong Kong Polytechnic University. Key Research Interests: Smart grids, building energy management, renewable energy integration, and decarbonization technologies. Notable contributions include novel energy modeling techniques for peak shaving, voltage regulation, and multi-scale urban energy systems. Articles Trends: His work spans smart building controls, IoT-enabled energy systems, stochastic optimization, and safety risk evaluation. Recent focus areas include distributed control strategies for HVAC clusters and voltage regulation in smart grids. Collaborations: Collaborated with Hong Kong Sun Hung Kai and AECOM on commercial building energy flexibility projects. Actively contributes to international journals and conferences, including chairing sessions at the International Conference on Applied Energy (ICAE). Future Directions: Developing intelligent digitalization for building, power, and transportation sectors to advance smart low-carbon systems through socio-technical integration.