جست‌وجوی استاد و پژوهشگر

Zhanyang Zhangمشاهده پروفایل

استاد

• Wireless Communication and Networks
• Cyber-Physical Systems & IoT/Sensor Networks
• Numerical Modelling and Simulation
• +۶ مورد دیگر

Dr. Zhanyang Zhang is a Professor of Computer Science at the City University of New New York, jointly appointed at the College of Staten Island and the CUNY Graduate Center. His expertise bridges wireless networking, cyber-physical systems, IoT, numerical simulation and high-performance computing, with recent emphasis on 5G/edge intelligence, ocean oil-spill modelling, and assistive technologies for the visually impaired. Education: Ph.D. in Computer Science, City University of New York M.S. in Computer Science, College of Staten Island B.E. in Computer Engineering, Jilin University, Changchun, China Research Interests: Dr. Zhang's research integrates communication theory, data science and large-scale simulation. He develops protocols for next-generation wireless networks, designs energy-efficient algorithms for underwater and terrestrial sensor systems, and applies lattice-Boltzmann methods to environmental disasters such as oil-spill propagation and urban flooding. His group also explores deep-learning-driven anomaly detection in 5G and edge-computing offloading strategies using game-theoretic and reinforcement-learning frameworks. Publication Trends: Across the past decade his scholarly output has shifted from foundational sensor-network coverage and localisation toward interdisciplinary applications: assistive smart-city services, GPU-accelerated ocean-surface transport models, and AI-based traffic and flood prediction. A recurrent methodological thread is the fusion of stochastic modelling (Markov, game, queueing) with high-performance simulation to derive scalable, real-time solutions. Grants & Projects: He currently leads or co-leads projects funded by the U.S. National Science Foundation and multiple CUNY schemes, totalling more than USD 1 M since 2020. Topics range from assistive cyber-connections for the visually impaired to next-generation oil-spill forecasting assimilating large sensor datasets. Labs & Teams: Dr. Zhang heads the Wireless & Cyber-Physical Systems Research Group at CSI, maintains affiliations with the CUNY High-Performance Computing Center, and supervises doctoral students within the Ph.D. Program in Computer Science at the Graduate Center.

• Deep Learning
• Hardware Architecture
• Computer Networks
• +۳ مورد دیگر

Brad McDanel is an Assistant Professor of Computer Science specializing in the intersection of deep learning, hardware architecture, and computer networks. His research focuses on optimizing deep neural networks (DNNs) through techniques like systolic array implementations, sparse architectures, and quantization. He has contributed to accelerating DNN inference on edge devices and distributed systems, with recent work addressing challenges in Large Language Models (LLMs) and multi-modal architectures. Education: Ph.D., Computer Science, Harvard University (2019) M.Sc., Computer Science, Wake Forest University (2012) B.Sc., Computer Science, Wake Forest University (2010) Research interests include speculative decoding, transformer optimization, and efficient deployment of AI systems. His work bridges theoretical frameworks and practical implementations, emphasizing computational efficiency and scalability. Notable contributions include innovations in systolic array optimization and distributed DNN architectures. Publications span conferences like ASPLOS, ICS, and ICDCS, with a focus on algorithmic and hardware-level optimizations for AI systems. He has pioneered techniques such as BranchyNet and Embedded Binarized Neural Networks, which enhance inference speed and resource utilization.

• Optical Fiber Sensing
• Wireless Communication Networks
• Internet of Things
• +۷ مورد دیگر

Huan Wu is an accomplished academic researcher with a substantial publication record spanning nearly three decades (1996-2025), comprising 63 indexed publications in the dblp database. Their scholarly work demonstrates expertise across multiple technical domains with significant contributions to optical communication systems, wireless networking, and artificial intelligence applications. Research interests focus on the intersection of electrical engineering and computer science, particularly in developing advanced sensing technologies using optical fibers, creating efficient communication protocols for IoT systems, and applying machine learning techniques to environmental monitoring and biomedical applications. Recent work shows a strong trend toward AI integration for infrastructure monitoring systems, including water pipe leak detection and hydrological modeling, as well as healthcare applications like non-invasive glucose monitoring and medical image analysis. The publication portfolio reveals a clear evolution from foundational work in signal processing and communication systems toward increasingly interdisciplinary research that bridges engineering with environmental science and healthcare. Recent publications (2023-2025) demonstrate particular strength in graph neural networks, distributed sensing systems, and AI-driven solutions for critical infrastructure monitoring. Based on publication patterns including consistent first-author contributions, supervisory roles evident in student collaborations, and extensive research output, Huan Wu appears to hold a senior academic position with significant research leadership. The collaborative nature of the work, involving multiple institutions across China and internationally, suggests active participation in major research initiatives and substantial grant funding, though specific details are not provided in the publication records.

• VLSI Design
• Integrated Circuits
• Computer Architecture
• +۶ مورد دیگر

Xiaoyang Zeng is a Professor at Tsinghua University's School of Information Science and Technology, Institute of Microelectronics, with an extensive research portfolio in VLSI design, integrated circuits, and hardware acceleration systems. With over 429 publications spanning from 2005 to 2025, Professor Zeng maintains an exceptionally active research program, particularly evident in the high publication volume in recent years (45 papers in 2024 and 28 projected for 2025). His collaborative network includes prominent researchers such as Yibo Fan, Jun Han, Xu Cheng, and Xiaoyong Xue. Professor Zeng's research focuses on cutting-edge areas including Compute-in-Memory architectures, neuromorphic computing, low-power circuit design, and hardware acceleration for AI applications. His work bridges theoretical innovation with practical implementation, as evidenced by numerous publications in top-tier IEEE journals including the Journal of Solid-State Circuits, Transactions on Circuits and Systems, and Transactions on VLSI Systems. Recent work demonstrates particular strength in RRAM-based CIM accelerators, energy-efficient converters, and advanced signal processing techniques. The publication trends show a strategic evolution from traditional circuit design toward emerging computing paradigms, with increasing focus on AI hardware acceleration, neuromorphic systems, and energy-efficient computing solutions. His research group has developed innovative approaches to address challenges in memory-centric computing, analog circuit design, and hardware implementation of machine learning algorithms, with applications spanning consumer electronics, medical devices, and edge computing systems. Selected Scientific Awards: IEEE Journal of Solid-State Circuits Best Paper Award (2022) National Natural Science Award of China (Second Class, 2020) IEEE Asian Solid-State Circuits Conference Best Paper Award (2019) Professor Zeng has successfully advised numerous graduate students who have become active contributors in the field, with several now leading their own research projects. His research has been supported by multiple national-level grants from the National Natural Science Foundation of China and the Ministry of Science and Technology, focusing on next-generation computing architectures and advanced circuit design methodologies. The research group maintains strong industry connections with leading semiconductor companies for technology transfer and practical implementation of research outcomes.

Jeremy Hollemanمشاهده پروفایل

دانشیار

• Low-power Circuits
• Analog/Mixed-Signal Design
• Neuromorphic Computing
• +۳ مورد دیگر

Dr. Jeremy Holleman is an Associate Professor and Program Coordinator of Electrical Engineering at the University of North Carolina at Charlotte. He directs the assessment efforts within the Electrical and Computer Engineering department. His research focuses on low-power analog/mixed-signal circuits, biomedical interface design, neuromorphic computation, and machine learning hardware for resource-constrained systems. He holds a Ph.D. (2009) and M.S. (2006) from the University of Washington and a B.S. (1997) from Georgia Institute of Technology. Dr. Holleman's work emphasizes energy-efficient computing architectures and hardware implementations of machine learning, including contributions to MLPerf Power benchmarks and TinyML standards. His academic background includes over 20 years of experience in analog circuit design, neuromorphic systems, and biomedical signal processing. Notable projects include a 1 Tera-OPS/Watt analog deep learning engine and ultra-low-power neural amplifiers for bio-potential recording. He has published extensively across IEEE journals and conferences, with over 50 peer-reviewed articles. His research has been applied in medical implants, wireless neural interfaces, and energy-harvesting systems. Current initiatives include advancing analog deep learning architectures and sustainable AI hardware optimization. Dr. Holleman’s lab collaborates on hardware-software co-design for embedded machine learning and neuromorphic systems.

Dora Blanco Herasمشاهده پروفایل

استاد

• High Performance Computing
• Parallel Computing
• GPU Computing
• +۷ مورد دیگر

Dora Blanco Heras is a Full Professor in the Department of Electronics and Computing at the University of Santiago de Compostela (Spain). She holds a PhD in Physics (cum laude) from the same institution and has served as Head of the Sustainable Development Office (2005–2010). Her research focuses on high performance computing, parallel computing, and GPGPU techniques applied to remote sensing and medical image processing. She leads the ARQCOMP research group in Computer Architecture and is affiliated with the Center for Research in Intelligent Technologies (CITIUS). Research interests include: High-performance computing for real-time image processing GPU acceleration of hyperspectral/multispectral analysis Anomaly detection in fluvial ecosystems Domain adaptation for Earth observation Machine learning for remote sensing classification Recent work emphasizes deep learning architectures (ResNet, GANs) for land-cover prediction and multispectral anomaly detection. Over 100 peer-reviewed publications span journals like IEEE Transactions on Geoscience and Remote Sensing, with impactful contributions to GPU-optimized algorithms and open-source tools like HypeRvieW. Professional service includes organizing HDCRS Summer Schools on high-performance geospatial AI and chairing Euro-Par workshops. Her team develops cutting-edge solutions for environmental monitoring and sustainable computing.

• Internet of Things
• 6G Software Architecture
• Machine Learning Systems
• +۵ مورد دیگر

Abhishek Kumar is a Researcher at the Center for Ubiquitous Computing, part of the Faculty of Information Technology and Electrical Engineering at the University of Oulu. He holds a PhD from the Department of Computer Science at the University of Helsinki (2018–2022). His research focuses on software-hardware co-integration in areas such as IoT, 6G, machine learning systems, energy efficiency, and environmental sensing, with a strong emphasis on trustworthy computing and UN Sustainable Development Goals (SDGs) like clean energy and sustainable cities. Teaching responsibilities include courses such as 521290S Distributed Systems (5 ECTS, 2025), High Performance Computing: Programming Parallel Supercomputers (3 ECTS, 2025), and Network Analysis and Graph Machine Learning (2 ECTS, 2025). Past courses include IoT and distributed systems modules from 2022 onward. He actively supervises 1 PhD student, 11 Master’s thesis students, and 4 independent studies, with prior supervision of 3 Bachelor’s, 4 Master’s, and 10 Advanced Practical Training students. Research collaborations span VTT, Ericsson Research, Alif Semiconductor, Aalto University, and University of Helsinki. He advises Soundxvision Oy, a Helsinki XR Center startup developing input modalities for XR headsets. Funding sources include the Finnish-American Research & Innovation Accelerator (FARIA), Indian-Finnish Consortia (FICORE), Nokia Foundation, and Walter Ahlström Foundation. His work bridges IoT, 6G, and AI to address environmental and societal challenges, leveraging software-defined systems and distributed computing paradigms. Recent publications explore metaverse integration, edge AI, and cybersecurity in emerging networks.

• Artificial Intelligence
• Reinforcement Learning
• Internet of Things (IoT)
• +۴ مورد دیگر

Dr. Qingzhi Liu is an Assistant Professor in the Information Technology Group at Wageningen University & Research, Netherlands. He holds a Ph.D. from Delft University of Technology (2016) and completed postdoctoral research at Eindhoven University of Technology (2016-2019). His expertise spans artificial intelligence, reinforcement learning, IoT, precision agriculture, and smart greenhouse systems. He previously worked at NEC China in 2009. Research Interests: Liu’s work focuses on applying machine learning techniques to agricultural challenges, IoT infrastructure optimization, and energy-efficient computing. His projects include crop classification using drone imagery, smart greenhouse climate modeling, and blockchain-based IoT security. He also explores edge computing and containerized cloud resource management for real-time applications. Publications: Over 30 peer-reviewed articles in top venues like IEEE TPDS, TITS, and TVT. Recent work covers reinforcement learning applications in microgrids, privacy-aware cloud scheduling, and hyperspectral disease monitoring in crops. Teaching: Involved in courses such as Big Data (INF33806), Artificial Intelligence (INF36306), and Advanced Machine Learning (FTE40306). Labs/Teams: Active contributor to the Information Technology Group, focusing on AI-driven solutions for agriculture and IoT ecosystems.

Maryam Parsaمشاهده پروفایل

استادیار

• Neuromorphic Computing
• Neural Architecture Search
• Bayesian Optimization
• +۶ مورد دیگر

Maryam Parsa is a Tenure-Track Assistant Professor in the Department of Electrical and Computer Engineering at George Mason University. Her research focuses on neuromorphic computing, Bayesian optimization, and algorithm-hardware co-design, aiming to develop energy-efficient, secure, and resilient AI systems for edge computing applications. She holds a PhD from Purdue University, supported by an Intel/SRC fellowship, and previously worked at Oak Ridge National Lab. Education: PhD in Electrical and Computer Engineering, Purdue University (2020) MS in Civil Engineering, Purdue University (Year unknown) MS in Electrical and Computer Engineering, University of Ottawa (Year unknown) BS in Electrical and Computer Engineering, Khaje Nasir Toosi University of Technology (Year unknown) Research Interests: Neuromorphic learning and bio-inspired robotics Bayesian optimization for materials discovery Privacy-preserving spiking neural networks Edge AI and real-time embedded systems Major Achievements: Lead a $2.4M 3-year project on 3D chip creation (2023) Recipient of Intel/SRC PhD Fellowship Current Work: Developing neuromorphic architectures for smart healthcare and cyber-physical systems Pioneering causal machine learning for materials innovation Advancing privacy & security in neuromorphic systems Labs/Teams: Active in Mason's neuromorphic computing research group with collaborations in national labs and industry partners.

Gines Garcia Avilesمشاهده پروفایل

استاد پژوهشی

• Telematics Engineering
• 5G/6G Networks
• Network Slicing
• +۲ مورد دیگر

Gines Garcia Aviles is a Research Professor in the Department of Information and Communication Engineering at the Faculty of Informatics, Universidad de Murcia. He obtained his doctorate from Universidad Carlos III de Madrid in 2021 with a thesis on network-slicing aware mobile protocols. Research Focus: Professor Aviles specializes in 5G/6G network architectures, O-RAN security, network virtualization, and intelligent networking systems. His work integrates cloud computing and AI into next-generation telecommunications infrastructure. Recent Work (2023-2024): Focuses on security vulnerabilities in Open RAN implementations, drone communication systems using 5G slicing, and architectural frameworks for natively intelligent 6G networks. His DAEMON project establishes a network intelligence plane for autonomous operations. Technical Contributions: Developed practical solutions like POSENS for end-to-end network slicing and Nuberu for virtualized RAN components, validated through experimental testbeds and EU collaborations.

• High-Performance Computing
• Parallel Computing
• Deep Learning
• +۳ مورد دیگر

Enrique S. Quintana-Ortí is a Professor at the Technical University of Valencia and Jaume I University , Spain, specializing in Computer Science of Systems and Computers . His work bridges High-Performance Computing (HPC) , Parallel Computing , and Deep Learning , with a focus on optimizing Matrix Algorithms for modern architectures. Key research areas: Quantized Inference , GEMM-Based Convolutions , GPU Acceleration , and Performance Portability across ARM, RISC-V, and NVIDIA processors. Recent projects include RED-SEA (European interconnect solutions), GreenLightningAI (decoupled AI systems), and Ginkgo (GPU-based linear algebra frameworks). His publications (2023–2025) emphasize edge computing , mixed-precision techniques , and energy-efficient AI . Collaborative efforts span institutions like Xilinx , Fujitsu , and co-authors such as Adrián Castelló , Héctor Martínez , and Francisco D. Igual .

Dinesh Kumar Sainiمشاهده پروفایل

استادیار

• Plant Breeding
• Sorghum Improvement
• Climate-Resilient Crops
• +۵ مورد دیگر

Dinesh Kumar Saini serves as a Research Assistant Professor in the Department of Plant and Soil Science at Texas Tech University, focusing on molecular and conventional breeding strategies for climate-resilient crops with emphasis on sorghum improvement. Education: Ph.D. in Plant Breeding from Punjab Agricultural University, India (awarded Chancellor Medal and University Gold Medal) Master's and Bachelor's from Chaudhary Charan Singh University (recipient of multiple gold/silver medals and Kisan Trust Prize) Saini's research spans plant breeding, genetics, genomics, and bioinformatics, specializing in trait mapping, marker-assisted breeding, and high-throughput phenotyping. He investigates physiological, molecular, and histological mechanisms for climate resilience and yield potential in grain sorghum, integrating data-driven approaches with physiological insights to accelerate crop improvement. His publication trends reveal intensive work on genomic selection models, meta-analysis of QTLs for abiotic stress tolerance in cereals and legumes, and AI-driven phenotyping techniques. Research spans sorghum, wheat, rice, and chickpea, targeting drought, heat, and salinity resilience through advanced breeding strategies. Scientific awards: Chancellor Medal University Gold Medal DST-INSPIRE Fellowship ICAR-SRF Fellowship Kisan Trust Prize Multiple Gold Medals Multiple Silver Medals Saini mentors graduate students and contributes to departmental teaching. As a core member of the Texas Coalition for Sustainable Integrated Systems Research Program (TeCSIS), he collaborates with breeders, physiologists, and machine learning experts to develop cutting-edge tools for high-throughput phenotyping and molecular breeding, aiming to enhance agricultural sustainability in vulnerable regions.

Hui Linمشاهده پروفایل

دانشیار

• Cyber-Physical Systems
• Smart Grid Security
• Network Security
• +۳ مورد دیگر

Hui Lin is an Associate Professor in the Department of Electrical, Computer and Biomedical Engineering at the University of Rhode Island. She leads the Dependable Cyber-Physical Systems (DeCPS) Laboratory, focusing on enhancing security and resilience in smart power grids through interdisciplinary approaches combining cybersecurity, computer networks, and artificial intelligence. Ph.D. in Electrical and Computer Engineering, University of Illinois at Urbana-Champaign (2017) M.S. in Electrical and Computer Engineering, University of Illinois at Chicago (2010) Her research spans cyber-physical security, IoT defense mechanisms, and smart grid resilience. Key methodologies include physics-informed graph neural networks, runtime semantic analysis, and software-defined networking virtualization. She has developed frameworks like HELICSAuto for co-simulation and DefRec for anti-reconnaissance defense. Recent publications focus on programmable switch security, prediction-based grid monitoring, and AI-driven attack mitigation. Funded by NSF, DoD, and DOE, she received the 2022 NSF CAREER Award and a best poster award at SoSSDN 2016. Her lab actively recruits PhD students and post-doctoral researchers. 2022 NSF CAREER Award Best Poster Award at SoSSDN Workshop 2016 Grants include: 2024 DoD ONR Co-PI: "Advancing Research on Cyber-Physical Security and Resilience" 2023 NSF PI: "SaTC Small - Enabling Programmable In-Network Security for an Attack-Resilience Smart Grid" 2023 NSF PI: "PARP: Mislead Physical-Disruption Attacks by Preemptive Anti-Reconnaissance" 2020 NSF PI: "Preempting Physical Damage from Control-Related Attacks"

Gian Luca Forestiمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +۲ مورد دیگر

Prof. Gian Luca Foresti is Full Professor of Computer Science at the University of Udine and Director of the Master in Intelligence and Emerging Technologies. His research spans artificial intelligence, computer vision, and cybersecurity with applications in medical imaging, industrial inspection, and aerial surveillance. Research develops novel neural architectures including capsule-enhanced models, physics-informed networks, and bio-inspired systems. Recent innovations address underwater imaging, federated medical AI, anomaly detection, and document analysis for cultural heritage preservation. Publications demonstrate leadership in multi-disciplinary applications, particularly medical imaging (MS segmentation, brain anomaly detection) and industrial automation (defect detection, quality control). Recent work shows emphasis on efficient models for edge deployment and security applications. Directs research initiatives in intelligence technologies, including drone swarm coordination for surveillance tasks and cybersecurity frameworks for containerized environments. Current projects span healthcare diagnostics, environmental monitoring, and heritage preservation.

• Artificial Intelligence
• Reinforcement Learning
• Internet of Things (IoT)
• +۵ مورد دیگر

Dr. Qingzhi Liu is an Assistant Professor at the Information Technology Group within Wageningen University & Research in the Netherlands. He holds a Ph.D. from Delft University of Technology (2016) and postdoctoral research experience at Eindhoven University of Technology (2016-2019). His research focuses on AI-driven solutions for precision agriculture, IoT systems, and edge computing. He teaches courses including Big Data (INF33806), Artificial Intelligence (INF36306), and Advanced Machine Learning (FTE40306). Research interests include reinforcement learning applications in microgrid energy management, deep learning-based crop classification using UAV imagery, and blockchain-enhanced IoT security frameworks. He has published in top venues like IEEE TPDS and IEEE TITS, addressing challenges in smart agriculture, distributed cloud scheduling, and energy-efficient computing. His work integrates computer vision techniques for agricultural monitoring (e.g., plant stem occlusion inpainting), low-code platforms for agri-tech development, and secure IoT data fusion methods. He actively contributes to interdisciplinary projects at the intersection of IT and life sciences.