جست‌وجوی استاد و پژوهشگر

Semih Doğuمشاهده پروفایل

استادیار

• Electromagnetic Theory
• Microwave Imaging
• Inverse Scattering Problems
• +۳ مورد دیگر

Semih Doğu is an Assistant Professor at the Department of Electronics and Communication Engineering , Faculty of Electrical and Electronics Engineering , Istanbul Technical University . His research focuses on Electromagnetic Theory , Microwave Imaging , Inverse Scattering Problems , and Antenna Design . Doctorate: Istanbul Technical University (2023) Master's: Istanbul Technical University (2017) Bachelor's: Yıldız Technical University (2015) His research interests emphasize microwave-based diagnostics, including: Microwave imaging for breast cancer detection Antenna optimization for medical and security applications Inverse problem solving in electromagnetic systems Through-the-wall imaging for surveillance Semih Doğu's recent publications demonstrate his expertise in: Neural networks for temperature monitoring in hyperthermia Ku/Ka-band antenna designs for satellite systems Microwave salinity sensing Algorithm development for improved imaging accuracy He contributes to the ITU Electromagnetics Research Group , participating in projects like: Microwave Brain and Breast Imaging Device Development Compressed Sensing for Energy-Efficient Communication Microwave Tissue Analysis

Pingbo Tangمشاهده پروفایل

دانشیار

• Civil Engineering
• Remote Sensing
• Human Systems Engineering
• +۵ مورد دیگر

Pingbo Tang is an Associate Professor in the Department of Civil and Environmental Engineering at Carnegie Mellon University, affiliated with the College of Engineering. He leads the Spatiotemporal Workflows and Resilient Management Laboratory (SWARM Lab). Tang holds a Ph.D. from Carnegie Mellon University (2009), a Master’s in Bridge Engineering from Tongji University (2005), and a Bachelor’s in Civil Engineering from Tongji University (2002). His research focuses on civil infrastructure operations, human systems engineering, and AI-driven solutions for predictive management. Key areas include remote sensing, data analytics, and Human-Cyber-Physical-Systems (H-CPS) in construction and infrastructure (e.g., airports, nuclear plants). He has published over 100 peer-reviewed articles and secured funding from NSF, DOE, NASA, and industry partners. Tang is a leader in professional organizations, including ASCE (Computing Division Chair), TRB (Bridge Management Committee), and ASTM International. He has received prestigious awards, including the NSF CAREER Award (2015), ASCE Halpin Award (2020), and multiple best-paper honors. His work integrates AI, digital twins, and safety innovation, with applications in aviation scheduling (e.g., Alaska Airlines collaborations) and nuclear power plant operations. Tang’s research emphasizes sustainable infrastructure, safety, and interdisciplinary solutions for complex systems.

• Wireless Communications
• 5G/6G Networks
• Non-Orthogonal Multiple Access (NOMA)
• +۸ مورد دیگر

Leila Musavian is a Professor of Wireless Communications at the University of Surrey's Department of Electrical and Electronic Engineering within the Faculty of Engineering and Physical Sciences. She is a leading researcher in next-generation wireless communication systems with a focus on 5G/6G technologies, particularly in the areas of non-orthogonal multiple access (NOMA), energy harvesting communications, physical layer network coding, and ultra-reliable low-latency communications (uRLLC). Her research has significant implications for vehicular networks, IoT applications, and future wireless infrastructure. Her research interests span multiple critical areas of modern wireless communications, including energy-efficient resource allocation, security in wireless networks, massive MIMO systems, and reconfigurable intelligent surfaces. She has pioneered work on the performance analysis of NOMA systems under statistical quality of service constraints, which has become increasingly important for emerging applications requiring ultra-reliable and low-latency communications. Her research bridges theoretical foundations with practical implementations, often incorporating machine learning techniques for optimization of wireless networks. Her publication portfolio shows a clear evolution toward cutting-edge topics in wireless communications, with recent focus on holographic beamforming via dynamic metasurface antennas, robotic wireless energy transfer, and deep reinforcement learning applications for vehicular optical camera communications. These works demonstrate her ability to identify and address emerging challenges in next-generation wireless systems, particularly those related to 6G technologies and beyond. Professor Musavian has successfully supervised numerous PhD students who have gone on to become active researchers in the field, with many continuing to collaborate with her on cutting-edge projects. Her research has been supported by significant grants from national and international funding bodies, though specific grant details are not visible in the current dataset.

Luke Olsonمشاهده پروفایل

استاد

• Numerical Analysis
• High-Performance Computing
• Scientific Computing
• +۴ مورد دیگر

Luke Olson is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), part of the College of Engineering. He holds an affiliate appointment in the Department of Mechanical Science and Engineering. His research focuses on numerical methods, high-performance computing, and parallel algorithms, particularly algebraic multigrid (AMG) solvers and sparse matrix computations. He leads the development of open-source libraries like PyAMG and RAPtor, advancing computational tools for scientific and engineering applications. Education: Ph.D. in Applied Mathematics from the University of Colorado Boulder (2003), M.S. in Mathematics from the University of Iowa (1999), and B.A. in Mathematics and Physics from Luther College (1997). Research Interests: Algebraic multigrid methods and preconditioners High-performance computing and parallel algorithms Numerical solutions to partial differential equations Scientific computing and software development Machine learning integration in numerical simulations Recent Articles Trends: Recent work merges machine learning with traditional numerical methods, such as neural network closures for turbulent combustion and reduced basis approximations using neural networks. Ongoing contributions include optimizing multigrid methods for exascale architectures and enhancing communication efficiency in distributed systems. Awards: Recognized with the NSF CAREER Award (2007), UIUC Campus Award for Excellence in Teaching (2024), and the Donald Biggar Willett Faculty Scholar distinction (2016). Active in conference organization, including the Copper Mountain Conference on Multigrid Methods. Teaching & Grants: Teaches courses like Numerical Methods for PDEs and Scientific Machine Learning. Leads projects funded by NSF and industry collaborations, emphasizing education innovation through the AE3 fellowship (2014–2016). Labs/Teams: Directs the Scientific Computing Group at UIUC and contributes to interdisciplinary initiatives like the Center for Exascale-enabled Scramjet Design (CEESD).

Linghao Songمشاهده پروفایل

استادیار

• Accelerator Architecture
• FPGAs
• Hardware Acceleration Systems
• +۴ مورد دیگر

Linghao Song is an Assistant Professor in the Department of Electrical & Computer Engineering at Yale University. His research focuses on accelerator architecture design, FPGA-based acceleration systems, and ReRAM-based computing. He is affiliated with the School of Engineering & Applied Science and holds expertise in sparse matrix processing, high-level synthesis, and task-parallel programming frameworks. Education: Ph.D. and M.S. from Duke University and University of Pittsburgh, respectively, with a B.S.E. from Shanghai Jiao Tong University. Research Interests: Song's work spans FPGA-accelerated computing, resistive memory (ReRAM) architectures for deep learning and graph processing, and high-performance sparse matrix operations. His recent projects include the TAPA framework for FPGA programming, ReFloat for iterative linear solvers, and the Sextans/Serpens accelerators for sparse matrix computations. Key Contributions: Over 20 peer-reviewed publications on topics like ReRAM-based accelerators (e.g., GraphR, PipeLayer), FPGA optimization frameworks (TAPA, RapidStream), and novel hardware designs for neural networks and graph analytics. His work emphasizes energy efficiency, scalability, and cross-domain applicability of hardware accelerators. Awards: Recipient of the Duke ECE Outstanding Dissertation Award (2021), EDAA Outstanding Dissertation Award (2020), and National Scholarship of China (2012). Labs/Teams: Leads research in FPGA acceleration and memory-based computing, collaborating on projects involving HBM integration, resistive memory architectures, and task-parallel dataflow frameworks.

• Cloud Computing
• Distributed Computing
• Parallel Computing
• +۱۰ مورد دیگر

Professor Javid Taheri is a leading academic at Karlstad University (2019–present), previously serving as Associate Professor (2015–2019) and Senior Lecturer (2015). His research focuses on cloud computing, edge computing, distributed systems, and AI-driven networking. He holds a Ph.D. in Information Technologies from The University of Sydney (2007) and an M.Sc./B.Sc. in Electrical Engineering from Sharif University of Technology (2000/1998). Research interests include cloud-edge continuum systems , resource optimization , 5G/6G networking , and AI for IoT . Notable contributions include frameworks like PerfSim (microservice performance simulation) and MultiScaler (auto-scaling for cloud applications). Publications highlight innovations in edge computing optimization, security for distributed systems, and machine learning for resource management. He has co-authored over 150 papers across top venues like IEEE Transactions and ACM conferences. Academic leadership includes roles as conference chair (IC2E 2023) and editorial work for journals on cloud and edge computing.

Haijian Sunمشاهده پروفایل

استادیار

• Wireless Communication
• 5G and Beyond Networks
• Internet of Things (IoT)
• +۴ مورد دیگر

Haijian Sun is an Assistant Professor at the University of Georgia's School of Electrical & Computer Engineering. His research focuses on advanced wireless communication systems, including 5G/6G networks, federated learning, mobile edge computing, and physical layer security. He explores cutting-edge topics like reconfigurable intelligent surfaces (RIS), hybrid active-passive symbiotic radio systems, and UAV-enabled communication. His work integrates machine learning and optimization techniques to address challenges in channel modeling, energy efficiency, and network security. Recent projects include autonomous agricultural monitoring via drones and energy-harvesting sensors, as well as secure IRS-VLC communication strategies. Publications highlight innovations in dynamic wireless charging for electric vehicles, graph-based phishing detection, and radio radiance field modeling. While no specific awards are listed, his contributions reflect significant engagement with industry-relevant 6G research. Research collaborations involve digital twin networks, IoT systems, and smart grid applications. His team develops practical solutions for real-world communication challenges, emphasizing both theoretical rigor and deployable technologies.

Per Stenströmمشاهده پروفایل

استاد

• Computer Architecture
• Memory Systems Optimization
• Energy-Efficient Computing
• +۳ مورد دیگر

Professor Per Stenström is affiliated with the Department of Computer Science and Engineering at Chalmers University of Technology . His research focuses on computer architecture , memory systems optimization , and energy-efficient computing , with significant contributions to DNN accelerator design and cache management . Research Trends : His recent publications emphasize Memory compression techniques for energy efficiency Hardware-software co-design for DNN acceleration Security in microarchitectural optimizations Hybrid memory systems for near-memory computing These works span both theoretical and applied aspects of computer architecture, with a particular focus on data redundancy elimination , parallel processing , and quality-of-service constraints . His work has influenced the development of energy-aware resource management frameworks and resilient EU HPC systems , as evidenced by his long-standing contributions to the field since the early 2010s.

Yifan Sunمشاهده پروفایل

استادیار

• GPU Architecture
• Computer Architecture Simulation
• Multi-GPU Systems
• +۵ مورد دیگر

Yifan Sun is an Assistant Professor in the Department of Computer Science at William & Mary, leading the Scalable Architecture Lab. He holds a Ph.D. in Electrical and Computer Engineering from Northeastern University (2020). His research focuses on GPU architecture, simulation tools, and multi-GPU system design. Recent work includes TrioSim (a lightweight DNN workload simulator) and NetCrafter (optimizing multi-GPU network traffic). He has published extensively at top venues like ISCA, MICRO, and IEEE Vis. Educations: Ph.D. in Electrical and Computer Engineering, Northeastern University (2020); M.S. and B.S. not explicitly stated but implied through academic progression. Research Interests: Developing explainable architecture tools, improving simulation frameworks (Akita/MGPUSim), and addressing challenges in wafer-scale GPU design. His work bridges hardware-software co-design with visualization techniques to enhance human understanding of complex architectures. Grants: Awarded NSF CCRI and CRII grants for simulation-as-a-service and explainable architecture projects. Collaborations include UVA, NUS, and Northeastern University. Labs/Teams: Scalable Architecture Lab (SARCHLAB), focusing on GPU systems, simulation, and visualization. Active in organizing workshops and GitHub repositories (e.g., https://github.com/sarchlab).

George Dasoulasمشاهده پروفایل

پژوهشگر

• Graph Machine Learning
• Structured Data Analysis
• Biomedical Informatics
• +۳ مورد دیگر

George Dasoulas is a Postdoctoral Researcher at Harvard University's Department of Biomedical Informatics, affiliated with the Zitnik Lab. He holds a PhD in Computer Science from École polytechnique in Paris, France, and previously worked at Huawei Technologies France. His research focuses on graph machine learning, particularly in biomedical applications and telecommunications, with contributions to graph neural networks (GNNs), attention mechanisms, and topological deep learning. Education: Ph.D., Computer Science (DaSciM group, LIX, École polytechnique); Diploma in Electrical & Computer Engineering (National Technical University of Athens). His work includes developing Lipschitz-normalized attention layers, parametrized graph shift operators, and modularity-aware graph autoencoders. He has been recognized with the 2022 Wojcicki and Troper Fellowship from Harvard's Data Science Initiative. Key Research Themes: Graph Representation Learning, Topological Neural Networks, Equivariant Learning, Multimodal Learning Applications: Biomedical Informatics, Telecommunications, Sustainable AI His articles emphasize scalable GNN architectures, graph-based unlearning strategies, and multimodal protein phenotyping. He has contributed to open-source projects like LipschitzNorm and PGSO, and actively publishes in top conferences (ICML, ICLR, NeurIPS).

• Renewable Energy Systems
• Thermal Energy Storage
• Building Energy Efficiency
• +۴ مورد دیگر

Professor Zhenjun Ma is a Professor and Deputy Director of the Sustainable Buildings Research Centre (SBRC) at the University of Wollongong. He holds a PhD from The Hong Kong Polytechnic University and has expertise in renewable energy systems, thermal energy storage, and building energy efficiency. His research focuses on advancing sustainable HVAC solutions, building control optimization, and demand flexibility in energy systems. He has received prestigious awards including the World Society of Sustainable Energy Technologies Innovation Award and Excellence in HVAC&R Research from AIRAH. Education: BEng and MSc from Xian Jiaotong University; PhD from Hong Kong Polytechnic University. Current academic roles include editorial board memberships in journals like Renewable Energy and Energy Conversion and Management , and leadership in initiatives like the NSW Decarbonisation Innovation Hub. Research Interests: Renewable energy integration in buildings, thermal storage technologies, data-driven building analytics, and grid-to-building energy systems. Active in over 40 funded projects, including grants from Australia's Department of Industry and the NSW Government. Awards: Invitational Fellowship from Japan Society for the Promotion of Science (2025), Fellow of AIRAH (2021), and multiple best paper awards. Supervises over 30 PhD/Master’s students on topics like energy flexibility optimization and net-zero building systems. Labs/Teams: Leads SBRC’s energy efficiency and sustainability research clusters. Collaborates with industry partners like BlueScope Steel on solar energy solutions.

Volker Rodehorstمشاهده پروفایل

استاد

• Computer Vision
• Image Analysis
• 3D Reconstruction
• +۵ مورد دیگر

Univ.-Prof. Dr.-Ing. habil. Volker Rodehorst is a full professor of computer vision at Bauhaus-Universität Weimar, holding positions in both the Faculty of Media and Faculty of Civil Engineering. His research focuses on photogrammetric computer vision, image analysis, 3D reconstruction, and structural health monitoring with applications in civil infrastructure inspection and urban modeling. He leads projects like ev.AI.luate and InfraCloud, leveraging AI and UAS technologies for infrastructure assessment. Education: PhD (2003): Technical University of Berlin, Faculty of Civil Engineering & Applied Geosciences Habilitation (2013): TU Berlin, Faculty of Electrical Engineering & Computer Science Computer Science Diploma (1994): TU Berlin Research Interests: UAS-based structural inspection using multi-view stereo and deep learning Crack detection and segmentation in concrete structures Automated building age estimation for energy modeling Flight path planning optimization for complex structures Integration of computer vision into BIM workflows Publications: Recent work emphasizes robust algorithms for crack detection (Omnicrack30k benchmark), UAS flight path optimization, and semantic segmentation challenges in bridge inspections. Key contributions include MVCrackViT and CISOL datasets advancing structural analysis methodologies. Awards: Best Academic Performance Prize (1994) - TU Berlin ISPRS Presidential Citation (2008) for WG III/2 leadership Grants & Labs: Leads Bauhaus' 3D-RealityCapture-ScanLab and coordinates EU projects like AISTEC-PRO. Active in developing modular solutions like smoodPLAN for infrastructure inspection. Teaching: Offers courses in photogrammetric computer vision, geodesy, and parallel systems. Supervises PhD students in structural health monitoring and computer vision.

• Machine Learning
• Deep Learning
• Cybersecurity
• +۴ مورد دیگر

Dr. Yi Wang is an Associate Professor and Department Chairperson of the Electrical and Computer Engineering Graduate Programs at Manhattan College, New York. He also serves as Director of the Electrical & Computer Engineering Graduate Program. His research focuses on machine learning, deep learning, cybersecurity, blockchain, and their applications in cyber-physical systems. Dr. Wang is an IEEE Senior Member (since 2021) and has secured NSF grants totaling over $149,000. Education: Ph.D. Computer Engineering, University of Alabama in Huntsville M.S. Computer Science, Wuhan University of Science and Technology B.S. Information Systems, Wuhan University of Science and Technology Research Interests: Machine learning and deep learning algorithms Cybersecurity for IoT and smart grids Blockchain applications in attribute-based access control Adversarial machine learning defense mechanisms Optical fiber communication systems Recent Publications Trends: His work spans blockchain platform comparisons, adversarial attack mitigation in power systems, and AI-driven smart home solutions. He frequently publishes in IEEE journals and conferences. Awards: Best Paper Award at 2017 IEEE UEMCON Best Paper Award at 2015 ICDIP Grants and Advising: Principal Investigator for NSF-funded UIRiSCS project (2022–2025). Co-Principal on NYC DOT Loading Zone Study. Recipient of Manhattan College Faculty Summer Grants (2021, 2017). Advises graduate students in cybersecurity and IoT research. Labs/Teams: Collaborates with the University of Zaragoza, Spain on smart systems research. Leads projects on plastic optical fiber networks and AI-driven smart home systems.

Vishal Saxenaمشاهده پروفایل

استاد

• Silicon Photonic Integrated Circuits
• Neuromorphic Computing
• CMOS Electronics
• +۳ مورد دیگر

Professor Vishal Saxena is a faculty member in the Department of Electrical and Computer Engineering at the University of Delaware since 2019. Previously, he held positions at Boise State University (2010–2016) as Assistant and Associate Professor, and served as the Micron Endowed Professor of Microelectronics at the University of Idaho (2016–2019). His research focuses on analog electronic and photonic integrated circuits (ICs), particularly in sustaining IC design advancements post-Moore scaling through hybrid CMOS-photonic integration, neuromorphic computing, and energy-efficient embedded intelligence. Dr. Saxena earned his B.Tech. in Electrical Engineering from IIT Madras (2002), followed by M.S. and Ph.D. in Electrical and Computer Engineering from Boise State University (2007–2010). He has industry experience in semiconductor and telecommunication engineering. His work is supported by NSF, AFOSR, DARPA, NASA, and industry collaborators. Notable awards include the NSF CAREER (2015), AFOSR YIP (2016), and DARPA YFA (2019). His research interests span silicon photonic ICs for optical interconnects, RF photonic systems, neuromorphic circuits using emerging NVM devices, and high-speed analog-to-digital converters. He has pioneered compact modeling techniques for photonic components and developed energy-efficient architectures for spiking neural networks. Dr. Saxena’s publications reflect advancements in photonic integration, neuromorphic hardware, and mixed-signal IC design. He actively contributes to the IEEE community through editorial roles and conference steering committees, including MWSCAS and ISCAS.

Adlen KSENTINIمشاهده پروفایل

استاد

• Mobile and Wireless Networks
• Software Defined Networking (SDN)
• Mobile Edge Computing (MEC)
• +۷ مورد دیگر

Adlen KSENTINI is a Professor at EURECOM's Communication Systems department, specializing in advanced networking technologies. His research focuses on Mobile and Wireless Networks, Software Defined Networking (SDN), Mobile Edge Computing (MEC), Network Function Virtualization (NFV), and Content Delivery Networks (CDN), with an emphasis on performance evaluation and network virtualization. He has contributed to projects like AC3 and 6G-BRICKS, exploring cloud-edge continuum integration and 6G infrastructure. Key research interests include virtualized mobile core networks, carrier cloud systems, and AI-driven network management. He has received Best Paper Awards at IEEE WCNC 2018 and IWCMC 2016 for works on network slicing and LTE modeling accuracy. His work often integrates machine learning for optimization, sustainability, and security in 5G/6G networks. Distinctions: Two Best Paper Awards Labs/Teams: Involved in EU-funded projects like AC3 and 6G-BRICKS Grants: Not explicitly listed, but active in collaborative research initiatives