جست‌وجوی استاد و پژوهشگر

• Geotechnical Earthquake Engineering
• Seismic Hazard
• Soil-Structure Interaction
• +۱۲ مورد دیگر

Mauro Aimar is a Fixed-term Assistant Professor in the Department of Structural, Geotechnical and Building Engineering (DISEG) at Politecnico di Torino, Italy. He is affiliated with the College of Civil and Building Engineering and serves as an invited member of the College of Environmental and Land Engineering. His academic and research activities focus on geotechnical earthquake engineering, seismic hazard, soil-structure interaction, and structural health monitoring. His research interests include: Seismic response and site characterization Scour of foundations and bridge piers Modal analysis and damping ratio estimation Numerical modeling of unsaturated soils Experimental testing in civil engineering Aimar's recent publications (2023–2025) center on seismic performance of bridges under scour, advanced site characterization using surface waves, and numerical modeling of soil-structure systems. His work combines experimental, numerical, and field-based approaches to assess infrastructure resilience. Key themes include Rayleigh wave analysis, MASW testing, FEM modeling of infiltration, and dynamic monitoring of bridge piers. He supervises PhD student Marlio Molina Manrique in the Civil and Environmental Engineering program. His teaching contributions span multiple degree levels, including courses on Soil-Structure Interaction, Geotechnical Earthquake Engineering, Seismic Risk, and InfraBIM. He has no listed scientific awards in the provided sources. Mauro Aimar actively contributes to research projects involving testing and measurement in civil engineering, with a focus on seismic resilience and infrastructure safety. He collaborates with Prof. Sebastiano Foti and other researchers in geotechnical and earthquake engineering.

• Machine Learning
• Biomedical Engineering
• Signal Processing
• +۳ مورد دیگر

Yanbo Zhang is a researcher affiliated with Nanyang Technological University , holding a PhD in Wireless Communication and Sensing (2022). His work spans machine learning , biomedical engineering , and signal processing with applications in healthcare, communication systems, and computer vision. Education: PhD from Nanyang Technological University, Singapore (2022) Research areas include: Machine learning for medical diagnosis (predicting infections, cardiovascular events, and cancer) Advanced analog-digital converter designs for precision electronics Diffusion models and evolutionary algorithms in AI Federated learning for imbalanced data and cybersecurity Recent publications focus on multi-label learning for disease prediction, secure communication architectures , and deep learning applications in medical imaging. Collaborations include institutions in Singapore, China, and international teams.

Mahdi Boloursaz Mashhadiمشاهده پروفایل

پژوهشگر

• Signal Processing
• Wireless Communications
• Machine Learning
• +۳ مورد دیگر

Mahdi Boloursaz Mashhadi is a researcher at Imperial College London's Department of Electrical and Electronic Engineering, affiliated with the Information Processing and Communications Lab. He holds a Ph.D. in Electrical Engineering from Sharif University of Technology (2018), with prior research roles at the University of Central Florida and Queen's University. His expertise spans signal processing, wireless communications, machine learning applications in communication systems, and biomedical signal processing. Dr. Mashhadi's research focuses on massive MIMO channel state acquisition , deep learning-driven pilot design , and semantic communication frameworks . His recent work explores token-domain multiple access, generative AI integration in communication systems, and federated learning optimizations. He has contributed to foundational studies in sparse signal reconstruction (e.g., iterative adaptive thresholding methods) and wearable health monitoring via PPG signals. Key achievements : Best Paper Award at EWDTS 2012, multiple grants (IEEE, national/regional), and patents (e.g., US Patent 9729160). Current projects include semantic-aware power allocation in generative communications and latency optimizations in distributed deep learning frameworks. Labs/Teams : Member of the Intelligent Systems and Networks (ISN) group at Imperial, collaborating on AI-driven communication systems and edge computing solutions. His work bridges theoretical signal processing with practical implementations in 5G/6G networks, biomedical devices, and distributed machine learning ecosystems.

Suraj Jogمشاهده پروفایل

پژوهشگر

• Wireless Networking
• RF Sensing
• IoT and Low Power Networks
• +۱ مورد دیگر

Suraj Jog is a Senior Research Scientist in the Networking Research Group at Microsoft Research, Redmond. He holds a PhD in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (UIUC), advised by Haitham Hassanieh, and a Bachelor of Technology in Electrical Engineering from IIT Bombay. His research focuses on Wireless Networking , RF Sensing , IoT and Low Power Networks , and Machine Learning . He has contributed to projects like FarmBeats and developed systems such as HawkEye for mmWave imaging. His work spans spectrum efficiency in satellite networks, mmWave beam alignment, and ambient signal utilization for IoT localization. Notable achievements include the Qualcomm Innovation Fellowship 2020, Third Place in the SIGCOMM 2020 Student Research Competition, and the Joan and Lalit Bahl Fellowship. He serves on editorial boards for IEEE Network Magazine and conference program committees like MobiCom 2023/2024. His research bridges theoretical advancements with practical applications, such as reducing environmental sensing costs by 50x and demonstrating mmWave imaging in foggy conditions. Advising: No formal advisees listed. Current roles include leadership in the Networking Research Group at Microsoft, and contributions to open-source projects and datasets for radar imaging and mmWave systems.

James Hopgoodمشاهده پروفایل

استاد

• Statistical Signal Processing
• Machine Learning
• Data Science
• +۵ مورد دیگر

James Hopgood is a Professor of Statistical Signal Processing at the University of Edinburgh's School of Engineering, leading the Institute for Data, Imaging, and Communications. He holds roles as Dean of Quality and Enhancement for the College of Science and Engineering and Director of the EPSRC/MoD CDT in Sensing, Processing & AI for Defence and Security (SPADS). His research focuses on statistical signal processing applications in medical imaging, acoustic sensing, and multi-target tracking. Hopgood earned his M.A., M.Eng., and Ph.D. from the University of Cambridge, joining Edinburgh in 2004. He has developed algorithms for Bayesian signal processing, acoustic source localization, and medical imaging, with over 100 publications. As Editor-in-Chief of the IET Journal of Signal Processing since 2011, he contributes to academic leadership. Teaching includes courses on signal processing, probability, and sensor networks, emphasizing visualization and real-world applications. Current projects span adaptive optical imaging for medical diagnostics and probabilistic graphical models for multi-target tracking. His work bridges academia and industry, collaborating with companies like Leonardo and Agilent Technologies. He supervises numerous PhD students and leads multidisciplinary initiatives in defense, healthcare, and autonomous systems. **Education**: PhD in Statistical Signal Processing, University of Cambridge (2001) MEng in Electrical and Information Sciences, University of Cambridge (1997) MA, University of Cambridge (2000) **Research Interests**: His work spans model-based Bayesian methods, acoustic source localization, medical imaging (e.g., fluorescence lifetime imaging), and multi-target tracking. Recent projects include optimizing UAV trajectories in dynamic environments and developing algorithms for distributed sensor networks. **Grants & Collaborations**: Funded by EPSRC/MoD, GSK, and Leonardo, his projects address defense sensing, drug development, and clinical imaging. Collaborations include Firefinch (software/data science) and the UDRC Summer School. **Labs/Teams**: Leads the Acoustics and Audio Group and the SPADS CDT, fostering innovation in sensing and AI for defense/security applications.

• Machine Learning
• Smart Grids
• Internet of Things (IoT)
• +۳ مورد دیگر

Assistant Professor in the Faculty of Engineering and Science at University of Agder, Norway. Active researcher in machine learning, smart grids, and wireless communication systems with specific focus on energy applications and IoT technologies. Current institution: University of Agder Email: surender.redhu@uia.no Research Interests: Specializes in applying advanced machine learning techniques to energy systems and IoT networks. Works on electricity price forecasting models, smart home energy optimization with cognitive data fusion, and wireless sensor management in constrained environments. Recent Article Trends: 15 publications from 2021-2023 show consistent focus on temporal-spatial neural networks for energy prediction, UAV-based IoT powering strategies, and biomedical signal interpretation with deep learning systems.

Jennifer Pestanaمشاهده پروفایل

مدرس ارشد

• Numerical Linear Algebra
• Scientific Computing
• Krylov Subspace Methods
• +۷ مورد دیگر

Dr Jennifer Pestana is a Senior Lecturer in the Department of Mathematics and Statistics at the University of Strathclyde, Faculty of Science. She has been a faculty member since 2015 and is actively involved in research and academic leadership. She is a principal investigator on multiple grants and serves as an editor for SIAM publications. Education: DPhil, University of Oxford, 2012 Postdoctoral positions: University of Manchester, University of Oxford Her research centers on numerical linear algebra and its applications in scientific computing . She investigates the convergence of Krylov subspace methods , develops preconditioners for specific applications, and explores the use of tropical algebra for matrix pre-scaling. Current projects involve fast solvers for fractional diffusion and analysis in broadband signal processing . Her work bridges theoretical numerical analysis with practical computational challenges. The recent publications reflect a strong focus on iterative methods, matrix analysis, and signal processing. Common themes include Toeplitz matrices , eigenvalue decomposition , preconditioning , and covariance estimation , demonstrating her expertise in both algorithmic development and application-driven numerical methods. Scientific Awards: Best Student Paper Award (2018) Dr Pestana actively supervises students and leads research projects, including EPSRC-SFI and Strathclyde ISP Joint PhD initiatives. She has secured significant funding for work on Krylov methods and efficient solvers. Her professional service includes organizing major conferences such as the Biennial Conference on Numerical Analysis and serving on program committees, including for the International Linear Algebra Society. She is also an invited speaker and visiting researcher, demonstrating national and international recognition. She is involved with research teams focused on numerical analysis and scientific computing, contributing to collaborative efforts in developing novel preconditioned iterative solvers. Her lab and project groups work on both theoretical and applied aspects of linear algebra in PDEs and signal processing.

Gary B Lamontمشاهده پروفایل

استاد

• Evolutionary Algorithms
• Multi-Objective Optimization
• Computational Intelligence
• +۷ مورد دیگر

Dr. Gary B. Lamont is a Professor in the Department of Electrical and Computer Engineering at the Air Force Institute of Technology (AFIT), part of Air University. His work bridges computational intelligence, evolutionary algorithms, and defense systems engineering, with extensive applications in aerospace, cybersecurity, and autonomous systems. Ph.D., University of Minnesota Institute of Technology, 1970 Master of Science in Electrical Engineering, University of Minnesota Institute of Technology, 1967 Bachelor of Physics, University of Minnesota Institute of Technology, 1961 Dr. Lamont’s research centers on multi-objective evolutionary algorithms (MOEAs) , with applications in UAV swarm mission planning, network intrusion detection, image processing, and protein structure prediction. He has pioneered the use of evolutionary computation in military and defense contexts, including radar waveform design, satellite constellation planning, and autonomous agent behavior generation. His work often integrates swarm intelligence, artificial immune systems, and distributed optimization techniques. The recent publications highlight a strong trend toward military and defense applications of computational intelligence, particularly in electronic warfare, space surveillance, and cyber defense. His work frequently employs multi-objective optimization to balance competing constraints in real-world systems such as radar, UAV swarms, and network security architectures. Image processing and signal transformation using evolved algorithms also remain active areas, especially for defense imaging and compression. IEEE Fritz Russ Bio-Engineering Award, 2008 IEEE Senior Life Member, 2004 Eta Kappa Nu AFIT Teacher of the Year, 2002 WPAFB Professional Employee of the Year, 1981 Best Presentation Award, Institute of Navigation, 2008 BEST PAPER AWARD, Gameon North America '09 Best Paper Award Nominee, IEEE SSCI 2007 Dr. Lamont has advised numerous graduate students, including Jeremy Stringer, Mark Kleeman, and Dustin Nowak, many of whom have co-authored significant publications with him. His research has been supported by the U.S. Air Force and other defense agencies, enabling high-impact work in autonomous systems, network security, and optimization under uncertainty. He has led projects involving multi-agent systems, self-organized swarms, and parallel evolutionary algorithms, often in collaboration with institutions like Wright-Patterson Air Force Base. His research group at AFIT operates at the intersection of computational intelligence and defense engineering, focusing on self-organized UAV swarms , evolutionary intrusion detection systems , and adaptive signal processing . The team utilizes high-performance computing environments and simulation platforms like Swarmfare to test swarm behavior and mission planning algorithms. The lab emphasizes software engineering discipline in AI systems, as seen in tools like jREMISA, and integrates biological metaphors such as immune systems and genetic algorithms into robust cyber defense frameworks.

• Microwave Engineering
• Automotive Radar Systems
• High-Frequency Electronics
• +۳ مورد دیگر

Jan Schöpfel is a Researcher in the Integrated Systems department at the Faculty of Electrical Engineering and Information Technology, Ruhr-Universität Bochum. His research focuses on advanced microwave and millimeter-wave systems, particularly in automotive radar technology, high-frequency integrated circuits, and SiGe-based solutions. He has contributed significantly to the development of compact, energy-efficient radar systems for automated driving applications, including innovations in power amplifiers, mixers, and phase-locked loops. His work spans topics such as harmonic radar for vulnerable road user detection, subharmonic mixing techniques, and inharmonic radar concepts for future automotive bands. Collaborations include projects on over-the-air synchronization for radar networks and low-phase-noise signal sources. He has published extensively in top journals like IEEE Transactions on Microwave Theory and Techniques and conferences such as the International Microwave Symposium (IMS). Key technical areas include SiGe BiCMOS technology, frequency multipliers, and MIMO antenna systems. His research addresses challenges in bandwidth optimization, power efficiency, and precision signal processing at millimeter-wave frequencies, with direct applications in next-generation automotive and 6G communication systems.

• Engineering and technology
• Computer hardware
• Computer theory
• +۳ مورد دیگر

Joris Van Kerrebrouck is a postdoctoral researcher at Ghent University within the Faculty of Engineering and Architecture , affiliated with the Department of Information Technology (EA05) . His work focuses on high-speed optical and electronic systems. Research Themes: Silicon photonics, radio-over-fiber technology, neuromorphic photonic circuits, heterogeneous integration, and millimeter-wave communication systems. Key Contributions: Development of ultra-high bandwidth modulator drivers, Tb/s optical transceivers, programmable photonic neurons, and advanced beamforming networks. Collaborations: Active partnerships with IMEC, academic institutions, and industry leaders in photonics and semiconductor technologies. Technical Expertise: SiGe BiCMOS, III-V integration, micro-transfer printing, and optical-electrical co-design.

Huacheng Zengمشاهده پروفایل

دانشیار

• Computer Networking
• Wireless Communication Systems
• Sensing Technologies
• +۳ مورد دیگر

Huacheng Zeng is an Associate Professor in the Department of Computer Science and Engineering (CSE) at Michigan State University (MSU), part of the College of Engineering. His research focuses on computer networking, wireless communication systems, and sensing technologies with applications in IoT security, signal processing, and machine learning. He received his Ph.D. in Computer Engineering from Virginia Tech in 2015 and was awarded the NSF CAREER Award in 2019. Dr. Zeng’s work spans innovative areas such as radar-based human motion tracking (e.g., RadEye), acoustic emotion decoding, and mmWave network optimization. His recent publications address challenges in device localization, vehicular communication, and secure RFID systems. His research often integrates machine learning techniques with traditional signal processing to enhance system performance and security. Education: Ph.D., Computer Engineering, Virginia Tech (2015) Awards: NSF CAREER Award (2019) His contributions to interference management, jamming-resilient communications, and distributed inference frameworks have advanced both theoretical and applied aspects of wireless networks. While no specific grants or advising details are listed, his extensive publication record reflects active collaboration in cutting-edge research domains.

• Network and Computing System Performance Analysis
• Computer Networks and Protocols
• Mobile and Wireless Communications
• +۵ مورد دیگر

Bouras Christos is a Professor in the Department of Computer Engineering at the University of Patras , focusing on advanced research and teaching in computer science and telecommunications. His work spans both theoretical foundations and practical applications. Network Performance Analysis 5G/6G Communication Systems Machine Learning for Network Optimization IoT and Telematics His recent research emphasizes 5G network optimization , with publications on beamforming techniques, resource allocation algorithms, and energy efficiency in MIMO and DUDe architectures. Key subtopics include: Deep learning for user allocation Game theory-based resource management IoT latency reduction Adaptive beamforming Network slicing optimization Techno-economic modeling He contributes to the academic community through leadership in the Division of Applications and Foundations of Computer Science, which includes laboratories focused on distributed systems, combinatorial algorithms, and machine learning applications.

Constandinos Mavromoustakisمشاهده پروفایل

استاد

• Internet of Things (IoT)
• Mobile and Wearable Computing
• Wireless Communications
• +۳ مورد دیگر

Constandinos Mavromoustakis is a Professor at the Department of Computer Science, University of Nicosia (School of Sciences and Engineering). He leads the Mobile Systems Lab (MOSys Lab) and holds leadership roles in IEEE, including Vice-Chair of the Cyprus Section and Chair of the Computer Society Chapter. He actively contributes to IEEE Communications Society committees and standardization groups like IEEE-SA SCC42 WG2040. Education includes: Dipl.Eng in Electronic and Computer Engineering from Technical University of Crete MSc in Telecommunications from University College London PhD from Aristotle University of Thessaloniki His research integrates Mobile Systems, IoT, and Wireless Communications, with emphasis on: 5G/6G network security using AI-driven strategies Resource optimization in cloud-edge ecosystems UAV-assisted networks for critical infrastructure Healthcare applications via IoMT and data analytics Recent publications (2024-2025) demonstrate strong focus on securing next-gen networks (O-RAN, 6G), optimizing edge computing for VR/IoT, and advancing smart healthcare/cities. Dominant themes include Deep Reinforcement Learning, quantum-inspired optimization, and latency minimization techniques. He participates in EU-funded initiatives including FP7, H2020, Eureka, and national projects. Industrial collaborations include consultancy for Intel Corporation. He directs the MOSys Lab, which researches mobile systems, IoT interoperability, and network security, with projects spanning UAV communications, medical IoT, and sustainable computing.

• Neural Networks
• Wireless Communications
• Signal Processing
• +۸ مورد دیگر

Ivan Milovanović is a researcher at the Department of Postgraduate Studies , University of Singidunum, Belgrade, Serbia. He holds a doctoral degree in Advanced Protection Systems from Singidunum University (2015) and has previously completed master's and bachelor's studies in Contemporary Information Technologies and Graphics and Design at the same institution. His career spans roles in software development, wireless communication research, and neural network applications for electromagnetic signal processing. B.Sc. in Graphics and Design (2008), Singidunum University M.Sc. in Contemporary Information Technologies (2009), Singidunum University Ph.D. in Advanced Protection Systems (2015), Singidunum University Milovanović's research focuses on applying neural networks to solve complex problems in wireless communications, particularly in direction-of-arrival (DOA) estimation for stochastic electromagnetic sources. His work addresses challenges in localization accuracy, signal correlation, and power variation. He has contributed to advancements in 2D/1D DOA estimation using hybrid neural models, experimental validation of antenna array measurements, and noise interference analysis in wireless systems. Publications span topics like wireless sensor networks, channel modeling, and MESH network security. Recent publications highlight a strong interdisciplinary focus combining neural networks with electromagnetic signal processing , antenna array optimization , and wireless communication systems . His work explores stochastic source tracking, coherent signal resolution, and noise mitigation techniques, often collaborating with researchers like Z. Stanković and N. Dončov. While no scientific awards are explicitly mentioned in the provided text, his extensive publication record indicates active contributions to applied electromagnetics and telecommunications engineering.

• RF/mm-Wave/THz Wireless Transceivers
• High-Speed SerDes
• PLLs/Frequency Synthesizers
• +۴ مورد دیگر

Xiaolong Liu serves as Assistant Professor at Southern University of Science and Technology's School of Microelectronics. His academic journey includes a Ph.D. from Hong Kong University of Science and Technology (2019), M.E. from Tsinghua University (2014), and B.E. from Beijing University of Aeronautics and Astronautics (2010). Prior to his current position, he worked as Staff Engineer at eTopus Technology in Silicon Valley (2019-2021) and completed a postdoc at HKUST. Ph.D. in Electronic and Computer Engineering, Hong Kong University of Science and Technology (2019) M.E. in Microelectronics, Tsinghua University (2014) B.E. in Electronic Engineering, Beijing University of Aeronautics and Astronautics (2010) Dr. Liu's research spans cutting-edge RF/mm-Wave/THz integrated circuit design, focusing on high-performance signal generators , ultra-wideband transceivers , and low-power frequency synthesis . His work addresses critical challenges in 5G/6G communications, terahertz spectroscopy, and high-speed wireline interfaces through innovative CMOS architectures. Technical emphases include varactor-less VCOs, harmonic enhancement techniques, and magnetic tuning mechanisms for millimeter-wave systems. His publication portfolio demonstrates consistent leadership in IEEE flagship journals/conferences (JSSC, TMTT, ISSCC, VLSI), with recent work advancing Class-F oscillators, sub-sampling PLLs, and sub-THz synthesizers. Notable trends include migration toward sub-THz frequencies (2023-2025) , multi-core oscillator architectures , and elimination of harmonic tuning for simplified design. IEEE VLSI Paper Technical Highlights (2019) IEEE CICC Invited Talk (2019) IEEE TVLSI 2023 Best Reviewer Award Top 4 Most Downloaded Paper in TCAS-II (July 2022) Dr. Liu actively mentors students through SUSTech's graduate programs, with advisees consistently earning university-level thesis awards and conference recognitions. His lab maintains strong industry ties through silicon validation and collaborates on cutting-edge tapeouts in 7nm/16nm nodes. Current projects focus on cryo-CMOS interfaces for quantum computing and ultra-wideband transceivers for 6G applications. His research group operates within SUSTech's state-of-the-art microelectronics facility, specializing in mm-Wave/THz characterization and high-speed SerDes validation. The team maintains partnerships with semiconductor foundries for advanced-node prototyping and collaborates with industry leaders on real-world implementation challenges.