جستجوی استاد راهنمای دانشگاهی

Jingfei Liu serves as an Assistant Professor in the Department of Mechanical Engineering at Texas Tech University and is a research faculty member at the Center of Excellence in Obesity and Cardiometabolic Research. Her work bridges mechanical engineering principles with biomedical applications, focusing on developing innovative ultrasound-based diagnostic and therapeutic technologies for medical use. Her core research encompasses biomedical acoustics—specifically the application of vibration, sound, and mechanical energy for medical diagnosis and therapy—alongside signal and image processing for medical instrumentation. A significant future direction involves transcranial ultrasound neuromodulation as a novel approach for treating obesity and associated cardiometabolic complications, aligning with the Center's mission to address obesity-related health challenges through interdisciplinary research. Analysis of her recent publications (2021-2025) reveals a concentrated expertise in advancing ultrasound elastography and surface acoustic wave techniques, with applications spanning cancer immunotherapy, tissue characterization, and obesity-related research. Her work demonstrates consistent innovation in both theoretical modeling (e.g., numerical simulations of wave propagation) and experimental validation (e.g., ex vivo tissue testing), establishing her as a contributor to next-generation biomedical imaging solutions. Dr. Liu actively participates in the Center of Excellence in Obesity and Cardiometabolic Research, leveraging interdisciplinary collaborations to develop engineering solutions for complex metabolic disorders. Her affiliation underscores a commitment to translating acoustic technologies into clinical applications for obesity treatment and cardiometabolic health improvement.

• Biomedical Acoustics
• Signal Processing
• Image Processing
• +۳ مورد دیگر

Dr. Chengbo Ai is an Associate Professor at the Department of Civil and Environmental Engineering , University of Massachusetts Amherst , specializing in transportation infrastructure and safety research. He holds a Ph.D. in Transportation Systems Engineering from Georgia Tech and a B.S. in Electrical Engineering from Peking University. Research Interests: Transportation Asset Management, Mobile LiDAR Applications, Roadway Safety Analytics, and Spatial Infrastructure Analysis Current Projects: Developing automated guardrail/sidewalk inventory systems, improving pedestrian infrastructure accessibility, and optimizing rail inspection methods Publications focus on integrating computer vision, machine learning, and geospatial technologies for infrastructure condition assessment. Recent work includes automated guardrail evaluation, rail gage inspection using consumer-grade devices, and pedestrian network analysis. Awards: Dwight David Eisenhower Transportation Fellowship recipients (2020-2022) Acorn Innovation Grant (2019) Best Poster Award at MassDOT Conference (2019) Students: Mentors Ph.D. researchers in pavement preservation, LiDAR processing, and transportation equity, with alumni working at FHWA, AECOM, and leading engineering firms Lab Facilities include advanced LiDAR systems (Theodore 1-3), dual-GPU workstations (Sullivan, Wazowski), and field vehicles like "Cynthia" for mobile mapping.

• Transportation Asset Management
• Pavement Preservation
• Roadway Safety
• +۶ مورد دیگر

Professor Vasileios Chatziioannou serves as Faculty Member and PhD Supervisor within the Department of Music Acoustics at the University of Music and Performing Arts Vienna. His academic leadership spans the Faculty of Music, where he guides doctoral candidates through the Structured Doctoral Program Performing Matters. His institutional role encompasses supervision across Ethnomusicology, Music in Society, and Musicology programs. His research focuses on the physics of musical instruments, with particular expertise in wind instrument aerodynamics and string-bow interaction mechanics. He employs advanced experimental techniques including particle image velocimetry (PIV) and high-speed motion capture alongside computational modeling. His work bridges theoretical acoustics with practical instrument design considerations, examining phenomena from tone hole geometry effects to friction dynamics in bowed strings. Analysis of his recent publications reveals a strong emphasis on energy-stable numerical methods for real-time simulation of musical instrument physics. His work consistently addresses the challenge of balancing computational efficiency with physical accuracy, particularly in modeling nonlinear phenomena like stick-slip friction and collision dynamics. The research trajectory shows increasing integration of experimental validation with computational models. While no specific scientific awards are documented in the provided materials, his sustained publication record in high-impact acoustics journals demonstrates significant scholarly contribution. His work regularly appears in specialized forums like the Journal of the Acoustical Society of America, particularly within special issues on musical instrument modeling. Professor Chatziioannou's supervision activities focus on computational and experimental approaches to musical instrument physics. His research program involves interdisciplinary collaboration between acousticians, instrument makers, and performers. Current projects include robotic replication of bowing techniques and 3D-printed instrument component optimization, reflecting his commitment to translating theoretical findings into practical applications for musicians and instrument designers.

• Musical Acoustics
• Physical Modeling of Musical Instruments
• Wind Instrument Aerodynamics
• +۴ مورد دیگر

Prof. Dr. Özgür Özdemir is a faculty member at Istanbul Technical University , where he serves in the Department of Electronics and Communication Engineering within the College of Engineering. He earned his PhD from the New Jersey Institute of Technology in 2000. Research Interests : Signal Processing, Electromagnetics, Microwave & Antenna Technologies, Biomedical Imaging Technical Expertise : Photoacoustic Imaging, Microwave Imaging, Antenna Design, Terahertz Engineering His recent publications demonstrate a strong focus on photoacoustic imaging (4 out of 15) with applications in immune cell characterization , drug treatment analysis , and medical imaging physics . He also contributes to antenna engineering with 3 publications on Vivaldi antennas , patch arrays , and reconfigurable surfaces . His work spans microwave imaging (3 publications) and acoustic modeling (2 publications). Active Research Projects : Gold Nanoparticles in Photoacoustic Tomography (2025-2026, BAP) Photoacoustic-Guided Photothermal Therapy for Solid Tumors (2025-2028, BAP) Oxygen Saturation Detection via Photoacoustic Tomography (2024-2025, BAP) In vitro Effects of Therapeutic Agents on Eosinophils (2024-2026, BAP) Phantom Development for Photoacoustic Tomography (2024-2025, TUSEB) Photoacoustic Systems for Skin Cancer Diagnosis (2020-2023, TUBITAK) Drone-based SAR for Environmental Imaging (2020-2021, BAP) Collaboration Network : International cooperation in biomedical imaging and microwave engineering, with recent work in layered media acoustics , spiral actuators for endomicroscopy , and sparsity regularization techniques .

• Signal Processing
• Electromagnetics
• Microwave and Antenna Technologies
• +۴ مورد دیگر

Yang Zhang is a Research Assistant Professor at the Illinois Institute of Technology (Illinois Tech), affiliated with the Department of Mechanical, Materials, and Aerospace Engineering within the Armour College of Engineering. His research focuses on experimental and computational aerodynamics, particularly in noise reduction mechanisms, flow control, and advanced measurement techniques such as particle image velocimetry (PIV). Key research interests include slat noise mitigation in aircraft systems, turbulent flow dynamics, and the application of smart materials and spectral analysis methods. Zhang’s work addresses challenges in high-lift systems, supersonic cavity flows, and aerodynamic efficiency optimization. His publications highlight contributions to noise prediction, flow visualization techniques, and passive/active control strategies for aerodynamic systems. Recent studies emphasize the integration of porous materials and dielectric elastomers for acoustic liner design, alongside advancements in data assimilation frameworks for turbulent boundary layer analysis.

• Aerospace Engineering
• Fluid Dynamics
• Noise Reduction
• +۳ مورد دیگر

Berend Jan van der Zwaag is an Assistant Professor at Universiteit Twente, affiliated with the Sensors and Smart Systems Research Centre. His work contributes to UN Sustainable Development Goals related to sustainable industry and innovation. He focuses on Artificial Intelligence, Cyber-physical systems, and Machine Learning applications in sensor networks and inertial measurement systems. Research interests include equine gait analysis using IMU sensors, machine health monitoring through ontology-based frameworks, and wireless sensor network design. Notable projects include the EquiMoves system for objective horse gait examination and an ontology framework for smart product-service systems in industrial IoT. Recent work emphasizes terrain classification for equine systems, gait event estimation, and speed estimation using body-mounted sensors. His publications span conferences like IEEE DCOSS-IoT and journals like Sensors and IEEE Internet of Things Journal. Accepting PhD Students Key collaborator: Prof. Paul Havinga (Twente) Research partnerships in Netherlands and international institutions

• Artificial Intelligence
• Cyber-physical systems
• Machine Learning
• +۳ مورد دیگر

Dr. Lijun Jiang is the Kummer Endowed Professor at the Department of Electrical and Computer Engineering, Missouri University of Science and Technology. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (2004) and has extensive industry and academic experience. He is an IEEE Fellow and ACES Fellow, recognized with awards including the IEEE Technical Achievement Award and IBM Research Technical Achievement Award. Education: Ph.D. in Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, USA M.S. in Electrical Engineering, Tsinghua University, China B.S. in Electrical Engineering, Beijing University of Aeronautics and Astronautics, China Research interests focus on electromagnetic modeling, high-speed electronic design, machine learning for signal/power integrity, microwave materials, and optics. His work bridges theoretical advancements with practical applications in antenna engineering and EDA solutions. Awards include IEEE Fellow (2019), ACES Fellow (2019), and the World’s Top 1% Scholar by ESI (2018). His recent research explores data-driven frameworks for electromagnetic near-field scanning and MIMO channel sounding. Key contributions include patents in antenna design and methodologies for electromagnetic compatibility. He has authored books on electromagnetic modeling and contributed chapters on metamaterials and angular momentum engineering.

• Electromagnetics
• Electromagnetic Compatibility (EMC)
• Signal Integrity
• +۵ مورد دیگر

Yuantao Fan is an Associate Senior Lecturer at Halmstad University's School of Information Technology, specializing in artificial intelligence and machine learning applications for industrial systems. His academic work bridges theoretical research with practical implementations in predictive maintenance and equipment monitoring. Dr. Fan's research focuses on Artificial Intelligence, Machine Learning, Anomaly Detection, and Data Mining , with particular emphasis on streaming data from real-world applications. His work spans equipment diagnosis and prognosis, predictive maintenance (PdM), and condition-based monitoring systems. His research integrates self-organizing models with expert knowledge to create robust diagnostic frameworks applicable to transportation systems and industrial equipment. Analysis of Dr. Fan's publication record from 2013-2025 reveals consistent specialization in predictive maintenance for transportation systems , particularly bus fleets and heavy vehicles. His work demonstrates progression from basic fault detection methods to sophisticated hybrid approaches combining physics-based models with deep learning. Recent publications increasingly address explainability in predictive maintenance systems and applications in healthcare analytics. Dr. Fan actively contributes to academic instruction as course responsible and examiner for Databases, Introduction to Linux and Python, and Linux administration system courses. He also teaches Artificial Intelligence, Data Mining, Perspectives on Computer Science and Engineering, Machine Learning for Predictive Maintenance, and AI Implementation courses. His technical expertise spans multiple domains including automotive systems diagnostics, battery health monitoring, acoustic signal processing, and healthcare prediction models. Dr. Fan's work demonstrates strong industry collaboration, particularly with Volvo and city bus fleet operators, translating academic research into practical industrial applications.

• Artificial Intelligence
• Machine Learning
• Anomaly Detection
• +۶ مورد دیگر

Andrea Bianco is a Full Professor at Politecnico di Torino in the Department of Electronics and Telecommunications. He serves as Vice Rector for Internal Affairs since 2024 and previously held roles such as Department Head and member of the Academic Senate. His research focuses on optical networks, software-defined networking, and networked music performance. He has authored over 220 publications with an h-index of 27 and received the Best Paper Award at IEEE GLOBECOM 2009. Education: Dr.Ing. in Electronics Engineering (1986) and Ph.D. in Telecommunications Engineering (1994), both from Politecnico di Torino. Research Interests: High-speed switching, SDN/NFV, network music performance, and energy-efficient optical networks. His work includes leading projects like SFINGI (virtualization in software routers) and ZeTON (Zero-Touch Optical Networks). He has collaborated with industries like Cisco, Alcatel, and BT. Bianco is an IEEE Senior Member and has co-chaired major conferences such as IEEE GLOBECOM and Networking.

• Networked Music Performance
• Optical Networks
• Software-Defined Networking (SDN)
• +۳ مورد دیگر

Duschia Bodet is a Postdoctoral Research Fellow at the Institute for the Wireless Internet of Things (WIoT) within Northeastern University’s College of Engineering, Department of Electrical Engineering. Her research focuses on sub-terahertz wireless communications, emphasizing modulation techniques, beam control, and MIMO systems. She has contributed to advancements in leaky-wave antenna arrays, Bessel beams, and 6G/7G network architectures. Bodet holds a PhD in Electrical Engineering from Northeastern University (2025). Research Interests: - Terahertz Communications : High-speed connectivity at 140 GHz and beyond. - MIMO Systems : Spatial multiplexing and array configurations. - Beamforming : Techniques for directional antennas and interference management. - 6G/7G Networks : Exploring mobile near-field terahertz applications and weather-resistant systems. Publications highlight cutting-edge work on reconfigurable modulators, Bessel beam self-healing, and protocol stack implementations. Her articles span 2022–2025, with a focus on experimental validation and practical challenges in terahertz systems. Awards: Best Paper Award at IEEE Globecom 2023 Paul Baran Young Scholar Award (Marconi Society) 2024 Outstanding PhD Student Award in Research Collaborations: IMDEA Networks Institute (Madrid) for channel measurement studies WIoT team for MIMO and antenna research Labs/Teams: Active contributor to the Institute for the Wireless Internet of Things (WIoT) and collaborates with IMDEA Networks Institute on experimental setups and channel analysis.

• Wireless Communications
• Terahertz Communications
• MIMO Systems
• +۳ مورد دیگر

Ju Feng is a Senior Researcher at the Department of Wind and Energy Systems, Technical University of Denmark (DTU), specializing in Wind Energy Systems , Wind Farm Optimization , and Control Strategies . He contributes to research in Floating Offshore Wind Farms , Multi-Disciplinary Optimization , and Noise Propagation . His work includes developing simulation tools, wake models, and control frameworks for wind farms in complex environments. Research Interests: Optimization of wind farm design and operation Advanced wake and flow modeling AI-driven wind energy solutions Noise control and acoustic propagation Multi-fidelity/multi-disciplinary modeling Cost-effective wind farm technologies Publication Trends: Ju Feng's recent work (2022-2024) focuses on floating offshore wind farms , noise constraints , and multi-disciplinary integration . Older articles (2012-2017) emphasize control algorithms , wake modeling , and optimization frameworks . Advising: Supervising PhD Student Wang R. (AI-Augmented Wind Farm Design Optimization, 2025-2028) Co-supervising Nyborg C. M. (Noise Control in Wind Farms, 2019-2023) Collaboration Network: Active in international projects like DecoWind, ModFarm, and FarmConners. Collaborates with institutions such as DTU Wind Energy and researchers including Shen W. Z. , Réthoré P.-E. , and Bredmose H. .

• Wind Energy Systems
• Floating Offshore Wind Farms
• Wind Farm Optimization
• +۶ مورد دیگر

Raoof Kosai is a researcher at Le Mans Université specializing in acoustics, signal processing, and wireless communication. His work bridges UAV detection, biomedical sensors, and machine learning. Research Focus: Acoustic localization, drone detection, acoustic myography, and non-orthogonal multiple access (NOMA) systems. Collaborations: Active in projects involving environmental sound classification, medical diagnostics, and sensor network optimization. Key Research Areas include: Acoustic signal processing for UAV tracking Machine learning in biomedical applications (e.g., diabetic neuropathy diagnostics) Optimization of SCMA systems for enhanced communication quality Design of noise-robust sensor arrays and acoustic chambers Recent Publications highlight advancements in beamforming algorithms, deep learning for gesture recognition, and RF front-end error compensation. His work often integrates acoustic myography and time-frequency analysis for real-time tracking and diagnostics. Education & Advisory: Supervised student Aymen CHAKHARI (2014) and collaborated with researchers like Jean-Hugh Thomas and Pascal Charge .

• Acoustics
• Signal Processing
• Machine Learning
• +۴ مورد دیگر

David Naso is a Full Professor at the Department of Electrical and Information Engineering (DEI) of the Polytechnic University of Bari, Italy. He teaches courses in feedback control, optimization, and system identification, and serves as the head of the DEI Robotics Laboratory and coordinator of the Master's Degree in Automation Engineering. PhD in Automatic Control from Polytechnic University of Bari (1998) Guest researcher at Technical University of Aachen (1997) Visiting professor at Saarland University (2013) His research focuses on robust control of aeronautical engines, control of high-speed electrical machines, mechatronic devices based on smart materials, distributed automation, and fault prevention with sensor networks. He has coauthored over 250 publications cited more than 3,000 times (h-index 29). Recent publications highlight expertise in control systems for dielectric elastomer actuators, energy management in hybrid-electric aircraft, and fault-tolerant microgrids. His work spans aerospace, robotics, and renewable energy systems. He leads the "Control Systems Team" at Energy Factory Bari (a GE Avio-Polytechnic partnership), and collaborates with AROL S.p.A for food industry robotics and Casillo Group for windmill monitoring systems. He sits on multiple Politecnico subsidiary committees. He has coordinated research projects with €2M+ funding and consulting contracts worth €400k. Currently serves as technical editor for IEEE/ASME Transactions on Mechatronics.

• Control Systems
• Mechatronics
• Smart Materials
• +۸ مورد دیگر

Robert Crittenden is a Professor of Cosmology at the University of Portsmouth's Institute of Cosmology and Gravitation (ICG), part of the Faculty of Technology. He holds additional roles as Faculty Director of Postgraduate Research and PhD Supervisor. His academic journey includes a PhD from the University of Pennsylvania (1993) under Paul Steinhardt, followed by postdoctoral positions at Princeton University and the Canadian Institute for Theoretical Astrophysics (CITA). He joined the ICG in 2002 as a Reader in Cosmology and was promoted to Professor in 2014. Affiliations: Institute of Cosmology and Gravitation, University of Portsmouth. Education: B.S., University of Maryland; Ph.D., University of Pennsylvania (1993). Research Interests: Crittenden's work focuses on understanding cosmic structure formation and evolution through observations of the cosmic microwave background (CMB), galaxy distribution, and weak gravitational lensing. He is renowned for pioneering studies on the integrated Sachs-Wolfe effect linking galaxies and CMB anisotropies, providing independent evidence for dark energy. His research spans cosmology, CMB physics, large-scale structure, dark energy, early universe phenomena, and magnetic fields in the cosmos. Publications: His recent work explores dark energy dynamics using DESI and eBOSS data, gravitational lensing effects, and relativistic cosmological simulations. Key themes include galaxy clustering, BAO analysis, and cosmological constraints from multiwavelength surveys. Grants & Advising: Supervised over 15 PhD theses, reflecting his role in training early-career cosmologists. His research is supported by grants focusing on observational cosmology and theoretical frameworks for dark energy. Labs/Teams: Active in the ICG's dark energy and gravitational lensing research groups, contributing to international collaborations like the Dark Energy Survey and DESI projects.

• Cosmology
• Cosmic Microwave Background
• Large Scale Structure
• +۳ مورد دیگر

Priyabrata Saha is a Research Fellow at the Georgia Institute of Technology, affiliated with the College of Engineering and the Department of Electrical and Computer Engineering. Research interests include: Deep learning applications in ocean acoustics Control systems for autonomous vehicles Reduced-order modeling of partial differential equations Robust perception in autonomous systems Spatiotemporal dynamics prediction His work spans interdisciplinary domains, combining machine learning with physical modeling for applications in oceanography , robotics , and sensor systems . Key trends in his publications highlight: Integration of physics-based models with generative deep learning Real-time control algorithms for multi-agent systems Resource-efficient architectures for autonomous perception Hybrid methods in dynamical system modeling

• Machine Learning
• Dynamical Systems
• Control Systems
• +۴ مورد دیگر