جستجوی استاد راهنمای دانشگاهی

• Geotechnical Engineering
• Pile Foundations
• Renewable Energy Infrastructure
• +۵ مورد دیگر

Dr. Hongyu Qin is a Lecturer in Civil Engineering at Flinders University's College of Science and Engineering. He holds a PhD from Griffith University, alongside a Master's from Hohai University and a Bachelor's from Zhengzhou University. As Discipline Lead for Civil Engineering Honours/Masters Thesis and Projects, he focuses on geotechnical engineering research, including geomaterial behavior, renewable energy pile foundations, and Measurement While Drilling (MWD). His work integrates industry collaboration, leading to innovations like an Engineering Calculator APP for foundation design. Notable grants include Flinders/Industry co-funded PhD scholarships and CSE Research Schemes. He has supervised students such as Chris Przibilla (2019 University Medal recipient). Recent research explores MWD and Machine Learning applications in geotechnics. Research Interests: Geotechnical Engineering, pile foundations for renewable energy, MWD technology, soil-structure interactions, and transportation infrastructure geotechnics. His projects address challenges in energy pile groups, dynamic pile responses, and slope stability. Key Awards: Flinders University CSE Workshop Support Scheme 2024 Endeavour International Postgraduate Research Scholarship (2006-2009) Griffith University Postgraduate Research Scholarship (2006-2009) Teaching: Coordinates and lectures courses such as ENGR8932 Engineering Geology, ENGR8931 Geotechnical Engineering, and advanced foundation design modules. His teaching emphasizes practical application through workshops like the Investigative Drilling Workshop (2023), fostering industry-academia collaboration.

Irene Violaمشاهده پروفایل

پژوهشگر

• Multimedia Compression
• Quality of Experience (QoE)
• Extended Reality (XR)
• +۳ مورد دیگر

Irene Viola is a senior tenured researcher at Centrum Wiskunde & Informatica (CWI) in the Distributed and Interactive Systems (DIS) group. Her work focuses on Quality of Experience (QoE) metrics, immersive multimedia systems , and point cloud streaming . Current projects: AMPLIFY (Horizon Europe), OPEN-DASH-PC (SPIRIT Open Call), INDUX-R (Horizon Europe), TransMIXR (Horizon Europe), VOXReality (Horizon Europe) Education: PhD in Electrical and Electronic Engineering (EPFL), MSc in Computer Engineering (Polytechnic University of Turin/EPFL), BSc in Cinema and Media Engineering (Polytechnic University of Turin) Her research bridges multimedia compression , human-centric XR systems , and adaptive streaming protocols . Recent publications analyze visual saliency in 3D, PCA-based quality metrics, and social interaction latency thresholds. Awards include the 2023 IEEE Multimedia Best Paper, 2019 APSIPA Sadaoki Furui Prize, and 2022 IEEE Best Demo. Projects highlight her expertise in AI/XR integration , open-source VR platforms (VR2Gather), and 5G/XR architectures . She collaborates across Europe (Horizon Europe) and with institutions like EPFL and Nagoya University.

Wang Junمشاهده پروفایل

استادیار

• Machine Learning
• Engineering Design
• Additive Manufacturing
• +۱۰ مورد دیگر

Jun Wang is an Assistant Professor in the Department of Mechanical Engineering at Santa Clara University (SCU) . He received his Ph.D. and M.S. in Mechanical Engineering from SUNY Buffalo (2019, 2015) and a B.S. from Xi’an Jiaotong University (2012). He previously served as a Postdoctoral Associate in Mechanical Engineering at University of Maryland, College Park and worked in the IDEAL Lab, focusing on Engineering Design, Optimization, and Machine Learning. Education: Ph.D., Mechanical Engineering, SUNY Buffalo (2019) M.S., Mechanical Engineering, SUNY Buffalo (2015) B.S., Mechanical Engineering, Xi’an Jiaotong University (2012) Dr. Wang’s research lies at the intersection of Engineering Design , Machine Learning/Artificial Intelligence , Advanced Manufacturing , and Topology Optimization . His lab, GIFTED (Geometric Informatics For Technology, Engineering, and Design) , develops AI-driven methods for geometric modeling, metamaterial design, and additive manufacturing. Specific projects include inverse/generative design frameworks, surrogate models for manufacturing simulation, and high-resolution multi-scale material synthesis. His recent publications focus on machine learning integration with isogeometric analysis, implicit surface-based inverse design of cellular materials, and optimization of non-periodic metamaterials. Notable collaborations include work on vision transformers for image enhancement and generative adversarial networks for engineering applications. Trends in his research emphasize bridging geometric modeling with cloud computing and big data to accelerate design cycles. Scientific Awards: Best Paper Award (IDETC-CIE 2021) for non-periodic cellular design Dr. Wang mentors graduate researchers Shaoliang Yang (Ph.D. candidate) and Kevin Wang (Master’s graduate). His former students Matthew Franke (now at Intel) and Yunchen Li (now at Hymson USA Inc.) have transitioned to industry roles. His NSF-funded project ( $249,829 ) explores data-driven inverse design of additively manufacturable materials. At SCU, he leads the GIFTED Lab , which combines geometric modeling , machine learning , and advanced manufacturing to revolutionize design workflows. The lab’s work targets performance-equivalent designs generated via AI, with applications in aerospace, automotive, and biomedical industries.

• Database Systems
• Machine Learning
• Graph Theory
• +۳ مورد دیگر

Shuai Ma is a researcher at Beihang University , School of Computer Science and Engineering, China. His work spans database systems , machine learning , and natural language processing , focusing on temporal knowledge graphs, graph neural networks, and privacy-preserving federated learning. He received his PhD from the University of Edinburgh , UK, in 2011. Research interests include graph theory , spatiotemporal data analysis , data mining , and anomaly detection . His recent publications address: 2025 : Technology mapping for ASICs, temporal network motifs, and 3D geometry compression. 2024 : Knowledge graph completion, scene mining for e-commerce, and secure aggregation for federated learning. Article trends reveal expertise in graph neural networks , temporal data processing , and privacy-aware systems . Collaborations with institutions like Concordia University and industry leaders underscore his interdisciplinary impact.

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +۳ مورد دیگر

Michael G. Rabbat is a researcher at Facebook AI Research in Montreal, Canada, affiliated with the College of Engineering. His work spans machine learning, artificial intelligence, and distributed computing. Key research interests: Federated learning, reinforcement learning, self-supervised learning, and optimization algorithms. Recent publications focus on neural network training acceleration, physics understanding in AI, and efficient distributed systems. His articles from 2025–2022 emphasize robust visual features (DINOv2), communication-efficient training (SlowMo), and privacy-aware federated systems. Collaborative projects include V-JEPA for video modeling and IntPhys for physics benchmarks.

• Additive Manufacturing
• Laser Processing
• Welding Technology
• +۵ مورد دیگر

Nicola Contuzzi serves as an Assistant Professor in the Department of Mechanics, Mathematics & Management at the Polytechnic University of Bari, Italy. His institutional contact details include email nicola.contuzzi@poliba.it and phone +39 0805963079, with office location at Via Orabona 4, Bari. Active in advanced manufacturing research, he contributes significantly to the field through experimental and computational methodologies. His research focuses on Additive Manufacturing (especially Wire Arc Additive Manufacturing), Laser Processing (welding, marking, and milling), and Joining of Dissimilar Materials . Utilizing integrated experimental-statistical-numerical approaches, he investigates microstructure-property relationships in metallic components, with emphasis on process optimization for automotive and aerospace applications. Key areas include parameter effects on bead geometry, energy efficiency in industrial processes, and non-destructive evaluation of welded joints. Analysis of his 15 most recent publications (2024-2025) reveals dominant themes in WAAM parameter optimization (feed rate, arc power), dissimilar material welding (aluminum-steel, copper-stainless steel), and laser process modeling. The research consistently employs statistical methods and numerical simulations to predict outcomes like weld geometry, microstructure evolution, and mechanical properties, demonstrating strong industry applicability. Scientific awards: No awards mentioned in available sources. Advising and grants: Student supervision details and grant funding information are not documented in the provided text. His active publication record suggests ongoing research projects, though specific collaborations or funding sources remain unspecified.

Shfaqat A Khanمشاهده پروفایل

استاد

• Greenland Ice Sheet
• Glaciology
• Geodesy
• +۳ مورد دیگر

Professor Shfaqat A Khan is affiliated with the Department of Space Research and Technology Geodesy and Earth Observation at the Technical University of Denmark . As a leading expert in Greenland ice sheet dynamics, he combines geodetic observations with glaciological modeling to study climate change impacts. Key techniques: GNSS, GRACE satellite data, altimetry Focus regions: Greenland, Arctic, High Mountain Asia Current projects: 3 active PhD studies on ice flow modeling and hydrodynamic coupling Research trends in his 2024-2025 publications reveal a comprehensive approach to ice sheet monitoring: Integrating multi-sensor data (GNSS/GRACE/ICESat) Quantifying 90+ years of glacier evolution Analyzing seismic impacts of extreme glacial events Developing Arctic-wide uplift and deformation models His work appears in 15+ major outlets including Nature and Science , with significant media coverage (300+ news outlets). As main supervisor for 5 PhD students, he drives cutting-edge research in cryospheric geophysics.

• Image Compression
• Real-Time Image Processing
• FPGA Architecture
• +۵ مورد دیگر

Yousef Baroud serves as a Research Associate at the Computational Imaging Systems (CIS) group within the Institute of Industrial IT (ITI) at the University of Stuttgart. His work bridges theoretical computer science and practical hardware implementation, focusing on real-time image and data processing solutions for industrial applications. His research spans image compression algorithms , FPGA-based hardware acceleration , and optical measurement systems . Key contributions include marker-free parallel decoding architectures for variable-length code streams, adaptive subsampling techniques for data compression, and real-time speckle noise reduction in hybrid optical systems. His work demonstrates strong integration of algorithm design with hardware implementation constraints. Analysis of his publication history (2012-2020) reveals consistent focus on real-time processing for high-bandwidth data streams, with increasing emphasis on adaptive compression techniques that balance computational efficiency with perceptual quality. His later work shows sophisticated integration of human visual system models into compression algorithms. Dr. Baroud collaborates extensively with researchers in optical engineering and fluid dynamics, particularly evident in his work on bubble concentration measurements using Raman spectroscopy and telecentric triangulation sensor systems. His technical approach consistently prioritizes hardware-friendly implementations suitable for industrial deployment.

Zhe Wangمشاهده پروفایل

پژوهشگر

• Image Processing
• Computer Vision
• Embedded Systems
• +۳ مورد دیگر

Zhe Wang is a Researcher at the Computational Imaging Systems department within the University of Stuttgart, Germany. Their work focuses on real-time image processing, data compression, and FPGA-based hardware architectures. Research Interests Real-time video/image compression Perceptual coding with JND models Parallel decoding architectures Embedded systems for high-throughput applications FPGA design for multimedia processing Publications Highlight Recent work spans FPGA-accelerated super-resolution, marker-free variable-length coding, and perceptual compression techniques. These contributions emphasize hardware efficiency, visual quality preservation, and high-speed data processing.

• Machine Learning
• Computer Vision
• Haptic Communication
• +۳ مورد دیگر

Furkan Kaynar is a researcher at the Chair of Media Technology, Technical University of Munich, affiliated with the Munich School of Robotics and Machine Intelligence (MSRM) and the Munich Institute of Robotics and Machine Intelligence (MIRMI). He holds a B.Sc. in Electrical and Electronics Engineering (2016) from Bogazici University and an M.Sc. in Electrical Engineering and Information Technology (2019) from TUM. His research focuses on Machine learning for task-oriented robotic grasping Computer vision and interactive segmentation Haptic communication systems Human-robot interfaces for teleassistance Recent work includes few-shot learning methods for grasp area segmentation via remote demonstrations. Project involvement includes Centre for Tactile Internet with Human-in-the-Loop (CeTI) , Teleoperation over 5G , and IEEE P1918.1.1 Haptic Codecs . Collaborative efforts appear in publications at international conferences. Teaching activities include: Organizing the Seminar on Topics in Signal Processing (WS19/20) Conducting tutorials for Image and Video Compression (SS20)

• Aerospace Propulsion
• Computational Fluid Dynamics
• Machine Learning
• +۶ مورد دیگر

Andrea Ferrero is an Associate Professor at the Polytechnic University of Turin , affiliated with the Department of Mechanical and Aerospace Engineering (DIMEAS) . He leads research in aerospace propulsion, computational fluid dynamics (CFD), machine learning, and reduced-order modeling, with a focus on turbulent flows, turbomachinery, and combustion. Scientific Director for projects like NextGenSProDesT (2023–2025) and PARSEC (2024–2027). Active in commercial research collaborations with companies like Avio Aero and GE. His work bridges theoretical and applied research, emphasizing AI/ML integration in aerospace systems. Research Interests Andrea Ferrero's research spans: Aerospace Propulsion : Advanced rocket nozzles, hybrid engines, and hydrogen turbofans. Computational Fluid Dynamics : Turbulent and compressible flow simulations, reduced-order models (ROM). Machine Learning : Data-driven turbulence modeling, stress correction with physical constraints. Combustion & Propellants : UV-curable solid propellants, photocurable materials for additive manufacturing. Recent Publications His 2025 articles address hydrogen turbofan fuel systems, rocket nozzle retro-flows, and UV-curable propellants, reflecting trends in sustainable propulsion and AI-enhanced CFD. Earlier works include turbomachinery simulations, resonant igniters, and microwave material uncertainty analysis. Teaching & Students He teaches Innovative Approaches to Turbulent Flow Simulation and Reduced Basis Methods in doctoral programs. His PhD advisees explore topics like: Turbulent flow modeling (Giacomo Gedda, Lorenzo Folcarelli) Hybrid rocket engine optimization (Leonardo Stumpo) Cryogenic fuel systems (Alessandra Zumbo) Topological heat exchanger design (Alessandro Chiodi)

• Modal analysis
• Nonlinear system identification
• Damping
• +۷ مورد دیگر

Luigi Garibaldi is a Full Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS) at the Polytechnic University of Turin, Italy. He has been actively involved in teaching and research for several decades, with a focus on mechanical systems dynamics, vibration analysis, and machine diagnostics. His work spans multiple research projects funded by competitive tenders, commercial contracts, and regional/national grants. Professor Garibaldi's educational background is reflected in his extensive teaching portfolio, which includes courses such as "Mechanics Applied to Machines," "Vehicle Noise and Vibration," and "Dynamics of Mechanical Systems" at both undergraduate and graduate levels. He has served on doctoral college committees for Mechanical Engineering since the 19th cycle (2003/2004) through the current 40th cycle (2024/2025). His research interests center on modal analysis and identification of linear and nonlinear dynamic systems, characterization and use of viscoelastic materials, and monitoring, diagnostics, and prognostics of rotating machinery and mechanical systems. His work aligns with several Sustainable Development Goals, particularly those related to industry, innovation, infrastructure, clean energy, and health and well-being. His research spans multiple domains including aerospace engineering, computational engineering, industrial design, machine learning applications in signal processing, and mechanical manufacturing engineering. Analysis of Professor Garibaldi's recent publications reveals a strong focus on vibration-based diagnostics, particularly for rotating machinery, structural health monitoring, and pyroshock testing applications. His work demonstrates expertise in signal processing techniques, nonlinear dynamics, and the application of machine learning to predictive maintenance problems. He has contributed significantly to the field through his editorial work as Associate Editor for Mechanics & Industry (since 2014) and Mechanical Systems and Signal Processing (since 2004). His leadership extends to roles such as Scientific Committee co-chair for the Surveillance 9 conference and Directorate of Research and Valorisation for INSA - Euro Mediterranée. Professor Garibaldi has supervised PhD students, including Riccardo Barbera (40th cycle, 2024-present), and has collaborated extensively with industry partners on commercial research projects related to machinery diagnostics, vibration analysis, and structural health monitoring. His research group, Dynamics of Mechanical Systems and Identification (DIMEAS), has undertaken numerous projects for companies including RFI S.p.A., focusing on railway catenary dynamics and interaction with pantographs.

Francesco Vaninمشاهده پروفایل

مدرس

• Earthquake Engineering
• Structural Dynamics
• Seismic Assessment
• +۳ مورد دیگر

Francesco Vanin is a Lecturer at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Architecture, Civil and Environmental Engineering. He works within the Civil Engineering department, focusing on earthquake engineering and structural dynamics. His teaching includes courses on structural dynamics and seismic design. Earthquake Engineering and Structural Dynamics Laboratory (EESD), ENAC, IIC SGC-ENS, ENAC-SGC His research centers on seismic behavior of masonry structures, equivalent frame modeling, and retrofitting of historical buildings. He investigates uncertainties in seismic assessment and develops numerical models for structural analysis. Vanin's publications emphasize finite element modeling, FRP strengthening, and experimental validation of masonry structures. His work spans both computational and physical testing methodologies. He supervises PhD student Bozulic Ivana and contributes to education in structural engineering through his teaching roles.

• Machine Learning for Visual Computing
• Big Data Visualization and Analytics
• Data Science and High-Performance Computing (HPC)
• +۴ مورد دیگر

Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the Indian Institute of Technology Kanpur (IITK) since 2022. He previously held positions at Los Alamos National Laboratory as a Postdoctoral Researcher (2018-2019) and Scientist II (2019-2022). Dr. Dutta earned his Ph.D. and M.S. in Computer Science from The Ohio State University (2011-2018) and a B.Tech in Electronics and Communication Engineering from the West Bengal University of Technology (2005-2009). His research lies at the intersection of Machine Learning , Visual Computing , Big Data Analytics , and High-Performance Computing (HPC) . He focuses on developing scalable solutions for extreme-scale data, such as exascale simulations, social media, IoT, and healthcare. His work emphasizes uncertainty quantification in AI models and interactive visualization techniques. Dr. Dutta’s recent publications highlight his expertise in in situ visualization for climate modeling, implicit neural representations for uncertainty-aware rendering, and statistical sampling for exascale systems. His funded projects include AI-driven data analytics frameworks and deepfake defense mechanisms supported by ISRO, SERB, and C3iHub. Scientific Awards include Best Reviewer (TVCG), Best Paper (ISAV, TopoInVis), and LAAP Award (LANL).

Steve Petruzzaمشاهده پروفایل

پژوهشگر ارشد

• High-Performance Computing
• Data Management
• Scientific Visualization
• +۷ مورد دیگر

Steve Petruzza is a Research Fellow at the Scientific Computing and Imaging Institute (SCI Institute) of the University of Utah. His work focuses on high-performance computing, data management, and visualization, particularly in dynamic runtime systems, high-performance I/O, and data streaming infrastructures. Research Themes : Neuroscience, material science, biomechanics, climate modeling, and exascale computing. Technical Expertise : Leading software engineering projects for Uintah, FEBio, and other simulation tools. Collaborations : Industry-academia partnerships in energy production, cardiovascular modeling, and orthopedic diagnostics.