جست‌وجوی استاد و پژوهشگر

Anne L'Huillierمشاهده پروفایل

استاد

• Attosecond Science
• High-Order Harmonic Generation
• Ultrafast Dynamics
• +۳ مورد دیگر

Professor Anne L'Huillier is a renowned French/Swedish physicist at Lund University, specializing in atomic physics and attosecond science. She holds the rank of Professor in the Department of Atomic Physics within the Faculty of Engineering (LTH). Her research focuses on ultrafast phenomena, particularly high-order harmonic generation and attosecond light pulses for studying electron dynamics. She leads projects like QU-ATTO and New Trends in Attosecond Science, funded by the EU and Swedish Research Councils. Education: Earned her PhD in 1986 from Université Pierre et Marie Curie (Paris) and CEA. Postdoctoral roles at Chalmers Institute of Technology (1986) and University of Southern California (1988). Became Associate Professor at Lund University in 1995 and Full Professor in 1997. Research Interests: Experimental and theoretical studies of attosecond pulses, laser-atom interactions, and ultrafast electron dynamics. Her work enables insights into quantum processes at the atomic scale. Collaborations span global institutions, advancing applications in condensed matter physics and quantum information science. Key Contributions: Pioneered methods to generate attosecond pulses, recognized by the 2023 Nobel Prize in Physics. Her team's innovations include optimizing attosecond source development and applying these pulses to study electron motion in matter. Awards: Nobel Prize in Physics (2023), Wolf Prize in Physics (2022), and multiple grants including Horizon Europe and Wallenberg Foundation funding. Active in conferences and mentoring, with 34 supervised works and 352 research outputs. Labs/Teams: Principal Investigator at NanoLund, a nanoscience center, and member of strategic initiatives like Light & Materials and Photon Science and Technology profile areas.

• Aerospace Engineering
• Fluid Dynamics
• Aerodynamics
• +۳ مورد دیگر

Dimitrios Rozakis is an Assistant Professor of Mechanical and Aerospace Engineering. He is actively engaged in research and teaching at the College of Engineering, where he leads the Aerodynamics & Propulsion Laboratory . Education PhD in Aerospace Engineering, National Technical University of Athens (2012) MSc in Fluid Mechanics, University of Manchester (2008) Diploma in Mechanical Engineering, Aristotle University of Thessaloniki (2006) Research Interests His research spans computational and experimental aerodynamics , with particular emphasis on: Transonic and supersonic flows Flow control using plasma actuators Hypersonic boundary-layer transition Reduced-order modelling and machine-learning techniques Turbomachinery aerodynamics Recent work has focused on high-fidelity simulations of buffet phenomena, experimental investigations of plasma-based separation control, and the development of data-driven surrogate models for unsteady aerodynamic loads. Selected Scientific Awards ASME Best Paper Award (2020) European Research Council Starting Grant (2018) AIAA Young Investigator Award (2016) Students, Grants & Funding He currently supervises three PhD students—Maria Koutsogianni, Panagiotis Giannakakis, and Eleni Christoforou—working on projects funded by the ERC, Horizon Europe, and the Greek Secretariat for Research & Technology. Active grants include an ERC Starting Grant on “Physics-informed machine learning for unsteady aerodynamics” (€1.5 M) and a Horizon Europe project on “Green regional aircraft technologies” (€4.2 M). Laboratory & Collaborations He directs the Aerodynamics & Propulsion Laboratory , which houses low-speed and transonic wind tunnels, a Ludwieg-tube facility for short-duration hypersonic experiments, and a high-performance computing cluster (>2 000 CPU cores). Ongoing collaborations include the von Karman Institute, DLR, and ONERA.

Yucheng Xieمشاهده پروفایل

استادیار

• Security in Machine Learning
• Smart and Mobile Healthcare
• Mobile Computing and Sensing
• +۳ مورد دیگر

Yucheng Xie is a Tenure-track Assistant Professor in the Graduate Department of Computer Science and Engineering at Yeshiva University, affiliated with the Katz School of Science and Health. He holds a Ph.D. in Electrical and Computer Engineering from Purdue University and a Master’s in Computer Science from Stevens Institute of Technology. His research focuses on Security in Machine Learning/AI Systems , Smart and Mobile Healthcare , and Mobile Computing and Sensing . Notable contributions include non-invasive wireless sensing via mmWave and Wi-Fi signals for health monitoring, adversarial attacks on activity recognition systems, and secure mobile deep learning. His recent articles explore topics like contactless human concentration monitoring, palm-based authentication, and environment-invariant eating behavior tracking. These works highlight innovations in mmWave technology, cybersecurity for AI systems, and healthcare applications. Awards : Best Paper Runner-up (IEEE Conference on Communications and Network Security, 2024) Best Paper Runner-up (IEEE International Conference on Computer Communications and Networks, 2022) Best Paper Award (EAI International Conference on IoT Technologies for HealthCare, 2019) While no advising or grant details are provided, his work emphasizes practical applications of wireless sensing and secure AI systems. No specific lab affiliations are mentioned, though his research often involves interdisciplinary collaboration.

Senol Ataogluمشاهده پروفایل

استاد

• Structural Mechanics
• Numerical Modeling
• Solid Mechanics
• +۳ مورد دیگر

Prof. Dr. Senol Ataoglu is a Professor in the Department of Civil Engineering at Istanbul Technical University (ITU). He holds administrative roles including Vice Rector (2024–present) and Acting Dean (2020–2020). His expertise includes Structural Mechanics, Numerical Modeling, Solid Mechanics, and Buried Pipes. He has advised numerous graduate students on topics like composite materials, thermal stress analysis, and pipeline design. His research focuses on civil infrastructure durability, material behavior under extreme conditions, and innovative engineering solutions for urban systems. He has published over 38 peer-reviewed articles and contributed to projects such as the 2017 BAP-funded study on concrete shear strength. Education: PhD in Structural Engineering from ITU (1997–1999). Academic career spans roles from Research Assistant (1993) to Professor (2017–present). He has served on editorial boards, including the El-Cezeri Journal of Science and Engineering, and holds patents in plastic pipe design. His work emphasizes practical applications in earthquake-resistant construction, utility tunnel systems, and composite material analysis. Recent research highlights include post-earthquake building assessments in Hatay, stress analysis of pretensioned structures, and utility tunnel applications for natural gas pipelines. He actively collaborates internationally and mentors students via thesis supervision, covering structural analysis, material characterization, and computational methods.

• Computer Vision
• Machine Learning
• Image Processing
• +۴ مورد دیگر

Adjunct Professor Shuai Wan is affiliated with the School of Engineering at RMIT University (City Campus, Australia). His research focuses on computer vision, machine learning, 3D point cloud compression, neural video coding, and remote sensing . Key contributions include lightweight deep learning frameworks for image/video compression, spatio-temporal context models for point clouds, and adaptive quantization techniques. Research Outputs Insights : Wan’s work spans 2024–2025 , emphasizing end-to-end deep learning solutions for challenges in Exemplar-based colorization with semantic attention Rendering-oriented 3D point cloud compression Slimmable video codecs with variable bitrate G-PCC standard enhancements for quantization and entropy coding Adversarial example detection in remote sensing Technical Domains : His articles intersect artificial intelligence, signal processing, and computer graphics , with applications in cloud gaming, SAR systems, and industrial data compression. Methods include transformers, attention networks, and 3D convolutional architectures .

Hugo Larochelleمشاهده پروفایل

دانشیار

• Neural Networks
• Deep Learning
• Machine Learning
• +۴ مورد دیگر

Hugo Larochelle is an Associate Professor at the Université de Montréal's Department of Computer Science and Operations Research (DIRO) within the Faculty of Arts and Sciences. His expertise spans Neural Networks, Deep Learning, and Machine Learning, with contributions to fields like Computer Vision and Natural Language Processing. A graduate of DIRO (Bacc 2004, PhD 2009), he held roles at the University of Sherbrooke (2011–2016) and co-founded Whetlab (acquired by Twitter in 2015). Currently at Google Brain, he balances academic and industrial research, leading projects like the UNIQUE initiative exploring neuroscience-AI intersections. He actively contributes to Quebec’s AI ecosystem through MILA and has received prestigious awards, including the 2019 Diplômé d'honneur. His work emphasizes ethical AI, reproducibility, and interdisciplinary applications. Education: Baccalauréat (Computer Science), Université de Montréal, 2004 Doctorat (Computer Science), Université de Montréal, 2009 Research Interests: Hugo’s work focuses on advancing deep learning techniques for real-world applications, including environmental monitoring (e.g., tree crown segmentation via drone imagery), AI ethics, and model unlearning. He explores intersections between neuroscience and AI, leveraging interdisciplinary approaches to solve complex problems. Recent efforts emphasize benchmark standardization (e.g., EEVEE/GATE) and improving model efficiency through architecture innovations like SoftMoE. Articles Trends: Recent publications highlight contributions to instance segmentation (SAM models), ethical AI (unlearning frameworks), and interdisciplinary applications (e.g., bird species modeling with remote sensing). His work bridges theoretical advancements with practical tools for industries and environmental science. Scientific Awards: 2019 Diplômé d'honneur (Université de Montréal). Advising & Grants: Supervised PhD students in topics like neural networks, code modeling, and program execution (e.g., Sara Hooker, David Bieber). Co-PI on the UNIQUE project (2019–2024, FRQNT-funded), exploring neuro-AI synergies. Recipient of CIFAR funding for ICRA research (2017–2022). Labs & Collaborations: Active contributor to MILA as an associate member. Leads interdisciplinary efforts in AI for environmental challenges and healthcare imaging optimization.

• Biomechanics
• Soft Tissue Mechanics
• Cardiovascular Biomechanics
• +۵ مورد دیگر

Gerhard Holzapfel is a Professor at TU Graz's Department of Biomechanics. His research focuses on biomechanics of soft biological tissues, cardiovascular systems, and advanced material modeling. He leads the Institute of Biomechanics, conducting experimental and computational studies on tissue mechanics, vascular diseases, and medical device design. Education : Details not explicitly provided in source text. Research Interests : Dr. Holzapfel's work spans constitutive modeling of soft tissues, computational fluid dynamics (CFD) in vascular systems, mechanobiology of cells and tissues, and biomaterials. He emphasizes translating biomechanical insights into clinical applications such as endovascular devices and surgical simulations. His studies often integrate microstructural analysis with macroscopic mechanical behavior to understand pathologies like atherosclerosis and aortic dissection. Publications : Recent work highlights advancements in fiber dispersion models for skin mechanics, Bayesian frameworks for material calibration, and CFD-driven evaluations of TEVAR (thoracic endovascular aortic repair). His articles emphasize predictive modeling of tissue behavior under various loading conditions, with applications in cardiology and regenerative medicine. Awards : No specific honors or fellowships mentioned in the provided text. Advising & Grants : No student/advisor relationships or grant details explicitly listed. The Institute of Biomechanics likely supports collaborative projects in biomechanical engineering and medical research. Labs/Teams : Director of TU Graz's Institute of Biomechanics, leading interdisciplinary teams in biomechanics, material science, and computational modeling. Collaborates with clinical partners on vascular biomechanics and surgical device development.

• Augmented Reality
• Virtual Reality
• Computer Graphics
• +۶ مورد دیگر

Dieter Schmalstieg is the Alexander von Humboldt Professor of Visual Computing at the University of Stuttgart and an adjunct professor at Graz University of Technology. He leads research in augmented reality (AR), virtual reality (VR), and visualization, with contributions to tracking, rendering, and medical applications. His work spans academia and industry, with over 400 publications and numerous awards, including the IEEE ISMAR Career Impact Award and Fellow of the IEEE. Education: PhD (1997), Habilitation (2001) from Vienna University of Technology. Research: Focuses on AR/VR systems, medical visualization, and real-time graphics. Key projects include the Christian Doppler Laboratory for Handheld AR and collaborations with Qualcomm and VRVis. Awards: START Prize (2002), IEEE Technical Achievement Award (2012), Humboldt Professorship (2023). His teaching includes courses on computer graphics, VR, and real-time rendering. He has advised over 30 PhD students, many of whom hold academic or industry leadership roles. Current research explores situated analytics, mixed reality telepresence (MRUnion), and AR applications in mining and medicine (MiReBooks).

• Artificial Intelligence
• Neural Networks
• Quantization
• +۳ مورد دیگر

Hang Li is a Researcher in the Department of Molecular Biophysics and Biochemistry at Yale University’s Yale School of Medicine. Their work focuses on advancing neural network architectures, quantization techniques, and spiking neural networks (SNNs). They are affiliated with the Molecular Biophysics and Biochemistry department and contribute to interdisciplinary research in artificial intelligence and computational neuroscience. Research interests include optimizing neural networks for efficiency through quantization, exploring spiking neural networks for low-power computing, and developing methods like hybrid SNN designs, post-training calibration, and neuromorphic architectures. Their recent work addresses challenges in extreme low-bit quantization, data augmentation for object detection, and temporal coding in SNNs. Publications highlight innovations in quantization methods (e.g., TesseraQ, GenQ), spiking transformer architectures, and workload-balanced pruning strategies. While no awards are explicitly listed, their contributions to model efficiency and neuromorphic computing are notable in the field. Hang Li collaborates on projects involving neuromorphic hardware, system inconsistency benchmarking (SysNoise), and data-driven spatio-temporal analysis. Their research bridges theoretical advancements and practical applications in AI and biomedical informatics.

• Medical Imaging
• Bioengineering
• Cardiovascular Hemodynamics
• +۶ مورد دیگر

Patrick Winter is a Senior Lecturer I in the Department of Bioengineering at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. His research focuses on advanced MRI techniques, cardiovascular imaging, and nanotechnology applications in molecular imaging. He specializes in developing imaging methodologies for diagnosing vascular diseases like atherosclerosis and aneurysms, with particular emphasis on 4D flow MRI, deep learning algorithms, and nanoparticle-based theranostics. Educations: No formal education details provided in text. Research Interests: His work bridges bioengineering and clinical medicine, addressing challenges in non-invasive disease detection. Key areas include: Development of super-resolution MRI techniques Machine learning applications in medical imaging Targeted nanoparticle delivery systems Quantitative hemodynamic analysis Preclinical cardiovascular modeling Recent articles highlight innovations in: Deep learning-enhanced 4D flow MRI for stroke prediction Ensemble learning for cardiovascular system imaging Multi-modal imaging of aneurysm dynamics Awards: No scientific awards explicitly mentioned in provided texts. Grants/Advising: No grant details or student advisees listed. Collaborations likely focus on interdisciplinary cardiovascular research. Labs/Teams: Engaged in bioengineering labs specializing in MRI technology advancement and nanomedicine applications.

• Biomedical Engineering
• Semiconductor Devices
• Digital Signal Processing
• +۴ مورد دیگر

Dr. Alok Kushwaha is a Researcher at the University of Adelaide's School of Electrical and Mechanical Engineering, affiliated with the Biomedical Engineering department. He has 24 years of experience in academia, research, and administration, with expertise in semiconductor devices, digital signal processing, and antenna design. Currently, he collaborates with Dr. Jiawen Li and Dr. Robert McLaughlin at the Institute for Photonics and Advanced Sensing (IPAS) on Optical Coherence Tomography (OCT) and Fluorescence systems. His research interests include biomedical engineering applications, advanced semiconductor technologies, and nanoelectronics. Notable projects involve developing handheld OCT probes for oral tissue imaging and analyzing terahertz communication systems. He has contributed to studies on molecular communication protocols and sensor technologies for livestock health monitoring. Research Trends : Recent work focuses on biomedical imaging innovations (e.g., 3D-printed OCT devices), semiconductor device optimization (e.g., subthreshold MOSFET performance), and high-speed antenna designs. His publications span nanotechnology, image processing, and materials science. Grants & Advising : No student advisees or grant details listed. Collaborates extensively within interdisciplinary teams at IPAS. Labs/Teams : Active member of the Institute for Photonics and Advanced Sensing (IPAS), contributing to advanced sensing and photonics research.

Mohammad Javad Latifiمشاهده پروفایل

پژوهشگر

• Mathematical Physics
• Geometry
• Applied Mathematics
• +۳ مورد دیگر

Mohammad Javad Latifi is a Researcher at Dartmouth College's Department of Mathematics. He holds a PhD in Mathematics from the University of Arizona and focuses on Mathematical Physics, Geometry, Applied Mathematics, and Data Science. His research bridges theoretical work in quantum field theory and dynamical systems with applied areas like machine learning and numerical modeling of physical systems. Research Highlights: Developed kernel smoothing techniques for sea-ice dynamics modeling. Advanced the theory of star transforms and V-line tomography in imaging and inverse problems. Contributed to tensor network approximations of Koopman operators in nonlinear dynamics. Collaborated on multi-level graph spanners and optimization algorithms. Teaching: Taught undergraduate courses including Differential Equations (Math 23) and Linear Algebra (Math 22). Designed visualizations for vector calculus and ODEs, emphasizing conceptual understanding and real-world applications. Software & Projects: Developed KSPoly , a Python package for smooth field approximations in polygonal geometries. Created a sound visualization tool exploring numerical sequences as musical patterns. Contributed to Lunewave's radar and autonomous vehicle software, implementing C++ algorithms for object tracking and data analysis.

Jannick Rollandمشاهده پروفایل

استاد

• Optical Engineering
• Freeform Optics
• Optical System Design
• +۵ مورد دیگر

Jannick Rolland is the Brian J. Thompson Professor of Optical Engineering at the University of Rochester's Institute of Optics and serves as CTO of LighTopTech, a company she co-founded. She leads the NSF I/UCRC Center for Freeform Optics (CeFO), focusing on graduate education, and directs the R.E. Hopkins Center for Optical Design and Engineering, which engages undergraduates in optical design, fabrication, and metrology. Her academic journey includes a Diploma from Institut D'Optique (1984), MS (1985) and PhD (1990) in optical science from the University of Arizona, followed by postdoctoral work at the University of North Carolina at Chapel Hill. Prior to her current role, she held positions at the University of Central Florida, where she established the Optical Diagnostics and Applications Laboratory (ODALab). Professor Rolland's research emphasizes optical instrumentation and system engineering, with a focus on freeform optics, optical coherence tomography (OCT), augmented reality (AR) displays, and biomedical imaging. Her work integrates physics-based modeling and image quality assessment across applications like medical visualization and optical system alignment. Key areas include metasurfaces, metaform optics, and the development of innovative optical systems for diverse fields such as astronomy and wearable technology. Her scientific awards include the 2014 OSA David Richardson Medal and the 2017 Edmund A. Hajim Outstanding Faculty Award. She is a Fellow of Optica, SPIE, and NYSTAR. Her editorial roles include associate editorships at Optics Letters and Optical Engineering . Her contributions span over 150 publications, reflecting her leadership in advancing optical engineering and its applications in healthcare, imaging, and emerging technologies.

• Artificial Intelligence in Radiology
• Machine Learning
• Health Informatics
• +۲ مورد دیگر

Dr. Axel W. E. Wismüller is a Professor of Radiology and Biomedical Engineering at the University of Rochester Medical Center . As Director of the AI Radiology Laboratory , he uniquely combines clinical practice in cardiothoracic/body imaging with pioneering research in Artificial Intelligence (AI) for medical imaging . Holding MD, MSc (Physics), and PhD (Electrical Engineering) degrees from German institutions, he maintains dual US and German medical licenses and board certifications. Ludwig-Maximilians-Universitaet München (1990-1996) Technical University of Munich (MD 1992, PhD 2006) Ludwig Maximilians University Munich (Residency 1997-2005) His research focuses on machine learning and network connectivity analysis in neuroimaging, particularly for HIV-associated neurocognitive disorders and multiple sclerosis . He invented the XOM algorithm (patented in US/EU) and developed AI systems for chest radiograph analysis , breast cancer imaging , and osteoporosis diagnostics . His work on large-scale Granger causality has transformed brain network analysis methodology. Scientific awards include SPIE Senior Membership (2016), NIH/NIDA R01 funding (2012-2015), and NYSTAR public-private partnerships . Supervising over 30 PhD/MSc students across engineering, computer science, and medicine , he bridges clinical and computational disciplines through active roles in ACR, RSNA, and SPIE .

Francesco Picarielloمشاهده پروفایل

پژوهشگر

• Electrical and Electronic Measurements
• Data Acquisition Systems
• Compressive Sampling
• +۷ مورد دیگر

Francesco Picariello is a Researcher at the Department of Engineering (DING) at the University of Sannio, specializing in Electrical and Electronic Measurements (ING-INF/07). His academic profile shows extensive expertise in measurement systems, data acquisition, and signal processing with applications across multiple domains including biomedical engineering, structural health monitoring, and wireless communications. His research interests focus on the design, implementation, and metrological characterization of mobile measurement systems, wireless sensor networks, data acquisition systems based on compressed sensing, signal processing, and image processing. These research activities have been applied to intelligent transportation systems, aerial photogrammetry using UAVs, indoor navigation, motion tracking, power consumption analysis of processors, wideband data acquisition systems, and physiological monitoring applications. Dr. Picariello's work demonstrates significant trends in compressive sampling techniques, with numerous publications on ECG signal processing, RF emitter localization, and structural health monitoring systems. His research shows a clear progression toward IoT integration, wearable medical devices, and security applications for communication channels. His scientific collaborations include institutions such as the University of Naples 'Parthenope', Military University of Technology of Warsaw, National Institute of Standards and Technology (NIST), Keysight Laboratories, CERN Institute, and the University of Tokyo, reflecting the international recognition of his work. As an active researcher with publications spanning from 2016 to 2025, Dr. Picariello has made substantial contributions to measurement science, particularly in the development of novel techniques for data acquisition, signal processing, and system metrological characterization across multiple application domains.