جست‌وجوی استاد و پژوهشگر

David Harleمشاهده پروفایل

استاد آموزشی

• Telecommunications Network Performance
• Broadband and Narrowband Networks
• ATM and SDH Technologies
• +۵ مورد دیگر

David Harle serves as a Senior Teaching Fellow within the Electronic and Electrical Engineering department at the University of Strathclyde's Faculty of Engineering. His academic career spans several decades with expertise in telecommunications and image processing technologies. His educational background includes a Doctor of Philosophy in Novel Techniques for Voice & Data Integration (1989) and a Bachelor of Science in Engineering (1984), both from the University of Strathclyde. Dr. Harle's research primarily focuses on telecommunications network performance evaluation , specializing in B-ISDN, ATM and SDH technologies, network interconnection, gateway performance, and traffic characterization. More recently, his work has expanded into hyperspectral imaging applications for agricultural quality inspection, particularly in rice seed classification and cattle tracking systems. His publications demonstrate a strong interdisciplinary approach combining telecommunications expertise with computer vision applications. His recent publications show a clear trend toward applying advanced image processing techniques to agricultural problems, with multiple papers on rice seed inspection using hyperspectral imaging between 2015-2020. These works demonstrate sophisticated approaches to classification and quality control using both spatial and spectral features. Dr. Harle has participated in numerous research projects as a Co-investigator, including the Automatic Rice Seed Inspection Using Hyper-Spectral Imaging (Newton Fund), Applicability of Localised Heating for metal forging, KTP-Cisco, Secure High availability Avionics Wireless Networks, and Light as a Service initiatives. He also participated in the Faculty Robotics and Automation Users Group Discussion in October 2017. His laboratory and team collaborations appear to be centered around the Centre for Signal & Image Processing at Strathclyde, working closely with colleagues including Christos Tachtatzis, Paul Murray, Ivan Andonovic, and Stephen Marshall on various imaging and telecommunications projects.

Nicolas Younes Cardenasمشاهده پروفایل

پژوهشگر ارشد

• Vegetation Phenology
• Remote Sensing
• Forest Flammability
• +۶ مورد دیگر

Nicolas Younes Cardenas is a Research Fellow at the Fenner School of Environment & Society, Australian National University. He specializes in remote sensing, vegetation phenology, and flammability analysis, with a focus on Australian eucalypt forests and global ecological systems. His work spans local-to-global scales, integrating hyperspectral sensors, machine learning, and biochemical analysis. Education: PhD in Earth and Environmental Sciences Master’s in Oil and Gas Exploration Master’s in Natural Resource Management Environmental Engineering Degree Research Focus: • OzFuel satellite mission development (world’s first for eucalypt flammability monitoring) • Vegetation phenology and environmental economics • Forest loss analysis in Upper Guinea and pygmy hippopotamus habitats • Intertidal ecosystem monitoring via geospatial technologies Publications & Projects: He has contributed to 15+ research outputs since 2016, including studies on fire behavior modeling, oil palm monitoring, and satellite system design. Active in the Hyper-NUE project (2023–2025) and a 2022 collaboration on carbon sequestration analysis.

Michael Straussمشاهده پروفایل

استاد

• Extragalactic Astronomy
• Observational Cosmology
• Galaxy Evolution
• +۴ مورد دیگر

Michael Strauss is Professor and Chair of the Department of Astrophysical Sciences at Princeton University. His research focuses on extragalactic astronomy and observational cosmology, utilizing large-scale sky surveys like the Sloan Digital Sky Survey (SDSS) and Hyper Suprime-Cam on Subaru. He studies galaxy distribution, AGN evolution, high-redshift quasars, and connections between galaxy properties and their cosmic environments. Strauss investigates the large-scale structure of the universe to constrain cosmological parameters, galaxy-environment relationships, and the nature of active galactic nuclei. His current work analyzes data from Hyper Suprime-Cam for distant quasar studies and contributes to future survey planning including the Subaru Prime Focus Spectrograph and Rubin Observatory Legacy Survey. His recent publications demonstrate a strong focus on high-redshift quasars observed with JWST and Chandra, supermassive black hole-galaxy connections, dark matter halo relationships, and fast radio burst host galaxies. Research consistently features multi-wavelength approaches combining optical, infrared, and X-ray data to study galaxy evolution mechanisms in the early universe.

Yair Litmanمشاهده پروفایل

پژوهشگر ارشد

• Theoretical Chemistry
• Quantum Dynamics
• Non-adiabatic Rate Theory
• +۶ مورد دیگر

Yair Litman is a DFG-supported Junior Research Fellow at Wolfson College and a theoretical chemist in the Yusuf Hamied Department of Chemistry , University of Cambridge. His work bridges quantum dynamics, advanced spectroscopy and machine learning to unravel how nuclei move at the atomic scale in aqueous, metallic and hybrid organic/inorganic systems. Education Diploma, University of Buenos Aires (2014) PhD, Fritz Haber Institute of the Max Planck Society, Berlin (2016–2020) Research Interests Litman’s research orbits around quantum mechanical descriptions of molecular motion . He develops adiabatic and non-adiabatic rate theories to treat hydrogen transfer and other light-atom reactions, where tunneling and zero-point energy dominate. He couples these theories with non-linear optical spectroscopies —sum-frequency generation, tip-enhanced Raman, 2D-IR—to obtain direct experimental fingerprints of elusive quantum effects at aqueous interfaces and on catalytic surfaces. Machine-learning-accelerated electronic-structure calculations provide the speed and accuracy required to simulate these complex many-body systems. Publications Trend Across 2022-2025, his publications reveal a concerted push toward first-principles spectroscopy : combining rigorous quantum-rate formulations with machine-learned potentials to predict and interpret spectra of interfacial water, defects in 2D materials and charge-transfer systems. The work is equally split between methodological advances (instanton theory, i-PI extensions, friction tensors) and high-impact applications (air-water interface fields, MX2 monolayers, dye-sensitized interfaces). Honours & Funding Deutsche Forschungsgemeinschaft (DFG) Fellowship Wolfson College Junior Research Fellowship Research Groups & Collaborations Litman is embedded in the Althorpe Group at Cambridge, continues collaborations with the Rossi Group at Max Planck Institute for Structure and Dynamics of Matter (Hamburg), and the Bonn Group at Max Planck Institute for Polymer Research (Mainz). He also contributes to the open-source i-PI and FHI-aims software ecosystems, fostering worldwide community development.

Rama Chellappaمشاهده پروفایل

استاد

• Computer Vision
• Pattern Recognition
• Signal and Image Processing
• +۵ مورد دیگر

Prof. Rama Chellappa is a College Park Professor at the University of Maryland, affiliated with the Electrical and Computer Engineering Department and the UMIACS Institute. He holds dual appointments in the A.J. Clark School of Engineering and Computer Science. His academic career includes roles as Chair of ECE (2011–2018) and previously at the University of Southern California. Education: B.E. (Honors) from University of Madras (1975), M.E. from Indian Institute of Science (1977), M.S.E.E. and Ph.D. from Purdue University (1978, 1981). He has held prestigious titles like Minta Martin Professorship and Distinguished Faculty Research Fellow. Research focuses on computer vision, biometrics (face/gait analysis), secure AI systems, and medical imaging applications. Key interests include 3D modeling from video, hyper-spectral processing, and AI-driven healthcare solutions like aging and tumor evolution modeling. Awards: NAI Fellow (2020), IEEE Jack S. Kilby Medal (2020), K.S. Fu Prize (2012), multiple IEEE/OSA fellowships, and teaching awards (e.g., Poole and Kent Senior Faculty Teaching Award). Service contributions include Editor-in-Chief of IEEE TPAMI, leadership roles in IEEE Signal Processing Society, and conference organization. Holds four patents and co-authored influential books on neural networks and image processing. Labs/Teams: Active in UMD’s Computer Vision and Pattern Recognition groups, collaborating on projects like AI+AgeTech collaboratories and multi-modal biometric systems.

Paul G Kwiatمشاهده پروفایل

استاد

• Quantum Optics
• Quantum Information
• Photonic Quantum Information Systems
• +۳ مورد دیگر

Paul G Kwiat is a distinguished Professor and John Bardeen Chair in Physics and Electrical Engineering at the University of Illinois at Urbana-Champaign, affiliated with The Grainger College of Engineering and the Department of Physics. He holds dual roles as a faculty member and founding Director of the Illinois Quantum Information Science and Technology Center (IQUIST). His research focuses on quantum optics, quantum information systems, and photonic quantum technologies, including entangled photon sources, quantum computing architectures, and hyper-entanglement applications. He earned his Ph.D. in Physics from UC Berkeley (1993), followed by postdoctoral work at the University of Innsbruck and Los Alamos National Laboratory. His honors include Fellowships from the Optical Society of America and American Physical Society, and the LANL Fellows Prize (1998). Notable contributions include pioneering work on quantum interrogation, quantum erasure, and optical implementations of quantum protocols. His research group actively explores quantum communication protocols (e.g., teleportation, superdense coding) and quantum computing via cluster-state paradigms. Recent projects include drone-based quantum key distribution, space applications of quantum networks, and attosecond-scale quantum metrology. Key achievements include developing high-efficiency single-photon sources, hyper-entangled photon pairs, and founding IQUIST in 2018 to advance quantum science education and collaboration across disciplines. His work bridges foundational quantum mechanics with applied technologies like quantum cryptography and quantum-enhanced imaging.

• Mathematics of Data Science
• Cryo-Electron Microscopy (Cryo-EM)
• Bayesian Inference
• +۴ مورد دیگر

Roy Lederman is an Assistant Professor in the Department of Statistics and Data Science at Yale University. He is affiliated with the Quantitative Biology Institute (QBio), Applied Math Program, Institute for Foundations of Data Science (FDS), and the Wu Tsai Institute (WTI). A Sloan Research Fellow (2023), his work spans cryo-electron microscopy (Cryo-EM), Bayesian inference, numerical analysis, and computational biology. Prior to Yale, he was a postdoc at Princeton University and held a Gibbs Assistant Professorship at Yale. Education includes a PhD in Applied Mathematics from Yale (advised by Vladimir Rokhlin and Raphy Coifman), with earlier degrees in Physics and Electrical Engineering from Tel-Aviv University. His research focuses on mathematical and algorithmic challenges in data science, particularly in recovering molecular structures from Cryo-EM data and developing methods for heterogeneous systems. Notable contributions include hyper-molecule models for dynamic structures, alternating diffusion for multi-sensor data, and Bayesian methods for inference on manifolds. His work bridges theoretical mathematics and computational tools, with applications in structural biology and signal processing. Recent publications highlight advancements in manifold learning, stochastic gradient descent for Cryo-EM refinement, and integrating molecular models with Cryo-EM data. He actively mentors postdocs and graduate students, emphasizing interdisciplinary approaches to data science challenges.

Jonathan Turnerمشاهده پروفایل

استاد

• Marine Environmental Monitoring
• Hyper-spectral Analysis
• Algorithms
• +۳ مورد دیگر

Jonathan Turner is a part-time Professor in the Department of Computer Science and Engineering at Washington University. He is no longer a full-time faculty member but remains active in research, focusing on instrument design for marine monitoring, hyper-spectral analysis, and computational methods. His professional recognitions include IEEE and ACM Fellowships, as well as membership in the National Academy of Engineering. Research Interests: Turner’s work combines environmental science and computer engineering to develop automated systems for long-term oceanic observation. He explores hyper-spectral imaging techniques for seawater analysis and contributes to algorithmic advancements in data processing. His expertise spans instrumentation design, spectral analysis, and foundational computer science disciplines such as algorithms and data structures. Scientific Awards: Fellow of the IEEE Fellow of the ACM Member of the National Academy of Engineering Advising & Grants: While no explicit student advisees or grant details are provided, Turner continues to engage in targeted research projects. The Turner Dissertation Award, named in his honor, was recently awarded to Yu Sun for contributions to computational imaging and machine learning.

Charles SOUSSENمشاهده پروفایل

استاد

• Inverse Problems
• Sparse Approximation
• Signal Processing
• +۳ مورد دیگر

Charles SOUSSEN is a Professor at CentraleSupélec and L2S (Laboratoire des Signaux et Systèmes). He holds a PhD from Université Paris-Sud (2000) and a Habilitation from Université de Lorraine (2013). His research focuses on inverse problems, sparse approximation, signal processing, and biomedical applications. He leads the Signals and Statistics Pole at L2S since 2020 and has coordinated the French ANR project BECOSE (2016–2020). His work spans areas like hyperspectral imaging, optical spectroscopy, and algorithm development for sparse representations. Education : Ecole Nationale Supérieure en Informatique et Mathématiques Appliquées (ENSI-MAG), Grenoble (1996) PhD in Signal and Image Processing, L2S, Université Paris-Sud (2000) Habilitation à Diriger des Recherches, Université de Lorraine (2013) Research Interests : His primary research areas include inverse problems, sparse approximation, signal processing algorithms, biomedical imaging, and hyperspectral data analysis. He develops methods for signal deconvolution, source separation, and modal analysis with applications in astronomy, geology, and medical diagnostics. Grants & Projects : ANR BECOSE (2016–2020): Sparse approximation algorithms for ill-conditioned inverse problems Labs & Teams : He is affiliated with L2S and leads the Signals and Statistics research group. His work involves collaborations in biomedical optics, hyperspectral analysis, and algorithm development for engineering and medical applications.

Șerban OPRIȘESCUمشاهده پروفایل

مدرس

• Color and Multi-/Hyper-Spectral Image Processing
• Digital Signal Processing
• Web Programming
• +۳ مورد دیگر

Șerban OPRIȘESCU is a Lecturer at the Department of Electronics and Computers, part of the Faculty of Electrical Engineering and Computer Science at the Transilvania University of Brașov. His research focuses on color and multi/hyper-spectral image processing, digital signal processing, and web programming. He has contributed to advancements in hyperspectral grassland analysis, polarization-based imaging for texture segmentation, and medical imaging applications like infantile hemangioma measurement. His publications span topics from environmental remote sensing to medical diagnostics, reflecting a blend of theoretical and applied research. Key works include a 2023 study on hyperspectral image segmentation for grassland analysis and a 2021 textbook on database systems. He is affiliated with the university's Politehnicii 1 campus, located in Brașov, Romania. No scientific awards or grants are explicitly listed in the provided materials. He has not disclosed advisee students or active laboratory teams in the available information.

• Observational Cosmology
• Cosmic Expansion Rate
• Dark Energy and Dark Matter
• +۷ مورد دیگر

Dr. Sherry H. Suyu is an Associate Professor at the Technical University of Munich (TUM) and a Max Planck Fellow at the Max Planck Institute for Astrophysics. Her research focuses on gravitational lensing applications for cosmology, particularly in measuring the cosmic expansion rate and studying dark energy/dark matter. She leads the HOLISMOKES program funded by an ERC Consolidator Grant. Her research group at TUM School of Natural Sciences Department of Physics investigates: Dark cosmos through gravitational lensing Supernova observations and Tidal Disruption Events Bayesian inference methods for astrophysical modeling Machine learning applications in lensed system analysis Scientific achievements include: 2021 Berkeley Prize from American Astronomical Society ERC Consolidator Grant for HOLISMOKES program Discovery of 330 high-quality lens candidates in Pan-STARRS survey She teaches courses including: Experimental Physics 1 (Winter 2024/5) Gravitational Lensing (Winter 2024/5) Introduction to Nuclear/Particle/Astrophysics (Summer 2025) The group includes 13 active members and 14 alumni across 7 institutions. Current work integrates HST and LSST imaging for next-generation lens discovery.

Andrea Bucchiمشاهده پروفایل

مدرس ارشد

• Computational Modelling
• Finite Element Method
• Fluid-Structure Interaction
• +۱۲ مورد دیگر

Andrea Bucchi is a Senior Lecturer in Engineering Sciences at the University of Portsmouth, affiliated with the School of Electrical and Mechanical Engineering and the Faculty of Technology. He serves as Associate Head (Research and Innovation) and is a member of the Biomedical Engineering Research Group. He joined the university in 2011 after serving as a Research Fellow at the University of Southampton, where he worked on the wave energy device Anaconda. His research focuses on computational modelling using finite element methods, particularly in cardiovascular mechanics, fluid-structure interaction, structural mechanics of hyper-elastic and piezoelectric materials, incompressible fluid dynamics, and wave energy systems . He has a strong interest in programming and numerical methods, with recent work emphasizing fluid-structure interaction in blood vessels and biomedical device design. The most recent publications show a multidisciplinary trend, bridging biomechanics, hydrodynamics, materials science, and medical device innovation . His work spans both theoretical mechanics and applied engineering, with applications in healthcare and maritime safety. Scientific Awards and Recognition: No specific awards mentioned in the text. Andrea Bucchi is actively involved in research supervision and is accepting PhD students. He has led multiple research projects funded by EPSRC, Innovate UK, and Diamond Light Source, including initiatives on spinal anaesthesia, airway devices, and maritime safety. His work contributes to UN Sustainable Development Goals related to health and clean energy. He has delivered invited talks, such as at the Institute of Materials, Minerals and Mining, and maintains a strong research presence with over 33 publications and datasets. He is part of the Portsmouth Centre for Advanced Materials and Manufacturing and collaborates extensively with researchers in biomechanics and engineering. His recent projects include real-time wave imaging for seakeeping, synchrotron imaging of heart valve scaffolds, and the development of anti-viral medical devices.

Johannes Lutzeyerمشاهده پروفایل

استادیار

• Graph Neural Networks
• Spectral Graph Theory
• Graph Representation Learning
• +۶ مورد دیگر

Johannes Lutzeyer is an Assistant Professor in the Data Science and Mining Team at the Computer Science Department of École Polytechnique in France. His academic journey includes a PhD in Mathematics from Imperial College London (supervised by Prof. Andrew Walden) and a postdoctoral fellowship at École Polytechnique with Prof. Michalis Vazirgiannis. Current role: Assistant Professor in Computer Science at École Polytechnique PhD: Mathematics Department, Imperial College London Postdoc: École Polytechnique His research lies at the intersection of Graph Representation Learning , Statistics , and Computer Science , focusing on Graph Neural Networks (GNNs) and spectral properties of graph shift operator matrices. Recent work explores robustness against adversarial attacks, virtual nodes, and theoretical advancements in GNN expressiveness. The 15 most recent publications span Machine Learning , Deep Learning , and Network Science , with subfields including GNN Robustness , Hyper-Heuristics , Bioinformatics Applications , and Spectral Analysis . Collaborations include leading conferences like ICML, ICLR, and AAAI. Scientific Recognition LOG Top Reviewer Awards (2024) ICML Best Reviewer Awards (2024) NeurIPS Top Reviewer Awards (2023) Johannes actively engages in academic knowledge dissemination through keynote talks, including titles like “Understanding Virtual Nodes in Graph Neural Networks” (ICLR 2025) and “Methodological Advances in Graph Neural Networks” (Ericsson Research 2024). He also invites applications for PhD positions, reflecting his mentorship role.

Barry Tillmanمشاهده پروفایل

استاد

• Agronomy
• Plant Breeding
• Crop Science
• +۳ مورد دیگر

Dr. Barry Tillman is a Professor of Agronomy at the North Florida Research and Education Center (NFREC). His research focuses on improving peanut varieties through conventional breeding and integrating genomic tools. He emphasizes sustainable agricultural practices, including water stress tolerance, herbicide efficacy, and precision agriculture technologies. His work bridges crop physiology, weed management, and technological innovation to enhance peanut production. Research interests include peanut breeding, drought resilience, and the application of hyperspectral imaging and robotics for crop monitoring. He explores transgenerational stress memory and melatonin seed priming to improve crop yield under environmental stress. Recent studies address aflatoxin risk modeling, weed control strategies, and disease management using hydrothermal time models. Dr. Tillman collaborates on developing cost-effective agricultural technologies, such as hyper-spectral cameras and robotic systems for plot-scale analysis. He has contributed to the registration of new peanut varieties like FloRun ‘T61’ and TUFRunner ‘297’, emphasizing functional food traits and disease resistance.

• Astronomy
• Extragalactic Astronomy
• Galaxy Formation
• +۵ مورد دیگر

Yuma Sugahara is an Assistant Professor at Waseda University's School of Advanced Science and Engineering , Department of Physics. Previously held research positions at the National Astronomical Observatory of Japan and Waseda's Research Institute for Science and Engineering. Specializes in astronomy with focus on extragalactic astronomy , particularly high-redshift galaxy evolution, starburst processes, and interstellar medium dynamics during cosmic reionization. PhD from University of Tokyo Graduate School of Science (2020) Master's from University of Tokyo Graduate School of Science (2017) Bachelor's from Kyoto University Faculty of Science (2015) Research employs JWST , ALMA , and Keck observations to study: Major mergers and starburst-induced morphological disturbances Dust temperature evolution across cosmic time Ionization parameter diagnostics for reionization-era galaxies Primordial galaxy analogs in local extremely metal-poor systems Key contributions include 15+ refereed publications as co-author on: High-redshift galaxy dynamics (z > 7) Quiescent galaxy formation pathways Outflow characterization in low-mass systems Multi-wavelength spectral energy distribution analysis