جست‌وجوی استاد و پژوهشگر

Bodo Wiltsمشاهده پروفایل

استاد

• Biophotonics
• Structural Color
• Nanotechnology
• +۳ مورد دیگر

Bodo Wilts is a Professor at the University of Salzburg in the Chemistry and Physics of Materials department. His research focuses on biophotonics , structural color , and photonic nanostructures in biological systems, particularly insects and beetles. Education Habilitation in Physics, University of Freiburg (2020) PhD in Physics, Rijksuniversiteit Groningen (2013) Diploma in Physics, University of Göttingen (2009) Research Trends Wilts’s recent publications highlight interdisciplinary work bridging biological optics , nanotechnology , and biomimetic materials . Key themes include: Photonic networks in insects (disordered vs. ordered structures) Self-assembly of block copolymers for advanced materials Applications of structural coloration in diagnostics and sensing 3D imaging of photonic nanostructures using X-ray tomography Projects Ra-Dia-M (2025–2026): Label-free SERS diagnostics for melanoma cells Unraveling butterfly scale morphogenesis (2022–2026): Genetics and biomechanics of butterfly scales High-aspect ratio optical structures (2024–2025): Simulation and characterization of optical metamaterials Activities Keynote on Dis/ordered photonic networks in insects (2024) Lectures on Multifunctional colors and nanostructure formation (2024) Presentations on Amorphous photonic networks (2023–2024)

• Materials engineering
• Mechanical engineering
• Machine learning
• +۱۲ مورد دیگر

Professor Nick Birbilis serves as the Executive Dean of the Faculty of Science Engineering and Built Environment at Deakin University. With a distinguished career spanning materials science, corrosion engineering, and machine learning applications, he leads one of Australia's most innovative academic faculties. His research has significantly impacted the fields of materials engineering, particularly in corrosion science, alloy development, and advanced manufacturing techniques. Professor Birbilis's educational background includes a Doctor of Philosophy, Graduate Certificate of Higher Education, and Bachelor of Engineering (with Honours) in Materials Engineering, all from Monash University. His research interests span materials engineering, mechanical engineering, machine learning, metals and alloy materials, electrochemistry, and glass technology. His recent publications demonstrate a strong focus on the intersection of traditional materials science with cutting-edge computational approaches. The research trends show increasing integration of machine learning techniques for alloy design and property prediction, alongside continued fundamental work on corrosion mechanisms in advanced materials including multi-principal element alloys, magnesium alloys, and additively manufactured components. Fellow, ASM International (2022) Fellow, Engineers Australia (2021) Fellow, International Society of Electrochemistry (2020) H.H. Uhlig Award, The Electrochemical Society (2020) Batterham Medal, Academy of Technological Sciences and Engineering (2017) Lee Hsun Award, Chinese Academy of Science (2015) Professor Birbilis currently supervises multiple doctoral students working on cutting-edge projects including sustainable engineering models, 3D printed glass behavior, corrosion-resistant coatings, and biomolecular extraction techniques. His research is supported by significant grants from the Australian Research Council, Department of Education, Office of Naval Research USA, and industry partners including Advanced Alloy Holdings, InfraBuild, and Bluescope Steel. These projects focus on hydrogen economy applications, corrosion-resistant alloys, advanced protective coatings, and steel innovation.

Yoann Altmannمشاهده پروفایل

استاد

• Statistical signal processing
• Image processing
• Bayesian inverse problems
• +۵ مورد دیگر

Yoann Altmann is Professor in the School of Engineering & Physical Sciences at Heriot-Watt University and a member of the Institute of Sensors, Signals & Systems. Since 2024 he holds the Chair in Electrical, Electronic & Computer Engineering (EECE), directing a research programme that bridges statistical signal processing, computational imaging and quantum & neuromorphic sensing. Education & career: 2010 – Eng. degree (Electrical Engineering), ENSEEIHT, Toulouse, France 2010 – M.Sc. (Signal Processing), National Polytechnic Institute of Toulouse 2013 – Ph.D. (Signal & Communications), IRIT Laboratory, Toulouse 2014-2017 – Post-doctoral Research Fellow, Heriot-Watt University 2017 – Royal Academy of Engineering Research Fellow & Assistant Professor, HWU 2024 – promoted to Professor, School of Engineering & Physical Sciences, HWU Research interests: Prof. Altmann develops mathematical and algorithmic tools for Bayesian inverse problems, with emphasis on single-photon LiDAR, low-illumination imaging, neuromorphic computational sensing, variational inference and sparse reconstruction. His work combines principled statistical modelling with efficient computational schemes to enable imaging in extreme scenarios such as underwater scattering, photon-starved environments, quantum metrology and real-time 3-D scene reconstruction. Publication trends: Across 160 outputs (2011-2025) his recent articles reveal a clear trajectory toward integrating modern machine-learning paradigms—variational autoencoders, diffusion generative models, spiking neural networks—with rigorous physics-based forward models. Applications span quantum parameter estimation, multimode-fiber endoscopy, hyperspectral & Compton imaging, nuclear safeguards and cultural-heritage spectroscopy, demonstrating both methodological breadth and high-impact interdisciplinary deployment. Honours & recognition: Royal Academy of Engineering Research Fellowship – competitively awarded (2017) Grants & datasets: He has generated four open datasets supporting reproducible research in quantum sensing, variational autoencoders, underwater single-photon LiDAR and multispectral fluorescence imaging, reflecting sustained funding and commitment to open science. Continuous peer-review service for IEEE and Elsevier journals since 2013 underlines his standing within the signal-processing community. Labs & teams: He leads the Bayesian Imaging & Sensing Computing (BISC) group ( https://bisc.site.hw.ac.uk ) which hosts post-docs, PhD researchers and international visitors working on statistical machine-learning for imaging, sensing and quantum technologies.

Dimitris Psaltisمشاهده پروفایل

استاد

• Optical Imaging
• Holography
• Biophotonics
• +۳ مورد دیگر

Dimitris Psaltis is a Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. He serves as Director of the Optics Laboratory and was Dean of the School of Engineering from 2007 to 2016. Previously, he held academic positions at Caltech, including Thomas G. Myers Professor of Electrical Engineering. Education : B.Sc., M.Sc., and Ph.D. in Electrical Engineering from Carnegie Mellon University (1974–1977). His research interests span optical imaging and holography , biophotonics , optofluidics , and renewable energy . Recent work explores machine learning integration into optical systems , such as photonic neural networks , diffraction tomography , and solar irradiance forecasting . Articles highlight advancements in subwavelength imaging , nonlinear optical computing , and AI-driven wavefront shaping . Scientific awards include: 1990 International Commission of Optics (ICO) Prize 2002 NASA Space Act Award 2003 Humboldt Research Award 2006 SPIE Dennis Gabor Award 2012 OSA Emmett N. Leith Medal 2016 OSA Joseph Fraunhofer Award Fellowships: SPIE (1986), OSA (1989), IEEE (2005), EOS (2012) His work has influenced biomedical imaging, optical computing, and energy applications, with recent projects focusing on lab-on-chip systems , multimode fiber endoscopy , and 3D printing of holographic elements . He leads the Optics Laboratory at EPFL, driving interdisciplinary research in photonic technologies.

Reza Zadehمشاهده پروفایل

استاد مدعو

• Machine Learning
• Distributed Computing
• Discrete Mathematics
• +۷ مورد دیگر

Reza Zadeh is a Computational Mathematics professor at Stanford University's School of Engineering and Founder & CEO of Matroid . He previously served as a Technical Advisory Board member for Databricks and leads the Spark Tutorial at Stanford. Research Interests: Specializing in Machine Learning and Distributed Computing , his work bridges theoretical mathematics with practical implementations in big data systems. Key focus areas include Optimization of Apache Spark 3D Convolutional Neural Networks Discrete Mathematics and Graph Theory Medical Imaging Applications Academic Contributions: His publications reveal trends across multiple disciplines: Adapting machine learning for medical diagnostics (2019-2022) Advancing distributed computing frameworks (2014-2016) Developing mathematical foundations for social networks (2009-2013) Creating scalable optimization algorithms (2014-2016) Scientific Awards: Best Paper Award runner-up at KDD 2016 Academic Leadership: He has taught SMACC Consulting and designed courses including CME 323: Distributed Algorithms and Optimization (2015-2024) and CME 305: Discrete Mathematics and Algorithms (2010-2017). His lectures cover graph theory, approximation algorithms, and spectral sparsification. Labs & Teams: Organized Spark Summit workshops and leads Scaled Machine Learning Conference . Collaborates with Stanford's ICME computational consulting services.

• Solidification
• X-ray Imaging
• Materials Science
• +۳ مورد دیگر

Ragnvald Mathiesen serves as Professor in the Department of Physics at the Norwegian University of Science and Technology (NTNU), Trondheim. His research leverages advanced synchrotron-based X-ray and neutron imaging techniques to investigate dynamic solidification processes in metallic alloys and geomaterials, with significant contributions to understanding microstructure evolution under varied conditions including microgravity. His primary research interests focus on solidification physics , in-situ X-ray radiography/tomography , and microstructure characterization of metallic systems. Key specialties include dendrite growth kinetics, phase transformation dynamics, grain refinement mechanisms in aluminum alloys, and the application of 4D imaging to capture transient phenomena in materials processing. His work bridges fundamental physics with industrial metallurgy applications, particularly in aluminum and magnesium alloy systems. Analysis of his 15 most recent publications (2019-2025) reveals a consistent emphasis on time-resolved imaging methodologies applied to solidification phenomena. His research demonstrates increasing sophistication in multi-modal imaging (X-ray/neutron), with growing applications in geomaterials and electro-active systems. The publications show strong international collaboration patterns, particularly with European synchrotron facilities like ESRF, and address both fundamental questions in solidification physics and practical challenges in materials processing. Professor Mathiesen actively contributes to the development of advanced X-ray microscopy techniques, as evidenced by his 2017 doctoral dissertation on high-energy X-ray transmission microscopy and recent publications on dark-field imaging and diamond lens optimization. His laboratory work utilizes NTNU's materials characterization infrastructure alongside major international facilities including the European Synchrotron Radiation Facility.

Wolfgang Rheinheimerمشاهده پروفایل

استاد

• Ceramic materials
• Sintering technologies
• Grain boundary engineering
• +۷ مورد دیگر

Prof. Wolfgang Rheinheimer is a Professor and Institute Director at the Institute for Ceramic Materials and Technologies , University of Stuttgart, since 2023. Previously, he held academic positions at RWTH Aachen (2022-2023), Forschungszentrum Jülich (Emmy Noether Group Leader, 2020-2022), TU Darmstadt (2020), and Karlsruhe Institute of Technology (2010-2017). Academic Rank: Full Professor Research Focus: Advanced sintering technologies, grain boundary engineering, defect chemistry, conductivity in ceramics, and microstructure evolution His research spans experimental and computational approaches to ceramic materials, with emphasis on field-assisted processing (electric/magnetic), grain boundary properties , and solid-state electrolytes . He has pioneered studies on flash sintering , cold sintering , and blacklight sintering mechanisms. Scientific Awards : Emmy Noether Fellowship (2020-2022) for establishing his independent research group His work integrates phase-field modeling with experimental characterization to optimize ceramic properties for energy applications (solid-state batteries, fuel cells) and structural uses. Collaborations include Robert Bosch GmbH and Purdue University (2018-2019 Visiting Professorship).

Julian Tachellaمشاهده پروفایل

پژوهشگر

• Signal Processing
• Machine Learning
• Computational Imaging
• +۵ مورد دیگر

Dr. Julian Tachella is a CNRS Research Scientist at the Sisyph Laboratory of École Normale Supérieure de Lyon, with co-founder/CSO roles at Blur Labs. His career spans signal processing, machine learning, and computational imaging, focusing on inverse problems and self-supervised learning. Affiliation: CNRS (French National Centre for Scientific Research), Sisyph Laboratory, École Normale Supérieure de Lyon Co-founder & CSO: Blur Labs (AI/Imaging startup) Research Interests: At the intersection of signal processing and deep learning , his work addresses imaging inverse problems through self-supervised methodologies (e.g., UNSURE, Generalized R2R) that eliminate ground-truth requirements. Key contributions include equivariant imaging frameworks for stability, spline sketches for photon-counting lidar compression, and uncertainty quantification techniques with equivariant bootstrapping. Recent Trends: 2025 publications emphasize lightweight architectures for multi-domain reconstruction (CT, super-resolution) and noise-agnostic SURE methods. 2024 works focus on audio declipping , compressed lidar , and nonlinear algorithm unrolling with applications in autonomous vehicles and medical imaging. Scientific Awards: Best Student Paper Award at ICASSP’22 Collaborations & Leadership: He leads the DeepInverse open-source project and develops algorithms for real-time 3D lidar reconstruction. His team includes researchers from University of Edinburgh and Grenoble INP, with applications in automotive lidar and underwater imaging.

• Numerical Analysis
• Uncertainty Quantification
• Inverse Problems
• +۳ مورد دیگر

Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.

Steven Leeمشاهده پروفایل

استاد

• Biophysical Chemistry
• Single-Molecule Fluorescence Spectroscopy
• Super-Resolution Imaging
• +۵ مورد دیگر

Professor Steven Lee is a leading figure in biophysical chemistry at the University of Cambridge , where he leads the TheLeeLab in the Yusuf Hamied Department of Chemistry . His research focuses on developing advanced single-molecule fluorescence and multidimensional super-resolution imaging techniques to probe fundamental biological processes at unprecedented spatial precision. Developed novel super-resolution microscopy approaches for 2D/3D visualization of T-cell membrane proteins and histone assembly in fission yeast nuclei Pioneer of 15-20nm resolution imaging strategies through fluorophore kinetics and image reconstruction algorithms Recipient of the 2017 Marlow Prize in Physical Chemistry , Lee's lab produces cutting-edge tools with applications in immunology , neurodegeneration , and cellular biophysics . His team maintains active collaborations with Prof Klenerman (FRS MedSci) and Prof Moerner (Nobel Chemistry 2014). Research Highlights : Molecular origins of immunity through T-cell membrane protein interactions 3D histone dynamics during DNA replication/repair Amyloid aggregate quantification for neurodegenerative disease diagnosis Volumetric imaging innovations via vLUME virtual reality platform

• Photoacoustic Imaging
• Ultrasound
• Tomography
• +۳ مورد دیگر

Srirang Manohar is a Full Professor at the TechMed Centre , University of Twente, specializing in Multi-Modality Medical Imaging . His work focuses on photoacoustic imaging, ultrasound, and tomography, with clinical applications in breast cancer, prostate cancer, and thyroid disorders.

Patrick Cordierمشاهده پروفایل

استاد

• Nanomechanical Testing
• Plastic Deformation of Minerals
• High-Pressure Deformation
• +۳ مورد دیگر

Patrick Cordier is a Distinguished Professor of Physics at the University of Lille, France, and a Senior Member of Institut Universitaire de France. He has held academic positions since 1989, including leadership roles at UMET Institute and significant contributions to mineral physics and materials science. Education: Habilitation (1995), PhD (1989), and M.S. (1985) in Materials Science from University of Lille. His research focuses on nanomechanical testing of minerals, plastic deformation mechanisms under high pressure, and multiscale numerical modeling of material behavior. He pioneered TEM-based defect characterization and developed physics-based models for mantle rheology. Recent publications highlight stress-induced amorphization, pure climb creep mechanisms, and TEM-based dislocation analysis, spanning materials science, geophysics, and solid-state physics with applications to Earth's mantle dynamics. Scientific Awards: Dana Medal (2016), ERC Advanced Grants (2011, 2018), Fellowships (American Geophysical Union, Mineralogical Society of America), and French academic honors. Cordier supervised 18 completed PhDs, 3 ongoing, and 8 habilitations. He served as Chief Editor of European Journal of Mineralogy and led major research groups and infrastructure projects at University of Lille.

Sarah Erickson-Bhattمشاهده پروفایل

استادیار

• Biophysics
• Optical Imaging
• Cancer Research
• +۲ مورد دیگر

Sarah Erickson-Bhatt is an Assistant Professor in the Physics Department at Marquette University , specializing in Biophysics and Biomedical Engineering. Her research focuses on applying optical imaging techniques to cancer diagnostics and surgical applications. Ph.D. in Biomedical Engineering from Florida International University (FIU), Miami Postdoctoral research at University of Illinois at Urbana-Champaign (UIUC) and University of Wisconsin-Madison Multidisciplinary expertise in biomedical optics, instrumentation development, and clinical translation Her work bridges physics, engineering, and clinical medicine, emphasizing optical coherence tomography , diffuse optical imaging , and multiscale cancer metabolism analysis . Recent publications highlight handheld imaging devices for intraoperative use and noninvasive breast cancer detection. Contact: sarah.erickson-bhatt@marquette.edu

Sorin Mitranمشاهده پروفایل

استاد

• Computational simulation of multiscale, multiphysics systems
• Data-driven constitutive relations for hyperelastic materials
• Nonlinear mode reduction
• +۴ مورد دیگر

Sorin Mitran is a Professor in the Department of Mathematics at the University of North Carolina at Chapel Hill. His research focuses on computational simulation of multiscale and multiphysics systems, data-driven constitutive relations for hyperelastic materials, and information geometry for reduced stochastic models. PhD in Aerospace Engineering from Politehnica University Bucharest (1995) Professional background includes fellowships at University of Tokyo (1993), Karlsruhe Institute of Technology (1998-1999), and University of Washington (1999-2002) His research develops numerical tools to predict macro-scale behavior from micro-scale interactions, such as plastic deformation of metals from lattice defect dynamics, microtubule mechanics from molecular dynamics, and protein folding from atomic-level simulations. Mathematical approaches include adaptive computation, machine learning for constitutive law prediction, and information geometry for stochastic process analysis. Recent publications (2023-2018) span computational biology, multiscale fluid dynamics, and medical applications of continuum mechanics. Articles frequently explore data-driven modeling, wave propagation in biological systems, and GPU-accelerated numerical methods like Lattice Boltzmann and Lattice Fokker-Planck formulations.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۲ مورد دیگر

Professor Yalin Zheng is a faculty member at the University of Liverpool, specializing in artificial intelligence, machine learning, and medical image analysis with applications in ophthalmic imaging. They hold a Ph.D. in Computer Science from the University of Southampton (2003) and have held research roles at King's College London and Medicsight PLC prior to joining Liverpool in 2008. Research interests focus on developing AI-driven solutions for eye disease diagnosis and management, including glaucoma, diabetic retinopathy, and corneal imaging. Their work integrates deep learning and novel imaging technologies like optical coherence tomography (OCT) for applications in ophthalmology and cardiology. Recent publications (2024-2025) emphasize AI techniques for OCTA vessel segmentation, corneal analysis, and cardiovascular risk prediction. They have secured significant research grants from organizations including the Medical Research Council, Wellcome Trust, and Procter & Gamble. Teaching roles include modules in Clinical Imaging and Applications (MSc) Ophthalmology Clinical Imaging (Module Co-ordinator) Medical Image Processing Professional activities include editorial roles in BMJ Open Ophthalmology (Associate Editor) Nature Scientific Reports (Editorial Board Member) and invited presentations on automated segmentation and ophthalmic imaging technologies.