جست‌وجوی استاد و پژوهشگر

Johanna Kerchمشاهده پروفایل

مدرس

• Glaciology
• Structural Geology
• Geophysics
• +۵ مورد دیگر

Dr. Johanna Kerch is a Lecturer and Postdoctoral Researcher at the University of Göttingen's Faculty of Geoscience and Geography, Department of Structural Geology and Geothermics. Her research focuses on computational structural glaciology, particularly statistical analysis of glacier ice properties, crystal anisotropy effects on ice flow, and geophysical methods for ice body structure analysis. She advocates for open-source software, digital literacy, and FAIR data practices in academia, while supporting gender equality and work-life balance initiatives in STEM. Education & Teaching: Offers 5 bachelor and 2 master's research project opportunities in glaciology and structural geology. Active in curriculum development emphasizing computational tools and ethical data management. Collaborations & Funding: Engaged in projects like the East Greenland Ice Stream (NEGIS) studies and EGRIP ice core research. Collaborates with institutions such as the Alfred Wegener Institute and international glaciology networks. Publications: Over 50 peer-reviewed articles and datasets on ice microstructure, isotopic records, and glacial dynamics. Key contributions include studies on shear margin dynamics in Greenland and Alpine glacier stratigraphy.

David Muddimanمشاهده پروفایل

استاد

• Mass Spectrometry
• Analytical Chemistry
• Biomolecular Analysis
• +۶ مورد دیگر

David Muddiman is the Jacob and Betty Belin Distinguished Professor of Chemistry at North Carolina State University, affiliated with the Department of Chemistry within the College of Sciences. He holds an AAAS Fellowship and leads the Muddiman Group, which focuses on advancing mass spectrometry techniques for biomedical applications. His work emphasizes the development of novel methodologies like IR-MALDESI (Infrared Matrix-Assisted Laser Desorption Electrospray Ionization) for imaging complex biological samples, including plant tissues, human cells, and biomedical materials. Education: B.S. Chemistry from Gannon University (1990), Ph.D. in Chemistry from the University of Pittsburgh (1995), and a Postdoctoral Fellowship at Pacific Northwest National Laboratory (1997). Research Interests: Innovations in mass spectrometry imaging, particularly IR-MALDESI for spatially resolved metabolomics, lipidomics, and proteomics. Key areas include tissue analysis, instrument optimization, and applications in biomedical research, environmental science, and plant biology. His group also explores machine learning integration for data analysis and quality control in MSI platforms. Scientific Awards: AAAS Fellow and Jacob and Betty Belin Distinguished Professorship honor his contributions to analytical chemistry and mass spectrometry. Advisees/Grants: While specific student names are not listed, the Muddiman Group actively engages in collaborative projects with funding from federal agencies and industry partnerships. Grants support research into imaging instrumentation, MSI standards, and biomedical applications. Labs/Teams: The Muddiman Group operates within the Department of Chemistry, specializing in analytical and biomedical mass spectrometry with a focus on developing cutting-edge imaging tools and methodologies.

Raphaël Pestourieمشاهده پروفایل

استادیار

• Scientific Machine Learning
• Inverse Design
• Electromagnetism
• +۷ مورد دیگر

Raphaël Pestourie is an Assistant Professor in the School of Computational Science and Engineering at Georgia Tech (2023–present). He holds a PhD in Applied Mathematics (Harvard University, 2020) and an MBA from ESSEC. His research focuses on scientific machine learning and inverse design in electromagnetism, combining AI with physics-based models for engineering optimization. Notable contributions include fast approximate PDE solvers, surrogate models for metasurface design, and end-to-end optimization frameworks. **Education**: PhD in Applied Mathematics, Harvard University (2020) Secondary field in Computational Science & Engineering, Harvard MBA, ESSEC Masters in Nanosciences (Paris Saclay), Engineering (École Centrale Paris), and Statistics (Harvard) **Research Interests**: His work bridges applied mathematics and AI to solve large-scale engineering problems. Key areas include: Inverse design of metasurfaces for optical applications Scientific machine learning for PDE-based optimization Hybrid models combining theory-driven and data-driven approaches End-to-end optimization of electromagnetic systems **Awards & Support**: Funded by MIT-IBM Watson AI Lab, DARPA, Simons Foundation, and others Arthur Sachs Fellow (French Fulbright Commission) Member of Harvard's Graduate School Leadership Institute **Advisees & Collaborations**: Advises PhD/MSc students at Georgia Tech and collaborates with MIT, Yale, and UC Berkeley researchers Current projects include metasurface design for chemical detection and imaging **Teaching**: Created new courses: CSE 8803 (Scientific Machine Learning) and CSE 8801 (Linear Algebra/Probability/Stats)

Nuutti Hyvönenمشاهده پروفایل

استاد

• Inverse Problems
• Electrical Impedance Tomography
• Numerical Analysis
• +۳ مورد دیگر

Nuutti Hyvönen is a Professor and Head of Department at the Department of Mathematics and Systems Analysis, Aalto University, School of Science. His research focuses on inverse problems, electrical impedance tomography, and numerical analysis with applications in biomedical imaging and engineering. He holds a Doctor of Science (Technology) degree and has extensive experience in developing mathematical methods for imaging and reconstruction algorithms. His work emphasizes improving the accuracy and robustness of tomographic techniques, particularly in handling modeling errors and electrode configurations. Key research areas include: Inverse problems for partial differential equations Electrical impedance tomography (EIT) and its biomedical applications Bayesian experimental design and uncertainty quantification Numerical methods for nonlinear imaging problems Recent publications highlight advancements in: Optimal electrode positioning Series reversion methods in Calderón problems Feasibility of EIT for monitoring intracerebral hemorrhages Edge-enhancing reconstruction algorithms He has contributed to over 79 peer-reviewed articles, with notable work on monotonicity-based reconstruction methods and stochastic Galerkin finite element approaches. His research bridges mathematical theory and practical applications in medical imaging, engineering, and computational science.

Renaud Toussaintمشاهده پروفایل

استاد

• Porous Media Physics
• Granular Flows
• Geophysics
• +۴ مورد دیگر

Renaud Toussaint is a Professor at the University of Oslo , affiliated with the Department of Physics within the Faculty of Mathematics and Natural Sciences . He leads research in the Porous Media Laboratory SFF , focusing on granular flows, geophysical phenomena, and fluid dynamics in disordered systems. His research interests include: Granular flow dynamics and seismic signal generation Porous media drainage and multiphase flow instabilities Earthquake mechanics and soil liquefaction Interstellar object modeling (e.g., 'Oumuamua) Fracture mechanics and thermal dissipation in materials Recent work emphasizes experimental and computational studies of granular media, with key contributions on drainage dynamics, seismic proxies for flow behavior, and interfacial fracture mechanics. Collaborations with institutions like the University of Oslo and international researchers highlight his interdisciplinary approach. Publications span Physical Review Letters , Nature Communications , and Journal of Geophysical Research , reflecting his impact in physics and geophysics. No awards or advisory roles are explicitly listed in the provided text.

Iryna Yakymenkoمشاهده پروفایل

استاد

• Quantum Physics
• Semiconductor Nanotechnology
• Spintronics
• +۳ مورد دیگر

Iryna Yakymenko is a Professor and Head of Unit at the Department of Physics, Chemistry and Biology (IFM) at Linköping University, Sweden. Her research focuses on fundamental quantum physics and semiconductor nanotechnology, particularly studying nanometer-sized semiconductor structures such as quantum dots and wires. She explores phenomena like Wigner localization, spin-polarization, and magnetization in quantum systems, with applications in spintronics and quantum informatics. Education: PhD in Physical and Mathematical Sciences, Bogolyubov Institute for Theoretical Physics, Kiev, 1991 MS in Radiophysics, Kiev State University, 1982 Research Interests: Her work addresses theoretical modeling of electron states in quantum structures, including magnetization dynamics, spintronics, and quantum transport. For instance, she investigates how quantum point contacts can selectively allow electrons of specific spin directions, advancing semiconductor spin electronics. Awards: Included in multiple 'Who's Who' editions internationally (1999–2004, 2000–2001, 2009). Collaborations: Leads the Theoretical Physics division and collaborates with researchers like Igor Abrikosov (Professor & Head of Division) and Sergey Simak (Professor). Her team explores nanophysics, nonlinear optics, and materials physics.

Germano S. Iannacchioneمشاهده پروفایل

استاد

• Condensed Matter Physics
• Thermo-Physical Properties
• Order-Disorder Phenomena
• +۷ مورد دیگر

Germano S. Iannacchione is a Professor of Physics at Worcester Polytechnic Institute (WPI), specializing in experimental research on thermo-physical properties of condensed matter, particularly soft-matter materials, colloids, and far-from-equilibrium systems. He joined WPI in 1998 and has mentored over 156 students (11 PhD, 14 M.Sc., 121 B.Sc.). Affiliations: Member of the Interactive Materials Design Center (iMDC) at WPI (2015–present) Former Program Director at NSF (Condensed Matter Physics Program, 2017–2020; Biomaterials Program, 2018–2019) Former Head of Physics Department at WPI (2006–2016) Founder of WPI's Nuclear Science & Engineering Program (2012–2017) and Master of Science in Physics for Educators (MPED) Program (2012–2016) Research Interests: His work focuses on phase transitions, liquid crystals, biomaterials, and self-assembly in complex fluids. He pioneered image-based analysis techniques for studying drying droplet patterns and their morphological evolution. His studies reveal insights into protein-LC interactions and crack formation mechanisms in colloidal systems. Key Contributions: Expanded the Physics Department at WPI to 20 faculty and $2M+ annual research funding Founded outreach initiatives: R.H. Goddard Cup Rocket Competition, Physicspalooza summer camps Established the STEM Faculty Launch Workshop (2015–2016) to mentor early-career academics Grants & Awards: NSF Division Director Appointment (January 2023) Sigma Xi Awards (2015) Highlighted in WPI News for STEM education and science communication efforts Labs & Teams: His lab integrates undergraduates into MQP projects, focusing on experimental condensed matter physics and interdisciplinary collaborations across materials science, biophysics, and engineering.

• Inverse Problems
• Image Processing
• Optimization
• +۵ مورد دیگر

Markus Grasmair is a Professor at the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU). His research focuses on inverse problems, mathematical methods in image processing, and optimization. He has contributed to nonsmooth variational methods and Lavrentiev regularization for ill-posed problems. Education: Habilitation in Mathematics (University of Vienna, 2011), PhD in Mathematics (University of Innsbruck, 2006), MSc in Mathematics (University of Innsbruck, 2003). Markus's research spans mathematical imaging, biomedical applications, and PDE-based modeling. He has developed regularization techniques for inverse problems, including Lavrentiev and L1 methods, and applied these to medical imaging and cervical cancer risk stratification. His work integrates sparse optimization, shape analysis, and data-driven approaches. Recent publications highlight interdisciplinary trends, combining inverse problems with medical imaging (X-ray calibration, cervical cancer prediction) and extending variational methods to PDEs, multiscale modeling, and graph-based systems. Key subfields include monotone operators, matrix factorization, and elastic shape analysis. He teaches courses in optimization and has held academic positions at the University of Vienna, Catholic University of Eichstätt-Ingolstadt, and University of Innsbruck. Contact: markus.grasmair@ntnu.no. Current Role: Professor (NTNU) Past Roles: Substitute Professor (Catholic University of Eichstätt-Ingolstadt, 2012-2013), Assistant Professor (University of Vienna, 2009-2012)

Mona Garvinمشاهده پروفایل

استاد

• Medical image analysis
• Ophthalmic imaging
• Graph-theoretic approaches for three dimensional image segmentation
• +۳ مورد دیگر

Mona Garvin is a Professor in the Department of Electrical and Computer Engineering at the University of Iowa's College of Engineering. She also holds researcher positions at the Iowa Institute for Biomedical Engineering and the Iowa Initiative for Artificial Intelligence , blending engineering principles with medical imaging applications. Education PhD in Biomedical Engineering, The University of Iowa (2008) MS in Biomedical Engineering, The University of Iowa (2004) BSE in Biomedical Engineering (2003) BS in Computer Science (2003) Research Focus Her work specializes in ophthalmic image analysis , leveraging machine learning and graph-theoretic approaches for 3D segmentation of retinal structures. Current projects involve differentiating optic disc pathologies (papilledema, NAION) using OCT and enhancing retinal blood flow analysis through computational models. Professional Affiliations Institute of Electrical and Electronics Engineers (IEEE) Society of Photographic Instrumentation Engineers (SPIE) Association for Research in Vision and Ophthalmology (ARVO) American Society for Engineering Education (ASEE) Innovative Contributions Developed tools like AxoNet 2.0 and eyeFusion for automated retinal segmentation and visual field quantification. Her team explores deep learning solutions for OCT analysis, latent variable modeling in retinal thickness patterns, and radiation effects on retinal structures.

Oliver Dürrمشاهده پروفایل

استاد

• Deep Learning
• Statistics
• Machine Learning
• +۱ مورد دیگر

Oliver Dürr is a professor of data science at the University of Applied Sciences Konstanz (HTWG) , affiliated with the Institute for Optical Systems (IOS) . He co-directs the newly established Thurgau Institute for Digital Transformation (TIDIT) and has over twenty years of experience in computational physics, data science, and mathematical modeling. His research focuses on deep learning and statistics , exploring their synergies for data analysis. He actively disseminates his work through academic profiles on Google Scholar , ORCID , and GitHub . Contact details: Alfred-Wachtel-Str. 8, 78462 Konstanz, Germany . Email addresses: oliver.duerr@htwg-konstanz.de and oliver.duerr@zhaw.ch .

Sudharman K. Jayaweeraمشاهده پروفایل

استاد

• Cognitive Radios
• Wireless Communications
• Statistical Signal Processing
• +۷ مورد دیگر

Dr. Sudharman K. Jayaweera is a Professor in the Department of Electrical and Computer Engineering at the University of New Mexico, Albuquerque, NM. He holds a PhD in Electrical Engineering from Princeton University (2003), an MS in Electrical Engineering from Princeton University (2001), and a BE in Electrical and Electronic Engineering with First Class Honors from the University of Melbourne, Australia (1997). He is a Senior Member of IEEE and serves as an editor for IEEE Transactions in Vehicular Technology. Dr. Jayaweera's research spans several key areas in modern communications and signal processing: Cognitive radios and autonomous learning systems Wireless communications and statistical signal processing Machine learning applications in communications Smart-grid technologies and cyber-physical systems Satellite communications and space networks Vehicular networks and distributed systems His recent work shows a clear trend toward integrating artificial intelligence with traditional communications systems, particularly focusing on UAV networks, spectrum management, and security aspects of cognitive radio systems. The publications reflect a strong emphasis on practical implementations of theoretical concepts, with applications ranging from smart grids to space communications. Dr. Jayaweera has received numerous scientific awards including the IEEE PACRIM 2011 Gold Award for Best Communications Paper, the IEEE AVSS '06 Best Paper Award, and the WPMC '03 Excellent Paper Award. He has also been recognized with fellowships including the National Research Council (NRC) Senior Fellow at the Naval Postgraduate School and ASEE Air Force Summer Faculty Fellow. As an advisor, Dr. Jayaweera has mentored numerous graduate students to completion of their PhD and MS degrees. His former students have gone on to successful careers at institutions including Syracuse University, SUNY Oswego, Qualcomm, Sandia National Labs, and other leading technology companies. He also directs the EYES Summer Internship program for international students at UNM. Dr. Jayaweera leads the Communications and Information Sciences Lab (CISL) and the Cognitive Radio Lab (CRL) at UNM, where his teams work on cutting-edge research in autonomous cognitive radios (which he terms "Radiobots"), machine learning for communications, and next-generation wireless systems.

• Quantum Information
• Quantum Entanglement
• Quantum Decoherence
• +۳ مورد دیگر

Dr. Arif Ullah is a Research Fellow in the Department of Quantum Science & Technology at the Australian National University, specializing in quantum information and atomic physics. His research primarily focuses on quantum entanglement, decoherence phenomena, and quantum dynamics in various noise environments. His research interests span quantum information theory, quantum entanglement dynamics, and quantum optics. Dr. Ullah has made significant contributions to understanding how quantum systems behave under different noise conditions, particularly examining multipartite entanglement, coherence preservation, and quantum correlations in classical environments. His work bridges theoretical quantum physics with potential applications in quantum computing and quantum information processing. Analysis of his publication record from 2009-2022 reveals a strong focus on quantum information processing, with particular emphasis on how noise affects quantum systems. His research trajectory shows progression from fundamental quantum phenomena to practical applications in quantum technologies, with increasing complexity in examining multipartite systems and different noise models. The publications demonstrate expertise in both theoretical modeling and experimental implications of quantum phenomena. Dr. Ullah has collaborated extensively with researchers across multiple institutions, particularly within Australia and internationally. His work appears in reputable journals including Quantum Information Processing, Journal of Physics A, and Laser Physics, reflecting the interdisciplinary nature of his research spanning physics, quantum information, and materials science. His research is conducted within the Atom Laser Group at ANU, which investigates quantum phenomena using atomic systems. The group's work encompasses both theoretical modeling and experimental implementations of quantum systems, with applications in quantum computing, quantum metrology, and fundamental tests of quantum mechanics.

Guillaume WEICKمشاهده پروفایل

دانشیار

• Condensed matter theory
• Mesoscopic quantum physics
• Strong light-matter coupling
• +۵ مورد دیگر

Guillaume WEICK serves as an Associate Professor at the University of Strasbourg within the Physics and Engineering Department, conducting research at the Institut de Physique et Chimie des Matériaux de Strasbourg (IPCMS). His academic journey began with a PhD in Condensed Matter Theory from Universität Augsburg and Louis Pasteur University (2003-2006), followed by postdoctoral positions at Freie Universität Berlin (2006-2009) and CNRS-IPCMS (2009-2012) before joining the faculty in 2012. Dr. WEICK's research program centers on theoretical investigations of quantum phenomena at the nanoscale. His work spans several interconnected domains: Mesoscopic quantum systems and transport phenomena Topological aspects of light-matter interactions Collective excitations in nanostructured materials Orbital magnetism in confined quantum systems Quantum effects in artificial materials mimicking graphene properties His theoretical approach combines quantum mechanics, electromagnetism, and statistical physics to model complex behaviors in engineered nanostructures. Analysis of his recent publications (2018-2025) reveals a strong focus on topological aspects of polaritonic and plasmonic systems, quantum transport in disordered environments, and magnetic properties of nanoscale objects. His work demonstrates increasing sophistication in handling complex light-matter interactions, particularly in systems exhibiting topological protection and edge states. The research shows clear progression from fundamental plasmonic phenomena toward more complex topological and many-body quantum effects. As an educator, Dr. WEICK teaches across the physics curriculum from undergraduate to master's level, covering foundational topics like electrostatics and fluid mechanics alongside advanced subjects including quantum mechanics, nonlinear physics, and statistical physics. His teaching portfolio reflects deep expertise in both classical and quantum theoretical physics. He actively participates in the GDR 2426 Mesoscopic Quantum Physics research network and contributes to the Interdisciplinary Thematic Institute QMat focused on quantum science and nanomaterials. His laboratory work is primarily theoretical, developing models to explain and predict quantum phenomena in nanoscale systems without experimental apparatus requirements.

Volker Kühnمشاهده پروفایل

استاد

• Wireless Communication Systems
• Information Theory
• Error-Correcting Coding
• +۴ مورد دیگر

Prof. Dr.-Ing. habil. Volker Kühn serves as Professor and Head of the Institute of Communications Engineering at the University of Rostock within the Faculty of Computer Science and Electrical Engineering. He concurrently holds the position of Vice Dean of the Faculty while directing academic operations as Chairman of both the Electrical Engineering and Medical Information Technology Examination Boards. His research integrates wireless communications theory with biomedical applications, specializing in spatial modulation techniques, information bottleneck optimization, and electrical impedance tomography signal processing. Current work focuses on machine learning-enhanced physiological monitoring systems and distributed compression algorithms for sensor networks, with significant contributions to bias-free spectral estimation from irregularly sampled data. Analysis of his 2021-2025 publications reveals three dominant research thrusts: (1) neural network applications for medical signal processing, (2) information-theoretic optimization of communication protocols, and (3) advanced spectral estimation methods for biomedical and sensor data. This interdisciplinary approach bridges communications engineering with healthcare technology development. Prof. Kühn actively mentors students as Study Advisor for Electrical Engineering and Academic Advisor for Medical Information Technology. His leadership extends to the ITG Technical Committee 5.1 on Information and Systems Theory and IEEE societies. The Institute of Communications Engineering under his direction maintains specialized laboratories for wireless communications testing, medical signal acquisition, and MIMO system prototyping, supporting both fundamental research and industry collaboration projects in 5G/6G technologies and healthcare IoT.

Paul Fortierمشاهده پروفایل

استاد

• Digital Communications
• Internet of Things
• Cyber-Physical Systems
• +۷ مورد دیگر

Paul Fortier is a Full Professor at the Department of Electrical Engineering and Computer Engineering , Université Laval, with a career spanning over three decades. His work focuses on Digital Communications , Internet of Things (IoT) , Cyber-Physical Systems , and Wireless Communication Systems , particularly exploring complexity-performance tradeoffs in hardware implementations. PhD in Electrical Engineering, Stanford University (1989) MSc in Statistics, Stanford University (1987) MSc and BSc in Electrical Engineering, Université Laval (1984, 1982) His research integrates Machine Learning with FPGA architectures for applications like real-time hand gesture recognition and hardware-in-the-loop simulation . Current projects include Advanced Digital Twin Development and Quantum-Resistant Communication Protocols under the Quebec Strategic Microsystems Group . Key contributions appear in IEEE Transactions on Vehicular Technology , IEEE Internet of Things Journal , and IEEE Transactions on VLSI Systems . He has secured major grants from Fonds de recherche du Québec and NSERC for initiatives like Hardware-in-the-Loop Simulators and Wireless Cybersecurity Research . Fellow, Engineering Institute of Canada Fellow, Canadian Academy of Engineering Senior Member, IEEE