Find Academic Supervisors

• Microwave Imaging
• Electromagnetic Scattering
• Inverse Problems
• +5 more

Ann Franchois is a Full Professor at Ghent University in the Faculty of Engineering and Architecture, specifically within the Department of Information Technology (EA05). She is affiliated with the Internet Technology and Data Science Lab, where she leads research in electromagnetic imaging and inverse problems. Her academic career spans several decades with continuous contributions to the field of microwave and millimeter wave imaging. Professor Franchois specializes in microwave imaging , electromagnetic scattering , and inverse problems , with significant applications in biomedical imaging (particularly breast cancer detection) and non-destructive testing (for concrete structures and steel fiber analysis). Her research integrates advanced mathematical techniques including regularization methods, numerical optimization, and computational electromagnetics to solve complex imaging problems. She has developed innovative approaches such as value picking regularization, piecewise smoothing techniques, and Huber regularization for improving reconstruction quality in quantitative imaging. Her recent publications (2013-2023) demonstrate a consistent focus on advancing 3D and 2.5D electromagnetic modeling techniques, with particular emphasis on medical applications and non-destructive testing. The research shows an evolution from fundamental electromagnetic theory to practical applications, especially in breast cancer detection through microwave tomography. Her work often involves collaborations with experts in image processing and signal reconstruction. Professor Franchois has supervised several PhD students to completion, including Funing Bai (2010-2014), Sara Van den Bulcke (2010), and Jürgen De Zaeytijd (2009). She has secured research funding from prestigious organizations, notably the Research Foundation - Flanders (FWO) for projects such as "Scattering-type scanning near-field mm-wave microscope" (2008-2013). Her laboratory work centers around the Internet Technology and Data Science Lab at Ghent University, where she develops and applies advanced computational methods for electromagnetic imaging. Current research directions include improving the accuracy and efficiency of microwave imaging systems, particularly for medical diagnostics and structural assessment applications.

Hassane SadokView profile

Professor

• Numerical Analysis
• Iterative Methods
• Linear Algebra
• +5 more

Hassane Sadok is a Professor and President at the University of the Littoral Côte d'Opale (ULCO), France. He is affiliated with the Department of Mathematics within the College of Science, where he contributes to teaching and research in numerical analysis and scientific computing. Position: Professor Institution: University of the Littoral Côte d'Opale (ULCO) Administrative Role: President since 2016 Department: Mathematics School: College of Science His research focuses on numerical linear algebra, particularly iterative methods for solving large linear systems, Krylov subspace methods (such as GMRES, Arnoldi, and CMRH), and regularization techniques for ill-posed problems. He has made significant contributions to the convergence analysis of minimal residual and orthogonal projection methods. His work bridges theoretical numerical analysis with practical computational algorithms. The recent publications reflect a strong trend in developing and analyzing block and global iterative solvers for multiple linear systems, convergence theory for eigenvalue problems, and regularization strategies like the L-curve and greedy Tikhonov methods. These works are grounded in applied mathematics and computational science, with applications in engineering and data analysis. Although no formal awards are listed in the provided texts, his leadership as university president and sustained research output indicate recognition in both academic and administrative domains. He advises graduate students in numerical analysis and computational mathematics, though specific names are not listed. He has not disclosed information on research grants. His teaching includes Master’s level courses in interpolation, quadrature, and numerical resolution of nonlinear equations. While no specific research lab or team is named, his collaborations with researchers like K. Jbilou, Y. Saad, and L. Reichel suggest active participation in a research group focused on numerical methods, likely within the Laboratoire de Mathématiques Pures et Appliquées (L.M.P.A.) at ULCO.

Balpreet Singh AhluwaliaView profile

Professor

• Optical Microscopy
• Super-resolution Microscopy
• Nanoscopy
• +7 more

Professor Balpreet Singh Ahluwalia is a distinguished academic at UiT The Arctic University of Norway, where he serves in the Department of Physics and Technology. His expertise lies at the intersection of advanced optical technologies and biomedical applications, with a focus on developing cutting-edge microscopy techniques for life science research. As a principal investigator of multiple high-impact research projects, including ERC-funded initiatives, he leads a dynamic research group focused on optical nanoscopy and bio-imaging. Dr. Ahluwalia's research spans several critical areas in modern optical science: Development of super-resolution optical nanoscopy techniques Quantitative phase imaging for live-cell analysis Integrated photonic circuits for biomedical applications Optical trapping and manipulation of biological specimens Raman spectroscopy for molecular fingerprinting His work has established UiT as a leading center for optical nanoscopy in Northern Europe, with particular emphasis on applications in marine biology, liver physiology, and pathogen detection. The research group has pioneered several novel approaches to high-speed, label-free imaging that overcome traditional limitations in resolution and speed. Analysis of Dr. Ahluwalia's recent publication record reveals a strong trajectory toward computational and AI-enhanced microscopy, with increasing focus on real-time imaging of dynamic cellular processes. His work bridges fundamental optical engineering with practical biomedical applications, particularly in the areas of nanoparticle-cell interactions, bacterial identification, and tissue imaging of marine organisms. Dr. Ahluwalia has received significant recognition through competitive research funding: ERC Starting Grant (16 M NOK) for high-speed chip-based nanoscopy ERC Proof-of-Concept grant (1.5 M NOK) for affordable photonic-chip based optical nanoscopy UiT Strategic Funding (13 M NOK) for Centre for Advanced Nanoscopy Multiple international collaborations including EU MSCA projects As an educator, Dr. Ahluwalia teaches FYS-8029 Optical Nanoscopy and mentors PhD students through multiple funded projects. His laboratory, part of the Ultrasound, Microwaves and Optics research group, maintains state-of-the-art facilities for optical nanoscopy, including custom-built super-resolution microscopes and integrated photonic platforms. The research environment fosters interdisciplinary collaboration between physicists, biologists, and computer scientists working toward next-generation imaging solutions.

Tamir BendoryView profile

Associate Professor

• Mathematics of data science
• signal processing
• optimization
• +4 more

Prof. Tamir Bendory serves as an Associate Professor in the Department of Electrical Engineering within Tel Aviv University's School of Electrical and Computer Engineering. His research bridges mathematical theory with practical signal processing applications, primarily focused on computational imaging challenges. His core research areas include Mathematics of data science, signal processing, optimization, statistics, cryo-electron microscopy, phase retrieval, and super-resolution. This interdisciplinary work develops theoretical frameworks for reconstructing biological structures from noisy imaging data, with particular emphasis on algorithmic solutions for cryo-EM and phase retrieval problems. Analysis of his 2017-2023 publications reveals consistent progression in mathematical cryo-EM methodologies, evolving from fundamental uniqueness theorems toward sparsity-constrained algorithms with improved sample complexity. His work increasingly integrates algebraic geometry with statistical signal processing to address computational bottlenecks in structural biology. Prof. Bendory actively recruits research students and acknowledges funding support from major international sources including ISF, NSF, BSF, and TAD. His laboratory focuses on developing mathematical tools for next-generation imaging technologies in structural biology.

Taotao CaiView profile

Lecturer

• Graph Data Processing
• Social Network Analytics
• Data Mining
• +6 more

Dr. Taotao Cai serves as an Honorary Lecturer and Postdoctoral Research Fellow in the School of Computing at Macquarie University, bringing expertise in graph analytics and machine learning. Previously, they held an Associate Research Fellow position at Deakin University's School of Information Technology. Their academic foundation includes a Ph.D. from Deakin University and a Master's in Computer Science from Shenzhen University, China. Education: Ph.D. in Computer Science, Deakin University, Australia Master of Computer Science, Shenzhen University, P.R. China Research spans graph data processing, social network analytics, and data mining with significant contributions to causal graph neural networks, influence maximization, and privacy-preserving machine learning. Recent work demonstrates strong interdisciplinary applications in remote sensing, healthcare decision systems, and battery management, while maintaining core focus on dynamic network analysis. The research portfolio reveals increasing sophistication in causal reasoning within graph structures and practical implementations for real-world systems. Publication trends from 2023-2025 show concentrated activity in graph neural networks (40%), privacy/security (25%), and remote sensing applications (20%), with emerging work in causal classification and medical AI. This evolution reflects both theoretical advancement and practical problem-solving across diverse domains. No formal scientific awards are documented in available sources. While no formal student advising is recorded, collaborative patterns indicate active mentorship within research teams. Grant activity remains unspecified, but extensive cross-institutional collaborations (particularly with Deakin University and international partners) suggest participation in multiple funded projects. Current trajectories point toward causal graph neural networks for explainable AI and privacy-enhanced federated learning systems as primary research frontiers. Active participation in international research networks is evident through co-authorship spanning Australia, China, and global institutions, with particular strength in graph algorithm development and social network analysis communities.

Yang YangView profile

Associate Professor

• Inverse Problems
• Medical Imaging
• Data Science
• +4 more

Yang Yang is an Associate Professor in the Department of Computational Mathematics, Science and Engineering at Michigan State University. Their research bridges inverse problem theory with computational innovation for medical and geophysical imaging technologies. Ph.D. in Mathematics (2014), University of Washington B.S. in Mathematics (2009), Zhejiang University Research Focus : Theoretical development of inverse boundary value problems in acoustics, optics, and elasticity using PDE theory, microlocal analysis, and differential geometry Geometric inverse problems for metric and vector field identification in Riemannian/Lorentzian geometries Algorithm design for tomographic reconstruction with applications in Ultrasound Computed Tomography, Photoacoustic Tomography, and Optical Tomography Scientific Contributions : NSF CAREER award recipient (2023) Developed non-iterative boundary control algorithms for acoustic inverse problems Established uniqueness/stability results for transversely isotropic perturbations in elasticity Created mathematically-justified imaging algorithms for media with unknown acoustic properties Publication Trends : Recent work focuses on conscious learning frameworks to address post-selection misconduct in AI, emergent Turing machines for autonomous programming, and ethical implications of deep learning's performance data. These align with their core expertise in inverse problem analysis and computational innovation.

Craig DouglasView profile

Professor

• Dynamic Data-Driven Application Systems
• Big Data
• High Performance Computing
• +6 more

Dr. Craig Douglas is a Professor of Mathematics and Statistics at the University of Wyoming's College of Engineering and Physical Sciences, with an Adjunct Professor appointment in Computer Science. He earned his Ph.D. from Yale University and is globally recognized for pioneering research in Dynamic Data-Driven Application Systems (DDDAS) for Big Data, high-performance computing, and multigrid algorithms for partial differential equations. He co-developed the first commercial DDDAS for oil and gas pipeline monitoring, deployed across 100+ countries. His work bridges theoretical mathematics and practical engineering challenges. Research Interests Dynamic Data-Driven Application Systems (DDDAS) High-Performance Computing (HPC) Multigrid Algorithms Big Data Analytics for Energy and Environment Computational Science Applications Recent Publications Advances in DDDAS frameworks Computational wildfire modeling CO 2 sequestration simulation Finite-difference and finite-element methods Cache optimization for multigrid algorithms Email: cdougla6@uwyo.edu

• Soil Physics
• Environmental Soil Science
• Porous Media Hydrodynamics
• +7 more

Scott B. Jones is Professor of Environmental Soil Physics in the Department of Plants, Soils and Climate at Utah State University's College of Agriculture and Applied Sciences. His research focuses on soil physics, water movement in porous media, and applications for agriculture, ecology, and space exploration. He maintains an active laboratory developing measurement techniques for soil properties and has been involved in multiple International Space Station experiments. Educational Background: PhD, Soil Science (Soil Physics), Utah State University, 1997 MS, Agricultural & Irrigation Engineering, Utah State University, 1992 BS, Civil Engineering, Brigham Young University, 1989 Professor Jones' research emphasizes development of cutting-edge measurement and modeling approaches for understanding porous medium properties and processes. His work spans agricultural, ecological, environmental and space-based applications, with a focus on mass and energy flow in soils. Current interests include machine learning of soil properties from dielectric measurements, microgravity effects on water supply to plant roots, porous media design for reduced gravity applications, soil water and nutrient flow, sensor development, and remote sensing of near-surface soil properties. His expertise bridges fundamental soil physics with practical applications in both terrestrial agriculture and extraterrestrial environments. Analysis of his recent publications reveals a strong focus on refining soil moisture measurement technologies, particularly electromagnetic and heat pulse methods. His work demonstrates a progression from fundamental soil physics principles toward increasingly sophisticated applications for water management challenges. A notable thread throughout his career has been the adaptation of soil physics principles for space exploration, evident in his numerous NASA-related projects and International Space Station experiments. Scientific Awards and Honors: International Professor of the Year, 2020 (College of Agriculture and Applied Sciences) Graduate Research Mentor of the Year, 2019 (College of Agriculture and Applied Sciences) Fellow, 2018 (Soil Science Society of America) Soil Physics & Hydrology Division Chair, 2016 (Soil Science Society of America) Faculty Researcher of the Year, 2012 (College of Agriculture) Undergraduate Mentor of the Year, 2008 (College of Agriculture) International Space Station Experiment, 2007 (with Russian Institute of Biomedical Problems) Professor Jones has mentored numerous graduate students through their research in soil physics and related fields. His research has been supported by various grants from NASA, USDA, and other agencies, particularly for his work on plant growth media for space applications and soil moisture measurement technologies. He has served as an Associate Editor for the Soil Science Society of America Journal and has been instrumental in organizing professional conferences in his field. His laboratory work focuses on soil physics instrumentation development, particularly sensors for measuring soil moisture, thermal properties, and other physical characteristics. Professor Jones has been a leader in designing experimental setups for studying water behavior in porous media under variable gravity conditions, including parabolic flight experiments and International Space Station investigations. His work on the ORZS (Optimization of Root Zone Substrates) project represents significant contributions to space agriculture research.

Yifei LouView profile

Associate Professor

• Compressive sensing
• Image analysis
• Numerical analysis
• +5 more

Yifei Lou is an Associate Professor jointly appointed in the Department of Mathematics and the School of Data Science and Society at the University of North Carolina at Chapel Hill . From 2014 to 2023 she served on the faculty of the University of Texas at Dallas, rising from Assistant to Associate Professor. Education: B.S., Peking University, 2005 Ph.D., Applied Mathematics, University of California, Los Angeles, 2010 (co-supervised by Prof. Andrea Bertozzi and Prof. Stefano Soatto) Postdoctoral Fellow, Georgia Institute of Technology, 2011–2012 (supervised by Prof. Allen R. Tannenbaum) Postdoctoral Fellow, University of California, Irvine, 2012–2014 (with Prof. Jack Xin and Prof. Hongkai Zhao) Research Interests: Dr. Lou’s work sits at the intersection of computational mathematics and data science . She develops efficient numerical tools to recover signals or images from limited measurements , exploiting sparsity, low-rankness, and tensor structures. Applications range from phase retrieval and hyperspectral imaging to seismic data completion and medical imaging . Core themes include compressive sensing , numerical optimization , and large-scale inverse problems . Recent Publication Trends: Her 2023–2025 corpus reveals a rapid pivot toward network-geometric approaches for pan-cancer genomics , neuroimaging , and immunotherapy response prediction . Papers leverage optimal mass transport , Ricci curvature , and multi-omics integration , translating sophisticated mathematical machinery into clinically actionable insights for breast cancer, melanoma, Alzheimer’s disease, and sarcoma. Scientific Awards: NSF CAREER Award (DMS-1846690), 2019–2024, “Mathematical Modeling from Data to Insights and Beyond” Grants & Workshops: NSF Workshop Grant (DMS-1821870), “Recent Developments on Mathematical/Statistical Approaches in Data Science,” May 31 – June 2, 2019 Laboratories & Collaborations: While no dedicated laboratory page is provided, Dr. Lou collaborates extensively across UNC Mathematics , the School of Data Science and Society , and interdisciplinary teams in medical imaging, oncology, and neuroscience.

Amit AshokView profile

Professor

• Inverse Problems
• Bayesian Inference
• Information Theory
• +8 more

Amit Ashok is a Professor of Optical Sciences at The University of Arizona's Wyant College of Optical Sciences and an Assistant Professor in the Department of Electrical and Computer Engineering. His research focuses on computational sensing and quantum-inspired imaging. Education: Ph.D. (2008), University of Arizona; M.S. (2001), University of Cape Town; B.Sc. (1998), University of Swaziland Professional Affiliations: Senior Member of The Optical Society (OSA), Member of IEEE, SPIE Senior Member His research leverages physical optics, machine learning, and information theory to design optical imagers and sensors. Key areas include inverse problems, compressive imaging, and quantum-limited resolution. The Intelligent Imaging and Sensing Lab (I2SL), led by Professor Ashok, develops computational sensing frameworks using reconfigurable optics and parallel computing architectures for applications in high-dimensional imaging and X-ray anomaly detection. Scientific Awards: SPIE Best Paper Award (2010), University of Cape Town Scholarships (1999, 2000), University of Swaziland Dean's, Vice-Chancellor, and Sino-Swazi awards (1995-1998)

• Computational Electromagnetics
• Inverse Problems in Electromagnetism
• Bioimpedance and Medical Diagnostics
• +3 more

Alice Reinbacher-Köstinger is an Associate Professor at the Institute for Fundamentals and Theory of Electrical Engineering, Graz University of Technology, Faculty of Electrical Engineering. Her research focuses on computational electromagnetics, inverse problems, and bioimpedance analysis. Role: Research and Teaching Research Areas: Transformer modeling, NFC systems, aortic dissection detection, and surrogate-based optimization Email: alice.koestinger@tugraz.at Her work combines advanced modeling techniques with machine learning, particularly in magnetic material characterization and bioimpedance signal analysis. Recent publications emphasize physics-informed neural networks, harmonic balance methods, and Bayesian experimental design. Current projects include optimizing sensor placement for magnetic property measurements and developing simulation tools for medical diagnostics. Her research bridges electrical engineering with biomedical applications through multiphysics simulations and data-driven methods.

Jovita LukasikView profile

Researcher

• Computer Vision
• Machine Learning
• Neural Architecture Search
• +4 more

Jovita Lukasik is a Ph.D. candidate in Computer Science at the University of Mannheim , affiliated with the Data and Web Science Group and the Computer Vision and Machine Learning Group at the Max Planck Institute for Informatics . Supervised by Prof. Dr.-Ing. Margret Keuper , her research focuses on Neural Architecture Search (NAS) and its applications in computer vision and graph-based learning systems. Education Ph.D. (2019–present), Computer Science, University of Mannheim M.Sc. (2016–2019), Mathematics in Business and Economics, University of Mannheim B.Sc. (2012–2015), Mathematical Finance, University of Konstanz Lukasik's research bridges generative NAS , graph neural networks , and inverse problem solutions . She has developed novel approaches for variational graph embeddings and stability analysis in NAS frameworks, contributing to the democratization of efficient deep learning architecture discovery. Her publication trends reveal expertise in automated machine learning with specific attention to search space optimization , graph-based NAS , and stability in differentiable architecture search . She has presented at premier venues including European Conference on Computer Vision and International Conference on Learning Representations . Lukasik actively contributes to academic communities through workshop organization (NAS @ ICLR 2021) and virtual seminar leadership on AutoML advancements.

Magdalena WysockiView profile

Researcher

• Medical Ultrasound Imaging
• 3D Computer Vision
• Neural Radiance Fields
• +3 more

Magdalena Wysocki is a researcher at Technical University of Munich's Department of Computer Science within the School of Computation, Information and Technology. She works under Prof. Nassir Navab's Chair of Computer Aided Medical Procedures at the Garching campus, focusing on medical ultrasound imaging and neural radiance fields for surgical applications. Her research integrates deep learning with medical physics to solve reconstruction challenges in ultrasound imaging. Her primary research interests include: Medical Ultrasound Imaging with neural field applications Physics-consistent 3D reconstruction from sparse views Geometry-aware deep learning for medical imaging Real-time surgical data science applications Confidence mapping in neural implicit representations Her recent publication trends show concentrated work on neural radiance fields for ultrasound (Ultra-NeRF), with expanding applications to tissue property estimation (UltRAP-Net) and surgical activity understanding (EgoExOR). Key methodological threads include physics-based constraints in deep learning and confidence quantification for clinical deployment. She actively supervises multiple Master's students on projects including: 4D Ultrasound SLAM (Sebastian Herz) Cardiac Shape Reconstruction with Motion Correction (Abdulkader Ghandoura) Confidence Maps research leading to MIUA 2024 publication (Bugra Yesilkaynak) Her GitHub repositories (ultra-nerf, ultron) demonstrate strong software engineering contributions to the medical imaging community. She contributes to the NARVIS and RobUSt research groups focusing on surgical vision systems and robotics.

• Solid State Physics
• Organic Electronics
• Charge Transport
• +4 more

Karin Zojer is an Associate Professor at the Institute of Solid State Physics, Graz University of Technology (TU Graz). Her research spans organic electronics, charge transport modeling, and porous materials analysis, with a focus on physics-informed computational approaches. Research interests include Interfacial engineering in organic semiconductors Computational modeling of charge/volatile transport μ-CT-based material characterization Kinetic Monte Carlo simulations Pore structure-property relationships Recent work emphasizes Physics-informed neural networks for transport phenomena Green hydrogen purification materials Multi-scale analysis of paper porosity Reactive diffusion in cellulose systems Quantification of descriptor correlations in porous media Her publications from 2020-2025 highlight interdisciplinary methods bridging theoretical physics, computational modeling, and industrial applications.

James M. DeGraffView profile

Research Professor

• Tectonics
• Structural Geology
• Fracture Mechanics
• +3 more

James M. DeGraff serves as a Research Professor in the Department of Geological and Mining Engineering and Sciences at Michigan Technological University. A Michigan Tech alumnus with BS and MS degrees, he also earned a PhD from Purdue University. After a distinguished career in the hydrocarbon industry with ExxonMobil, he returned to his alma mater to advance research and education in structural geology and reservoir characterization. His academic credentials include: PhD in Geology, Purdue University MS in Geophysics, Michigan Technological University BS in Geology, Michigan Technological University Dr. DeGraff's research centers on structural geology, tectonics, and fracture mechanics, particularly in the Midcontinent Rift System. He investigates fracture network formation, deformation timing, and fluid flow, integrating field, geophysical, and modeling approaches. His work bridges fundamental geological processes and practical applications in reservoir engineering, with a growing focus on automated data analysis techniques. Analysis of his recent publications (2018-2025) reveals a consistent focus on the Keweenaw fault system and Midcontinent Rift, utilizing LiDAR, aeromagnetic, and seismic data to unravel structural evolution and fracture patterns. This research has significant implications for understanding rift dynamics and optimizing hydrocarbon recovery in fractured reservoirs. Committed to education, Dr. DeGraff trains future geoscientists and has led research teams in industry and academia. His collaborative projects with reservoir engineers at ExxonMobil addressed complex fracture reservoir challenges, demonstrating the value of interdisciplinary approaches. He currently leads research initiatives at Michigan Tech that integrate field studies, geophysical data, and computational modeling to solve geological problems, continuing his legacy of impactful science and mentorship.