جست‌وجوی استاد و پژوهشگر

Martin Zachمشاهده پروفایل

پژوهشگر ارشد

• Biomedical Imaging
• Inverse Problems
• MRI Reconstruction
• +۲ مورد دیگر

Martin Zach is a postdoctoral researcher at the Center for Biomedical Imaging (CIBM), EPFL , specializing in inverse problems in biomedical imaging. He joined the Mathematical Imaging Section under the supervision of Prof. Michael Unser in September 2024. PhD : Graz University of Technology (2024), advised by Thomas Pock Martin’s research bridges model-based reconstructions and data-driven approaches in imaging, with a focus on MRI reconstruction and diffusion models . His work explores regularization techniques, energy-based priors, and probabilistic modeling. Recent publications highlight advancements in Gaussian mixture models , non-linear inversion , and Langevin sampling for biomedical imaging. His Google Scholar profile reveals a strong focus on inverse problems (2020–2025), with applications in MRI , CT , and quantitative phase imaging . Key methodologies include diffusion models , generative priors , and regularization algorithms , spanning disciplines from machine learning to computational biology . Martin’s current role at EPFL involves collaborative research in mathematical imaging , with affiliations to the BioMedical Imaging (BIG) Group in Lausanne, Switzerland.

Kirill Golubnichiyمشاهده پروفایل

پژوهشگر ارشد

• Mathematical Finance
• Partial Differential Equations
• Inverse Problems
• +۳ مورد دیگر

Kirill Golubnichiy serves as a Post Doctoral Fellow in the Department of Mathematics & Statistics at Texas Tech University, specializing in mathematical finance and computational methods for financial modeling. Education: Ph.D. in Mathematics, University of Washington, Seattle (2022) His research centers on developing mathematical tools for analyzing partial differential equations governing financial asset pricing, solving inverse problems with physics applications, and creating machine learning algorithms for forecasting financial markets. This interdisciplinary work bridges theoretical mathematics, computational finance, and artificial intelligence to address complex problems in quantitative finance. Analysis of his 15 most recent publications (2021-2025) reveals a dominant focus on solving the Black-Scholes equation for option pricing through innovative combinations of numerical methods and machine learning. His research shows increasing integration of deep learning techniques with traditional mathematical finance models, particularly in handling ill-posed problems and volatility forecasting. Secondary research streams include theoretical physics applications involving Einstein equations and academic textbook development in economic analysis. He participates in Texas Tech's Mathematical Finance Program, collaborating with faculty leads Dr. Zari Rachev and Dr. Brent Lindquist on quantitative finance research and graduate training initiatives.

Anna Midlenkoمشاهده پروفایل

مدرس

• Breast Cancer
• Surgical Oncology
• Cancer Genetics
• +۳ مورد دیگر

Anna Midlenko serves as an Instructor in the Department of Medicine at Nazarbayev University School of Medicine, bringing extensive clinical expertise in surgical oncology since joining in 2017. Her work bridges clinical practice and academic research in cancer treatment. Her educational foundation includes: Doctor of Medicine, Ulyanovsk State University, Russia (with honors) Residency in General Surgery (2007-2009) Internship in Surgical Oncology (2009-2010) Ph.D. in Surgical Oncology, Bashkir State Medical University, Ufa, Russia (2012) Dr. Midlenko's research centers on breast cancer biology and treatment innovations, with specific focus on genetic mechanisms, early detection methodologies, elderly patient care protocols, and oncoplastic surgical techniques. She actively develops AI-driven diagnostic tools using thermal imaging to improve accessibility of breast cancer screening. Her recent publications (2023-2024) reveal two dominant research trajectories: computational approaches applying physics-informed neural networks and deep learning to thermography-based detection, and population-level studies examining breast cancer epidemiology and genetic biomarkers within Kazakhstan's healthcare system. As Co-Principal Investigator for the colorectal cancer biomarker project (2019-2020), she contributes to translational research while mentoring through clinical teaching workshops including the University of Pittsburgh Master Class. Her conference participation spans oncology congresses in Salzburg and Shanghai, focusing on gastrointestinal cancers and breast pathology diagnostics.

Alexander Korotinمشاهده پروفایل

استادیار

• Generative Modeling
• Unpaired Learning
• Optimal Transport
• +۲ مورد دیگر

Alexander Korotin is an Assistant Professor at the Skolkovo Institute of Science and Technology (Skoltech) where he heads the Generative AI research group. He is also a senior research scientist at the Artificial Intelligence Research Institute (AIRI), leading the "Foundations of Generative AI" group. His academic journey includes a PhD in Math & Physics from Skoltech (2023), an MSc in Computer Science from the Higher School of Economics (HSE), and a BSc in Mathematics also from HSE. Dr. Korotin's research focuses on generative modeling, with particular emphasis on developing novel algorithms based on Optimal Transport and Schrodinger Bridges. His work bridges theoretical mathematics with practical machine learning applications, contributing significantly to the field of generative artificial intelligence. He has pioneered approaches to make Schrodinger Bridge solvers more efficient and practical, most notably with his "Light Schrödinger Bridge" framework that simplifies complex computational procedures while maintaining theoretical rigor. His publication record shows a clear progression toward making advanced generative modeling techniques more accessible and computationally efficient. Recent work demonstrates increasing sophistication in handling complex distribution matching problems through physics-inspired approaches (like electrostatic field matching) and novel distillation techniques that accelerate inference. The research spans from theoretical foundations to practical applications in image processing, semi-supervised learning, and reinforcement learning. Dr. Korotin has received recognition for his contributions to neural optimal transport and Schrodinger Bridges, with his papers frequently appearing in premier machine learning venues. His work on efficient computational methods for optimal transport has established him as a rising expert in these specialized areas of machine learning. As an academic leader, Dr. Korotin advises research students and collaborates extensively with colleagues across institutions, contributing to the advancement of generative AI through both theoretical developments and practical implementations. His work continues to push the boundaries of what's possible in generative modeling, with recent publications focusing on making advanced mathematical approaches more computationally efficient for real-world AI applications.

Jeremy Hoskinsمشاهده پروفایل

استادیار

• Numerical Analysis
• Fast Algorithms
• Quantum Optics
• +۴ مورد دیگر

Jeremy Hoskins is an Assistant Professor in the Department of Statistics at the University of Chicago. His research focuses on fast algorithms and numerical analysis with diverse applications including quantum optics, lens design, glaciology, computational biology, and inverse problems. Research Interests: Specializing in Numerical Analysis and Fast Algorithms , he applies computational methods to cross-disciplinary challenges in Quantum Optics , Lens Design , Glaciology , and Computational Biology . His work addresses Inverse Problems requiring high-performance computing solutions. Contact: Email: jeremyhoskins@uchicago.edu Institution: University of Chicago Department: Department of Statistics

Dany Lauzonمشاهده پروفایل

استادیار

• Geostatistics
• Stochastic Hydrogeology
• Facies Modeling
• +۵ مورد دیگر

Dany Lauzon is an Assistant Professor at the Department of Civil, Geological and Mining Engineering at Polytechnique Montréal . He is affiliated with the Institute for Research in Mining and the Environment (IRME) and the Geothermal and Hydrogeology Research Group . His research focuses on stochastic subsurface modeling using geostatistical and machine learning methods, inverse problems in hydrogeology, and uncertainty quantification in geological models. Education: B.Eng. (Polytechnique Montréal), Ph.D. (Polytechnique Montréal) Postdoctoral training: Karst Systems Modeling (University of Neuchâtel), Mineral Prospectivity (INRS) His work develops geostatistical algorithms and deep learning surrogates for groundwater flow and subsurface risk mitigation. Publications highlight spectral methods, graph neural networks, and hybrid modeling approaches for environmental and geological applications. Current supervision: Ph.D. student Innocent Boris Tagne Nkounga Master's student Olivier Brisebois

Peter Melchiorمشاهده پروفایل

استادیار

• Statistical Astronomy
• Machine Learning
• Data Fusion
• +۵ مورد دیگر

Peter Melchior is an Assistant Professor of Astrophysical Sciences at Princeton University, with a joint appointment at the Center for Statistics and Machine Learning. He leads the Princeton Astro Data Lab, where his team develops novel algorithms to extract information from astronomical observations despite instrumental limitations and noise. His educational background includes a Ph.D. in Physics (2010) and a Diplom/M.S. in Physics (2006), both from the University of Heidelberg. Prior to his current position at Princeton, he held postdoctoral positions at The Ohio State University (2011-2015) and the University of Heidelberg (2010). Dr. Melchior's research focuses on statistical methods for large astronomical surveys. His primary interests include: Physics-based machine learning for astronomical data analysis Source separation and data fusion techniques Optimal combination of multiple datasets from different surveys Development of neural network approaches for astronomical problems Application of statistical methods to hydrologic modeling His recent publications demonstrate a strong trend toward interdisciplinary work that combines astronomy with machine learning and environmental science. The research spans from fundamental astronomical data analysis techniques to practical applications in water resource management across the United States. Among his notable achievements: PI of a project funded by the Schmidt Futures Foundation to optimize target selection for the Prime Focus Spectrograph survey Lead developer of the HydroGEN project funded by NSF for hydrologic scenario generation Author of approximately 300 papers in major peer-reviewed journals Developer of open-source software including pyGMMis for Gaussian mixture modeling Dr. Melchior actively mentors students and has organized the Undergraduate Summer Research Program and Data Science Seminar at Princeton. His work bridges astronomy, statistics, and machine learning, with growing applications in environmental science.

Emilie Chouzenouxمشاهده پروفایل

پژوهشگر

• Optimization
• Image Processing
• Signal Processing
• +۷ مورد دیگر

Emilie Chouzenoux is a researcher at the Laboratory Digital Vision Center , Université Gustave Eiffel. Her work bridges optimization theory, signal processing, and machine learning, with applications in medical imaging , drug repositioning , and social media analysis . She has co-authored numerous publications on proximal algorithms , deep learning architectures , and stochastic optimization . Her recent research focuses on unrolled deep networks for signal restoration, graph-based matrix factorization for biomedical applications, and primal-dual methods for large-scale inverse problems. She collaborates widely with researchers like Jean-Christophe Pesquet and Angshul Majumdar. Dr. Chouzenoux's publications highlight interdisciplinary trends merging computer science , applied mathematics , and life sciences . Key subfields include image denoising , collaborative filtering , hate speech detection , and cryptocurrency forecasting .

Filip Elvanderمشاهده پروفایل

استادیار

• Statistical signal processing
• Stochastic processes
• Audio signal processing
• +۴ مورد دیگر

Filip Elvander is an Assistant Professor in the Department of Information and Communications Engineering at Aalto University, Finland. Previously, he served as a postdoctoral research fellow at KU Leuven (2020-2022), supported by the Research Foundation - Flanders (FWO). PhD in Mathematical Statistics (2020) and MSc in Industrial Engineering and Management (2015) from Lund University Assistant Professor at Aalto University since 2022 Leader of the Structured and Stochastic Modeling Group (SSMG) His research focuses on statistical signal processing, particularly inverse problems and optimal transport theory. Key application areas include acoustic localization, spectral estimation, audio processing, and spectroscopy. Current research directions involve: Optimal transport for geometric signal space modeling Spatio-temporal signal modeling in remote sensing and audio Misspecified modeling impacts and mitigation Optimal sampling schemes for efficient data collection Recent publications demonstrate trends in optimal transport applications for multi-pitch estimation, room acoustics, sensor networks, and audio restoration. His group includes 5 PhD students working on these topics. Awards include FWO postdoctoral fellowship (2021-2022). Collaborations span Lund University, KU Leuven, and Aalto University research teams.

Henri Gavinمشاهده پروفایل

استاد

• Earthquake Engineering
• Structural Control
• Vibration Isolation
• +۵ مورد دیگر

Henri Gavin is a Professor in the Department of Civil and Environmental Engineering. His research focuses on developing innovative solutions for structural resilience, control systems, and earthquake engineering. He leads interdisciplinary projects integrating sensor technology, IoT systems, and machine learning for real-world applications such as earthquake early warning systems. Research Interests: Professor Gavin's work spans earthquake engineering, structural dynamics, vibration isolation, robotics, optimization, and machine learning applications in civil infrastructure. His signature project—Pilot Earthquake Early Warning in Kathmandu (Data+, 2022)—demonstrates his approach to combining AI models with geophone sensors and IoT networks for on-site seismic alerts. Publication Trends: His recent articles (2020-2024) emphasize structural reliability, decentralized control of robotic systems (UAVs/swarms), optimization algorithms, and seismic protection strategies. This reflects a consistent focus on theoretical rigor applied to practical engineering challenges, particularly in nonlinear dynamics and multi-agent coordination. Projects and Advising: He mentors students through Duke's Data+ program, guiding projects in energy data analytics and earthquake early warning systems. Notable funded initiatives include instrumentation and modeling of seismic isolation systems (EAGER, 2012) and performance analysis of base-isolated structures during seismic events.

• Inverse Problems
• Mathematical Imaging
• Computational Mathematics
• +۳ مورد دیگر

Prof. Otmar Scherzer is a distinguished academic at the Faculty of Mathematics, Department of Mathematics, University of Vienna . His work spans Inverse Problems, Mathematical Imaging, Computational Mathematics, and Applied Mathematics , with a focus on tomography, neural networks, and medical imaging. Over nearly two decades, he has published extensively in journals like Inverse Problems , SIAM Review , and IEEE Transactions . 2025 : Advanced data-driven regularization and neural network applications in inverse problems. 2024 : Explored diffraction tomography, Hilbert space approaches to scattering, and motion detection algorithms. 2023 : Addressed uncertainty quantification, visco-acoustic imaging, and Doppler ultrasound interpolation. His recent research emphasizes uncertainty-aware blob detection, strain reconstruction in elastography , and Fourier-based tomography . Publications frequently integrate numerical experiments and computational inverse methods , reflecting a blend of theoretical and applied mathematics. Prof. Scherzer collaborates with institutions like RICAM (Johann Radon Institute) and contributes to hybrid imaging modalities such as quantitative optical coherence elastography and photoacoustic tomography . He has supervised projects in 3D shape reconstruction and non-convex regularization , though specific student names are not listed in the provided data.

• Geophysics
• Paleomagnetism
• Computational Modeling
• +۵ مورد دیگر

Dr. F. Out is a Researcher at Utrecht University's Faculty of Geosciences, Department of Earth Sciences, specializing in Micromagnetic Tomography (MMT) and paleomagnetism. Their work focuses on developing mathematical and computational methods to extract reliable magnetic signals from rock samples at the grain level. Research interests center on micromagnetic tomography , paleomagnetic signal analysis , and computational modeling of rock magnetism . Their work bridges geophysics, mathematics, and computer science to improve accuracy in reconstructing Earth's historical magnetic field. Key methodologies include combining micro X-ray computed tomography with magnetic surface scans to analyze individual magnetic grains. Publications since 2022 demonstrate consistent advancement in MMT methodology, with focus on mathematical uncertainties, grain-size distributions, and computational libraries. Research spans theoretical mathematical exploration, practical implementation of MMT techniques, and applications to basalt samples including Hawaiian lava flows. Active participant in the Friday Earth Sciences Talk series (2021) Teaching experience includes multiple mathematics and physics courses for Earth Sciences students, demonstrating strong foundation in quantitative methods essential for geophysical research.

Michael Taroudakisمشاهده پروفایل

استاد

• Wave Propagation
• Mathematical Modeling
• Acoustical Oceanography
• +۴ مورد دیگر

Michael Taroudakis is a Professor at the University of Crete, holding a Ph.D. from the National Technical University of Athens (1988). His research focuses on mathematical modeling of physical phenomena with emphasis in wave propagation, particularly as applied to acoustical oceanography and inverse problems. Professor Taroudakis's research interests span several interconnected fields including wave propagation, mathematical modeling, acoustical oceanography, inverse problems, seismic signal analysis, and geoacoustic inversion. His work demonstrates a consistent focus on applying advanced mathematical techniques to solve complex problems in underwater acoustics and oceanographic research. He has made significant contributions to statistical characterization of acoustic and seismic signals, development of inversion techniques, and applications of machine learning in acoustical oceanography. Analysis of his publication record reveals a strong emphasis on statistical methods for signal characterization, particularly using wavelet transforms and hidden Markov models. His research has evolved from fundamental mathematical approaches to more applied problems in ocean monitoring and environmental assessment, with recent work expanding into socioeconomic analysis related to labor markets in Crete. This interdisciplinary approach demonstrates his ability to apply mathematical expertise to diverse real-world problems. Professor Taroudakis has maintained an active research program with publications spanning over three decades, showing continued productivity through 2025. His work has contributed significantly to the field of acoustical oceanography, particularly in the Mediterranean region, and has helped advance methodologies for geoacoustic inversion and ocean acoustic tomography.

• Mathematical Physics
• Quantum Integrable Systems
• Algebraic Geometry
• +۴ مورد دیگر

Paul Zinn-Justin is a Professor in Mathematical Physics at the School of Mathematics and Statistics . His research spans the intersection of quantum integrable systems , algebraic geometry , and combinatorics , with a focus on connections between these fields. Education: PhD and Habilitation from Universite Pierre et Marie Curie (Paris VI) Recent projects include Shuffle Algebras and Vertex Models (2024-2027) and Elliptic Schubert Calculus (2021-2026), funded by ARC, NHMRC, and MRFF grants . His work applies lattice models and quantum algebra to problems in symmetric function theory and Schubert calculus. Paul contributes to computational algebra through the CotangentSchubert Macaulay2 package (2024), enabling computation of motivic Chern and Segre classes of Schubert cells. His scholarly output includes over 93 works, with recent emphasis on exactly solvable lattice models, shuffle algebras, and quantum group structures.

Philip Clemsonمشاهده پروفایل

پژوهشگر ارشد

• Time Series Analysis
• Complex Systems
• Chaotic Systems
• +۷ مورد دیگر

Dr. Philip Clemson is a Senior Research Associate in the Department of Physics at Lancaster University, specializing in the time series analysis of complex systems. His work focuses on chaotic and stochastic data, particularly from non-autonomous (time-dependent) systems, with significant applications in biomedical physics and living systems. He has extensive experience in industry, having developed algorithms for medical devices and wearable technology. His research also encompasses numerical Bayesian statistical techniques using Markov chain Monte Carlo and sequential Monte Carlo methods. Current projects include phase coherence analysis for autonomic neuropathy diagnosis and network dynamics-based standards for endothelial health, reflecting his interdisciplinary approach between physics and biomedical applications. 2024: Phase coherence in autonomic neuropathy diagnosis 2023-2024: Network dynamics for endothelial health standards His recent publications demonstrate expertise in time-localized analysis, cardiovascular dynamics, and chronotaxic systems. Dr. Clemson is part of the Nonlinear and Biomedical Physics research group within the Physics Department.