Jean-Christophe Pesquet is a Professor affiliated with the Laboratory Digital Vision Center. His work spans optimization, inverse problems, artificial intelligence, signal and image processing . He actively collaborates on advanced algorithm design and computational methods for imaging and signal recovery. Key research areas: Optimization, Wavelet Analysis, Blind Source Separation, Deep Learning Recent publications focus on nonconvex optimization, primal-dual splitting, and deep unfolding techniques. His methodological contributions include proximal algorithms, stochastic subspace approaches, and Bregman divergences. Applications range from biomedical imaging (e.g., PET, CT scans) to seismic data analysis and aerospace defect detection. Notable collaborations include researchers like Emilie Chouzenoux , Audrey Repetti , and Caroline Chaux . Current projects integrate neural networks with classical signal processing frameworks (e.g., lifting schemes, MCMC algorithms).
Jacob Seifert is a postdoctoral researcher in the Nanophotonics group at Utrecht University's Faculty of Science. He completed his Ph.D. in 2024 with a thesis titled "Differentiable Modeling for Computational Imaging." His research focuses on advanced metrology for logic semiconductor circuits through wavefront shaping, in collaboration with industrial partners, with the aim of developing highly precise nanoscale overlay (OVL) and alignment metrology techniques. He is based at the Leonard S. Ornstein Laboratory in Utrecht. Dr. Seifert's research interests center on computational imaging techniques, particularly ptychography, which reconstructs high-resolution images from diffraction patterns. His work bridges theoretical optics, computational physics, and practical applications in semiconductor metrology. He has developed expertise in noise modeling, automatic differentiation for optimization, and machine learning integration to improve imaging results. His technical skills include programming in Python, C++, Mathematica, and Matlab, along with data visualization and computational modeling. Analysis of Dr. Seifert's publication record reveals a strong progression from fundamental algorithm development to practical applications. His work demonstrates increasing sophistication in handling noise, optimizing illumination, and applying machine learning techniques to computational imaging problems. Recent publications show growing collaboration with industrial partners, highlighting the practical relevance of his research to semiconductor manufacturing processes. Dr. Seifert has not been mentioned as receiving any specific scientific awards in the available information. While no formal students are listed in the provided information, Dr. Seifert has collaborated extensively with researchers including Allard Mosk (his primary collaborator), Yiyi Shao, Sander Weerdenburg, and others in the Nanophotonics group. His research has been supported through academic-industrial partnerships focused on semiconductor metrology applications. Dr. Seifert is part of the Nanophotonics research group at Utrecht University, which focuses on advanced optical techniques for imaging and measurement at the nanoscale. His work integrates computational methods with optical physics to solve challenging problems in semiconductor manufacturing and nanoscale characterization, particularly in the area of extreme ultraviolet (EUV) imaging which is critical for next-generation chip fabrication.
Gil Robalo Rei is a Research Associate at the Institute for Numerical Mechanics within the TUM School of Engineering and Design at the Technical University of Munich (TUM). He has been working at the institute since 2021, contributing to research in computational mechanics and numerical methods, and is actively involved in teaching courses related to numerical methods and computational mechanics. His educational background includes: Master of Science (M.Sc.) in Mechanical Engineering from Technical University of Munich (2021) Bachelor of Science (B.Sc.) in Mechanical Engineering from Technical University of Munich (2018) Gil's research focuses on advanced computational methods for solving complex engineering problems. His primary interests span Uncertainty Quantification , Bayesian Methods , and Inverse Problems , with applications across multiple domains including solid-state battery technology, biomedical modeling, and materials science. His work often involves developing novel computational frameworks that integrate statistical methods with physics-based simulations to address challenges where traditional approaches fall short, particularly when dealing with computationally expensive forward models. Analysis of Gil's publication record reveals a strong trend toward interdisciplinary research that bridges computational mechanics with statistical inference. His work demonstrates expertise in applying Bayesian methods to inverse problems in diverse contexts such as tumor growth modeling, solid-state battery optimization, and powder system characterization. A notable pattern is his focus on developing computationally efficient approaches for problems with expensive forward models, often leveraging Gaussian processes and active learning techniques to reduce computational costs while maintaining accuracy. Gil has actively contributed to academic mentoring through supervision of student projects: Multiple Bachelor's and Master's theses in computational mechanics and related fields Research internships focused on engineering simulations Term papers and visualization labs exploring numerical methods His collaborative approach is evident in co-supervision with other researchers like Christoph Schmidt and Jonas Nitzler, reflecting the interdisciplinary nature of his work and the research environment at TUM. As part of the research group led by Prof. Wolfgang A. Wall, Gil contributes to the QUEENS framework development and participates in the broader activities of the Institute for Numerical Mechanics. His work connects with several research teams focusing on computational mechanics applications in energy storage systems, biomedical engineering, and advanced materials, demonstrating the versatility and applicability of his methodological contributions across different scientific domains.
Wolfgang Wall is a full Professor and founding Director of the Institute for Computational Mechanics at the Technical University of Munich (TUM). Born near Salzburg (Austria), he studied at the University of Innsbruck and received his PhD from the University of Stuttgart. He is a co-founder of AdCo Engineering GW GmbH and Ebenbuild GmbH, and currently serves as Rector of the International Centre for Mechanical Sciences (CISM) in Udine, Italy. A member of both the Austrian and Bavarian Academies of Sciences, he has received numerous prestigious awards including the O.C. Zienkiewicz Award and ERC Advanced Grant. 1983: Matura, Höhere Technische Bundeslehranstalt Salzburg (with distinction) 1991: Dipl.-Ing. degree from University of Innsbruck (with distinction) 1999: Dr.-Ing. (summa cum laude) from University of Stuttgart His research focuses on application-motivated fundamental research in computational mechanics, spanning coupled multifield/multiscale problems (fluid-structure interaction, contact dynamics, electro-chemo-mechano-thermo interaction) and applications in energy storage systems (all-solid-state batteries), additive manufacturing, and computational biophysics/biomedical engineering (patient-specific respiratory/cardiac modeling, cancer nanomedicine, musculoskeletal systems). His group develops advanced computational methods, software frameworks, and physics-based models for high-performance computing. Recent emphasis includes uncertainty quantification, inverse analysis, and machine learning integration. The 15 most recent publications reveal trends in computational mechanics (8/15 articles), biomedical engineering (5/15), and energy storage/additive manufacturing (7/15). Notable themes include novel finite element frameworks for multiphysics problems, Bayesian calibration methods for biological systems, and multiscale modeling of nanomedicine and battery materials. 1986-1988: Excellency in Studying Awards (~ top 1%) 1991: Best graduation ever in Civil Engineering at Innsbruck University 1994: European Academic Software Award 2000: Fritz-Peter-Müller Award, University of Karlsruhe 2000: Rotary Award for doctoral thesis, Stuttgart 2005: Golden Teaching Awards (TUM students) 2008: Fellow Award of the International Association of Computational Mechanics 2011: Chuo University Guest Professorship Award 2012: IACM Computational Mechanics Award 2013: Heinz Maier-Leibnitz Medal 2016: Prandtl Medal (ECCOMAS) 2018: EUROMECH Fellows Award 2021: ERC Advanced Grant 2022: JSCES Grand Prize 2024: O.C. Zienkiewicz Award (IACM) As a dedicated educator, he teaches courses ranging from foundational engineering mechanics (1000+ students) to specialized graduate topics like discontinuous Galerkin methods and biomedical applications. His leadership extends to founding the Munich School of Engineering (2010-2012), establishing the Center for Computational Biomedical Engineering (2012), and serving on multiple editorial boards (IJNME, CMAME, IJNMBE) and scientific councils.
Prof. Dr. David Ginsbourger is a Professor of Statistical Data Science at the University of Bern , leading the Uncertainty Quantification and Spatial Statistics Group within the Institute of Mathematical Statistics and Actuarial Science. He has held visiting roles at institutions like the Isaac Newton Institute (Cambridge, UK) and actively collaborates across engineering , geosciences , and medicine . University of Bern (2021-present) Idiap Research Institute (2015-2020) Swiss Academy of Sciences (elected member, 2025) Education : PhD in Applied Mathematics, École des Mines de Saint-Etienne (2009) Double Graduate Diploma, École des Mines de Saint-Etienne & Berlin Technical University (2005) Master’s in Applied Mathematics, Jean Monnet University & École des Mines de Saint-Etienne (2005) Licence in Mathematics, Joseph Fourier University (2002) David’s research focuses on uncertainty quantification , Gaussian process modeling , Bayesian optimization , and design of experiments . His work spans theoretical developments (e.g., kernel design, excursion set estimation) and applications in climate science , medical diagnostics , and engineering . Recent collaborative projects address inverse problems in hydrogeology , autonomous ocean sampling , and high-impact weather forecasting . Publications highlight trends in adaptive experimental design , kernel methods for equivariant models , and uncertainty quantification in multidisciplinary contexts . Key themes include excursion set estimation , Bayesian optimization , and spatial distributional modeling . Awards & Memberships : Elected member, Swiss Academy of Sciences (2025) Elected member, International Statistical Institute (2023-) Member, ELLIS Society (2024-) Long-term member, Swiss Mathematical Society Advising & Collaboration : David has advised numerous PhD and master’s students, including Athénaïs Gautier , Cédric Travelletti , and Mickael Binois . He has led projects at Idiap Research Institute and collaborates with institutions like the Oeschger Center for Climate Change Research and the Center for Artificial Intelligence in Medicine . Labs & Teams : He founded the Uncertainty Quantification and Optimal Design group at Idiap (2015-2020) and currently leads research at the University of Bern , integrating with multidisciplinary initiatives in climate change and infectious diseases .
Ганна Анатоліївна Шишканова is an Associate Professor at the Department of Applied Mathematics within the Faculty of Computer Science and Technologies at Zaporizhzhia Polytechnic National University. With over two decades of academic experience since 1999, she holds the academic title of Associate Professor and a Candidate of Physical and Mathematical Sciences degree. Her expertise spans applied mathematics, continuum mechanics, and mathematical modeling, with significant contributions to contact mechanics and econometrics. Zaporizhzhia State University (Applied Mathematics, Specialist) Zaporizhia National Technical University (Entrepreneurship, Master's degree, 2019) Dr. Shyshkanova's research focuses on solving complex mathematical problems in engineering and economics. Her work in spatial contact problems with unknown contact regions has practical applications in structural engineering and green building design. She has developed innovative approaches to multidimensional integro-differential equations and applied catastrophe theory to various scientific domains. Her recent research has expanded into econometrics, applying mathematical models to business optimization and economic forecasting. Her 77 scientific publications demonstrate consistent scholarly output across multiple disciplines, with recent work addressing sustainable construction, cylindrical structure deformation, and precision alloy production. The publications reveal a trend toward interdisciplinary research that bridges applied mathematics with practical engineering and economic challenges. Certificate of Merit on the 110th anniversary of ZNTU (2010) Certificate of Merit on the 115th anniversary from Zaporizhzhia City Council (2015) Honorary Certificate for long-term work and contribution to technical education (2021) Appreciation from NU "Zaporizhzhia Polytechnic" for 50th anniversary (2023) Certificate from Zaporizhzhia Regional State Administration (2023) Dr. Shyshkanova teaches Higher Mathematics, Probability Theory and Mathematical Statistics, Optimization Methods and Models, and Econometrics. Her pedagogical approach integrates traditional instruction with innovative distance learning technologies to enhance student engagement with mathematical concepts. She has contributed to educational methodology through research on student evaluation systems and the application of mixed learning approaches to mathematical disciplines.
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 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 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 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 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 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 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 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 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.
