Find Academic Supervisors

• Signal and Image Processing
• Bayesian Statistics
• Markov Chain Monte Carlo (MCMC) Methods
• +4 more

Jean-François Giovannelli is a Professor at IMS Bordeaux , affiliated with the Université de Bordeaux . His work focuses on Signal and Image Processing within the SPECTRAL team. Collaborations span institutions like CEA , CNRS , and industry partners ( STMicroelectronics , Stellantis ), emphasizing Bayesian methods and MCMC algorithms for diverse applications. Current Affiliation : Professor, IMS Bordeaux, Université de Bordeaux Research Group : Signal and Image Processing Team : SPECTRAL Collaborations : CEA, CNRS, CESTA, Thales, NXP Research Interests include: Bayesian inference for inverse problems MCMC sampling techniques Signal/image reconstruction Mass spectrometry data analysis Adaptive optics in astronomy Medical imaging algorithms Article Trends show a focus on Bayesian modeling across disciplines: biomedical data, astronomical imaging, and microwave mapping. Key methods include MCMC samplers , regularized inversion , and statistical validation . Professional Contributions involve developing tools like NiftyRec for tomography and advancing Adaptive Optics restoration algorithms. His work bridges theoretical statistics and practical applications in healthcare, space, and defense.

Panagiota BirmpaView profile

Assistant Professor

• Generative modeling
• Scientific Machine learning
• Uncertainty Quantification
• +4 more

Panagiota Birmpa is an Assistant Professor in the Department of Actuarial Mathematics & Statistics at Heriot-Watt University's School of Mathematical and Computer Sciences, Edinburgh. Her research bridges advanced mathematical theory with cutting-edge machine learning applications, focusing on uncertainty-aware methodologies for complex data systems. She actively supervises PhD students and maintains strong collaborative ties with international research groups in applied mathematics and computational science. Her academic credentials include: BSc+MSc (integrated master) in Applied Mathematics and Physical Sciences (majors in Analysis and Statistics) from National Technical University of Athens (NTUA), 2011 MSc in Pure Mathematics from National and Kapodistrian University of Athens (NKUA), 2014 PhD in Mathematics from University of Sussex, UK, 2018 (Thesis: Quantification of Mesoscopic and Macroscopic Fluctuations in Interacting Particle Systems) Dr. Birmpa's research program integrates theoretical mathematics with modern computational challenges through seven core domains: Generative modeling, Scientific Machine learning, Uncertainty Quantification, Probabilistic Graphical models, Interacting Particle Systems, Optimal transport Theory, and Partial Differential Equations. Her interdisciplinary approach enables innovative solutions for complex data analysis problems across scientific domains, particularly where traditional statistical methods face limitations in high-dimensional spaces. Analysis of her publication trajectory reveals a progression from foundational statistical physics (2017-2018 interface dynamics research) toward contemporary machine learning applications (2021-2024). Her recent work demonstrates increasing sophistication in merging deep learning architectures with uncertainty quantification frameworks, especially for scarce high-dimensional data scenarios where conventional approaches fail. This evolution reflects broader trends in mathematical data science toward robust, interpretable AI systems. No scientific awards or fellowships are currently listed in her professional profile. Dr. Birmpa accepts PhD candidates for projects exploring deep learning-graphical model interfaces with uncertainty quantification, building on her prior AFOSR-funded postdoctoral research at UMass Amherst (2021-2022). Her grant history includes significant support from the Air Force Office of Scientific Research for developing particle-based generative algorithms. She maintains active supervision of graduate researchers while pursuing methodological innovations in probabilistic modeling. Her collaborative research network spans multiple institutions including University of Massachusetts Amherst, with interdisciplinary teams developing novel mathematical frameworks for scientific machine learning. Current projects focus on Lipschitz-regularized gradient flows and generative particle algorithms for high-dimensional data, extending her earlier work on non-equilibrium fluctuations in particle systems.

Dongdong ChenView profile

Assistant Professor

• Machine Learning
• Computer Vision
• Image Processing
• +4 more

Dongdong Chen serves as an Assistant Professor in the Department of Computer Science at Heriot-Watt University's School of Mathematical and Computer Sciences in Edinburgh, United Kingdom. His research profile indicates active supervision of PhD students with full scholarships available, demonstrating his established position within the academic community. Current research outputs show consistent publication activity from 2020 through 2024 with increasing impact. Dr. Chen's research interests center on machine learning applications for imaging systems, with particular expertise in image processing, computer vision, computational imaging, and inverse problems. His work bridges theoretical machine learning with practical applications, especially in medical imaging contexts. The fingerprint analysis of his publications reveals strong connections to Deep Learning (31%), Unsupervised Learning (25%), and Inverse Problems (43%), indicating a cohesive research trajectory focused on unsupervised frameworks for imaging challenges. Analysis of his 15 most recent publications (2021-2025) shows a clear evolution toward unsupervised and equivariant learning approaches for inverse problems, with increasing focus on diffusion models and medical applications. The research demonstrates strong theoretical foundations combined with practical implementations, particularly in MRI and medical diagnostics. Citation metrics indicate significant impact, with several papers exceeding 40 Scopus citations. His notable scientific achievements include: IES Best Student Paper Award (2014) MICCAI'18 BIA Best Paper Nomination (2018) Dr. Chen actively mentors PhD students and has attracted research attention through media coverage of his ICCV paper on Equivariant Imaging. His research contributes to UN Sustainable Development Goals through imaging technology development. Current activities include recruiting PhD students for projects in Edinburgh with full scholarship support, indicating ongoing research expansion and team development.

Cédric HERZETView profile

Research Fellow

• Statistical Learning Theory
• Optimization
• Operational Research
• +3 more

Cédric HERZET is a Permanent Member of CREST at INRIA Rennes, focusing on statistical learning theory, optimization, and inverse problems. His work bridges theoretical analysis with practical algorithm development. Primary affiliation: INRIA Rennes (French National Institute for Research in Digital Science) Research Interests: Statistical Learning Theory, Optimization algorithms, Operational Research, Inverse Problems. His work particularly addresses sparse approximation, compressed sensing, and iterative thresholding methods. Key Contributions: Development of Bayesian pursuit algorithms, geometric analysis of subspace clustering with outliers, and analysis of state evolution in dense graph message passing. His theoretical work has direct applications in signal/image processing and machine learning. Software: Created MATLAB implementations of sparse approximation algorithms and Bernoulli-Gaussian Lab toolbox for Bayesian pursuit simulation.

• Magnetic Resonance Imaging
• Deep Learning
• Compressed Sensing
• +3 more

Mehmet Akcakaya is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Minnesota. His research focuses on machine learning for medical imaging, especially MRI reconstruction using deep learning and compressed sensing. He leads NIH-funded projects and is accepting PhD students. His research interests include physics-informed deep learning , compressed sensing , and MRI reconstruction . He develops algorithms for high-resolution, accelerated MRI with applications in cardiac and brain imaging. Key challenges addressed are reference-free reconstruction and robustness in inverse problems. Recent work shows a trend toward self-supervised and unsupervised deep learning for MRI, with innovations in cycle-consistent learning and diffusion models for inverse problems. There is increasing emphasis on few-shot and zero-shot adaptation to handle limited training data. Dr. Akcakaya leads the NIH-funded project Robust and Efficient Learning of High-Resolution Brain MRI Reconstruction and collaborates on the Center for Mesoscale Connectomics . He is accepting PhD students and mentors research in medical imaging and machine learning. His work is conducted at the University of Minnesota's Center for Magnetic Resonance Research, leveraging interdisciplinary collaborations in biomedical engineering and neuroscience.

Miriam SchulteView profile

Professor

• High-Performance Computing (HPC)
• Parallel and Distributed Systems
• Scientific Computing
• +5 more

Miriam Schulte is a Professor at the Institute for Parallel and Distributed Systems (IPVS) at the University of Stuttgart . As Dean of Studies SimTech , she leads academic programs in simulation technology. Her research focuses on high-performance computing , multi-physics simulations , and scientific software development , with significant contributions to coupling libraries like preCICE and biophysical frameworks like OpenDiHu . Key Research Areas: High-Performance Computing (HPC) Multi-physics and Fluid-Structure Interaction (FSI) Sparse Grids and Hierarchical Numerical Methods Machine Learning in Simulation Software Parallel and GPU-Accelerated Algorithms Advising: Guided student projects on quantum neural networks , GPU-optimized sparse grids , and SYCL-based HPC frameworks . Coordinated SimTech Research Modules and IPVS/SGS team initiatives. Software Leadership: Maintains preCICE (coupling library for multi-physics) Develops OpenDiHu (neuromuscular simulations) Advances PLSSVM (parallel SVM library) and SG++ (sparse grids) Her recent publications (2022–2025) emphasize machine learning integration with multi-physics simulations , including groundwater heat pumps , brain tumor modeling , and neuromuscular EMG prediction . She actively promotes open-source software sustainability and collaborative research infrastructure at the University of Stuttgart.

Zheyu JiangView profile

Assistant Professor

• Process Systems Engineering
• Industrial Decarbonization
• Advanced Separation Processes
• +3 more

Dr. Zheyu Jiang is an Assistant Professor in the Department of Chemical Engineering at Oklahoma State University , leading the Computational Laboratory for Advanced Manufacturing and Sustainability (CLAMS). His research bridges process systems engineering with digital agriculture and sustainability , focusing on industrial decarbonization , food-water-energy nexus , and explainable AI applications. Education : Ph.D. in Chemical Engineering from Purdue University (2018), B.Ch.E. (Hons) from University of Minnesota (2014) Research Expertise spans multi-scale modeling, physics-informed machine learning, and optimization techniques applied to: AI-driven digital agriculture solutions Decarbonization of chemical processes Advanced separation technologies Process monitoring through digital twins Scientific Contributions include: Pioneering physics-constrained machine learning for soil moisture estimation Developing MOLA (Multi-block Orthogonal LSTM Autoencoder) for industrial process monitoring Advancing decentralized optimization algorithms for renewable energy integration Research Recognition : 2025 NSF CAREER Award ($500k grant for digital agriculture) Featured in AIChE Journal Futures Issue (top 10% downloads) Multiple travel awards and Corteva Agriscience People's Choice Awards Laboratory : CLAMS trains students in computational sustainability, with members like graduate researcher Saba Ghasemi Naraghi and undergraduate success stories such as Tylee Kareck (offered Ph.D. positions at Texas A&M/UT Austin)

• Signal Processing
• Probability
• Inverse Problems
• +7 more

Christian Heinrich is a Professor at the University of Strasbourg, affiliated with the ICube - IMAGeS team (Images, Modeling, Learning, Geometry and Statistics). His research focuses on signal processing, inverse problems, machine learning, and optical imaging, particularly polarimetric imaging and biological tissue analysis. He collaborates closely with Jihad Zallat (ICube-TRIO) and has contributed to the development of methodologies for medical imaging and computational neuroscience. Education: Electrical Engineering (Supélec, 1990), Master's in Signal Processing (Université Paris-Sud/Supélec, 1994), PhD in Signal Processing (Université Paris-Sud/Supélec, 1997), Aggregation in Applied Physics (1998), Habilitation (University of Strasbourg, 2008) His research spans polarimetric imaging techniques for tumor monitoring, brain connectivity analysis through multimodal MRI and fMRI, and advanced image processing methods like non-local means filtering and spatial mixture models. Recent work explores cryo-EM reconstruction, topological preservation in surgical planning, and uncertainty quantification in medical imaging. Christian Heinrich co-leads the IRIV master’s degree (Imaging, Robotics, and Engineering for the Living) and the Franco-German DaSAI double degree program in Data Science and Artificial Intelligence. He has published extensively in journals like Biomedical Optics Express , IEEE Transactions on Biomedical Engineering , and Medical Image Analysis .

Céline MeillierView profile

Associate Professor

• Signal Processing
• Image Processing
• Statistical Signal Processing
• +5 more

Céline Meillier is an Associate Professor at the University of Strasbourg, affiliated with ICube research laboratory in Strasbourg, France. She serves as co-president of the GRETSI 2025 conference alongside Vincent Mazet, demonstrating her leadership position within the signal and image processing community. Her research focuses on modeling and analysis of spatially structured signals, with applications in astronomy hyperspectral data, functional MRI, and brain connectivity analysis. Methodologically, she specializes in sparse decomposition problems integrating expert knowledge into regularization constraints and multiple test thresholding procedures for low signal-to-noise event detection. ANR JCJC DynaSTI Project (2023-2026): Modeling Spatial-Temporal Dynamics of Functional Connectivity in resting fMRI DYNAMIC project funded by GdR ISIS (2019-2021) Dr. Meillier teaches multiple courses including Image Processing and Registration, Data Processing, Statistics, and Random Signal Processing for the FIP TI Santé program. Her research has practical applications in functional MRI for Alzheimer's, depression, optogenetics in small animals, and schizophrenia in humans. She has supervised three doctoral students: Argheesh Bhanot (2017-2021), Emma Menant (2023-present), and Massyl Moudoud (2023-present), as well as one post-doctoral fellow Duong Hung Pham (2018-2019).

• Computational Statistics
• Machine Learning
• Applied Probability
• +7 more

O. Deniz Akyildiz is an Assistant Professor in Statistics at the Department of Mathematics, Imperial College London. He holds a PhD in Signal Processing from Universidad Carlos III de Madrid (UC3M) and completed postdoctoral work at Warwick CS and The Alan Turing Institute. His research bridges computational statistics, machine learning, and applied probability, focusing on stochastic sampling methods, generative modeling, and optimization techniques for statistical inference and inverse problems. Education : BSc/MSc in Electronics and Communications Engineering (İTÜ), PhD in Signal Processing (UC3M). His work explores diffusion-based parameter estimation, score-energy-latent generative models, and overdamped/underdamped Langevin dynamics for probabilistic solutions to PDEs. He has developed adaptive importance samplers and contributed to high-dimensional stochastic filtering with real-world data. Recent efforts include lecture notes and code for stochastic simulation courses and organizing workshops on Gradient Flows for Sampling (Royal Statistical Society, 2023) and Accelerating Generative Models (Alan Turing Institute, 2022-2023). His publications emphasize error bounds in diffusion models, physics-informed neural processes, and convergence analysis of optimization-driven sampling algorithms.

Vissarion PapadopoulosView profile

Professor

• Structural Engineering
• Computational Mechanics
• Stochastic Analysis
• +3 more

Vissarion Papadopoulos is a Professor in the Department of Structural Engineering at the School of Civil Engineering, National Technical University of Athens (NTUA). His office is located at the Statics and Aseismic Research Laboratory, with contact details including email vpapado@central.ntua.gr and phone 210 772 4158. He has maintained an active research profile since at least 2016, focusing on advanced computational methods in structural engineering. His primary research domains include: Structural Engineering and Computational Mechanics Stochastic Analysis and Uncertainty Quantification Multiscale Modeling of Composite Materials Machine Learning for Engineering Simulations Optimization of Structural Systems Thermomechanical Behavior of Advanced Materials Analysis of his recent publications reveals a decisive shift toward integrating deep learning frameworks with traditional computational mechanics. His work demonstrates consistent innovation in accelerating solutions for parametric and transient structural problems through transformer networks, Bayesian inference, and physics-informed neural networks. Key application areas include carbon nanotube reinforced composites, sustainable automotive design, and seismic-resistant structures, with emphasis on enhancing computational efficiency while maintaining accuracy. He is affiliated with NTUA's Statics and Aseismic Research Laboratory, which specializes in structural dynamics, earthquake engineering, and advanced computational methodologies for civil infrastructure analysis and design.

Quan ZhangView profile

Assistant Professor

• Machine Learning
• Statistics
• Interpretable Learning
• +7 more

Quan Zhang is an Assistant Professor in the Department of Accounting and Information Systems at the Broad College of Business, Michigan State University. With a Ph.D. in Information, Risk and Operations Management from the University of Texas at Austin (2020), an M.S. in Biostatistics from the University of Minnesota (2015), and a B.S. in Biological Science and Economics from Peking University (2012), his academic journey bridges diverse disciplines. Education Ph.D., Information, Risk and Operations Management - University of Texas at Austin (2020) M.S., Biostatistics (Ph.D. study) - University of Minnesota at Twin Cities (2015) B.S., Biological Science and Economics - Peking University (2012) His research focuses on interpretable machine learning , particularly balancing model interpretability and capacity using Bayesian and variational inference methods. Applications span telemedicine , quantitative marketing , medical data analysis , and online finance , with recent work on watermarking for copyright protection and robust representation learning. Selected publications include his 2025 paper on Beyond Invisibility: Learning Robust Visible Watermarks , 2023 work on Weibull Racing Survival Analysis , and foundational 2018 contributions to Nonparametric Bayesian Lomax Delegate Racing . His methodological innovations in Bayesian multinomial regression and survival modeling have been published in Advances in Neural Information Processing Systems and the Journal of Machine Learning Research . He contributes to software development with Python and R implementations for Bayesian models, available in his GitHub repository. His teaching includes graduate-level ITM885 Machine Learning and Optimization , emphasizing practical applications in business analytics.

Benedikt WirthView profile

Professor

• Optimisation and calculus of variations
• image processing
• scientific computing
• +9 more

Prof. Dr. Benedikt Wirth is a Professor of Mathematics at the University of Münster, Germany, affiliated with the Institute for Analysis and Numerics within the Department of Mathematics and Computer Science. He is an active researcher and educator specializing in optimization and calculus of variations, with significant contributions to mathematical imaging and shape analysis. His research interests include image processing, scientific computing, numerical analysis, optimization, shape spaces, geodesics in shape space, variational methods, elastic deformation, and optimal transport. Wirth has developed innovative mathematical frameworks for shape analysis, particularly focusing on Riemannian metrics for shape spaces and variational approaches to shape comparison and optimization. His recent publications (2023-2025) demonstrate continued leadership in mathematical optimization, with particular focus on PET reconstruction, dimension reduction techniques, manifold embeddings, and branched transport theory. His work bridges theoretical mathematics with practical applications in medical imaging and computer vision, showing particular strength in connecting geometric analysis with computational methods. CRC 1450 - A05: Targeting immune cell dynamics by longitudinal whole-body imaging and mathematical modelling CRC 1450 - A06: Improving intravital microscopy of inflammatory cell response by active motion compensation EXC 2044 - C1: Evolution and asymptotics EXC 2044 - C2: Multi-scale phenomena and macroscopic structures EXC 2044 - C3: Interacting particle systems and phase transitions EXC 2044 - C4: Geometry-based modelling, approximation, and reduction Prof. Wirth actively supervises numerous bachelor's and master's students, with over 40 theses completed under his guidance since 2015. His teaching portfolio includes courses on inverse problems, numerical methods for partial differential equations, shape spaces, optimization, and optimal transport. He has consistently maintained an active research program while contributing significantly to the education of the next generation of mathematicians.

• Physics
• Chemistry
• Machine Learning
• +5 more

Giuseppe De Luca is a Research Fellow at the Department of Physics and Chemistry - Emilio Segrè, University of Palermo. His work spans interdisciplinary domains including theoretical physics, biophysics, machine learning, and social dynamics. Research Themes : Model averaging techniques, liquid-liquid phase separation, quantum complexity, holography, and social exclusion effects in education. Methodologies : Bayesian inference, multiscale analysis, and machine learning optimization. Recent publications focus on weighted-average least squares estimation, structural degradation in biomaterials, and virtual reality applications in psychological research. He contributes to the SHARE project with statistical expertise in survey weighting and imputation strategies.

• Statistics
• Big data analytics
• Spatial statistics
• +3 more

Laura Šaltytė-Vaisiauskė is an Associate Professor and currently serves as Vice-Rector for Research and Innovation at Klaipėda University, Lithuania. She is affiliated with the Department of Informatics and Statistics within the Faculty of Marine Technology and Natural Sciences, with additional connections to the Faculty of Social Sciences and Humanities and the Institute of Baltic Region History and Archaeology. Dr. Šaltytė-Vaisiauskė defended her doctoral thesis in Mathematics at Vilnius University in 2013 and has since established herself as a versatile researcher with publications spanning multiple disciplines while maintaining core expertise in statistical methodology. Her research interests center around Statistics, Big data analytics, and Spatial statistics, with significant contributions to Image recognition and Discriminant analysis. Analysis of her publication record reveals a strong focus on developing advanced statistical methods for spatio-temporal data analysis, particularly using Hidden Markov Models with spatial weighting. Her work demonstrates increasing interdisciplinary reach, extending from pure statistical methodology to applications in finance, healthcare, energy systems, and social sciences. Recent publications show her applying statistical techniques to diverse problems including financial volatility modeling, electric vehicle optimization, and even therapeutic interventions for depression. Her scholarly output demonstrates consistent productivity across multiple domains, with publications in reputable journals and conference proceedings. Her work shows particular strength in developing methodological innovations with practical applications, often collaborating across disciplinary boundaries to address complex real-world problems. As Vice-Rector for Research and Innovation, Dr. Šaltytė-Vaisiauskė oversees research strategy and development at Klaipėda University. Her leadership role complements her active research career, allowing her to influence both the direction of her own work and the broader research landscape at her institution. Her recent projects include studies on the impact of COVID-19 restrictions on businesses in the Klaipėda region, demonstrating her commitment to addressing timely societal challenges through rigorous research.