Find Academic Supervisors

Tore Selland KleppeView profile

Professor

• Monte Carlo Methods
• Bayesian Statistics
• Stochastic Volatility Models
• +5 more

Tore Selland Kleppe is a Professor of Mathematics at the University of Stavanger, affiliated with the Faculty of Science and Technology and the Department of Mathematics and Physics. His research focuses on computational statistics, Bayesian methods, Monte Carlo techniques, and their applications in econometrics and energy economics. Key research interests include Hamiltonian Monte Carlo (HMC) methods, stochastic volatility modeling, commodity price dynamics, and Markov-switching models. He has contributed to advancements in numerical integration for stochastic differential equations, adaptive sampling algorithms, and efficient computation in high-dimensional Bayesian models. Notable work includes developing dynamically rescaled HMC algorithms, incorporating transport maps and importance sampling for hierarchical models, and analyzing commodity futures using state-space frameworks. His publications span top journals like Journal of Computational and Graphical Statistics , Statistics and Computing , and Energy Economics . Collaborations involve experts in econometrics (e.g., Roman Liesenfeld, Atle Oglend) and computational methods. His recent work addresses challenges in restricted domain sampling, storage constraints in energy markets, and adaptive step-size strategies for MCMC efficiency. No awards or grants are explicitly mentioned, but his extensive publication record reflects sustained academic contributions. He actively participates in conferences like the International Conference on Econometrics and Statistics and Norwegian Statistical Association meetings.

Axel KlarView profile

Professor

• Kinetic Theory
• Traffic Flow Modeling
• Pedestrian Dynamics
• +6 more

Prof. Axel Klar is a Professor in the Department of Technomathematics at the Rheinland-Palatinate University of Applied Sciences (RPTU) Kaiserslautern-Landau. His research focuses on mathematical modeling of complex systems, with a strong emphasis on kinetic theory, fluid dynamics, and applied analysis. Key areas of expertise include traffic flow dynamics, fiber dynamics in industrial processes, pedestrian movement patterns, and biological systems such as cell motion and tumor growth. He develops and analyzes numerical methods for partial differential equations and stochastic systems, often addressing challenges in technical textile production, non-equilibrium thermodynamics, and multi-physics simulations. His work bridges microscopic particle-based models with macroscopic continuum descriptions, emphasizing asymptotic methods and domain decomposition techniques. Notable contributions include stochastic fiber lay-down models for nonwoven manufacturing, mesh-free particle methods for granular and rarefied gas flows, and kinetic-based traffic models with applications to autonomous vehicles and crowd control. Current research explores data-driven approaches for coupled systems in socio-economic and biomedical contexts. Prof. Klar’s interdisciplinary projects involve collaborations with industry partners (e.g., in textile engineering) and academic institutions globally. His team develops open-source numerical toolkits for applications ranging from radiation therapy optimization to climate-resilient infrastructure design. Recent efforts focus on AI-driven parameter estimation for high-dimensional PDE systems and uncertainty quantification in multi-scale modeling.

Som DuttaView profile

Assistant Professor

• Computational Fluid Dynamics
• High Performance Computing
• Machine Learning for Fluid Dynamics
• +3 more

Som Dutta is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Utah State University . His research focuses on high-fidelity computational fluid dynamics (CFD) modeling with applications to environmental flows, aerospace systems, and machine learning (ML) integration. Education: PhD in Civil Engineering (2017), MS in Civil Engineering (2012) from University of Illinois at Urbana-Champaign, BS in Civil Engineering (2008) from Birla Institute of Technology, Mesra. Research Interests: Som investigates turbulent multiphase flows, environmental fluid mechanics, and ML-based surrogate models for digital twins of energy and space systems. His work spans applications in river/coastal mechanics, hybrid rocket propulsion, and bioregenerative life support systems for lunar exploration. Awards: Recognized with the Undergraduate Research Mentor of the Year (2029, 2021), Bayfield Visiting Researcher award (2019), and prestigious fellowships including ATPESC (2017) and CSE Fellowship (2011). Teaching: Courses include Fluid Mechanics, Computational Fluid Dynamics, and Numerical Methods at both undergraduate and graduate levels. Publications: His recent work includes studies on lunar life support systems, droplet fragmentation physics, and Great Salt Lake hydrodynamics. He employs GPU-accelerated CFD and DNS/LES methods for multiscale flow analysis.

• Algebra
• Group Theory
• Module Theory
• +3 more

Prof. Simion Breaz is a Professor at Babeş-Bolyai University's Faculty of Mathematics and Computer Science, Department of Mathematics. His research focuses on algebraic structures, including module theory, group theory, and homological algebra. He has contributed to studies on silting/cosilting modules, abelian groups, and matrix decompositions. He serves on editorial boards for journals like Mathematica Bohemica and Studia Universitatis Babeş-Bolyai Mathematica . Key research projects include PN-III-P4-ID-PCE-2020-0454 (2020), PN-II-RU-TE-2011-3-0065 (2011), and others funded by Romanian research councils. His work bridges abstract algebra with categorical methods, emphasizing structural properties of modules and groups. Recent publications explore topics such as cotorsion theories in triangulated categories, matrix decompositions over finite fields, and inert subgroup properties in abelian groups. He collaborates with leading mathematicians like Ulrich Albrecht, Grigore Călugăreanu, and Jan Žemlička.

Adrien MERLINIView profile

Researcher

• Computational Electromagnetics
• Integral Equations
• Neuroimaging
• +3 more

Adrien MERLINI is a Researcher at IMT Atlantique's Microwave Department in Brest, France. His work focuses on computational electromagnetics, integral equation methods, and neuroimaging applications. He specializes in developing numerical techniques for low-to-high-frequency electromagnetic modeling, preconditioning strategies for integral equations, and machine learning-enhanced solutions for inverse problems. His research bridges fundamental theory and applied engineering, addressing challenges in medical imaging, material characterization, and high-performance computing. Key areas of expertise include: High-frequency spectral analysis of boundary integral operators Stabilized formulations for low-frequency electromagnetic simulations Supervised learning approaches for electrical source imaging Quasi-Helmholtz projector-based preconditioning techniques Fast direct solvers for integral equations His innovations in numerical methods have advanced applications in brain-computer interfaces, microwave-based medical imaging, and terahertz dosimetry. Collaborative projects include developing the simBCI framework for EEG simulation and the Pythran compiler for accelerating scientific Python codes. Research contributions span over 30 peer-reviewed articles since 2015, with recent emphases on conditioning analysis of electromagnetic integral equations and regularization strategies for neuroimaging inverse problems.

• Computer Vision
• Machine Learning
• Continual Learning
• +5 more

Tomasz Trzcinski is an active computer vision and machine learning researcher with a prolific publication record across top-tier conferences and journals including CVPR, ECCV, WACV, NeurIPS, and IEEE Access. His work spans multiple institutions, primarily collaborating with researchers from Polish academic and research organizations as evidenced by co-author patterns and institutional affiliations in his publications. 229+ publications documented in DBLP (2012-2025) Active contributor to computer vision and machine learning communities Regular presence at major conferences (CVPR, ECCV, NeurIPS) Trzcinski's research primarily focuses on continual learning , where he has made significant contributions to addressing catastrophic forgetting and knowledge retention. His work extends to 3D vision , particularly neural radiance fields (NeRF), Gaussian splatting, and point cloud processing, with applications in robotics and scene understanding. He also explores medical imaging applications, collaborating with medical researchers on projects involving pulmonary artery pressure estimation and fetal birth weight prediction. His methodological contributions include innovations in hypernetworks, vision transformers, and test-time adaptation techniques. Analysis of his recent publications (2023-2025) reveals a strong emphasis on continual learning methodologies, with approximately 40% of his work addressing challenges in this domain. His research demonstrates a consistent trajectory toward more efficient and robust neural network architectures, with increasing focus on practical applications in robotics, medical imaging, and real-world adaptation scenarios. The integration of vision-language models and contrastive learning approaches represents his latest research direction, indicating adaptation to emerging trends in the field. As a senior researcher, Trzcinski frequently serves as a corresponding author and collaborates with numerous junior researchers, suggesting an active supervisory role. His work shows evidence of securing research funding through collaborations with institutions involved in medical imaging and computer vision applications. Trzcinski maintains active research collaborations across multiple teams, particularly with groups focused on continual learning (Twardowski, Deja, Cygert), 3D vision (Kania, Kowalski), and medical applications (Sitek, Grzeszczyk). His research group appears to maintain strong connections between theoretical machine learning advancements and practical applications in healthcare and robotics.

• Mathematical Analysis
• Numerical Analysis
• Semiconductor Physics
• +3 more

Nella Rotundo is an Associate Professor (Ricercatrice di tipo B) in Mathematical Analysis (MAT/07) at the University of Florence, Department of Mathematics. Her research focuses on mathematical modeling of semiconductors, partial differential equations, and numerical methods. She has held postdoctoral positions at the Weierstrass Institute (Berlin), McGill University (Montreal), and the University of Calabria. She is a Principal Investigator in the MATH+ Incubator project IN-B3 addressing doping variations in silicon crystals. Her academic background includes a PhD in Mathematics applied to Engineering (University of Catania), and a Master's in Mathematics (University of Calabria). She has contributed to over 9 peer-reviewed publications since 2010, with recent work emphasizing numerical methods for semiconductor simulation and inverse problems in materials science. Rotundo has presented research at conferences including the Young Applied Mathematicians Conference (Rome 2024) and AMaSiS (Berlin). Her technical contributions span finite element methods, optimal control theory, and computational approaches to semiconductor characterization.

Max PitzView profile

Lecturer

• Infinite Graph Theory
• Topology
• Set Theory
• +2 more

Max Pitz is a Lecturer in Discrete Mathematics at Universität Hamburg, with research focused on infinite graph theory, topological methods, and combinatorial structures. His work develops foundational theorems for infinite graphs and end spaces. Recent publications solve open problems in graph orientation, tree decompositions, and choosability. Research employs set-theoretic and topological approaches to extend combinatorial principles to infinite domains.

Stefan KlusView profile

Associate Professor

• Data-driven model reduction
• Transfer operator approximation
• Statistical learning theory
• +7 more

Stefan Klus is an Associate Professor at the School of Mathematical & Computer Sciences, Department of Mathematics, Heriot-Watt University. He holds a concurrent position as a Fellow at the Free University of Berlin (2022–2026). His research focuses on data-driven model reduction, transfer operator approximation, and applications in dynamical systems, molecular dynamics, and fluid mechanics. Key interests include spectral graph theory, tensor decompositions, and high-performance computing. Klus is actively involved in advancing machine learning methodologies for analyzing complex systems and networks. His work contributes to UN Sustainable Development Goals through interdisciplinary applications in ecology and climate science. He accepts PhD students and maintains collaborations across multiple institutions, with a strong emphasis on bridging theoretical mathematics and real-world computational challenges. External Positions: Fellow, Free University of Berlin (1 July 2022 – 30 June 2026) Research Interests: Data-driven modeling and transfer operators Kernel-based learning and statistical theory Quantum mechanics and molecular dynamics simulations Spectral graph theory and network dynamics Low-rank tensor decompositions and real-time computing Recent Research Trends: Klus’s 2023–2025 articles emphasize spatiotemporal network analysis, Koopman operator applications, and optimization methods for graph problems. His work bridges abstract operator theory with practical challenges in ecology, climate modeling, and agent-based systems.

Mark GiesbrechtView profile

Professor

• Symbolic Mathematical Computation
• Computer Algebra
• Algebraic Algorithms
• +5 more

Mark Giesbrecht is a Professor in the David R. Cheriton School of Computer Science and currently serves as the Dean of the Faculty of Mathematics at the University of Waterloo, Canada. His research focuses on Symbolic Mathematical Computation, including computational methods in algebra, number theory, and the development of computer algebra systems like Maple and LinBox. He has held significant administrative roles, including Director of the Cheriton School of Computer Science (2014–2020) and Director of Undergraduate Studies (2002–2005). His awards include ACM Distinguished Scientist (2013) and an NSERC Synergy Award (2003). Research Interests: Symbolic Computation, Sparse Polynomial Algorithms, Algebraic Complexity, and Computer Algebra Systems. He has supervised numerous graduate students, many of whom have pursued academic and industrial careers. His work bridges theoretical foundations and practical implementations, contributing to both algorithm design and software development. Professional Activities: He has served as Program Chair of ISSAC (2013), editor of the Journal of Symbolic Computation (2003–2020), and organizer of workshops on symbolic-numeric computing. His contributions span editorial roles, conference organization, and community leadership in computer algebra. Grants & Funding: While specific grants are not detailed here, his research has been supported by NSERC and industry collaborations, reflected in awards like the NSERC Synergy Award. Labs & Teams: He is affiliated with the Symbolic Computation Group at Waterloo and the Ontario Research Centre for Computer Algebra (ORCCA).

• Carbon Capture and Storage
• Electrochemical Engineering
• Process Simulation
• +5 more

Dr. Thomas Moore is a Senior Lecturer in Chemical Engineering at Queensland University of Technology (QUT), affiliated with the School of Mechanical, Medical & Process Engineering within the Faculty of Engineering. He holds a PhD from the University of Melbourne and undergraduate degrees from the University of Queensland. Prior to QUT, he was a Staff Scientist at Lawrence Livermore National Laboratory, leading modelling efforts in carbon capture technologies. His research focuses on carbon capture process design, CO₂ electrolysis, and techneconomic analysis of green technologies. Key areas include electrification of gas separation processes, direct air capture (DAC), and the application of data science for predictive modeling. He teaches thermodynamics, reactor engineering, and process design. Dr. Moore’s work contributes to UN Sustainable Development Goal 7 (Affordable and Clean Energy) through innovations in low-carbon energy systems. His recent publications emphasize scalable carbon capture solutions, electrochemical systems, and cross-scale catalyst modeling. His research projects integrate computational modeling, experimental validation, and industrial collaboration to advance climate technologies. Current efforts address the economic and technical challenges of scaling up CO₂ conversion systems.

• Blind Signal Processing
• Bayesian Estimation
• Particle Filters
• +3 more

Ayşın Baytan Ertüzün is a Professor at Boğaziçi University, specializing in signal processing and statistical methodologies. Their research focuses on blind signal processing, Bayesian estimation techniques, particle filters, and seismological applications. They have contributed extensively to defect detection in textiles using advanced signal processing methods like wavelet transforms and independent component analysis, as well as geophysical signal analysis through Bayesian deconvolution approaches. Over 25 years of academic work includes pioneering work in non-Gaussian processes, time-varying autoregressive models, and particle filtering algorithms for complex signal environments. Their research bridges theoretical advancements with practical applications in manufacturing quality control and seismic data analysis. Key technical contributions include hybrid deconvolution methods for Bernoulli-Gaussian processes, subband domain subspace analysis for texture defects, and entropy-based 2D lattice filter structures. Their work often emphasizes robust statistical frameworks for non-stationary and non-Gaussian signal environments, with applications spanning from environmental forecasting (wind speed modeling) to medical signal analysis (respiratory sound coherence). Publications span from foundational 1990s lattice filter innovations to modern Bayesian approaches. Their methodologies are characterized by interdisciplinary fusion of signal processing theory with real-world industrial and geophysical challenges. No specific awards are listed in the provided materials, though their extensive publication record indicates sustained academic impact.

Joseph P. HavlicekView profile

Professor

• Signal Processing
• Image Processing
• Video Processing
• +7 more

Dr. Joseph P. Havlicek is the Gerald Tuma Presidential Professor in the Gallogly College of Engineering at the University of Oklahoma, where he has served since 1997. He directs the OU Center for Intelligent Transportation Systems and is a full member of the OU Institute for Biomedical Engineering, Science, and Technology. His extensive career spans both academic and professional realms, with significant contributions to signal processing, image analysis, and transportation systems. His educational background includes: BS in Electrical Engineering from Virginia Tech (1986) MS in Electrical Engineering from Virginia Tech (1988) PhD in Electrical and Computer Engineering from the University of Texas at Austin (1996) Dr. Havlicek's research spans multiple domains including signal, image, and video processing; modulation domain signal processing; target tracking; medical imaging; and intelligent transportation systems. His work bridges theoretical signal processing with practical applications in healthcare and transportation. He has pioneered techniques in AM-FM signal and image modeling, uncertainty measures, and developed innovative approaches for medical image analysis and infrared target tracking. Analysis of his recent publications reveals a strong interdisciplinary trend, with significant work at the intersection of signal processing and medical imaging, particularly in bone marrow assessment and cancer treatment monitoring using PET/CT imaging. His computer vision research focuses on infrared object detection and tracking, with recent work on dual-band infrared systems improving small object detection. The publications also demonstrate his continued theoretical contributions to signal processing, particularly in modulation domain techniques and entropy-based uncertainty measures. His scientific achievements have been recognized with numerous awards: University of Oklahoma Outstanding Faculty Advisor Award (2006) University of Oklahoma College of Engineering Brandon H. Griffith Faculty Award (2003) University of Oklahoma IEEE Favorite Instructor Award (1998, 2000) Department of the Navy Award of Merit for Group Achievement (1990) University of Texas Engineering Foundation Award for Exemplary Engineering Teaching (1992) Dr. Havlicek has been exceptionally successful in securing research funding, serving as PI or co-PI on more than 100 externally funded grants and contracts totaling over $25 million. His professional service includes editorial positions with IEEE Transactions on Image Processing and IEEE Transactions on Industrial Informatics, as well as leadership roles in major conferences including ICIP and ICASSP. He has chaired both graduate and undergraduate studies committees in the ECE department. He leads the OU Center for Intelligent Transportation Systems, which develops advanced technologies for traffic management and traveler information systems. His biomedical engineering work through the OU Institute focuses on medical image analysis, particularly for cancer treatment monitoring and bone marrow assessment. His research teams regularly collaborate with medical institutions to translate signal processing techniques into clinical applications.

• High-dimensional Statistics
• Machine Learning
• Signal Processing
• +5 more

Karim Lounici is a Professor at the Centre de Mathématiques Appliquées (CMAP) of Ecole Polytechnique. His research focuses on high-dimensional statistics, machine learning, signal/image processing, probability theory, and optimization. He explores theoretical foundations and practical applications in areas like stochastic processes, quantum tomography, and dynamical systems. His work bridges mathematical rigor with computational methods, addressing challenges in data analysis, uncertainty quantification, and algorithmic efficiency. Recent contributions include equivariant representation learning, robust covariance estimation, and learning Koopman operators for dynamical systems. Publications span topics like neural conditional probability, spatio-temporal Monge alignment, and domain adaptation, reflecting expertise in both statistical theory and applied machine learning. No scientific awards explicitly mentioned. No advising or grant details provided in text. Affiliated with CMAP, a leading applied mathematics research center at Ecole Polytechnique.

• Hybrid Variational/DL methods under topological constraints
• Hybrid Variational/DL methods under geometric constraints
• PDE Analysis and numerical simulation
• +5 more

Carole Le Guyader is an Associate Professor in the Mathematical and Software Engineering Department at the National Institute of Applied Sciences of Rouen (INSA Rouen), France. She has been actively contributing to the fields of image processing, mathematical analysis, and computational methods since joining INSA Rouen in 2009, after previously serving as an Assistant Professor at INSA Rennes from 2005 to 2009. Her academic leadership extends to various administrative roles including Head of Mathematics teaching at the STPI INSA Rouen department since 2019 and Director of the Mathematical Engineering Department since 2013. Dr. Le Guyader earned her Habilitation to Supervise Research on November 16, 2012, at Rouen University with the thesis titled "Contribution to the mathematical analysis of images: segmentation, registration and decomposition." Her habilitation jury included distinguished mathematicians such as Grégoire ALLAIRE from Ecole Polytechnique and Gabriel PEYRE from University Paris-Dauphine. Her primary research interests focus on the development and application of variational methods and deep learning techniques for image processing, with particular emphasis on incorporating topological and geometric constraints. She investigates mathematical models for image segmentation, registration, and decomposition, applying these techniques to biomedical imaging, geophysics, and other scientific domains. Her work bridges theoretical mathematics with practical applications in medical diagnostics and industrial settings. Le Guyader's publication record demonstrates a consistent focus on advancing image processing methodologies, particularly in the integration of traditional variational approaches with modern deep learning techniques. Her recent work shows increasing emphasis on medical applications, especially in radiotherapy planning and MRI reconstruction, where geometric and topological constraints play critical roles in ensuring clinically meaningful results. Dr. Le Guyader has successfully supervised multiple PhD students throughout her career, demonstrating her commitment to mentoring the next generation of researchers. Her collaborative approach is evident in her extensive network of academic and industrial partnerships across Europe and the United States. She has secured and managed numerous research projects including the LMI project "Mathematical Imaging and Digital Analysis" at CRIHAN since 2013, and the wind field modeling project with LABEX AMIES since 2014. Her leadership extends to coordinating INSA teams at the GENCI/CRIANN Hackathon since 2017 and participating in regional and European research initiatives.