Find Academic Supervisors

• Applied Mathematics
• Mathematical Modeling
• Multiphase Flow
• +8 more

Steinar Evje is a Professor of Applied Mathematics at the University of Stavanger, affiliated with the Faculty of Science and Technology and the Department of Energy and Petroleum Technology. His research bridges mathematical modeling with applications in petroleum engineering and cancer biology. His research interests include: Mathematical modeling of multiphase flow in porous media Conservation laws and PDEs Tumor cell migration and biomechanics Fluid-structure and cell-fluid interactions Data-driven discovery of PDEs Numerical analysis and scientific computing His recent publications (2023–2024) show a strong trend in combining machine learning with physical modeling, particularly in learning flux and diffusion functions from data, solving conservation laws using graph neural networks, and modeling tumor progression under fluid pressure. These works span disciplines from petroleum engineering to computational oncology, reflecting interdisciplinary impact. Scientific awards are not mentioned in the provided text. He actively collaborates with researchers in both engineering and biomedical fields. While specific students are not listed, his extensive publication record suggests mentorship of graduate students and postdocs. He has contributed to modeling in oil recovery, well control, and tumor microenvironment dynamics, with no mention of external grants but evident sustained research output. He is involved in modeling efforts related to: Two-phase and three-phase flow in porous media Cell migration under interstitial fluid flow Spontaneous imbibition and wettability alteration Gas migration in leaking wells Low salinity flooding in oil recovery

Shuchin AeronView profile

Associate Professor

• Data Science
• Statistical Signal Processing
• Inverse Problems
• +8 more

Shuchin Aeron is an Associate Professor in the Department of Electrical and Computer Engineering at Tufts School of Engineering, with joint appointments in the Departments of Computer Science and Mathematics. He holds a Ph.D. from Boston University (2009) and completed postdoctoral research at Schlumberger Doll Research, focusing on borehole acoustic signal processing. His research spans statistical signal processing, machine learning, compressed sensing, and information theory, with applications in geophysics, bioengineering, and imaging. Aeron has authored over 175 publications and holds patents in acoustic signal processing. He received the NSF CAREER Award (2016) and is a Senior Member of the IEEE. Educations: Ph.D., Electrical Engineering, Boston University, 2009 M.S., Electrical Engineering, Boston University, 2004 B.Tech., Indian Institute of Technology, 2002 Research Interests: Statistical signal processing (SSP), inverse problems, compressed sensing, information theory, convex optimization Machine learning applications in geophysical signal processing, imaging, and bioengineering His work emphasizes optimal sampling and recovery of multidimensional signals, with contributions to compressed sensing architectures and generative models for particle physics experiments. He leads NSF-funded projects on data science and domain generalization, and collaborates with industry partners like Schlumberger and Mitsubishi Electric Research Labs. Awards: NSF CAREER Award (2016) Mitsubishi Electric Research Lab Research Gift (2015) Grants and Funding: NSF HDR TRIPODS (2019–2023) AFOSR: Enabling Trusted Human-Like Artificial Teammates (2018–2023) NSF: Optimal Sampling and Recovery for Multilinear Signals (2013–2016) Aeron teaches advanced courses in probabilistic systems analysis, information theory, and machine learning. He directs the Tufts Data Science undergraduate and graduate programs, and serves on editorial boards of journals including Frontiers in Signal Processing and IEEE Transactions on Geoscience and Remote Sensing .

• Nonlinear Partial Differential Equations
• Cross-Diffusion Systems
• Mathematical Modeling in Semiconductor Devices
• +5 more

Ansgar Jüngel is a Full Professor for Analysis of Nonlinear Partial Differential Equations at TU Wien, Austria. He leads the Institute of Analysis and Scientific Computing within the Faculty of Mathematics and Geoinformation. His research focuses on mathematical modeling, analysis, and numerical simulation of complex systems, particularly in semiconductor physics, quantum hydrodynamics, and biological transport phenomena. Jüngel holds a PhD from TU Berlin (1994) and habilitation (1997), with prior academic roles at University of Konstanz and University of Mainz. His work spans nonlinear PDEs, cross-diffusion systems, entropy methods, and applications in semiconductor modeling (e.g., quantum drift-diffusion, Maxwell-Stefan systems). Notable contributions include rigorous derivation of energy-transport models, analysis of quantum fluid models, and development of entropy-dissipative numerical schemes. Awards include the ERC Advanced Grant (2021) and Tsungming-Tu Award (2011). Teaching emphasizes advanced mathematical topics: cross-diffusion systems, partial differential equations, and computational finance. Active in PhD supervision and collaborative projects, he co-authored books on transport equations, entropy methods, and semiconductor modeling. His team includes postdocs and PhD students working on topics like multi-species populations, tumor growth modeling, and stochastic cross-diffusion systems.

• Computational Mechanics
• Numerical Methods (Meshfree/XFEM/Isogeometric)
• Mixed Variational Methods
• +3 more

Jack HALE is a Research Scientist at the University of Luxembourg's Faculty of Science, Technology and Medicine (FSTM), Department of Engineering. He joined Prof. Stéphane Bordas' team in 2013, focusing on computational mechanics and numerical methods. His work integrates advanced techniques like meshfree methods, XFEM, and isogeometric analysis to address challenges in solid mechanics and high-performance computing. Education : PhD in Aeronautics, Imperial College London (2009-2013), supervised by Dr. Pedro M. Baiz Villafranca. MEng in Engineering, University of Bristol (2004-2008). Research exchange at Rice University (2006-2007) on cross-flow filtration processes. Research Interests : Implicit boundary methods for medical image-based simulations. Development of scalable meshfree/XFEM/isogeometric analysis frameworks. Mixed variational methods to resolve locking phenomena in solid mechanics. High-performance computing for distributed parallel systems. Publications & Software : His 50+ publications emphasize open-access research via ORBilu, with a focus on FEniCSx-based tools (e.g., DOLFINx, FEniCS-shells). Recent work addresses Bayesian model selection, melt instability identification, and SAR data assimilation in aquifer modeling. Collaborations : Open to academic/industrial partnerships in computational mechanics, material science, and biomedical engineering.

Herbie LeeView profile

Professor

• Bayesian statistics
• Computer models
• Spatial inverse problems
• +7 more

Herbie Lee is a Professor in the Department of Statistics at the Jack Baskin School of Engineering, University of California, Santa Cruz. He also serves as the Interim Dean of Social Sciences. His faculty office is located in 537A Baskin Engineering, and he can be reached at 831-459-1655 (though email is preferred). Lee completed his educational journey with a strong foundation in mathematics and statistics: B.S. in Mathematics from Yale University with a specialty in statistics Ph.D. in Statistics from Carnegie Mellon University (1998), advised by Larry Wasserman Post-doctoral work at Duke University's ISDS department Professor Lee's research focuses on Bayesian statistics, particularly in the areas of computer models, spatial inverse problems, simulator emulation, adaptive experimental design, and optimization. His work bridges theoretical statistics with practical applications across diverse domains including cosmology (dark energy modeling), aircraft damage detection, electronic health records analysis, and environmental monitoring. He has made significant contributions to the understanding of neural networks within a statistical framework. An analysis of his recent publications reveals a strong emphasis on Gaussian process modeling, Bayesian optimization, and nonstationary spatial statistics. His work increasingly addresses complex real-world problems requiring sophisticated statistical approaches for mixed data types, constrained optimization, and real-time decision making. The interdisciplinary nature of his research is evident in collaborations with institutions like Lawrence Livermore National Laboratory and the Naval Postgraduate School. Among his notable achievements: Supervised PhD student Bobby Gramacy won the prestigious Savage Award Authored two books on Bayesian optimization and multiscale modeling Published over 50 peer-reviewed articles in top statistics and machine learning journals Professor Lee has supervised numerous graduate students, with his most recent PhD student graduating in 2024. His research has been supported by various grants, though specific funding sources aren't detailed in the provided text. His teaching portfolio includes courses on Bayesian statistics, design and analysis of computer simulation experiments, and statistical methods for biological, environmental, and health sciences. He is actively involved in several professional statistical societies including the International Society for Bayesian Analysis (ISBA), American Statistical Association (ASA), and Institute of Mathematical Statistics (IMS).

Sarah Eberle-BlickView profile

Senior Lecturer

• Numerical Analysis
• Inverse Problems
• Partial Differential Equations
• +7 more

Sarah Eberle-Blick is a Senior Lecturer at the Institute of Mathematics , Goethe University Frankfurt, Department of Computer Science and Mathematics. Her work bridges numerical methods, inverse problems, and wave propagation in elasticity. Research Focus : Numerical analysis of PDEs, monotonicity methods for inclusion detection, FEM-BEM coupling, multiscale seismic data processing. Key Contributions : DFG-funded projects on elastic wave reconstruction; development of stable integral formulations for acoustic and thermoelastic wave equations; implementation of 3D wave simulations. Article Trends : Recent papers emphasize inverse problems in linear elasticity, time-harmonic wave equations, and multiscale analysis using wavelets. Collaborative work spans geophysics, computational mechanics, and mathematical modeling. Projects : Includes monotonicity-based regularization, Lipschitz stability estimation, and FEM-BEM coupling with convolution quadrature. Contact: eberle@math.uni-frankfurt.de | Room 103, Institute of Mathematics, Frankfurt am Main, Germany.

Noemi PetraView profile

Associate Professor

• Large-scale inverse problems
• PDE-constrained optimization
• Uncertainty quantification
• +4 more

Noemi Petra is an Associate Professor in the Department of Applied Mathematics at the University of California, Merced , with research focusing on large-scale inverse problems , PDE-constrained optimization , uncertainty quantification , and optimal experimental design . Her work bridges mathematical theory with practical applications in geophysics and energy systems. Current Role: Associate Professor at UC Merced Research Interests: Large-scale inverse problems, PDE-constrained optimization, uncertainty quantification Her research has produced significant contributions in Bayesian inference for ice sheet modeling, power system estimation , and scalable optimization algorithms . She has developed open-source software frameworks like hIPPYlib-MUQ for data-model integration. Key research trends include: Bayesian Methods for geophysical inverse problems Optimization Techniques for power systems and ice dynamics Scalable Algorithms for high-dimensional uncertainty quantification Software Development for large-scale inverse problems Scientific awards: NSF CAREER award (2017) for large-scale Bayesian inverse problems She actively collaborates on ice sheet dynamics and power grid modeling , with software tools adopted in academic and industrial applications.

Rui ZhuView profile

Assistant Professor

• Data-driven modeling
• Simulation of complex systems
• Optimization
• +9 more

Rui Zhu is an Assistant Professor at the School of Industrial and Systems Engineering , The University of Oklahoma , specializing in data-driven modeling, simulation, and optimization of complex systems with applications in healthcare, manufacturing, and engineering education. She earned her Ph.D. in Industrial Engineering from The Pennsylvania State University (2021), M.S. in Human Factors in Design, and B.S. in Industrial Design from Harbin Institute of Technology (2014, 2017). Research Interests: Rui's work integrates deep learning-assisted simulation , predictive modeling under model uncertainty , and optimization of advanced sensing systems . She explores virtual reality (VR) simulation and eye-tracking data analytics in VR environments, with cross-disciplinary impact on health and medical systems and cyber-physical-social systems . Publication Trends: Recent work highlights AI/ML in healthcare diagnostics ( Bayesian survival analysis , cardiac electrophysiology ), VR applications in manufacturing and education ( problem-solving modeling , adaptive virtual assistants ), and sensor optimization for industrial systems. Her research bridges theoretical innovation (e.g., spatiotemporal regularization) with practical applications (e.g., noise quality evaluation). Scientific Awards: Best Track Paper, Work Systems & Services, IISE Annual Conference (2022) Best Track Paper, Human Factors & Ergonomics, IISE Annual Conference (2021) Honorable Mention, INFORMS Conference on Service Science Best Student Paper Competition (2020) Marcus Fellowship, Penn State University (2019) Honor Graduate, Harbin Institute of Technology (2016, 2014) Advising: Actively mentoring Ph.D. students including Weixuan Dong (Fall 2022–Present) and Yuning Cui (Fall 2021–Present). She teaches undergraduate and graduate courses like ISE 3293 Applied Engineering Statistics and ISE 4633 Probabilistic Systems Models . Labs & Teams: Rui leads a lab focused on virtual reality simulation and eye-tracking analytics, collaborating with institutions like Penn State University and Harbin Institute of Technology. Her team applies interdisciplinary methods to solve challenges in healthcare, manufacturing, and educational technologies.

Alberto SorrentinoView profile

Associate Professor

• Numerical Analysis
• Inverse Problems
• Medical Imaging
• +3 more

Alberto Sorrentino serves as an Associate Professor in the Department of Mathematics at the University of Genoa, actively teaching Mathematical Methods for Bioengineering, Numerical Calculation, and Reverse Problems courses for Mathematics and Bioengineering degree programs through the 2025-2026 academic year. His research specializes in Numerical Analysis and Inverse Problems , developing computational frameworks for Medical Imaging and Neuroscience applications. Key contributions include probabilistic source localization tools for M/EEG, validation methodologies for electrical source imaging, and Bayesian approaches to neuroimaging inverse problems under extreme conditions like space environments. Recent publications (2023-2024) reveal a consistent focus on uncertainty quantification in brain imaging, advanced EEG analysis pipelines for space medicine, and optimization of regularization techniques for MEG connectivity. His work bridges computational mathematics with biomedical challenges through interdisciplinary collaboration. Information regarding academic advising, research grants, laboratory facilities, or team structures is not documented in the available materials.

• Optimization
• Evolutionary Algorithms
• Inverse Problems
• +4 more

Maciej Smołka, PhD hab., serves as a University Professor at the Institute of Computer Science within the Faculty of Computer Science at AGH University of Science and Technology in Kraków, Poland. His office is located at D-17, Kawiory 21, room 2.25, and he actively contributes to academic governance through the Computer Science Discipline Council and College of the Faculty of Computer Science. His research centers on computational optimization, specializing in metaheuristics, evolutionary algorithms, and inverse problems. He addresses complex challenges in non-convex optimization, time-delay systems, and stabilization of forward solvers, with significant contributions to hierarchical memetic strategies including the pyHMS Python library. His interdisciplinary work extends to Music Informatics, where he investigates urban soundscapes as carriers of local identity and applies computational methods to musical harmonization. Recent publications (2022-2025) demonstrate advancements in auto-configured metaheuristics for engineering optimization, socio-cognitive approaches to time-delay control, and LLM-generated algorithm design. His work bridges computational intelligence with practical applications in thermal systems and Gaussian mixture modeling, while maintaining a parallel research thread on musicology and urban acoustic ecology.

Nicolas Garcia TrillosView profile

Associate Professor

• Applied Analysis
• Computational Probability
• Statistics
• +5 more

Nicolas Garcia Trillos is an Associate Professor in the Department of Statistics at the University of Wisconsin-Madison, with research spanning applied analysis, computational probability, statistics, and machine learning. His work focuses on the intersection of calculus of variations, optimal transport, and partial differential equations in learning systems. His educational background includes: Bachelor's degree in Mathematics from Universidad de Los Andes, Bogotá, Colombia (2010) Ph.D. in Mathematics from Carnegie Mellon University (2015) Prager Assistant Professor (postdoctoral position) at Brown University (2015-2018) Garcia Trillos' research centers on the mathematical foundations of machine learning , developing tools in optimal transport and calculus of variations to analyze graph-based learning and continuum models. Key contributions address adversarial robustness , federated learning , and spectral clustering , providing theoretical guarantees for large-scale problems through PDE frameworks. Recent publications (2022-2025) demonstrate a cohesive focus on geometric approaches to adversarial robustness , using optimal transport to derive classification bounds and study solution existence. He has advanced federated learning via consensus-based bi-level optimization (CB2O) and developed Fermat distances for metric approximation, bridging pure mathematics with statistical learning theory. No scientific awards were mentioned in the provided text. There is no information available regarding student advising, research grants, or laboratory affiliations in the source material.

• Digital Rock Physics
• Computational Pore-Scale Physics
• Tomographic Imaging
• +9 more

Professor Christoph Arns is a full Professor at the University of New South Wales (UNSW) School of Engineering, specializing in Mineral and Energy Resources Engineering. He has been at UNSW since 2008 and has held the position of full Professor since 2014. Currently, he leads the Geoenergy and Geostorage discipline within the school. Prior to joining UNSW, he was a research fellow at the Australian National University from 2001-2008. Professor Arns obtained his Dipl. Phys. from RWTH Aachen, Germany in 1996 and his PhD in Petroleum Engineering from UNSW Sydney in 2002. His academic background combines physics and petroleum engineering, creating a unique interdisciplinary perspective for his research. His primary research focus is on Digital Rock Physics, where he has pioneered computational pore-scale physics based on tomographic images. He specializes in integrating 3D tomographic imaging technology with NMR techniques for petrophysical applications, with particular emphasis on heterogeneity analysis. His work spans multiple sub-disciplines including pore-scale modeling, digital core analysis, rock physics, reservoir characterization, and Minkowski functionals for structural analysis. Professor Arns has developed the MPI-parallel software package 'morphy' for computational physics operating on segmented tomographic images, which includes sophisticated NMR response modeling, electrical property calculations, permeability estimation, elastic moduli determination, and morphological property analysis. His research has substantial industry relevance, as evidenced by his role as a founding member of Digital Core Pty Ltd, an ANU/UNSW spin-off that was sold to FEI for $76 million in 2014 and is now part of ThermoFisher. He has received significant research support through three successive Australian Research Council (ARC) fellowships. His scholarly contributions are reflected in numerous publications spanning from 2000 to 2025, with recent work focusing on advanced computational methods, machine learning applications in rock physics, and detailed analysis of fluid-rock interactions at the pore scale. Professor Arns maintains active leadership roles in multiple professional societies including Interpore (lifetime member, former council chair 2016-2019), Society of Core Analysts (Australasia Regional Director since 2016), Society of Petrophysicists & Well Log Analysts, Society of Exploration Geophysicists, and Society of Petroleum Engineers.

• Signal Processing
• Medical Imaging
• Machine Learning
• +3 more

Dr. Saïd Moussaoui is a Professor in the Department of Automation and Robotics at École Centrale de Nantes , affiliated with the Nantes Digital Sciences Laboratory (LS2N) and the Signal, Image and Sound research team. His work spans machine learning, medical imaging, and signal processing, with a focus on EEG-based mental workload classification, PET reconstruction, and 4D Flow MRI optimization. Research Areas : Signal Processing, Medical Imaging, Machine Learning, Neuroscience, Biomedical Engineering Labs/Teams : LS2N Laboratory, Signal, Image and Sound Team Recent publications highlight trends in graph learning for EEG analysis, deep learning regularization in PET imaging, and super-resolution techniques in cardiovascular MRI. His research integrates physics-based models with data-driven approaches for applications in healthcare and industrial predictive maintenance. The LS2N laboratory provides a multidisciplinary environment for his work, combining advanced computational methods with real-world applications in biomedical engineering and robotics. Collaborations with institutions like IEEE and projects on digital twins underscore his technical leadership in applied research.

Rongrong WangView profile

Associate Professor

• Applied and Computational Harmonic Analysis
• Machine Learning
• Neural network training and generalization
• +6 more

Rongrong Wang serves as Associate Professor in both the Department of Computational Mathematics, Science and Engineering (CMSE) and Department of Mathematics at Michigan State University, based in the Engineering Building with contact email wangron6@msu.edu . Her academic journey includes: B.S. in Mathematics and B.A. in Economics from Peking University, Beijing Ph.D. in Applied Mathematics from University of Maryland College Park under John Benedetto and Wojciech Czaja Postdoctoral fellowship at University of British Columbia with Ozgur Yilmaz and Felix Herrmann Her research spans Applied and Computational Harmonic Analysis , Machine Learning , and Compressed Sensing with focus areas including neural network training dynamics, learning theory, tensor analysis, and inverse problems. She investigates theoretical foundations of deep learning while developing applications for medical imaging and signal processing. Recent publications (2024-2025) demonstrate strong interdisciplinary work at the intersection of deep learning theory and medical imaging, particularly exploring edge-of-stability phenomena in neural networks and diffusion-guided reconstruction techniques. Her work also advances tensor decomposition methods and in-context learning mechanisms in language models. Professor Wang actively recruits self-motivated graduate and undergraduate students with backgrounds in mathematics, computer science, or electrical engineering for research opportunities in her lab.

• Geophysics
• Bayesian Inference
• Inverse Problems
• +2 more

Malcolm Sambridge is a researcher at the Research School of Earth Sciences, Australian National University . His work focuses on advancing Bayesian inference methods for geophysical problems, particularly through the development of trans-conceptual sampling frameworks. In the article "Trans-Conceptual Sampling: Bayesian Inference With Competing Assumptions" , Sambridge and colleagues propose a novel approach to Bayesian sampling that generalizes trans-dimensional methods, enabling exploration across models with varying conceptual assumptions, basis functions, and forward physics. This framework eliminates the need for bespoke parameter transformations, allowing automated sampling with modern algorithms like Hamiltonian MCMC. The research demonstrates that trans-conceptual sampling achieves results comparable to reversible-jump MCMC in standard cases but extends applicability to scenarios where traditional trans-dimensional methods would fail, such as models with non-local basis functions or heterogeneous parameterizations. Applications range from airborne electromagnetic inversion to synthetic seismic tomography, showcasing the method's versatility in handling complex geophysical problems.