Find Academic Supervisors

Alireza DoostanView profile

Professor

• Uncertainty Quantification
• Computational Stochastic Mechanics
• Model Reduction
• +7 more

Alireza Doostan is a Professor in the Smead Aerospace Engineering Sciences department at the University of Colorado Boulder. He is also an Affiliated Faculty member in Applied Mathematics at the same institution. His research focuses on uncertainty quantification, computational stochastic mechanics, and scientific machine learning, with applications to aerospace and mechanical systems. PhD, Structural Engineering, The Johns Hopkins University (2006) MA, Applied Mathematics and Statistics, The Johns Hopkins University (2006) MS, Structural Engineering, Sharif University of Technology (2002) BS, Civil Engineering, Sharif University of Technology (2000) His work emphasizes uncertainty quantification (UQ), model reduction for stochastic systems, and data assimilation in mechanics. He develops methods for statistical inverse analysis , design under uncertainty , and data-driven model discovery applied to fluid and structural dynamics. Recent publications focus on scientific machine learning (e.g., PINNs for Li-ion batteries), bi-fidelity methods for data reduction, and stochastic optimization in aerospace design. These works span topics like turbulence modeling, spacecraft trajectory planning, and adaptive data compression for PDE systems. Selected scientific awards include the H. Joseph Smead Faculty Fellow (2018), NSF and DOE Early Career Awards (2015, 2011), and multiple teaching accolades. He leads the UQ and Data-Driven Lab within the Aerospace Mechanics Research Center (AMREC).

Houman OwhadiView profile

Professor

• Numerical Approximation
• Machine Learning
• Uncertainty Quantification
• +3 more

Houman Owhadi is the IBM Professor of Applied and Computational Mathematics and Control and Dynamical Systems at the California Institute of Technology. His research explores interplays between numerical approximation, statistical inference, and learning from a game-theoretic perspective, with applications in numerical homogenization, operator-adapted wavelets, fast solvers, Gaussian process regression, and uncertainty quantification. His current research focuses on computational information games, kernel flows, and the development of universal scalable solvers. He has made fundamental contributions to uncertainty quantification through adversarial approaches and the development of optimal statistical estimators. Professor Owhadi has received numerous awards including the Vannevar Bush Faculty Fellowship (2024), Germund Dahlquist Prize (2019), and SIAM Fellowship (2022). His research has been supported by AFOSR, DARPA, DOE, NASA, NSF, and ONR.

Valery VishnevskiyView profile

Lecturer

• Biomedical Imaging
• Magnetic Resonance Imaging (MRI)
• Ultrasound Imaging
• +3 more

Dr. Valery Vishnevskiy is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich , affiliated with the Biomedical Imaging Group . His research focuses on advanced medical imaging techniques, particularly in cardiac MRI , ultrasound , and machine learning applications . He contributes to projects involving 4D flow MRI reconstruction , speed-of-sound imaging , and motion-corrected diffusion tensor analysis . Primary Affiliation : ETH Zürich, Department of Information Technology and Electrical Engineering Research Interests : Biomedical Imaging, MRI Reconstruction, Ultrasound Physics, Cardiac Imaging, Deep Learning His recent work emphasizes self-supervised learning for medical imaging, including the development of FlowMRI-Net for accelerated 4D flow MRI and Speed-of-Sound Imaging using diverging waves. Publications span hyperpolarized 13C metabolic imaging , viscoelasticity reconstruction , and probabilistic sampling optimization . Dr. Vishnevskiy's contributions to deformable image registration include methods for handling sliding interfaces in abdominal and cardiac imaging, with applications in respiratory motion compensation and tissue ablation monitoring . He also explores mathematical models for enzyme activity analysis and behavioral pattern detection.

• Signal Processing
• Image Processing
• Machine Learning
• +7 more

Jian Qiu Zhang is a Professor at Fudan University in the School of Information Science and Technology , affiliated with the Key Laboratory for Information Science of Electromagnetic Waves and Research Center of Smart Networks and Systems in Shanghai, China. Previously, he was at the University of Greenwich (1999-2002) and earned his PhD in 1996 from Harbin Institute of Technology in the Department of Electrical Engineering . His research interests span Signal Processing , Image Analysis , and Machine Learning , with a focus on applications in Biomedical Imaging , Hyperspectral Data Analysis , and Smart Network Systems . His work often integrates Wavelet Transforms , Tensor Decomposition , and Bayesian Filtering to solve complex problems in Medical Imaging and Wireless Sensor Networks . Recent publications highlight advancements in Deep Learning for 3D Ultrasound , PARAFAC Decomposition , and Graph Neural Networks for Hyperspectral Classification . His technical contributions include Adaptive Filtering Algorithms , Nonlinear Unmixing , and Wavelet-Based Sensor Analysis . Key collaborations include researchers from institutions such as Harbin Institute of Technology, University of Greenwich, and international teams in IEEE Transactions and IGARSS conferences.

Douglas CochranView profile

Academic

• Remote Sensing
• Signal Processing
• Mathematics
• +4 more

Douglas Cochran is a Research Professor in the School of Mathematical and Statistical Sciences and an Emeritus Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University (ASU). He has held roles such as Program Manager for Mathematics at the U.S. Air Force and DARPA, and served as a Visiting Faculty Fellow at the ASU Barrett Honors College. His research focuses on mathematical aspects of remote sensing, particularly for national security applications, with expertise in signal processing, radar systems, and information theory. Education: Ph.D. Applied Mathematics (Harvard, 1990), S.M. Applied Mathematics (Harvard, 1986), M.A. Mathematics (UC San Diego, 1980), S.B. Mathematics (MIT, 1979). Key Roles: Program Manager (U.S. Air Force, DARPA), Consultant (Australian Defense Science), Editor (IEEE Transactions on Signal Processing, Sampling Theory in Signal and Image Processing). His research interests include radar signal processing, multi-channel detection, and information-geometric foundations of sensing systems. Recent work addresses topics like cyclostationary signal analysis, radar clutter cancellation, and eigenvalue distributions in statistical signal processing. Notable awards include the IEEE Fellowship (2015), U.S. Department of Defense Medal for Exceptional Public Service (2005), and multiple teaching excellence recognitions. He has led over 20 grants, including NSF-funded projects on mathematical methods in medical imaging and MURI initiatives on information theory for adaptive systems. Cochran has advised numerous students and contributed to industry through consulting roles at companies like Motorola and Raytheon. His academic service includes editorial roles and organizing IEEE conferences, reflecting his broad impact in academia and defense applications.

Toshiya HachisukaView profile

Associate Professor

• Computer Graphics
• Light Transport Simulation
• Computational Statistics
• +3 more

Toshiya Hachisuka is an Associate Professor in the Department of Computer Science at the University of Waterloo, part of the School of Computer Science. His research focuses on combining applied mathematics, computer science, and physics to address challenges in visual simulation, particularly in computer graphics, light transport, and computational statistics. He holds a Ph.D. from the University of California, San Diego (2011) and a B.Eng. from the University of Tokyo (2006). Research Interests: Hachisuka’s work spans light transport simulation , Monte Carlo rendering , numerical analysis , and fluid dynamics . He develops algorithms for efficient rendering and simulation, including methods like segment-based light transport and quantum ray marching. His techniques often integrate mathematical rigor with computational efficiency. Publications: His recent work explores advancements in Monte Carlo methods, fluid simulation, and quantum computing applications in rendering. Notable themes include denoising techniques , boundary element methods , and gradient-domain rendering . Awards: No scientific awards explicitly mentioned in the text. Advising & Grants: No specific students or grants listed, but his extensive publication record suggests active research collaboration. Labs/Teams: Involved in computer graphics research groups at the University of Waterloo, contributing to cutting-edge rendering and simulation projects.

• 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.

• Neural Data Science
• Systems Neuroscience
• Computational Neuroscience
• +3 more

Benjamin Adric Dunn is an Associate Professor in (Neural) Data Science at NTNU's Department of Mathematical Sciences. Formerly, he held positions as a PhD researcher and postdoc at NTNU's Kavli Institute for Systems Neuroscience, with prior education including an MS in Computational Engineering from Purdue University and a BS in Applied Mathematics from the University of Connecticut. His career also includes industrial experience as a Computational Methods Engineer at Pratt & Whitney, UTC. His research focuses on integrating mathematical and computational approaches to understand neural systems, particularly spatial cognition, grid cells, and topological data analysis in neuroscience. Key contributions include work on toroidal representations in grid cells, posture coding in neocortex, and cohomological feature extraction from neural data. Publications emphasize multidisciplinary methods bridging algebraic topology, statistical physics, and neuroscience. Teaching includes courses like TMA4255 (Applied Statistics) and TMA4285 (Time Series). Outreach activities highlight engagement in scientific communication, including media interviews about posture-coding neurons and grid cell research.

Pedro DiezView profile

Researcher

• Computational Science
• Geophysics
• Civil Engineering
• +3 more

Pedro Diez is a researcher specializing in computational methods for geotechnical and geophysical applications. His work focuses on developing data-driven models, particularly Reduced-Order Models (ROM) and Bayesian solvers for inverse problems. He explores advanced techniques like kernel Principal Component Analysis (kPCA) combined with Proper Orthogonal Decomposition (POD) to optimize dimensionality reduction in parametric forward problems. His research emphasizes uncertainty quantification using Markov-Chain Monte Carlo (MCMC) strategies, addressing challenges in geophysical crust dynamics and large-scale parameter identification.

DEBROUX NoémieView profile

Assistant Professor

• Image Processing
• Numerical Analysis
• Topology Optimization
• +7 more

DEBROUX Noémie is a French academic specializing in image processing and numerical analysis. She earned her Ph.D. in 2018 from INSA Rouen, supported by the M2NUM project co-financed by the European Union and Normandie Regional Council. Currently, she is a Maître de Conférences (Assistant Professor) at Clermont Auvergne University since 2019. Her research focuses on variational models, nonlocal methods, and topology-preserving segmentation for shape analysis and biomedical imaging applications. Ph.D. in Mathematics (2018), INSA Rouen MCF (Assistant Professor) at Clermont Auvergne University (2019–present) Research partnerships with University of Cambridge, UC Los Angeles, and Monash University Her work spans three key areas: variational models for image segmentation and registration, nonlocal characterization of total variation and shape descriptors, and topology optimization for 3D printing. She integrates mathematical rigor with practical applications in biomedical imaging, materials science, and computational geometry. Her publications demonstrate expertise in joint segmentation/registration frameworks (2016–2020), nonlocal topology preservation (2017), and crack recovery in bituminous surfacing (2018). Collaborations include Dassault Systèmes (2015) and CEREMA (2015–2016). She has presented at major conferences: SIAM Imaging Science (2016), Applied Inverse Problems (2017), and ISMRM (2019). Scientific Awards: Bourse Newton (2017) for Cambridge research L’Oréal-UNESCO for Women in Science nominee (2017) Prix de la Vocation Scientifique et Technique des Filles (2017) She has supervised engineering students' projects and taught mathematical analysis, variational methods, and machine learning (2016–2018). As an elected member of INSA Rouen's Scientific Council (2015–2016), she contributed to academic governance.

• Mathematical data processing
• Imaging sciences
• Ill-posed inverse problems
• +5 more

Mohamed Jalal Fadili is a Professor in Image and Signal Processing at the University of Caen, France, affiliated with GREYC CNRS UMR 6072 and ENSICAEN. His work bridges mathematical data processing, optimization, and computational harmonic analysis with applications in medical and astronomical imaging. Education: Engineering Degree (1996), MSc (1996), PhD (1999), and Habilitation (2010) in Signal and Image Processing from the University of Caen Research interests span ill-posed inverse problems, sparse recovery algorithms, nonsmooth and nonconvex optimization, and statistical learning. He collaborates on projects like Σ-Vision ERC (co-leader) and Sparse-Astro ERC , focusing on super-resolution and low-complexity models. Scientific awards include being a Junior member of the Institut Universitaire de France (2013-2018). He has advised numerous PhD students and postdoctoral researchers, many of whom now hold academic or research positions globally. Software: MCALab (Morphological Component Analysis), various toolboxes for image decomposition, inpainting, denoising, and fMRI analysis Organized minisymposiums at SIAM conferences on operator splitting algorithms, sparse recovery, and super-resolution

Philippe PoncetView profile

Professor

• Numerical Analysis
• High Performance Computing
• Fluid Dynamics
• +5 more

Philippe Poncet is a Professor at the Université de Pau et des Pays de l'Adour (UPPA) and Vice-President for Digital Environment. He leads the Numerical Methods and Complex Fluids team at the Laboratory of Mathematics and their Applications (LMAP). His expertise spans particle/vortex methods, high-performance computing, and applications in geoscience, microfluidics, and biomathematics, with a focus on reactive flows and porous media. Poncet's research integrates numerical analysis with practical challenges in environmental and biomedical engineering. Key interests include Lagrangian methods for fluid dynamics, optimization algorithms, pore-scale modeling, and cystic fibrosis biomechanics. His work bridges theoretical mathematics and industrial-scale simulations. Recent publications demonstrate advances in pore-scale dissolution modeling, Bayesian neural networks for inverse problems, and viscous flow simulations. His articles consistently address multi-scale challenges in geosciences and biomedicine using innovative computational frameworks. He has led significant projects including the ANR-funded MucoReaDy (mucus dynamics in respiratory diseases) and MicroMineral (CO₂ mineral storage). These collaborations involve multidisciplinary teams addressing environmental and health-related fluid dynamics.

Pierre BorgnatView profile

Research Professor

• Graph Signal Processing
• Complex Networks
• Time-Frequency Analysis
• +3 more

Pierre Borgnat is a CNRS Research Professor at École Normale Supérieure de Lyon, leading the Signals, Systems & Physics group. His research integrates signal processing, graph theory, and machine learning for complex data analysis. Key contributions include: Multiscale methods for graph signals and networks Time-frequency analysis of nonstationary data Applications in transportation, neuroscience, and climate science Graph-based learning with optimal transport He directs the 'Signals, Systems & Physics' group and serves as Area Editor for IEEE Transactions on Signal Processing. Recent projects include ACADEMICS (machine learning for complex models) and climate extremes forecasting.

Christian FreudeView profile

Researcher

• Computer Graphics
• Rendering
• Monte Carlo Methods
• +3 more

Christian Freude is a PreDoc Researcher at the Department of Computer Graphics , Faculty of Informatics , Vienna University of Technology (TU Wien) , focusing on Visual Computing and Human-Centered Technology . His work bridges photorealistic rendering , Monte Carlo methods , and real-time simulation . Active in projects: Toward Optimal Path Guiding (2023–2027, WWTF-funded), ACD (2020–2028) Supervised PhD/Master's students : Lukas Lipp, Christian Jauernik, Johannes Lurf Research spans Monte Carlo denoising , radiative thermal transport , subsurface scattering , and point cloud rendering . His 2025 work on Inverse Simulation of Radiative Thermal Transport introduces novel methods for efficient heat distribution modeling. Key scientific awards include the OCG Förderpreis 2016 . Publications from 2015–2025 highlight advancements in rendering algorithms , stereoscopic film , and uncertainty visualization .

Fatemeh GhoreishiView profile

Assistant Professor

• Machine Learning
• Bayesian Statistics
• Autonomous Systems
• +4 more

Fatemeh Ghoreishi is an Assistant Professor at Northeastern University, holding joint appointments in the Department of Civil and Environmental Engineering and the Khoury College of Computer Sciences . Her research focuses on machine learning, Bayesian statistics, and decision-making under uncertainty, with applications in autonomous systems, transportation, and cyber-physical systems. She leads the Institute for Experiential AI and collaborates across disciplines to advance AI-driven solutions for complex systems. Education: Ph.D./M.Sc., Mechanical Engineering (Minor in Applied Statistics), Texas A&M University (2019/2016) M.Sc., Biomedical Engineering, Iran University of Science & Technology (2014) B.Sc., Mechanical Engineering, University of Tehran (2012) Research Interests: Dr. Ghoreishi develops data-driven methodologies for uncertainty quantification, reinforcement learning for autonomous systems, and human-AI collaboration. Her work integrates Bayesian inference, optimization, and sensor data to solve real-world challenges in transportation, robotics, and infrastructure security. Recent projects include AI for disaster response, privacy-preserving federated learning, and safe control of autonomous vehicles. Awards & Grants: $900,000 U.S. Army grant (2024): 'Interactive Human-Agent Teaming Via Multiple Feedback Types' $60,000 U.S. Army grant (2023): 'BRITE: Bayesian Inference and Preference Learning' IEEE Senior Member (2025) 2025 AIAA SciTech Best Paper Finalist 2024 MECC Conference Best Paper Award Advising & Teams: She mentors Ph.D. students (e.g., Negar Asadi) and undergraduates (e.g., Ise Okhiria, Alyssa Lee). Her lab emphasizes interdisciplinary collaboration, with affiliations at Northeastern’s Institute for Experiential AI and partnerships with the University of Utah and University of Michigan.