Find Academic Supervisors

Vakhtang PutkaradzeView profile

Professor

• Mathematical Physics
• Fluid Dynamics
• Nonlinear Dynamics
• +4 more

Vakhtang Putkaradze is a Centennial Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. His research spans mathematical physics, fluid dynamics, robotics, and nonlinear systems. He leads a research group offering graduate positions in areas such as Hamiltonian systems, porous media, and sensor technology. Research interests include geometric mechanics, fluid-structure interactions, and applications in robotics and biomedical engineering. Key topics involve the dynamics of flexible tubes, control theory in sports mechanics, and mathematical models of social conformity. He has developed novel methodologies such as Lie-Poisson neural networks for Hamiltonian systems and CLPNets for multi-part dynamical systems. His work integrates theoretical analysis with experimental applications, such as olfactory sensor devices and inflatable solar towers. Recent articles highlight contributions to variational principles in porous media, integrability in figure skating trajectories, and the thermodynamics of active materials. Putkaradze collaborates on interdisciplinary projects combining mathematics, physics, and engineering. His group focuses on solving complex problems through geometric and computational approaches, with applications ranging from energy systems to biomedical sensing technologies.

• Computational Fluid Dynamics
• Biomedical Engineering
• Numerical Analysis
• +7 more

Johan Hoffman is a Professor of Numerical Analysis at KTH Royal Institute of Technology, Stockholm, Sweden, and Deputy Head of the Division of Computational Science and Technology. He leads research in computational science, with a focus on fluid dynamics, biomedical engineering, and numerical methods. His work integrates fundamental mathematics with applied research in aerodynamics, urban planning, renewable energy, and biomedicine. Hoffman is a founder of the FEniCS open-source software project and a partner in the EU’s GENEX project on digital twins. Education: Ph.D. in Applied Mathematics, Chalmers University of Technology Postdoctoral Fellow, Courant Institute, New York University Visiting Researcher at Oxford University, Stanford University, and others Research Interests: Hoffman’s research spans computational fluid dynamics, turbulence modeling, finite element methods, and biomedical applications such as cardiovascular simulations. He develops adaptive numerical methods and high-performance computing frameworks for complex systems. Publications Trends: Recent work emphasizes fluid-structure interaction in biomedical contexts (e.g., mitral valves), turbulence analysis, and multiphysics simulations in renewable energy. His methods prioritize stability, adaptivity, and scalability for large-scale problems. Awards: ERC Proof of Concept Grant (2015) Ingvar Carlsson Award (2005) Leslie Fox Prize (2005) Bill Morton Prize (2001) Advising & Grants: Hoffman’s funding includes grants from the Swedish Research Council, ERC, and EU Horizon 2020. He supervises research on digital twins, turbulence modeling, and biomedical simulation. His lab collaborates with industry and academic partners globally. Labs/Teams: Core contributor to the FEniCS project and leader of computational fluid dynamics teams at KTH, advancing open-source tools for multiphysics simulation.

William RistenpartView profile

Professor

• Transport phenomena
• Colloid science
• Electrokinetics
• +5 more

William Ristenpart is a Professor of Chemical Engineering at the University of California, Davis, and Director of the UC Davis Coffee Center. His research focuses on transport phenomena, colloid science, electrokinetics, and food science, with recent work on aerosol dynamics, granular materials, and coffee science. He leads a lab equipped with high-speed cameras and advanced optical systems to study complex fluid dynamics and particle interactions. Ristenpart’s research interests span diverse applications, including droplet behavior in electric fields, vocal fold aerosol formation, and coffee brewing science. He has pioneered studies on the physics of coffee roasting, flavor profiles, and brewing techniques. His work also addresses practical challenges like mask efficacy in reducing aerosol transmission and optimizing lab-on-a-chip devices using vibrational forces. His publications highlight interdisciplinary approaches, blending engineering principles with food science and biomedical applications. Notable contributions include modeling expiratory aerosols during speech, optimizing facemask designs, and developing theoretical frameworks for vibrational particle manipulation. The Coffee Center, under his leadership, bridges academic research with industry needs in coffee production and sensory analysis. Ristenpart’s teaching innovations, such as the Design of Coffee course, engage over 1,800 students annually, emphasizing hands-on learning in chemical engineering principles. His lab’s equipment includes Phantom high-speed cameras (100,000 fps), microscopes, and laser systems to capture dynamic phenomena at unprecedented resolution.

Ivan YotovView profile

Professor

• Numerical Analysis
• Scientific Computing
• Fluid Dynamics
• +5 more

Ivan Yotov is a Professor in the Department of Mathematics at the University of Pittsburgh, affiliated with The Dietrich School of Arts and Sciences. He holds a PhD in Computational and Applied Mathematics from Rice University (1996). His research focuses on numerical analysis, scientific computing, and multiphysics systems, with expertise in fluid flow, porous media, and domain decomposition methods. Key research areas include mixed finite element methods, multiscale modeling, and coupled systems (e.g., fluid-poroelastic interactions). He has contributed to reservoir simulation frameworks like UT-MB and collaborated on projects involving multiphase flow, geomechanics, and biomedical applications. Yotov teaches advanced courses in numerical analysis, linear algebra, and scientific computing. His work emphasizes accurate discretizations and scalable algorithms for complex systems, addressing challenges in energy, environmental, and biomedical engineering. Notable projects include the Department of Energy-funded multiblock reservoir simulation initiative and collaborations with institutions like Argonne National Laboratory.

Charles WolgemuthView profile

Professor

• Biological Physics
• Cell Motility
• Biophysics
• +3 more

Charles Wolgemuth is a Professor of Physics and Molecular and Cellular Biology at the University of Arizona, serving as Director of Undergraduate Studies. His research integrates theoretical, computational, and experimental approaches to study cellular biophysics, particularly focusing on mechanisms of cell motility and force generation. Key areas include bacterial motility, cancer metastasis, wound healing, and Lyme disease progression. He holds a PhD in Physics from the University of Arizona (2000) and leads the Wolgemuth Cellular Biophysics Group. Research interests emphasize the biophysical principles underlying cell shape maintenance, migration, and interactions. His work explores how biochemical reactions drive physical responses in cells, with applications to developmental biology, disease progression, and immune response. Notable studies investigate motility in Lyme disease spirochetes, collective cell migration dynamics, and cytoskeletal mechanics in neurons. Publications span bacterial swimming mechanics, viscoelastic fluid dynamics, and computational modeling of biological systems. The lab's interdisciplinary approach bridges physics, biology, and mathematics to address complex biological phenomena at multiple scales.

• Control Systems
• Drilling Engineering
• Hyperbolic Partial Differential Equations (PDEs)
• +5 more

Ole Morten Aamo is a Professor in the Department of Engineering Cybernetics at NTNU. His research focuses on advanced control systems for complex engineering problems, particularly in drilling engineering and hyperbolic partial differential equations (PDEs). He holds a prominent position within the field of adaptive control, nonlinear systems, and fluid dynamics. His work often addresses challenges in offshore drilling, including vibration control, leak detection, and real-time data-driven decision-making. He employs cutting-edge methodologies like physics-informed neural networks and backstepping control designs. Key contributions include models for percussive drilling, distributed damping systems for reducing stick-slip vibrations, and adaptive control strategies for hyperbolic PDE systems. Recent publications highlight advancements in fault diagnosis for drilling operations, boundary control of hyperbolic systems, and the integration of machine learning for lithology classification. His interdisciplinary approach bridges theoretical control systems with practical applications in energy and mining sectors. He has supervised numerous PhD and master’s students, including Haavard Holta, Nils Christian Aars Wilhelmsen, and Mia Olea Vettestad. His research groups collaborate with industry partners to translate theoretical innovations into deployable solutions for drilling automation and process safety. Notable projects include the development of real-time kick/loss detection systems and the design of decentralized damping subs for drilling rigs. His work is frequently published in top-tier journals like Automatica , IEEE Transactions on Automatic Control , and SPE Journal .

• Computational Fluid Dynamics
• Low Reynolds Number Hydrodynamics
• Integral Equation Methods
• +3 more

Dr. Mary Catherine Kropinski is a Professor (On Leave) in the Department of Mathematics at Simon Fraser University (SFU), part of the Faculty of Science. She holds a PhD in Applied Mathematics from Rensselaer Polytechnic Institute (1993), followed by a postdoctoral appointment at the Courant Institute at NYU. Her research focuses on computational fluid dynamics and low Reynolds number hydrodynamics, with an emphasis on developing integral equation methods for solving fluid dynamics equations like Stokes and Navier-Stokes. These methods are applied to complex systems such as microfluidic flows, sedimentation processes, and biological flows. Dr. Kropinski has been recognized with the NSERC Women's Faculty Award and has contributed to peer review for journals like the Journal of Computational Physics . Her work bridges theoretical advancements in numerical methods with practical applications in engineering and environmental science. She has served on NSERC committees and has authored numerous influential papers on fluid dynamics modeling and algorithm development.

John StockieView profile

Professor

• Fluid Dynamics
• Industrial Mathematics
• Scientific Computing
• +7 more

John Stockie is a Professor in the Department of Mathematics at Simon Fraser University (SFU), part of the Faculty of Science. He earned his Ph.D. in Applied Mathematics from the University of British Columbia in 1997. His research focuses on fluid dynamics, industrial mathematics, and scientific computing, with an emphasis on developing numerical methods for solving complex physical systems governed by nonlinear PDEs. He combines asymptotic analysis, finite volume methods, and adaptive mesh techniques to address applications in fuel cells, biofluid dynamics, and porous media flow. Stockie’s work bridges theoretical and applied mathematics, addressing real-world challenges such as hydrogen fuel cell efficiency, tree sap exudation mechanisms, and glacier dynamics. His teaching includes courses like MACM 316 Numerical Analysis I. He has contributed to open-access educational resources, including a clicker question bank for numerical analysis. His research trends span environmental modeling (e.g., atmospheric dispersion, glacier simulation), biomedical applications (biofilm dynamics), and industrial problems (emission estimation). He emphasizes interdisciplinary collaboration, often involving computational tools like level set methods and Bayesian inversion approaches. Stockie’s lab focuses on developing robust numerical algorithms and their application to fluid-structure interactions, multiphase flows, and environmental systems. His work has implications for climate science, biomedical engineering, and sustainable energy technologies.

Iman BorazjaniView profile

Associate Professor

• Computational Fluid Dynamics
• Fluid-Structure Interaction
• High-Performance Computing
• +2 more

Iman Borazjani is Associate Professor of Mechanical Engineering and Sallie and Don Davis '61 Faculty Fellow at Texas A&M University. He holds a Ph.D. in Mechanical Engineering from the University of Minnesota (2008), M.S. from Georgia Tech (2005), and B.S. from Sharif University of Technology (2002). His research focuses on computational fluid dynamics, fluid-structure interaction, high-performance computing, cardiovascular flows, and bio-inspired propulsion. Professor Borazjani has received prestigious recognitions including AIAA Associate Fellow (2021), ASME Fellow (2020), NSF CAREER Award (2015), and Fulbright Scholarship (2017-2018). He directs the Scientific Computing and Biofluids Lab, developing advanced simulation tools for complex fluid-structure interaction problems. His recent work demonstrates three primary thrusts: 1) Cardiovascular engineering with focus on heart valve dynamics and blood coagulation; 2) Bio-inspired propulsion systems using high-fidelity simulations of fish swimming; and 3) Active flow control techniques including traveling wave morphing surfaces for aerodynamic applications. His group develops specialized computational methods including curvilinear immersed boundary techniques, parallel large eddy simulations, and machine learning approaches for inverse problems in biomechanics.

A. Daniel HillView profile

Professor

• Production Engineering
• Well Stimulation
• Production Logging
• +1 more

A. Daniel Hill is Noble Chair and Regents Professor in Petroleum Engineering at Texas A&M University. With industry experience and advanced degrees from UT Austin, his research advances well engineering technologies including production optimization, stimulation design, and complex well performance. Research develops analytical and computational methods for improving hydrocarbon recovery, particularly in challenging reservoir environments. Innovations include advanced production logging interpretation techniques, multilateral well modeling approaches, and hydraulic fracturing optimization protocols applied in major basins worldwide. Honors from the Society of Petroleum Engineers recognize transformative contributions to production engineering fundamentals and technology transfer. Patents cover improved recovery methods implemented by international energy companies.

Ding ZhuView profile

Professor

• Well Stimulation
• Hydraulic Fracturing
• Complex Well Performance
• +4 more

Ding Zhu is a Professor of Petroleum Engineering and McCain Engineering Chair at Texas A&M University's Harold Vance Department of Petroleum Engineering within the College of Engineering. His research focuses on well stimulation, hydraulic fracturing, intelligent completions, and distributed sensing technologies. He holds a Ph.D. from The University of Texas at Austin (1992), M.S. (1988), and B.S. from Beijing University of Science & Technology (1982). Research interests include acid stimulation mechanisms, fracture conductivity optimization, and downhole monitoring via distributed temperature/acoustic systems. Notable contributions involve integrating DAS/DTS data for fracture diagnostics and developing models for acid fracturing in carbonate reservoirs. Recent publications emphasize fracture propagation detection, proppant transport dynamics, and novel applications of 3D printing in fracture modeling. His work bridges experimental studies (e.g., acid jetting experiments) with advanced numerical simulations for reservoir optimization. Awards: SPE Distinguished Member (2011), Excellence in Teaching Awards (2015–2019) Grants/Advising: Active in industry-funded research; advising focuses on graduate students in hydraulic fracturing and completion technologies.

• Numerical Analysis
• Soft Materials and Complex Fluids
• Free Boundary Problems
• +2 more

Hector D. Ceniceros is a Professor in the Department of Mathematics at the University of California, Santa Barbara (UCSB), within the College of Letters and Science. He holds roles as Partner of the Complex Fluids Design Consortium and Faculty Advisor of the UCSB SIAM Chapter. His research focuses on numerical analysis, soft materials, free boundary problems, and data science. He has held academic positions since 1995, including Vice-Chair for Graduate Affairs (2016-2018) and visiting roles at institutions like Caltech and Universidade de Brasília as a Science Without Borders Fellow. Education: Ph.D. and M.S. in Mathematics, Courant Institute, NYU (1995, 1993) B.S. in Physics, Escuela Superior de Física y Matemáticas, Instituto Politécnico Nacional (1989) Research Interests: Dr. Ceniceros specializes in computational methods for complex fluids, including polymer self-consistent field theory, phase separation dynamics, and machine learning integration. His work bridges numerical analysis with applications in soft matter physics and materials science. Recent efforts emphasize data-driven approaches for material design and pandemic modeling through stochastic games and deep learning. Awards & Recognition: Fellow of the American Mathematical Society (2019) Plenary Speaker at Colombian Congress of Applied Mathematics (2022) Mochizuki Teaching Award (2002) Advising & Grants: As academic leader, he has advised numerous research initiatives and contributed to NSF-funded projects. His editorial roles include Associate Editorships at Multiscale Modeling and Simulation and Orbita Mathematicae . Labs & Collaborations: Collaborates with the Complex Fluids Design Consortium, advancing computational tools for soft materials. Active in interdisciplinary research, integrating fluid dynamics, materials science, and data science.

Lisa Maria KreusserView profile

Associate Professor

• Partial Differential Equations
• Machine Learning
• Data Analysis
• +4 more

Dr. Lisa Maria Kreusser is a Reader (equivalent to Associate Professor) at the University of Bath's Department of Mathematical Sciences and Fellow at the Institute for Mathematical Innovation. Her research integrates partial differential equations, data analysis, and machine learning with applications spanning biology, climate modeling, and engineering. Her research program focuses on: Mathematical foundations of machine learning Numerical methods for PDEs Multiscale modeling in climate science Applications in healthcare technology Innovations in optimization algorithms Her recent publications (2019-2025) demonstrate strong interdisciplinary focus with 33% in machine learning foundations, 27% in scientific computing, 20% in applied mathematics, and 13% in healthcare/climate applications. Core themes include diffusion models, optimization theory, and computational methods for physical systems. Significant scientific recognition includes: Reinhart Heinrich Award from ESMTB (2020) Rising Stars in Computational and Data Sciences (2020) Multiple grants including ICMS Knowledge Exchange Catalyst Fellowship (2023-24) Nevile Junior Research Fellowship at Magdalene College, Cambridge (2019-2022) She leads interdisciplinary projects on digital health interventions for chronic diseases and renewable energy auctions. As Environmental Extremes Lead at IMI, she coordinates climate-related mathematical research.

Mohammad FarazmandView profile

Assistant Professor

• Applied Dynamical Systems
• Control Theory
• Optimization
• +4 more

Mohammad Farazmand is an Assistant Professor in the Department of Mathematics at North Carolina State University (NC State). His research focuses on applied dynamical systems, control theory, optimization, and data-driven methods. He holds a PhD in Mathematics from ETH Zurich (2014). His work emphasizes stochastic modeling in epidemiology and environmental systems, fluid dynamics, and the development of numerical methods for partial differential equations (PDEs). Key contributions include enforcing conservation laws in numerical discretizations, quantifying uncertainties in stochastic compartmental models, and predicting extreme events like wildfires using sparse sensor data. He also explores reduced-order modeling and shape-morphing techniques for efficient simulation of complex systems. Recent publications span journals like Nonlinear Dynamics , Journal of Computational Physics , and Proceedings of the Royal Society A , highlighting interdisciplinary applications in fluid mechanics, epidemiology, and wildfire risk assessment. Farazmand collaborates with research groups in Control, Optimization, and Modeling; Numerical Analysis and Scientific Computing; and Ordinary/Partial Differential Equations. His methods bridge deterministic and probabilistic frameworks to address challenges in state estimation and rare event prediction.

Thomas AbadieView profile

Assistant Professor

• Fluid Mechanics
• Multiphase Flows
• Interfacial Phenomena
• +5 more

Thomas Abadie is an Assistant Professor at the University of Birmingham's School of Chemical Engineering. His research focuses on fluid mechanics, multiphase flows, and interfacial phenomena with applications in environmental and industrial challenges. He employs experimental techniques (e.g., Particle Imaging Velocimetry) and numerical methods (e.g., interface capturing/tracking) to study bubble dynamics, complex fluids, and mass transfer processes. His work spans scales from microreactors to industrial aeration systems. Abadie holds a PhD in Fluid Mechanics (2013) and an MSc in Fluid Mechanics, Energy, and Transport Phenomena from the University of Toulouse (2010). He teaches courses on transport phenomena and supervises undergraduate and graduate research projects. His research explores topics such as viscoelastic fluid dynamics, Richtmyer-Meshkov instabilities, and graphene synthesis. Recent studies emphasize machine learning integration with multiphase flow modeling and data-driven approaches to drop size distributions. His work bridges computational methodologies with real-world applications in energy and environmental systems. Abadie serves as a reviewer for journals like Chemical Engineering Science and Journal of Fluid Mechanics , and consults on CFD and multiphase systems. His collaborations span academia and industry, addressing challenges in wastewater treatment and renewable energy systems.