Find Academic Supervisors

• Optimal Control
• Numerical Analysis
• Finite Element Methods
• +2 more

Jakob Wagner is a Research Fellow at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology and the Department of Mathematics. He works within the Chair of Optimal Control under Prof. Boris Vexler and serves as an Invited Lecturer at Kutaisi International University in Georgia since 2022. His educational background includes a Master's degree in Mathematics from TUM (2019-2020, grade 1.0) and a Bachelor's degree in Mathematics from TUM (2014-2018, grade 1.2). Wagner's research focuses on Optimal Control of fluid dynamics equations, particularly the Navier-Stokes and Stokes systems. He specializes in Finite Element Methods and rigorous Error Estimates for discretizations of partial differential equations. His work addresses time-dependent problems, state constraints, and boundary control mechanisms in computational fluid dynamics, with significant contributions to stream-function formulations and pressure boundary conditions. Analysis of his publication record reveals a concentrated research trajectory in numerical analysis of incompressible flow control. His work consistently advances theoretical foundations for finite element discretizations, emphasizing fully discrete error analysis and pointwise constraints across Stokes and Navier-Stokes frameworks. Key innovations include novel approaches to blood flow modeling and ocean current reconstruction through coupled ODE systems. Wagner actively mentors students, supervising Master's theses by Alexandro Jedaidi (2025, ocean current reconstruction), Chenhong Lin (2025, Stokes equations), and Hendrik Bruhse (2023, parabolic problems), plus Noah An der Lan's Bachelor thesis (2025, Bayesian experiment design). His teaching portfolio spans Analysis, Optimization, and Numerical Methods courses at TUM and Kutaisi International University. He operates within TUM's Chair of Optimal Control research ecosystem, contributing to international collaborations through conference presentations at GAMM Annual Meetings and specialized symposia, while maintaining active involvement in computational mathematics projects including robotic swarm applications for radio imaging.

Noel CressieView profile

Professor

• Spatial Statistics
• Environmental Informatics
• Bayesian Statistics
• +3 more

Noel Cressie is a Distinguished Professor of Statistics at the University of Wollongong (UOW), Australia, affiliated with the School of Mathematics and Applied Statistics and the National Institute for Applied Statistics Research Australia (NIASRA). He is also the Director of the Centre for Environmental Informatics (CEI). His academic journey includes a PhD from Princeton University (1975) and a B.Sc. with First Class Honours from the University of Western Australia (1972). His research focuses on spatial and spatio-temporal statistics, Bayesian methods, environmental informatics, and applications in climate science. Notable projects include work on atmospheric CO2 flux inversion (WOMBAT framework), Antarctic environmental research (SAEF initiative), and statistical remote sensing for NASA. He has secured over $20 million in research funding and authored four influential books, including Statistics for Spatial Data . Cressie has received prestigious awards such as the COPSS R.A. Fisher Award (2009), Pitman Medal (2014), and Fellowship of the Australian Academy of Science (2018). He leads interdisciplinary teams addressing global challenges like carbon cycle dynamics and biodiversity modeling. His contributions to statistical methodology and environmental science have been recognized through international collaborations and advisory roles.

Andrew NgView profile

Adjunct Professor

• Artificial Intelligence
• Machine Learning
• Robotics
• +4 more

Andrew Ng is an Adjunct Professor at Stanford University's Computer Science Department and a globally recognized leader in AI. He is the Founder of DeepLearning.AI, Executive Chairman of LandingAI, General Partner at AI Fund, and Co-Founder of Coursera. His work has revolutionized machine learning and online education, with over 200 research papers in AI, robotics, and related fields. He was named to the 2023 Time100 AI list of most influential figures in AI. Ng's research focuses on machine learning, deep learning, reinforcement learning, and their applications in robotics and education. He pioneered the development of massive open online courses (MOOCs), notably through Stanford's early experiments in 2011 that attracted hundreds of thousands of learners. His contributions include foundational work in algorithms like Latent Dirichlet Allocation (LDA) for text analysis and advancements in spectral clustering and inverse reinforcement learning. His publications span topics from robotic hand design to scalable deep learning systems, emphasizing practical and scalable solutions. Ng's educational initiatives, such as the Machine Learning and Deep Learning Specializations, have educated millions worldwide. He advocates for accessible AI education and ethical AI development, emphasizing collaboration between academia and industry.

Johannes BjervaView profile

Professor

• Natural Language Processing
• Artificial Intelligence
• Linguistic Typology
• +5 more

Johannes Bjerva is a Full Professor at Aalborg University's Department of Computer Science (Campus Copenhagen), leading the Copenhagen branch and conducting interdisciplinary NLP research integrating linguistic typology. His work focuses on low-resource languages, language model security, and societal AI impact. PhD (University of Groningen, 2017): Thesis on multitask/multilingual lexical modeling M.A. & B.A. in Computational Linguistics (Stockholm University) Research interests span linguistically-informed NLP , language model security , and low-resource language technology . Current projects include the DFF Sapere Aude grant (2025) for language model detection security and the LM2-SEC project (2025–2030). His 2024 ACL paper on embedding inversion security and 2024 EMNLP paper on typological diversity exemplify recent work. Scientific awards include: 2021: Teacher of the Year (AAU Computer Science) 2019: Google Cloud research credits 2022: Carlsberg Semper Ardens (5M DKK) 2024: Novo Nordisk Data Science grant (~10M DKK) Supervision includes 8 PhD students across projects like CreoleVal and HiFi-KPI . He serves on the Industrial Researcher Committee at Innovation Fund Denmark and is a member of Det Unge Akademi (2023–2028).

Shunxiang CaoView profile

Assistant Professor

• Fluid-Structure Interaction
• Computational Fluid Dynamics (CFD)
• Shock/Bubble Interaction
• +6 more

Shunxiang Cao is an Assistant Professor at Tsinghua Shenzhen International Graduate School in China, where he has worked since July 2022. He earned his Ph.D. in Aerospace Engineering from Virginia Tech (United States) between August 2014 and August 2020 and completed his B.S. in Aerospace Engineering at Beihang University (China) from September 2010 to May 2014. Prior to his current role, he served as a Postdoctoral Scholar at the California Institute of Technology (United States) from November 2019 to May 2022. Teaches courses such as Advanced Numerical Analysis, Numerical Methods for PDEs, and Fluid-Structure Interaction simulations. Research Interests include fluid-structure interaction, computational fluid dynamics, shock/bubble dynamics, material failure analysis, embedded-boundary methods, resolvent-based optimization, Kalman inversion, high-intensity focused ultrasound, and digital twin technology. His work focuses on numerical modeling, optimization, and fluid-solid coupling mechanisms in diverse applications like underwater propulsion, energy storage membranes, and medical acoustics. Scientific Awards include the USNCCM16 Conference Award (2021), Graduate Fellowship at Virginia Tech (2014-2015), Merit Undergraduate Student in Beijing (2014), and Singapore Technology Engineering Fellowship (2010-2013).

• Computational stochastic mechanics
• Machine learning in engineering
• Engineering safety assessment
• +7 more

Dr Dee Wu serves as a Senior Lecturer at the School of Civil and Environmental Engineering at the University of Technology Sydney (UTS), specializing in the integration of computational mechanics, machine learning, and engineering design. With a strong research profile focused on structural reliability and safety assessment, Dr Wu develops innovative frameworks that bridge theoretical mechanics with practical engineering applications, particularly in the realm of composite materials and uncertain structural behavior. Dr Wu's research interests center on computational stochastic and non-stochastic mechanics, with particular emphasis on machine-learning-aided engineering safety assessment, nondeterministic methods for isogeometric analysis with polymorphic uncertainties, and AI techniques for composite material design. Their work addresses critical challenges in structural engineering where uncertainty quantification becomes essential for safety evaluation. The research output reveals a clear trajectory toward developing virtual modeling techniques that significantly enhance computational efficiency while maintaining accuracy in structural analysis. Dr Wu's publications demonstrate expertise in phase-field methods, support vector regression variants (including Extended SVR, Capped SVR, and Twin SVR), and uncertainty quantification frameworks that handle both aleatoric and epistemic uncertainties. These techniques have been successfully applied to fracture mechanics, buckling analysis, vibration analysis, and impact assessment problems. Dr Wu actively pursues funded research in three main areas: Digital twin applications in Civil Engineering, Machine learning aided engineering analysis and design, and Safety assessment for Smart City initiatives. Currently, they are a key participant in the ARC Discovery Project 'Assessment of Dynamic Pile Driving Using Machine Learning' (DP230102781), running from June 2023 to May 2026, working alongside researchers Khabbaz M, Fatahi B, and Zhang X. In teaching, Dr Wu delivers courses including Introduction to Civil and Environmental Engineering (48310), Advanced Engineering Computing (48371), and Finite Element Analysis (49047), demonstrating commitment to both foundational and advanced engineering education. Their ORCID identifier is 0000-0002-7284-5024, and they maintain an active Google Scholar profile reflecting their substantial research contributions in computational structural engineering.

• Granular Mechanics
• Mechanical Engineering
• Neutron Diffraction
• +4 more

Associate Professor Chris Wensrich is a faculty member in the School of Engineering at the University of Newcastle, specializing in Mechanical Engineering. He holds a PhD, Bachelor of Mathematics, and Bachelor of Engineering from the same university. His research focuses on granular mechanics, neutron diffraction, and strain tomography, with pioneering work in Bragg-edge transmission strain tomography and granular dynamics modeling. Wensrich has held visiting appointments at Clare Hall College, Cambridge University, and the Isaac Newton Institute for Mathematical Sciences in the UK. He currently serves as President of the Australian Neutron Beam User Group (ANBUG) and is a member of the ACNS Program Advisory Team at ANSTO. His expertise spans applied mechanics, computational modeling (DEM), and experimental techniques involving neutron diffraction. His research interests include granular material behavior, stress distribution measurement, and validation of computational models using neutron imaging. Notable contributions include studies on silo quaking dynamics, force chain analysis in granular assemblies, and residual stress characterization in additive manufacturing. Wensrich has supervised 11 PhD students and secured over $5.4M in grants, including ARC Discovery Projects and industry-linked initiatives. Publications span granular mechanics, strain tomography, and material characterization, with over 60 journal articles and 38 conference papers. His work bridges theoretical, computational, and experimental methods to advance understanding of particulate systems and engineering materials.

Yuting ZhangView profile

Research Fellow

• Probabilistic Geotechnics
• Reliability-Based Design
• Data-Driven Site Characterisation
• +3 more

Dr Yuting Zhang serves as a Research Fellow within the Department of Civil, Maritime, and Environmental Engineering at the University of Southampton's Faculty of Engineering and Physical Sciences. He is an integral member of the Royal Academy of Engineering Chair Centre of Excellence for Intelligent & Resilient Ocean Engineering (IROE), focusing on machine learning applications for geotechnical site characterization. His educational background includes a Bachelor's degree and MPhil in Geotechnical Engineering from Wuhan University, China, followed by a 2024 PhD from the University of Newcastle, Australia, specializing in probabilistic calibration of resistance factors for piling designs. Zhang's research centers on probabilistic geotechnics and reliability-based design methodologies, with particular emphasis on data-driven site characterization techniques. His work bridges machine learning algorithms with geotechnical and geophysical data analysis to enhance foundation engineering practices, especially in offshore and marine environments. Current projects investigate spatial soil variability effects on pile group reliability, optimization of resistance factors, and innovative data augmentation approaches for rock fracture prediction. His publication record demonstrates consistent output in high-impact journals since 2022, with recent 2025 publications indicating active research momentum. The articles reveal strong thematic focus on probabilistic methods for pile design, integration of diverse geotechnical data sources, and machine learning applications in subsurface characterization. As part of the Infrastructure Group and Southampton Marine and Maritime Institute, Zhang contributes to ocean energy research initiatives while maintaining active collaborations with international researchers including Jinsong Huang, Jiawei Xie, and Anna Giacomini. His work supports the development of more resilient offshore infrastructure through advanced geotechnical reliability frameworks.

• Bayesian nonparametrics
• causal inference
• variational inference
• +5 more

Kolyan Ray is a Senior Lecturer (equivalent to Associate Professor) in Statistics at the Department of Mathematics, Imperial College London. He holds affiliations within the Faculty of Natural Sciences and contributes to the Statistics research group. His research focuses on Bayesian nonparametrics, causal inference, variational inference, inverse problems, and asymptotic statistics. Academic Background: PhD in Statistics from the University of Cambridge (supervised by Richard Nickl) Postdoc at Leiden University under Aad van der Vaart Lecturer (Assistant Professor) at King’s College London before joining Imperial Professional Roles: Associate Editor at the Electronic Journal of Statistics His work bridges theoretical and applied statistics, with emphasis on foundational methods and interdisciplinary applications. He maintains an active research program in statistical methodology and collaborates across academic institutions.

Zhou YingView profile

Associate Professor

• Seismology
• Geophysics
• Computational Seismology
• +3 more

Zhou Ying is an Associate Professor of Geophysics at Virginia Tech's College of Science, Department of Geosciences. Her research focuses on understanding seismic heterogeneities in the Earth's interior through finite-frequency wave propagation theory and diffractional tomography. Ph.D. in Geophysics from Princeton University (2005) Her research interests include: Seismic Wave Propagation in Heterogeneous Media Global and Computational Seismology Finite-Frequency Seismic Tomography 3-D Anelasticity (Q) Structure in the Mantle Seismic Anisotropy in the Upper Mantle and Transition Zone Core-Diffracted Waves and Core-Mantle Boundary Structure Signal Processing and Array Data Analysis Zhou's 15 most recent publications span seismology, geophysics, and unexpected interdisciplinary topics like molecular biology. Most studies focus on mantle discontinuities, finite-frequency tomography, and outer core dynamics, with specific attention to 410-km/660-km discontinuities and surface-wave sensitivity kernels. Current graduate advisee: Zhen Guo (Ph.D. candidate in Geophysics). Former advisees include Youyi Ruan (Ph.D. 2012), Kui Liu (Ph.D. 2014), Kai Deng (MS 2014), and Jing Xue (MS 2014). She teaches undergraduate courses in Physical Geology and Earthquake Seismology, and graduate courses in Theoretical Seismology, Advanced Seismology, and Seismic Data Analysis.

Dimitris KarlisView profile

Professor

• Computational Statistics
• Mixture Models
• EM Algorithm
• +12 more

Dimitris Karlis is a Professor in the Department of Statistics at the Athens University of Economics and Business (AUEB), within the School of Information Sciences and Technology. He has been a key academic figure since earning his BSc and PhD in Statistics from AUEB in 1992 and 1999, respectively, and was promoted to Associate Professor in 2012 before advancing to full Professor. Education: BSc in Statistics, AUEB (1992) PhD in Applied Statistics, AUEB (1999) His research spans computational statistics, mixture models, EM algorithms, copulas, multivariate discrete data, and applications in sports, insurance, and seismicity. He has published extensively in top-tier statistical journals such as the Journal of the Royal Statistical Society and Statistics in Medicine . The 15 most recent publications reveal a strong focus on multivariate count data, integer-valued time series, model-based clustering using copulas, and applications in actuarial science and health. His work frequently involves mixture models, Bayesian inference, and innovative extensions of Poisson-based frameworks. Scientific Service and Recognition: Associate Editor: Metron, Communications in Statistics, IMA Journal of Management Mathematics, Stochastic Environmental Research and Risk Assessment Editor: Biometrics Bulletin of IBS Member: American Statistical Society, International Statistical Institute, International Association of Statistical Computing, Hellenic Statistical Institute Publicity Officer: Eastern Mediterranean Region, International Biometric Society Advising and Grants: He has supervised 4 completed PhDs and 18 Master’s theses, with several more in progress. He has led and participated in research projects funded by the European Union and EUROSTAT, particularly in official statistics. His advising spans methodological and applied topics in statistics. Labs and Teams: While no formal lab is named, he collaborates extensively with researchers in actuarial science, transportation, biostatistics, and environmental risk, often through joint projects and publications.

Michael ThorneView profile

Associate Professor

• Global seismic wave propagation
• Global seismic structure and dynamics
• Core-mantle boundary structure
• +9 more

Michael Thorne is an Associate Professor in the Department of Geology & Geophysics at the University of Utah, where he has been faculty since July 2014. His research focuses on using seismology to investigate Earth's structure, with specialization in mapping seismic structure of the deep mantle and developing numerical techniques for seismic wave propagation modeling. He teaches courses in geophysics, the dynamic Earth, and seismology. Dr. Thorne's educational background includes: B.S. in Physics from Indiana University Bloomington (1991-1996) Ph.D. in Geological Sciences from Arizona State University (2000-2005) His primary research interests span global seismic wave propagation, Earth's deep interior structure, and the dynamics of the core-mantle boundary region. Dr. Thorne specializes in studying ultra-low velocity zones (ULVZs), D" discontinuity structure, and seismic array processing techniques. His work combines advanced waveform modeling with seismic observations to understand Earth's deep structure and dynamics, including connections between deep mantle features and surface geology. Dr. Thorne's recent publications demonstrate a consistent focus on ultra-low velocity zones at the core-mantle boundary, with increasing sophistication in modeling techniques. His work has evolved from basic detection of ULVZs to detailed characterization of their morphology, elastic properties, and potential origins. Recent papers incorporate advanced Bayesian inversion methods and multidimensional modeling to better understand these enigmatic features and their implications for Earth's thermal and chemical evolution. Dr. Thorne actively mentors graduate students through thesis research courses and has secured numerous research grants to support his work. His current projects include: "Mapping the Lateral Variability of Groundwater Input into the Great Salt Lake Using Electrical Methods" (2024-2025) "NSFGEO-NERC: Advancing Capabilities to Model Ultra-Low Velocity Zone Properties Through Full Waveform Bayesian Inversion" (2024-2027) "The Future of Glaciers Using a Novel, Interdisciplinary Approach" (2024-2026) "Global Search for D Discontinuity Structure" (2022-2026) His research group utilizes advanced computational methods and collaborates with institutions worldwide to investigate Earth's deep interior structure using seismic observations and modeling.

Kyle T SpikesView profile

Associate Professor

• Seismic Reservoir Characterization
• Rock Physics Modeling
• Geophysical Inversion Techniques
• +4 more

Kyle T Spikes is an Associate Professor in the Department of Earth and Planetary Sciences at the Jackson School of Geosciences, The University of Texas at Austin. His research focuses on integrating geologic data with quantitative tools for seismic reservoir characterization, emphasizing forward and inverse problems in rock physics, stochastic modeling, and seismic inversion. He works across scales from sub-micron rock samples to surface seismic data, developing effective medium models and numerical techniques to estimate heterogeneous and anisotropic rock properties. His work spans applications in carbonates, shales, and fractured reservoirs, with a strong emphasis on Bayesian methods, stochastic inversion, and machine learning for data analysis. Recent studies include fluid flow effects in porous media, distributed acoustic sensing (DAS), and rock physics modeling of unconventional reservoirs like the Haynesville Shale. He has contributed to CO2 sequestration monitoring through inversion of 3D VSP data at the Cranfield site. Notable trends in his publications include advancements in Bayesian-based rock physics modeling, integration of multi-scale data (laboratory to field scale), and innovative applications of DAS technology for seismic monitoring. His research bridges geophysics, petrophysics, and reservoir engineering, addressing challenges in reservoir characterization and monitoring under varying fluid and stress conditions. Dr. Spikes advises postdoctoral researchers and graduate students in the Jackson School, though specific advisee names are not listed. His work is supported by collaborative projects with industry and academic partners, focusing on practical solutions for subsurface reservoir challenges.

• Nuclear Engineering
• Reactor Design and Safety
• Thermal-Hydraulics
• +3 more

Tomasz Kozlowski is an Associate Professor and Associate Head for Undergraduate Programs at the University of Illinois at Urbana-Champaign's Grainger College of Engineering, Department of Nuclear, Plasma, and Radiological Engineering (NPRE). He holds additional positions as Associate Professor at Poland's National Centre for Nuclear Research (NCBJ) and Affiliated Professor in Computational Science and Engineering at UIUC. His research focuses on multi-physics modeling, reactor design/safety, computational methods, and thermal-hydraulics. He has taught courses like NPRE 200 (Mathematics), NPRE 455 (Neutron Transport), and advanced modeling topics. Education: B.S., M.S., and Ph.D. in Nuclear Engineering from Purdue University (2000–2005), followed by a Docent Habilitation in Nuclear Power Safety from the Royal Institute of Technology (KTH, 2011). He has collaborated on a $2M DOE grant for fuel storage solutions and contributed to UIUC's submission for a micro-reactor license application. His work includes advanced reactor design, uncertainty quantification, and computational tools like TRACE and MCNP-ORIGEN. Research emphasizes reactor analysis methods, numerical solver development, and inverse uncertainty quantification. Over 100 publications span topics like TRISO fuel performance, BWR instability, and hydrogen production integration with microreactors. He serves as Associate Editor for Nuclear Technology and actively engages in international benchmarks (e.g., BEAVRS, OECD/NEA).

Youssef MarzoukView profile

Professor

• Uncertainty Quantification
• Bayesian Computation
• Inverse Problems
• +4 more

Youssef Marzouk is a Professor of Aeronautics and Astronautics at MIT, serving as co-director of the MIT Center for Computational Engineering and director of the Aerospace Computational Design Laboratory. His research focuses on integrating physical modeling with statistical inference, emphasizing Bayesian computation, uncertainty quantification, and optimal experimental design. He holds a SB, SM, and PhD from MIT and has been recognized with prestigious awards including the DOE Early Career Award and the Junior Bose Teaching Prize. Education: PhD in Aeronautics and Astronautics, MIT SM in Aeronautics and Astronautics, MIT SB in Aeronautics and Astronautics, MIT Research Interests: Uncertainty Quantification techniques for complex systems Bayesian computational methods and inverse problem solutions Optimal experimental design strategies Interdisciplinary applications in geophysics, environmental science, and engineering Awards: 2022: Report to the President, Center for Computational Science and Engineering 2021: Bayesian Inference Software Framework (hIPPYlib-MUQ) 2012: MIT School of Engineering Junior Bose Award 2010: DOE Early Career Research Award Labs & Leadership: Aerospace Computational Design Laboratory (Director) MIT Center for Computational Engineering (Co-Director) Editorial Board roles: SIAM Journal on Scientific Computing, Advances in Computational Mathematics