Find Academic Supervisors

• Computational Mathematics
• Applied Mathematics
• Stochastic Processes
• +5 more

Di Liu is a Professor in the Department of Mathematics at Michigan State University. His research focuses on computational mathematics, stochastic processes, and quantum mechanics, with applications in chemical kinetics and nano-optics. He teaches courses such as Applied Linear Algebra and Readings in Mathematics. Education: Ph.D. in Applied and Computational Mathematics, Princeton University (1999–2003) B.S. in Computational Mathematics and Its Applied Softwares (1994–1999) His work spans numerical methods for solving Kohn-Sham equations, multiscale modeling of nano-optical systems, and stochastic simulation algorithms with applications in chemical reaction networks. Recent publications highlight network embedding techniques for energy landscape analysis and error estimation in delayed reaction systems. Professor Liu's research trajectory shows consistent innovation in computational methods for quantum dynamics and stochastic systems, with a particular emphasis on adaptive algorithms and multiscale approaches. His publications bridge theoretical mathematics and practical applications in physics and chemistry.

Mauro BeckerView profile

Professor

• Sedimentology
• Stratigraphy
• Basin Evolution
• +3 more

Mauro Becker is a Professor of the Practice at Texas A&M University, specializing in sedimentology, stratigraphy, and reservoir geoscience. He holds a PhD in Geology from The University of Texas at Austin (1993-1996), an MSc from Universidade Federal de Ouro Preto, Brazil (1983-1984), and a BSc from Universidade Federal do Rio Grande do Sul, Brazil (1977-1981). His research focuses on sedimentological and stratigraphic characterization of deep-water reservoirs, including turbidite systems, fluvial deposits, and carbonate reservoirs. He emphasizes integrating outcrop studies, seismic data, and stochastic modeling to improve reservoir management and field development strategies. Key regions of study include the Campos Basin (Brazil), Gulf of Mexico, and Northeast Brazilian basins. Becker has contributed extensively to reservoir heterogeneity analysis, fluid flow simulation, and the application of geophysical methods like seismic inversion for reservoir characterization. His work bridges academic research and industry needs, with a focus on optimizing oil field development in complex sedimentary systems. His publications span over two decades, addressing challenges in deep-water reservoirs, outcrop analogs, and pre-salt reservoirs in Brazil. Notable projects include the Barracuda and Cascade fields, where he applied advanced geostatistical and geological methods to enhance reservoir understanding and production strategies.

Philip GreulichView profile

Lecturer

• Mathematical Modelling of Stem Cells
• Cell Lineage Dynamics
• Cell Population Dynamics
• +3 more

Dr. Philip Greulich is a Lecturer in Applied Mathematics at the University of Southampton, specializing in mathematical modeling of biological systems. He is affiliated with the Institute for Life Sciences and the Mathematical Modelling research groups. His work focuses on understanding stem cell dynamics, cell lineage architectures, and population-level interactions using stochastic processes and dynamical systems theory. Education: PhD in Theoretical Physics from the Universities of Cologne and Saarland (2010). Postdoctoral research at the University of Edinburgh (2010–2012) and the University of Cambridge’s Cavendish Laboratory (2013–2016). Research Interests: Mathematical modeling of stem cell fate decisions Universal features of cell lineage structures Cell population dynamics under environmental constraints Bayesian model comparison with experimental data Teaching: Modules include MATH3090/6140 (Structure and Dynamics of Networks) and MATH3052 (Mathematical Biology). Grants & Awards: Recipient of the Alan Turing Fellowship (2021) and Cancer Research UK funding. Received the Blackboard VLE Award for Best Learner Support (2022). Lab/Team: Collaborates with the Applied Mathematics and Theoretical Physics groups, contributing to interdisciplinary projects at the interface of mathematics and biology.

Andrea BonitoView profile

Professor

• Numerical Analysis
• Partial Differential Equations
• Adaptive Finite Element Methods
• +3 more

Andrea Bonito is a Professor in the Department of Mathematics at Texas A&M University, affiliated with the College of Arts and Sciences. His research focuses on numerical methods for partial differential equations (PDEs), including adaptive finite element methods, free boundary problems, geometric PDEs, and non-Newtonian fluid dynamics. He explores topics like finite element approximations of thin structures, fractional operators, and optimal learning algorithms. His work emphasizes applications in material science and fluid dynamics, such as modeling bilayer plates, folding mechanics, and electroconvection of thin liquid crystals. Recent contributions include advancements in PINNs (Physics-Informed Neural Networks) for elliptic PDEs and gamma-convergent methods for large deformations. Bonito’s research bridges theoretical analysis with computational implementation, addressing challenges in high-dimensional approximation and incomplete information scenarios. His academic output spans over two decades, with notable publications on geometric PDEs, spectral fractional diffusion, and adaptive methods for elliptic problems. Bonito collaborates on interdisciplinary projects, leveraging numerical analysis to solve complex physical phenomena.

Julio Enrique Castrillon-CandasView profile

Academic

• Uncertainty Quantification
• Partial Differential Equations
• Integral Equations
• +5 more

Julio Enrique Castrillon-Candas is a Research Assistant Professor at Boston University's Department of Mathematics and Statistics within the College of Arts & Sciences. He is a prominent member of the Probability and Statistics research group, focusing on computational mathematics and uncertainty quantification. His academic journey includes a Ph.D. in electrical engineering and computer science from MIT. His research spans several interconnected fields including Uncertainty Quantification, Partial Differential Equations, Integral Equations, Fast Multi-Level Kriging for large spatial datasets, Fast Radial Basis Function Interpolation, and Machine Learning applications. His work bridges theoretical mathematics with practical computational approaches for solving complex problems in science and engineering. Analysis of his recent publications reveals a strong focus on developing efficient computational methods for uncertainty quantification, particularly through multi-level approaches to spatial statistics and stochastic collocation methods for PDEs with random domains. His research demonstrates a consistent progression from foundational mathematical theory to practical implementations for large-scale problems. Dr. Castrillon-Candas has secured significant funding including an NIH grant titled 'Stochastic Dynamic Modeling of Cellular Protein Interactions' (Award Number: 1R01GM131409-01, $323,280) and an NSF/DOE AMPS grant on 'Uncertainty Quantification for Stochastic Analysis of Electrical Power Networks' (Award Number: 1736392, $229,279). He maintains an active research collaboration network with prominent academics including Raul Tempone (KAUST), Fabio Nobile (EPFL), Marc G. Genton (KAUST), and Rio Yokota (Tokyo Institute of Technology). His work has been presented at numerous invited talks at prestigious institutions including Harvard University, Tufts University, and MIT.

Panos ParpasView profile

Associate Professor

• Mathematical Optimization
• Computational Mathematics
• Scientific Computing
• +3 more

Panos Parpas is a Reader in Computational Mathematics at the Department of Computing, Imperial College London. His research focuses on mathematical optimization algorithms, computational mathematics, and scientific computing, with applications in science, engineering, and finance. He previously held roles as a postdoctoral fellow at MIT and a quant at Credit Suisse. Key research interests include dynamical systems, numerical methods for optimization, and stochastic processes. He teaches courses such as Computational Optimization and Computational Finance with C++. His work bridges theoretical optimization with practical applications in areas like water distribution networks, financial modeling, and machine learning. Panos has authored numerous publications on topics like multilevel optimization methods, stochastic mirror descent algorithms, and the analysis of saddle points using Witten Laplacians. His research emphasizes algorithmic efficiency and scalability, particularly in high-dimensional and distributed settings. Notable collaborations include projects on optimizing water distribution systems (e.g., pump scheduling and pressure management) and developing robust algorithms for financial and engineering systems. His contributions span journals like Automatica , SIAM Journal on Scientific Computing , and Journal of Optimization Theory and Applications .

Eric Charles DykemanView profile

Senior Lecturer

• Mathematical Biology
• Virology
• RNA Structure Prediction
• +3 more

Dr. Eric Charles Dykeman is a Senior Lecturer in the Department of Mathematics at the University of York. His research bridges mathematical biology, virology, and computational science, focusing on RNA structure prediction, viral assembly mechanisms, and viral evolution. He develops computational tools to study RNA folding kinetics and employs systems approaches to understand viral life cycles and host interactions. Education: Ph.D. in Physics, Arizona State University (2008) Postdoctoral appointments: University of York (2008-2013) Fellowships: Leverhulme Trust Early Career Fellow (2014-2016) Research Interests: • Mathematical and biophysical modeling of viral assembly, particularly ssRNA viruses with icosahedral symmetry • RNA folding prediction algorithms and kinetic modeling of RNA structural transitions • Systems-level analysis of viral life cycles and temporal coordination of infection processes • Quasi-species theory and evolutionary models grounded in viral biology Selected Publications (Recent Trends): Recent works focus on mRNA gene circuits, ribosome kinetics modeling, and genome-regulated viral assembly. Key themes include computational approaches to RNA dynamics, protein-RNA interactions, and multiscale modeling of viral infections. Supervision: Offers computational-based Ph.D. projects in viral systems modeling and RNA secondary structure analysis.

Marina Iuliana KnightView profile

Professor

• Wavelet Multiscale Methods
• Nonstationary Time Series Analysis
• Long Memory Processes
• +6 more

Marina Iuliana Knight is a Professor of Statistics in the Department of Mathematics at the University of York. She holds a BSc in Mathematics from Bucharest, an MSc in Stochastic Processes and Theoretical Statistics from Bucharest, and a PhD in Statistics from the University of Bristol. Her research focuses on multiscale methods for nonstationary time series, with applications in energy, biology, and neuroscience. She has led projects such as the EPSRC-funded NeST programme grant and collaborates widely across disciplines, including with Prof Seth Davis (Biology), Dr Elena Geangu (Psychology), and Prof Guy Nason (Imperial College London). Current roles include Head of the Statistics Section and membership in the University Impact Leads for Science Departments group. Her work involves developing wavelet-based techniques for complex network data, with R packages like 'GNAR' and 'lpacf' available on CRAN. She has supervised numerous PhD students, including Maddie Shelley (current), Dingjia Cao (2024), and Jonathan Embleton (2021). Her research has addressed topics such as circadian rhythms, EEG/LFP data analysis, and wearable sensor integration for infant studies. She actively participates in international conferences and has authored 33+ peer-reviewed publications. Key achievements include advancing methods for locally stationary time series forecasting and network time series modelling. Her work bridges statistical theory with practical applications in medicine and environmental science, reflecting a commitment to interdisciplinary impact.

Brian Van KotenView profile

Assistant Professor

• Applied Probability
• Numerical Analysis
• Computer Simulation of Molecular Systems
• +4 more

Brian Van Koten is an Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. His research focuses on applied probability and numerical analysis, with applications to molecular systems simulation, variance reduction in statistical physics, and numerical methods for saddle point identification. He teaches courses such as Math 652 (Numerical Analysis II), emphasizing computational methods for partial differential equations and optimization in machine learning. Key research interests include computer simulations of biological and materials systems, rare event sampling techniques, and atomistic/continuum coupling methods. He has presented work at institutions like the Institute for Computational and Experimental Research in Mathematics (ICERM) and the Institute for Pure and Applied Mathematics (IPAM), focusing on topics like stratification for Markov chain Monte Carlo and the string method for minimum energy paths. Van Koten's work bridges theoretical numerical analysis with practical computational challenges, addressing issues in multiscale modeling and error analysis. His teaching philosophy integrates advanced numerical methods with real-world applications, supported by detailed course materials on topics like finite difference methods and optimization algorithms for large-scale data science problems.

Linda PetzoldView profile

Professor

• Computational Science and Engineering
• Systems Biology
• Bioengineering
• +7 more

Linda Petzold is the Mehrabian Distinguished Professor in Computer Science and Mechanical Engineering at the University of California, Santa Barbara (UCSB). Her roles span academic leadership, research, and interdisciplinary collaboration. She holds affiliations with the National Academy of Engineering, National Academy of Sciences, and multiple UCSB centers including the Institute for Collaborative Biotechnologies, Center for Bioengineering, and Center for Control, Dynamical Systems & Computation. Dr. Petzold earned her PhD and BA in Computer Science and Mathematics from the University of Illinois. Her research focuses on modeling, simulation, and software development for multiscale systems in biology, materials, and social networks. Notable projects include the Stochastic Simulation Service (StochSS), a cloud-based platform for biological modeling, and collaborations on topics like circadian rhythms, coagulopathy, and social sentiment analysis. She also leads work in computational neuroscience, including studies of neuronal activity in brain organoids. Her scientific contributions are recognized through prestigious awards, including the Sidney Fernbach Award and ACM Prize in Computational Science. Petzold’s research emphasizes bridging computational methods with experimental data, fostering interdisciplinary partnerships to address complex challenges in healthcare, ecology, and materials science. Her lab develops algorithms for stochastic systems, with applications ranging from trauma patient outcomes to fibrinolytic mechanisms. Publications highlight advancements in numerical methods, machine learning, and systems biology. Key themes include neural ordinary differential equations, prompt tuning for LLMs, and multimodal scientific datasets. Her work integrates computational tools with real-world biomedical and ecological problems, reflecting a commitment to translational research. Dr. Petzold advises students and collaborates extensively, with grants supporting projects like Bayesian neural networks for parameter inference and GAN-based imputation for medical data. Her leadership in academic and professional societies underscores her role as a visionary in computational science and engineering.

• Kinetic Models
• Fokker-Planck Equations
• Epidemic Dynamics
• +4 more

Mattia Zanella is a Researcher at the Department of Mathematics, University of Pavia, within the 'Methods and Models for Applied Sciences' group. His work focuses on kinetic theory, computational methods, and their applications to epidemic dynamics, opinion formation, and socio-economic systems. He develops mathematical models to study emergent phenomena in complex systems, with particular emphasis on uncertainty quantification and control strategies. Research interests include kinetic equations (Fokker-Planck, Boltzmann-type models), agent-based systems, and data-driven approaches for understanding collective behavior. His studies address topics such as epidemic spreading, wealth distribution, traffic flow, and social networks, often integrating stochastic processes and numerical analysis. Key contributions involve analyzing consensus dynamics, optimizing control mechanisms for epidemic models, and modeling the impact of social heterogeneity on disease transmission. His work bridges theoretical developments with practical applications, such as biomedical image segmentation and policy evaluation in public health. Recent publications emphasize the interplay between opinion dynamics and epidemic control, uncertainty quantification in kinetic equations, and the mathematical modeling of socio-economic systems. Zanella collaborates on interdisciplinary projects, applying advanced computational techniques to real-world problems in epidemiology, engineering, and urban studies.

• Optimal Control
• Partial Differential Equations
• Mean Field Games
• +6 more

Associate Professor at the University of Pavia's Department of Economics and Management, specializing in optimal control, partial differential equations, and mean field games with applications to economics and finance. Holds an Italian qualification (ASN) for associate professorship and a French qualification (maître de conférences). Research focuses on dynamic optimization, stochastic systems, and economic modeling of human capital, environmental quality, and pandemic impacts. Active in teaching mathematics for economics and optimization at undergraduate and graduate levels. Education: PhD in Mathematics from University of Padua (2015), postdocs at University of Graz (Austria) and University of Padua. Extensive visiting collaborations in France, Chile, and Brazil. Research Highlights: Pioneering work on mean field games modeling spatial interactions in human capital dynamics and environmental quality regulation. Contributions to sparse optimization, inverse problems in fracture mechanics, and large deviations in stochastic volatility models. Recent Publications Trends: Focus on infinite-dimensional mean field games (e.g., Hilbert spaces), pandemic economics, and environmental policy modeling. Over 15 peer-reviewed articles spanning optimal control, PDEs, and economic applications. Grants & Leadership: Coordinator of Italian GNAMPA projects on MFGs and environmental models. Organized conferences including AMASES 2022 and special sessions at international optimal control conferences. Referee for top journals like SIAM J. Control Optim. and J. Mathematical Economics. Teaching: Courses include 'Mathematics for Management,' 'General Mathematics,' and PhD-level 'Static and Dynamic Optimization.' Oversees tutoring programs and international collaborations via ERASMUS+.

Mitia DuerinckxView profile

Associate Professor

• Mathematical Physics
• Partial Differential Equations
• Stochastic Homogenization
• +7 more

Mitia Duerinckx is an Associate Professor in the Mathematics Department at Université Libre de Bruxelles (ULB), Belgium. He holds a joint appointment as CNRS Research Associate at Université Paris-Saclay (LMO), currently on leave since January 2022. His academic career includes positions as F.R.S.-FNRS Research Associate at ULB (2022-2024), Visiting Assistant Professor at UCLA (2020-2022), and various postdoctoral positions at ENS Lyon and ULB. Professor Duerinckx's research focuses on the role of microstructural disorder in mathematical physics, particularly in deriving effective equations and phenomenological laws of continuum physics rigorously from atomistic models, in the tradition of Hilbert's Sixth Problem. His work combines partial differential equations with tools from probability theory, with specific emphasis on particle systems, homogenization theory, scaling limits, and fluid-structure interactions. His current research is supported by an ERC Starting Grant (2023-2028) titled 'Scaling limits of particle systems and microstructural disorder' (PASTIS). His publication record demonstrates consistent contributions to the mathematical understanding of particle systems, homogenization, and effective properties of disordered media. Recent work includes rigorous derivations of effective sedimentation speed and viscosity for random suspensions, analysis of transport-Stokes models for sedimentation, and applications of hyperuniformity concepts to particle systems. His scientific achievements have been recognized through numerous awards including: ERC Starting Grant (2023) BMS Young Scholar Award (2021) Adolphe Wetrems Prize in Mathematics (2019) LMBP PhD Thesis Award (2019) Aguirre-Basualdo Prize (2018) Olbrechts-Tyteca PhD Thesis Award (2018) Professor Duerinckx actively supervises multiple postdoctoral researchers and research interns, currently including Fabrizio Caragiulo, Corentin Le Bihan, Matthieu Ménard, and Elise Bonhomme. He has established a productive research group focused on mathematical aspects of particle systems and microstructural disorder, supported by substantial research funding including the ERC grant and an ARC Advanced Grant 'Partial Differential Equations in Interaction'. He maintains an active presence in the international mathematical community through invited talks at major conferences and workshops, organization of research events, and service as a referee for leading mathematical journals.

Agnes Lamacz-KeymlingView profile

Researcher

• Analysis of PDEs
• Multiscale problems and homogenization
• Wave phenomena

Dr. Agnes Lamacz-Keymling is a researcher at the University of Duisburg-Essen in the AG Optimal Control of Partial Differential Equations. Her research focuses on analysis of PDEs, multiscale problems, homogenization, and wave phenomena. PhD in Mathematics (2011) and Diploma in Mathematics (2008) from TU Dortmund Her work spans homogenization of periodic structures, wave propagation in heterogeneous media, negative index meta-materials, and multiscale modeling. She has received third-party funding through a DFG project on wave propagation in periodic structures and negative refraction. Recent publications highlight trends in Bloch wave homogenization, dispersive wave models, and photonic crystal analysis. Her teaching includes Mathematics E3 and E4 in the winter semester 2021/22. Third-party funding: DFG project on wave propagation in periodic structures and mechanisms of negative refraction (2014)

• Genomics
• Epigenetics
• Regulatory Networks
• +5 more

Pengyu Ni is a Research Fellow at Yale School of Medicine, specializing in genomics and computational biology. His research focuses on understanding cis-regulatory elements, gene annotation, and regulatory networks across species and tissues. He contributes to large-scale projects like GENCODE and explores applications of machine learning in protein phase transition prediction and functional genomics. His work bridges experimental and computational approaches to uncover fundamental mechanisms in gene regulation and neurodevelopmental disorders. Recent studies include single-cell transcriptomic analysis of human brains, comparative genomic studies of regulatory elements in humans and mice, and improving methodologies for functional genomics assays. Ni’s research also addresses challenges in motif discovery, epigenetic state modeling, and validation of regulatory sequences. His findings advance our understanding of genomic regulation in health and disease contexts such as schizophrenia and autism spectrum disorders. No formal academic awards or grants are explicitly listed in the provided information. Collaborations involve multi-cohort transcriptomic dissections and interdisciplinary teams leveraging AI-driven approaches. While specific lab affiliations aren’t detailed, his work aligns with genomic and computational biology initiatives at Yale School of Medicine.