Find Academic Supervisors

Jianfeng LuView profile

Professor

• Mathematical Analysis
• Algorithm Development
• Computational Physics
• +5 more

Jianfeng Lu is the James B. Duke Distinguished Professor of Mathematics at Duke University, with affiliations in the Department of Mathematics, Chemistry, and Physics within the Trinity College of Arts & Sciences. He holds a Ph.D. from Princeton University (2009). His research focuses on mathematical analysis, algorithm development for computational physics, theoretical chemistry, materials science, and machine learning, particularly in high-dimensional PDEs, quantum dynamics, and multiscale modeling. Notable achievements include the 2024 James B. Duke Distinguished Professorship. His work spans interdisciplinary collaborations, with grants from the National Science Foundation (NSF) and Department of Energy (DOE), addressing challenges in quantum computing, electronic structure theory, and AI-driven material design. Research interests include quantum Markov processes, reinforcement learning theory, and numerical methods for PDEs. He advises numerous graduate students and postdocs, mentoring in applied mathematics and computational physics. His labs and teams contribute to projects like the Duke Materials Initiative and the ELSI collaboration for electronic structure theory.

Paul L BendichView profile

Adjunct Professor

• Topological Data Analysis
• Machine Learning
• Computational Topology
• +4 more

Paul L Bendich is an Adjunct Professor of Mathematics at Duke University's Trinity College of Arts & Sciences. He holds a Ph.D. from Duke University (2008). His research focuses on adapting topological and geometric methods for data analysis, particularly in topological data analysis (TDA). He has pioneered TDA methodologies for applications in machine learning, sensor fusion, and environmental modeling. Current appointments include leading research initiatives in multi-modal data analysis and reinforcement learning optimization. Key areas of expertise include computational topology, persistent homology, and topological signal processing. He teaches courses on topological data analysis (COMPSCI 434, MATH 412) and has developed educational programs like Data+ at Duke. Grants include NSF-funded projects (BIGDATA: F: DKA: CSD) and Air Force Office of Scientific Research initiatives. Recent work emphasizes topological methods in AI safety (topological parallax), reinforcement learning efficiency, and geophysical feature tracking. Professional activities include conference presentations on TDA applications and editorial work for journals. His research bridges theoretical mathematics with practical data-driven challenges in science and engineering.

Stefan M. WildView profile

Adjunct Professor

• Numerical Optimization
• Machine Learning
• Scientific Computing
• +3 more

Stefan M. Wild serves as Director of the Applied Mathematics and Computational Research (AMCR) Division and Senior Scientist at Lawrence Berkeley National Laboratory, while holding an adjunct faculty position in the Industrial Engineering and Management Sciences (IEMS) department at Northwestern University's McCormick School of Engineering. He also serves as a Senior Fellow at NAISE (Northwestern Initiative for AI and Society). Dr. Wild earned his Ph.D. and M.S. in Operations Research from Cornell University (2009, 2007) and his M.S. and B.S. in Applied Mathematics from the University of Colorado, Boulder (2003, 2002). His academic journey includes an Argonne Director's Postdoctoral Fellowship (2008-2010) and the DOE Computational Science Graduate Fellowship (2005-2008). His research program focuses on developing numerical optimization and automated learning algorithms for challenging science and engineering problems at interfaces involving computer simulations, complex data, and physical experiments. Dr. Wild leads multiple community software projects including BAND, parMOO, libEnsemble, deepHyper, NUCLEI, POptUS, and surmise, with applications spanning nuclear physics, materials science, and astrophysics. His work bridges derivative-free optimization, uncertainty quantification, high-performance computing, and scientific machine learning. Dr. Wild has advised numerous postdocs who have established successful careers at national laboratories and academic institutions. His editorial responsibilities include Mathematical Programming Computation, INFORMS Journal on Computing, Data Science in Science, and SIAM Review. U.S. Department of Energy Early Career Research Award (2020) STS Forum Future Leader (2018) IDC HPC Innovation Excellence Award (2015) SIAM SIGEST Award (2014) Strategic Laboratory Leadership Program (UChicago Booth School) (2013) DOE Computational Science Graduate Fellowship (2005-2008) As AMCR Division Director, he leads a diverse team of applied mathematicians, computational scientists, and software engineers addressing some of the world's most challenging computational problems across scientific and engineering disciplines. His leadership emphasizes both technical excellence and commitment to inclusion, diversity, equity, and accountability in scientific research.

James H. NolenView profile

Professor

• Probability
• Partial Differential Equations
• Stochastic Dynamics
• +3 more

James H. Nolen is a Professor of Mathematics at Duke University's Trinity College of Arts & Sciences. He holds a Ph.D. from the University of Texas, Austin (2006) and a B.S. from Davidson College (2000). His research focuses on probability, partial differential equations, and stochastic dynamics, with applications to biological models and reaction-diffusion systems. Notable awards include the 2025 Bass Chair and an NSF Postdoctoral Fellowship (2006-2008). Leadership: Directed Duke's Mathematics Postdoctoral Training Program (2019-2023) Grants: Co-PI for NSF RTG grant on Analysis and Applications (2021-2026), PI for NSF CAREER award on stochastic dynamics (2014-2020) His work bridges theoretical analysis with applications, including studies on random walks, interacting particle systems, and front propagation in heterogeneous media. He serves as an Associate Editor for SIAM Journal on Mathematical Analysis and SIAM Multiscale Modeling and Simulation.

• 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.