Consulting service
We would like to offer you, free of charge, a piece of advice on many aspects associated with Computational Mathematics as well as Scientific Computing in general. We are a group of graduate students and postdoctoral fellows who are passionate about computational aspects of many scientific and engineering disciplines. If you have a problem, for instance, with the implementation of sophisticated boundary conditions in your finite-element-based solver, or maybe you wondering which linear system algorithm would be the most effective for your simulation, then we invite you to contact one of our tutors or simply send an email to siam@math.mcmaster.ca. In the below table, you can find the list of our tutors that might help in solving your problem. If you are looking for more advanced expertise in some subfield, for example you are confused about your eigenvalues coming from a sophisticated system of elliptic PDEs, then please send an email before visiting us so we could arrange office hours with an appropriate tutor. However, if your question involves some basics of numerical analysis, programming languages or other related aspects, you are welcome to come by in the indicated time.
Please keep in mind that we are not here to solve your homework or course projects for numerical analysis-related courses, which does not mean that we must not discuss the problems you are struggling with!
Tutors
| Name | Areas of expertise | Environment |
| John Ernsthausen | Text Based Computing, Static Websites, Content Managemnet, Search Engine Optimization, Data Coordination, Data Management, Numerical Methods for ODEs and DAEs, and more. | Ruby, C/C++ |
| Pritpal 'Pip' Matharu | Numerical Methods for ODEs and PDEs, Computational Fluid Dynamics, Spectral Methods, Finite Difference Methods, High Performance Computing: OpenMP, MPI, CUDA, GPU Computing | MATLAB, CUDA, C |
| Carlos Hinrichsen Picand | Data Science, Statistical Learning, Clustering Algorithms, Dynamic Optimization, Simulation, Statistical Methods, Mathematical and Financial Modelling | R, Python |
| Jonathon Riddell | Numerical linear algebra, computational methods for quantum and classical statistical mechanics (Monte Carlo, tensor networks and exact methods), MPI | Mathematica, C++ |
| Geoffrey Woollard | Image analysis, especially for transmission electron microscopy, Fourier transforms (DFT, FFT), Numerical linear algebra, Inverse problems, Basics of Parallel Programming in Python | Python (numpy, scipy, pandas, numba) |
| Dewan F. Wahid | Large scale data processing, action-oriented machine learning models, designing and delivering data-driven solutions to complex business problems. Solid background in natural language processing, large-scale optimization, operations research, and network sciences. | Python, Java, SQL, MATLAB |