CSE Seminars - School of Computational Science & Engineering - Faculty of Science

News

We hope to see you all in our upcoming events. Please see seminar announcements for locations.

-Seminar chairs

CSE Seminars, The Goals

The Scientific Computing Seminar is an interdisciplinary, student-oriented event which serves as a venue for dissemination of information about various aspects of scientific computing such as:

applications of scientific computing to various disciplines, including some non-traditional ones,
technical aspects of high-performance computing,
tutorials concerning practical aspects of computing (hardware/software issues, code development, parallelization, debugging, etc.).

The selection of topics highlights both the breadth and depth of research in scientific computing at McMaster.

All Fall 2025 Seminars will be held from 12:30pm – 1:30pm in Hamilton Hall 410 on the following dates.

Upcoming CSE Seminars

CSE Seminar: November 26, 2025

Speaker: TBD
When: November 26th, 2025
Time: 12:30 -13:20pm
Where: Hamilton Hall, 410

Past Seminars

CSE Seminar: Menu Optimization for Meal Delivery Platforms

Speaker: Dr. Sheng Liu
When: October 1st, 2025
Time: 12:30 -13:20pm
Where: Hamilton Hall, 410

CSE Seminar: October 29, 2025
Speaker: TBD
When: October 29th, 2025
Time: 12:30 -13:20pm
Where: Hamilton Hall, 410

CSE Seminar: November 12, 2025
Machine learning methods for sensitivity analysis of climate-economic models.

Speaker: Daniel Presta
When: November 12th, 2025
Time: 12:30 -13:20pm
Where: Hamilton Hall, 410

Abstract: Large scale integrated climate-economic models typically involve numerous underlying parameters with different amounts of uncertainty, some arising from econometric estimates using historical data, some arising from experimental measurement of physical and atmospheric relationships. Because of the intrinsic nonlinear nature of these models, it is often impossible to employ traditional methods for local sensitivity analysis based on comparative statics to understand the effect of a particular parameter on the outcome of the model. Conversely, the high computational cost of the models makes it impractical to use simulation-based methods for global sensitivity analysis. Accordingly, we explore the use of machine learning methods to understand and quantify the influence of multiple parameters, taking full account of nonlinearities, while still being computationally feasible. We illustrate the techniques in the context of an existing stock-flow consistent climate-economic model.

Bio: Daniel Presta is a fourth year PhD Candidate at McMaster University, researching climate-economic modelling under the supervision of Dr. Matheus Grasselli.