Past Events

Speaker: Jack Hughes, (Concordia University) Title: Travelling waves and wave pinning (polarity): Switching between random and directional cell motility Abstract: 

Speaker: Dr. Mufan Li Affiliation: University of Waterloo Title: The Proportional Scaling Limit of Neural Networks 

Speaker: Ed Thommes Title:  Long-range forecasting of seasonal influenza vaccine uptake using web search data Abstract:

Speaker: Richard Zhao (McMaster University) Title: Improving infectious disease prevalence estimation and parameter inference using number of tests and positivity data

Speaker: Benjamin Benteke Affiliation: PhD student at University of Guelph Title: Expanding optimization ensemble model methods for forecasting seasonal influenza in the U.S.

Speaker: Jenny Lawson, Univ. Calgary. Title: Resource Driven Dispersal Models with Harvesting  Abstract: 

Speaker: Sarah Hagen (University of Illinois at Urbana-Champaign)  Title: Quantum Secret Sharing with Three and Four Qubits

Speaker: Dr. Guanru Feng (Senior Scientist at SpinQ technology Inc.) Title: Desktop quantum computing platform for education Abstract: 

Speaker: Dr. Warren Hare Title:  Cosine Measures and Derivative Free Optimization Abstract:  A positive spanning set (PSS) is a set that contains vectors pointing into every half-space. This provides the exact property required to ensure convergence for certain derivative free optimization algorithms. The quality of a PSS can be studied via the cosine measure. In this talk, we discuss the cosine measure, what it captures, how to compute it, and some of the remaining challenges linked to its mathematics.

Abstract:  Different teams read generative AI differently: IT thinks platform, Legal thinks risk, Customer Service thinks fewer tickets. Data scientists bring another lens: decisions that rely on models need validation, uncertainty estimates, bias checks, and monitoring in production. LLMs are also strong NLP tools: they can turn call transcripts, service notes, and workflow logs into structured features. Because we care about decision quality and data pipelines, data scientists are well placed to help shape enterprise AI strategy.