Discipline of Mathematical Sciences Colloquium 2025

Date: Fri, 14 Mar - Fri, 19 Dec 2025
Location: Napier G04 Lecture Theatre, North Tce campus (subject to change)
Contact: Dr Michael Albanese
Email: michael.albanese@adelaide.edu.au

Date	Speaker	Presentation
Friday 14^th March 2:10 to 3:00 pm	Dr Radu Cimpeanu	Interfacing with the real world: How to break mathematical models (and put them back together again) In this talk I will explore the interplay between classical - as well as modern - analytical techniques in applied mathematics and high performance computing. When not completely disjointed (or indeed one completely absent), these two approaches are often deployed with a verification-oriented mindset. By contrast, I will strive to convince you of the power of analytically-informed computational approaches (or indeed computationally-informed analytical methods) that draw elements from mathematical modelling, differential equations, asymptotic and complex analysis, and control theory. Interfaces will be the main theme of the talk not only as the intersection space between different methodologies, but also in view of the applications motivating this work, which include interfacial flow problems such as elucidating the dynamics of droplets, bubbles and liquid films in non-trivial (and highly non-linear!) regimes as the setup for hybrid technique development.
Friday 23^rd May 2:10 to 3:00 pm	Associate Professor Cheng Soon Ong	Recommending Actions with Machine Learning Using the lens of the scientific method, I unpack the main concepts behind machine learning. Machine learning (in its latest popular incarnation as AI) has been widely used to analyse data, but this talk will focus on the opposite problem: given knowledge about the world, what data should we measure next? By considering a practical problem in engineering biology, we will discuss how machine learning is used to successfully explore fitness landscapes in life sciences. The question of where to measure is mapped to machine learning algorithms such as active learning, bandits, and design of experiments. We will cover novel approaches to choosing where to measure, that we call active generation. The talk concludes with my opinions on opportunities and challenges in recommending actions.

Date

Speaker

Presentation

Friday 14^th March

2:10 to 3:00 pm

Dr Radu Cimpeanu

Interfacing with the real world: How to break mathematical models (and put them back together again)

In this talk I will explore the interplay between classical - as well as modern - analytical techniques in applied mathematics and high performance computing. When not completely disjointed (or indeed one completely absent), these two approaches are often deployed with a verification-oriented mindset. By contrast, I will strive to convince you of the power of analytically-informed computational approaches (or indeed computationally-informed analytical methods) that draw elements from mathematical modelling, differential equations, asymptotic and complex analysis, and control theory. Interfaces will be the main theme of the talk not only as the intersection space between different methodologies, but also in view of the applications motivating this work, which include interfacial flow problems such as elucidating the dynamics of droplets, bubbles and liquid films in non-trivial (and highly non-linear!) regimes as the setup for hybrid technique development.

Friday
23^rd May

2:10 to 3:00 pm

Associate Professor Cheng Soon Ong

Recommending Actions with Machine Learning

Using the lens of the scientific method, I unpack the main concepts behind machine learning. Machine learning (in its latest popular incarnation as AI) has been widely used to analyse data, but this talk will focus on the opposite problem: given knowledge about the world, what data should we measure next? By considering a practical problem in engineering biology, we will discuss how machine learning is used to successfully explore fitness landscapes in life sciences. The question of where to measure is mapped to machine learning algorithms such as active learning, bandits, and design of experiments. We will cover novel approaches to choosing where to measure, that we call active generation. The talk concludes with my opinions on opportunities and challenges in recommending actions.

School of Computer and Mathematical Sciences

Tagged in mathematical sciences