AIML 3-minute thesis competition (Practice Round, Batch 2)

Date: 7th December 2021
Time: 14:00-16:00
Location: Hybrid Meeting (G.07)
Talks

Title: Talking About Resources
Speaker: Filip Smola
Abstract:
Resources control what you do, be it in what you are working with or what you are working towards. My project is to make a computer understand what we mean by these resources, so that together we can better understand the processes they control.

Title: Data Terms of Use, Now Automated and Forever
Speaker: Rui Zhao
Abstract:
Dealing with data Terms of Use (DToU) is often a shadow of modern data-processing activities, inherited from the biggest lie on the Internet — I have read and agree to… My research proposes a novel machine-understandable language to model the DToU, to allow machines to automatically check the compliance of most data-processing activities, reducing the burdens for humans. The language is based on formal logic, taking advantages of accountability and extensibility. Last but not least, this framework acknowledges that processes can change DToU for each output data based on those for input data, and makes the compliance reasoning sustainable by deriving DToU for output data.

Title: Here be Dragons – Navigating Unfamiliar Mathematics using Networks
Speaker: James Vaughan
Abstract:
Deep learning fact selectors will learn specific representations of mathematical features to make suggestions, which need to be updated as users define new constants, types, and theories. Instead, by representing formal mathematics as a network, we can develop models that are fitted only to the network structure and generalise to any new features.

Title: Proactive Side Effect Prediction: Using AI to Race Against Time
Speaker: Lauren DeLong
Abstract:
Imagine going to the doctor to treat an eye infection, then ending up with itchy hives, or going for pain relief, but now your medicine causes stomach cramps! Such side effects can upset patients, dampen trust in doctors, and cost medical companies loads of money. To predict these side effects before they happen, we used network prediction methods, similar to those which generate friend recommendations for you on social media. Novel predictions can help to identify harmful side effects before a patient like you might experience them.

Title: Trusting the Transfer: From Scotland to the Antipodes
Speaker: Jake Palmer
Abstract:
Single Transferable Vote (STV) is a family of algorithms for counting ranked ballots in multi-winner elections, typically carried out by hand. We verify using a general characterisation of STV that, regardless of the existing or not-yet-existing variant used, it is correct and terminates. This extends to covering Meek’s method of STV — a computer-counting variant that relies on the convergence of a vector under iteration of a specific function — used in several places including some elections in New Zealand.

Title: You Can Survive the Maze of Death
Speaker: Mark Chevallier
Abstract:
Every turn you take in the maze of death might lead to fortune or disaster. And you don’t know which way to go! But we can prove, beyond any doubt, that by following some simple rules, you will be able to learn the absolute best way to navigate the maze. Want to know the rules? Better listen to the talk.

Title: Foundations of Physics
Speaker:  Richard Schmoetten
Abstract:
The physical theories describing the subatomic world have been experimentally verified to famously high degrees of accuracy. Yet conceptual problems remain: in fact, it is doubtful that the standard formulation of these theories is entirely well-defined. I aim to study one candidate remedy to these troubles, the Haag-Kastler axioms, and investigate well-founded models of reality with the help of a proof assistant.

Title: Follow the Patient Flow
Speaker: Petros Papapanagiotou
Abstract:
Hospital staff often work under a lot of pressure and have to adhere to ten of pages of policies and guidelines. They often have to improvise their workflow which leads to errors and delays that put patients at risk. Our research aims towards smart systems to model and manage patient flows, improve safety and lead to better, more consistent care.