The paper’s problem of interest is the identification of an epidemic’s patient zero given a network of contacts and a set of infected individuals, under the assumptions that the infection states of only a few individuals are initially observed and the epidemic has evolved. To tackle this issue, they formulate the problem as an active querying problem and propose a number of active querying strategies inspired by active learning. Their results suggest that in the limited information scenario it is possible to achieve source inference performance comparable to when the infection states of all individuals are observed.

A paper based on controllable models for simplifying medical text, co-authored by our colleague Rosni Kottekulam Vasu and external collaborators, was accepted at the 37th AAAI Conference on Artificial Intelligence! The paper is titled “Med-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Texts” and was jointly conducted with Chandrayee Basu, Michihiro Yasunaga from Stanford University, and Qian Yang from Cornell University.

The paper’s vision is an interactive automatic medical text simplification system, which can enable medical practitioners and patients to simplify the contents of a text or conversation selectively and have controllability over the type of desired textual transformations.

Rosni presented the work at AAAI, orally and on a poster and was accepted to the 2023 AAAI student scholarship and volunteer program. Congratulations!

In order to facilitate and coordinate this evaluation campaign, we implemented a Web-based software infrastructure – dubbed Alan’s Speakeasy – that provides a graphical interface for human users to connect and have conversations in chatrooms and allows students to connect their conversational agents. Speakeasy also allows students to evaluate the conversational agents they talk to, using a survey that asks users to assess the accuracy of the conversational agents.   

We are honored to have been granted new funding by the UZH Teaching Fund via the innovation program that will allow us to extend the scope of Speakeasy. This project has a two-fold goal: Firstly, we would like to extend the implementation of the current software infrastructure – Speakeasy – to incorporate further features that will make the software more usable and more elaborate for running evaluation campaigns for conversational agents within the ATAI class.  Secondly, we would like to expand the scope of our software to reach a larger audience. Specifically, we would like to make our software useful for other organizations. We would like other research groups working on various aspects of AI (inside and outside UZH) to be able to reuse our software for teaching practical aspects of AI.