9:00 – 9:10 Welcome and Overview
9:10 – 10:30 Session 1

5 Student Presentations (12 min + 3 min Q&A)

A Framework for Participatory Budgeting with Resource Pooling
(Jeremy Vollen, UNSW Sydney)

Using Liquid Democracy For Attention Aware Social Choice
(Shiri Alouf Heffetz, Ben Gurion University)

Reliable Neuro-Symbolic Abstractions for Planning and Learning
(Naman Shah, Arizona State University)

Interpretability and Fairness in Machine Learning: A Formal Methods Approach
(Bishwamittra Ghosh, National University of Singapore)

Predictive Modelling of Human Reasoning Using AGM Belief Revision
(Clayton K Baker, University of Cape Town)
10:30 – 11:00 Coffee Break
11:00 – 12:30 Invited Talk

How to Speak to the Public about Artificial Intelligence
Michael Wooldridge (University of Oxford)
12:30 – 14:00 Lunch Break
14:00 – 15:30 Session 2

5 Student Presentations (12 min + 3 min Q&A)

Human-Centred Multimodal Deep Learning Models for Chest X-Ray Diagnosis
(Chihcheng Hsieh, Queensland University of Technology)

Object Detection in Real Open Environment
(Xiaowei Zhao, Beihang University)

AI Techniques for Urban Traffic Control and Mobility
(Saumya Bhatnagar, University of Huddersfield)

Exploring Multilingual Intent Dynamics and Applications
(Ankan Mullick, IIT Kharagpur)

Argumentation for Interactive Causal Discovery
(Fabrizio Russo, Imperial College London)
15:30 – 16:00 Coffee Break
16:00 – 17:30 Career Panel

Pin-Yu Chen (IBM Research AI)
Kate Larson (University of Waterloo)
Toby Walsh (University of New South Wales)
(more career panelists will be confirmed later)
17:30 – 17:40 Closing Remarks

Invited Talk

Michael Wooldridge (University of Oxford)

Since everything went crazy in AI, around 2012, I, like many other members of our community, have frequently found myself put in the position of having to talk about our field to a non-specialist audience. I’ve been interviewed on TV and radio, and spoken to endless university committees, government committees, and industrial conferences. More recently, following the publication of my two popular science books (the Ladybird Expert Guide to AI [2018], and The Road to Conscious Machines [2020]), I’ve even begun speaking at a literary festivals (believe me, I never expected to be doing this as a PhD student studying multiagent systems back in 1989). In this talk, I will relate these experiences, the mistakes I made, and what I learned from them – how our field is perceived, what people fear, hope, and expect from it, and how best to communicate excitement about the very real progress we’ve made recently with a realistic understanding of where we are and where we are going.
About the invited speaker
Michael Wooldridge is a Professor of Computer Science at the University of Oxford, and a programme director for AI at the Alan Turing Institute. He is a Fellow of the ACM, the Association for the Advancement of AI (AAAI), and the European Association for AI (EurAI). From 2014-16, he was President of the European Association for AI, and from 2015-17 he was President of the International Joint Conference on AI (IJCAI). As well as more than 400 technical articles on AI, he has published two popular science introductions to the field: The Ladybird Expert Guide to AI (2018), and The Road to Conscious Machines (Pelican, 2020).

Career Panel

Pin-Yu Chen (IBM Research AI)
Pin-Yu Chen is a principal research scientist at IBM Thomas J. Watson Research Center, USA. He is also the chief scientist of RPI-IBM AI Research Collaboration and PI of ongoing MIT-IBM Watson AI Lab projects. Pin-Yu Chen received his Ph.D. in electrical engineering and computer science from the University of Michigan in 2016. His recent research focuses on adversarial machine learning of neural networks for robustness and safety. His long-term research vision is to build trustworthy machine learning systems. He received the IJCAI Computers and Thoughts Award in 2023. He is a co-author of the book “Adversarial Robustness for Machine Learning”. He has published more than 50 papers related to trustworthy machine learning at major AI and machine learning conferences.

Kate Larson (University of Waterloo)
Kate Larson is a Professor at the Cheriton School of Computer Science at the University of Waterloo. She also currently hold a University Research Chair and the Pasupalak AI Fellowship. She completed her Ph.D in Computer Science at Carnegie Mellon University. Kate is interested in issues that arise in settings where self-interested agents interact, where these agents may be AI-agents, humans, or a combination. In particular, she is interested in understanding how computational limitations influence strategic behavior in multiagent systems, as well as developing approaches to address computational issues which arise in practical applications of multiagent systems.

Toby Walsh (University of New South Wales)
Toby Walsh is an ARC Laureate Fellow and Scientia Professor of AI at UNSW and CSIRO Data61. He is Chief Scientist of UNSW.AI, UNSW’s new AI Institute. He is a strong advocate for limits to ensure AI is used to improve our lives, having spoken at the UN, and to heads of state, parliamentary bodies, company boards and many others on this topic. This advocacy has led to him being “banned indefinitely” from Russia. He is a Fellow of the Australia Academy of Science, and was named on the international “Who’s Who in AI” list of influencers. He has written three books on AI for a general audience, the most recent is “Machines Behaving Badly: the morality of AI”.

List of mentors

  • Piotr Faliszewski (AGH University)
  • Tim Miller (The University of Queensland)
  • Panagiotis Kouvaros (Imperial College London)
  • Markus Brill (University of Warwick)
  • Julian McAuley (University of California, San Diego)
  • Parisa Kordjamshidi (Michigan State University)
  • Lirong Xia (Rensselaer Polytechnic Institute)
  • Peter Stone (University of Texas at Austin)
  • Maria Gini (University of Minnesota)
  • Debasis Ganguly (University of Glasgow)
  • Matthew E. Taylor (University of Alberta)

List of reviewers

  • Ann Nowé (Artificial Intelligence Lab Brussels)
  • Arthur Choi (Kennesaw State University)
  • Bo Li (University of Illinois at Urbana-Champaign)
  • Christopher Amato (Northeastern University)
  • Claude-Guy Quimper (Université Laval)
  • Daniel Le Berre (Université d’Artois)
  • David V. Pynadath (USC Institute for Creative Technologies)
  • Diana F. Adamatti (Universidade Federal do Rio Grande)
  • Dominique Longin (CNRS)
  • Edith Elkind (University of Oxford)
  • Fei Fang (Carnegie Mellon University)
  • Francesco Amigoni (Politecnico di Milano)
  • Francesco Ricci (Free University of Bozen-Bolzano)
  • Frans A. Oliehoek (Delft University of Technology)
  • Georgina Stegmayer (Universidad Nacional del Litoral)
  • Hanghang Tong (University of Illinois at Urbana-Champaign)
  • Haris Aziz (UNSW Sydney)
  • Harko Verhagen (Stockholm University)
  • Jan Ramon (INRIA)
  • Jérôme Lang (CNRS)
  • Jiwen Lu (Tsinghua University)
  • Judy Goldsmith (University of Kentucky)
  • Kagan Tumer (Oregon State University)
  • Kate Larson (University of Waterloo)
  • Lirong Xia (Rensselaer Polytechnic Institute)
  • Makoto Yokoo (Kyushu University)
  • Maria Gini (University of Minnesota)
  • Markus N. Rabe (UC Berkeley)
  • Mohamed Siala (LAAS-CNRS @ INSA Toulouse)
  • Mohan Sridharan (University of Birmingham)
  • Nathan R. Sturtevant (University of Alberta)
  • Nicola Basilico (Università degli Studi di Milano)
  • Ofer Arieli (Tel-Aviv Academic College)
  • Paolo Torroni (University of Bologna)
  • Parisa Kordjamshidi (Michigan State University)
  • Piotr Faliszewski (AGH University)
  • Pradeep Varakantham (Singapore Management University)
  • Samarth Swarup (University of Virginia)
  • Sandip Sen (University of Tulsa)
  • Silvia Schiaffino (UNICEN)
  • Stephen Cranefield (University of Otago)
  • Stephen Guy (University of Minnesota)
  • Tim Miller (The University of Queensland)
  • Ulle Endriss (University of Amsterdam)
  • Wangmeng Zuo (Harbin Institute of Technology)