AI, ML and Friends is a weekly seminar series within the School of Computing on Artificial Intelligence, Machine Learning, and related topics. We are open to attendees and presenters external to the school. Please sign up to the mailing list to receive weekly announcements including zoom details, and email the seminar organiser to schedule a talk.

Upcoming Seminars

29 June 2026, 13:00#

Action Model Learning from Lexicons#

Speaker: Siqi Cheng

Abstract: In this talk, Siqi will present research focusing on action model learning and its connection to narrative planning. The presentation is divided into three parts, explaining how to automatically learn action models from VerbNet to ultimately facilitate narrative planning. First, Siqi will present a rule-based system that maps VerbNet annotations to formal planning preconditions and effects. Second, the talk will cover the limitations of VerbNet in action modelling, and how to utilise information learned from natural language to resolve those limitations. Finally, Siqi will outline future plans for this research, particularly regarding its links to practical narrative planners and more complex story generation.

Bio: Siqi is a first-year MPhil student at the ANU School of Computing, focusing on action model learning, particularly from existing lexicons. Siqi’s research currently encompasses information extraction from lexicons and natural language, as well as surveys of action model representation, with the aim of connecting existing lexicons to practical narrative problems to advance the scalability and complexity of narrative planning.

Where: Building 145, room 3.41

01 July 2026, 11:00#

Algorithmic advances for biological sequence analysis: from locality-sensitive hashing to transcript assembly#

Speaker: Prof. Mingfu Shao

Abstract: Genomic and transcriptomic sequences have become fundamental to modern biology and medicine. In this seminar, I will present two lines of our work that address major challenges in biological sequence analysis. The first focuses on the design of locality-sensitive hashing (LSH) functions for the edit distance, a central problem in comparing biological sequences that contain mutations and sequencing errors. I will introduce SubseqHash and SubseqHash2, in which a sequence is hashed to its minimized subsequences; to make this work, we made a major algorithmic innovation that overcomes the challenge posed by the exponential number of subsequences. As constructing LSH functions for edit distance is known to be difficult, we generalize it to locality-sensitive bucketing (LSB) functions, which allow a sequence to be mapped to multiple values. We proved that optimal LSB functions exist and designed LSB functions using combinatorial optimization and machine learning. In the second part of the talk, I will introduce our work on reconstructing full-length linear and circular RNAs from diverse RNA-seq data, a problem known as transcript assembly. I will first present its mathematical formulation and our work for solving it. I will then focus on the suite of practical assemblers we have developed (e.g., Scallop, Scallop2, Aletsch, Beaver, TERRACE, and EquiRep) and highlight the new algorithms behind them; these methods leverage key signals in RNA-seq data, such as paired-end reads, barcode-linked reads, and information from multiple samples, to improve the accuracy of transcript reconstruction.

Bio: Mingfu Shao is an Associate Professor in the Department of Computer Science and Engineering at The Pennsylvania State University. He received his PhD in Computer Science from the École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, in 2015. Following that he pursued postdoctoral training as a Lane Fellow at Carnegie Mellon University before joining Penn State in 2018. Mingfu and his research group work on a variety of topics in algorithmic computational biology. One line of their work is the design of locality-sensitive hashing algorithms for edit distance, enabling efficient comparison of large-scale sequencing data with high error and mutation rates. They also develop algorithms and tools for assembling full-length transcripts from RNA-seq data; examples include Scallop and Scallop2 for single-sample RNA-seq assembly, TERRACE and EquiRep for circular RNA assembly, and Aletsch and Beaver for multi-sample transcript assembly. Mingfu is a recipient of the Dimitris N. Chorafas Foundation Award (2015) and the US NSF CAREER Award (2022).

Where: Building 145, room 3.41

03 September 2026, 11:00#

Preaching to the converted. Tailoring large language model dialogue to differentiate idea endorsement and system evaluation.#

Speaker: Prof. Giles Hirst

Abstract: Large language models (LLMs) are not only a relevant persuasive tool for politics, conspiracy theories, and contested social issues, but also for ‘big business’. We examine how LLMs monetize and persuade in interactions, impacting (a) perceptions and support of new ideas (message) and (b) attitudes toward the Artificial Intelligence (AI) system (messenger). We theorize and support a two-sided commercial transaction: an LLM “speaking to” a user’s moral values lead users to evaluate both message and messenger more positively: they are more likely to endorse, use, and pay for a novel concept and they perceive the LLM understands them better. Morally congruent framing sells the object as well as enhances perceptions of the user-AI relationship. Crucially, for message recipients, this effect is most pronounced for those with more polarized political identities, addressing who is most malleable through AI influence.

Bio: Giles Hirst is Professor of Leadership at the Research School of Management, The Australian National University, and Fellow at the Judge Business School, University of Cambridge. An organisational psychologist by training, he completed his PhD at the Melbourne Business School. Developing creative ideas is one of mankind’s greatest gifts, and this is one of Giles main interests helping individuals and leaders unlock their creativity in partnership with technology. Giles’ research examines leadership, creativity, and innovation—particularly how leaders and employees can harness technology to enhance creativity and inclusion at work. His work also explores social impact themes such as improving refugee employment outcomes and addressing precarious work. He has published widely in top management journals including the Academy of Management Journal, Academy of Management Review, Leadership Quarterly, Journal of Management, and Journal of Applied Psychology. Recognised among the world’s top 2% most-cited scholars, Giles serves as Associate Editor of the Journal of Organizational Behavior and Consulting Editor for the Journal of Applied Psychology. An award-winning educator, Giles has received Vice-Chancellor’s Awards for Teaching Excellence and led leadership programs with government and commerce. He brings rich industry experience, having worked as a management consultant, served on not-for-profit social housing boards, and commercialised new business ventures. His programs help leaders connect with purpose, use their strengths, and achieve greater impact.

Where: Building 145, room 1.33

24 September 2026, 11:00#

Informed Machine Learning and Explainability for Binary Image Processing via Mathematical Morphology#

Speaker: Dr. Diego Marcondes

Abstract: The explainability of machine learning methods has recently become an issue in virtually all domains of application, and important research lines aiming to explain high-performance black boxes have been explored within the explainable AI initiative. Alternatively, this talk presents a different line of research focused on developing fully interpretable models that can evolve into high-performance methods. In particular, we will discuss how informed machine learning, characterised by the insertion of strong domain knowledge into the model design, can be leveraged to obtain interpretable methods. The main concepts will be discussed in the context of binary image processing and recent developments in mathematical morphology, in particular the discrete morphological neural networks.

Bio: Diego Marcondes earned a BS in Statistics and a PhD in Applied Mathematics from the University of São Paulo, Brazil. He is currently a research fellow at the Mathematical Data Science Centre, Mathematical Sciences Institute, The Australian National University and member of the IRL FAMSI. Previously, he was a postdoc at the Computer Science Department, Institute of Mathematics and Statistics, University of São Paulo (2022-2024), and a visiting postdoctoral scholar at the Department of Electrical and Computer Engineering, Texas A\&M University (2023-2024). His research interests in data science are at the intersection of probability theory, statistics, applied mathematics and computer science. In particular, he is interested in developing learning methods with a strong mathematical basis which do not only have a high performance, but are controllable and interpretable, with applications to scientific problems, and signal and image processing.