The following speakers have graciously agreed to give public lecture, keynotes, and panel discussion at ALTA 2026. Other speakers will be anounced soon!

Prof. Eduard Hovy - Executive Director of Melbourne Connect, University of Melbourne, School of Computing and Information Systems

Exploring the Representation Space of LLMs

Abstract: We know that LLMs can be trained to capture simple facts, images, inference rules, generalized concepts, sentiment values, formatting, stylistic features, social/pragmatic features, and a host of other abstractions, many of which we can only vaguely articulate. How is all this represented? Is the representation space ‘linear’ (whatever precisely that means), as the Linear Representation Hypothesis postulates? Or is it approximately isotropic, or mainly linear but locally anisotropic in spots? Given that concepts are represented as complex patterns in LLMs, and given that multiple concept facets superpose within a neuron, it is impossible to even identify exactly where and how simple concepts are represented, or to easily perform effective ablation. So how can one investigate these questions? And further, how would one prove that one’s claims are valid? I describe some explorations that students, colleagues, and I have been conducting over the past 2 years.

Biography

Eduard Hovy is the Executive Director of Melbourne Connect (a research and tech transfer centre at the University of Melbourne), a professor at the University of Melbourne’s School of Computing and Information Systems, and an adjunct professor at the Language Technologies Institute in the School of Computer Science at Carnegie Mellon University. In 2020–22 he served as Program Manager in DARPA’s Information Innovation Office (I2O), where he managed programs in Natural Language Technology and Data Analytics. Dr. Hovy completed a Ph.D. in Computer Science (Artificial Intelligence) at Yale University in 1987 and was awarded honorary doctorates from the National Distance Education University (UNED) in Madrid in 2013 and the University of Antwerp in 2015, as well as several other international awards. He is one of the initial 17 Fellows of the Association for Computational Linguistics (ACL) and is also a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI).

Dr. Hovy’s research focuses on computational semantics of language and addresses various areas in Natural Language Processing and Data Analytics, including in-depth machine reading of text, information extraction, automated text summarization, question answering, the semi-automated construction of large lexicons and ontologies, and machine translation. In early 2026 his Google h-index was 113, with over 75,000 citations.

Dr. Hovy is the author or co-editor of eight books and over 400 technical articles and is a popular invited speaker. He regularly co-taught Ph.D.-level courses and has served on Advisory and Review Boards for both research institutes and funding organizations in Germany, Italy, Netherlands, Ireland, Singapore, and the USA.

From 2003 to 2015 he was co-Director of Research for the Department of Homeland Security’s Center of Excellence for Command, Control, and Interoperability Data Analytics, a distributed cooperation of 17 universities. In 2001 Dr. Hovy served as President of the international Association of Computational Linguistics (ACL), in 2001–03 as President of the International Association of Machine Translation (IAMT), and in 2010–11 as President of the Digital Government Society (DGS).

Distinguished Professor Karin Verspoor FTSE FAIDH - Executive Dean, School of Computing Technologies, RMIT University

In the post-LLM world, does structured knowledge still have a role in science?

Abstract: LLMs have absorbed vast volumes of scientific knowledge captured in digital artefacts including publications, ontologies, and databases, and threaten to disrupt disciplines such as bioinformatics which have traditionally relied on curated, structured knowledge. Models which rely on implicit knowledge representation through complex, data-driven mathematical models, and which reproduce knowledge through language, stand in stark contrast to carefully constructed, curated information captured through relational structures and formal semantics. In this talk, I will explore the tensions this creates, while identifying connections between these apparently opposing paradigms. This will lead to consideration of why and how science might benefit from bringing the two paradigms closer together.

Biography

Distinguished Professor Karin Verspoor is Executive Dean of the School of Computing Technologies at RMIT University in Melbourne, Australia. She is a Fellow of the Australian Academy of Technological Sciences and Engineering, a Fellow of the Australasian Institute of Digital Health, and a 2021 “Brilliant Woman in Digital Health”. Her project “EINSTEIN AI” was selected as a “community impact” finalist in the 2026 Australian Financial Review awards, and she was also selected as a finalist in the Women in AI Australia/New Zealand Awards 2022 for “AI in Innovation”. Karin is passionate about using artificial intelligence to enable biological discovery and clinical decision support from data. Her work has a specific emphasis on the use of natural language processing to transform unstructured data in biomedicine into actionable information.

Karin held previous posts as Director of Health Technologies and Deputy Head of the School of Computing and Information Systems at the University of Melbourne, as the Scientific Director of Health and Life Sciences at NICTA Victoria Research Laboratory, at the University of Colorado School of Medicine, and at Los Alamos National Laboratory. She also spent 5 years in tech start-ups during the US Tech bubble, where she helped design an early artificial intelligence system. Karin received a BA with a double major in Computer Science and Cognitive Sciences from Rice University in Houston, TX, USA, and completed both a MSc and PhD in Cognitive Science and Natural Language at the University of Edinburgh, UK.

Distinguished Professor Mohit Bansal - Director of the MURGe-Lab, Core AI Lead of ENGAGE NSF-AI Institute, UNC Chapel Hill

AI Challenges: Trustworthy Collaboration, World Discovery, and Long-Horizon Memory

Abstract: In this talk, I will discuss 3 major pitfalls and challenges of current state-of-the-art AI agents, and present potential solutions for: (1) Teaching agents to be trustworthy and reliable collaborators based on: social/pragmatic multi-agent interactions via speaker-listener confidence calibration, learning to balance positive and negative persuasion, and multi-agent AI safety through the lens of compositional attacks, theory-of-mind, and belief-steering; (2) Discovering and improving skills/world models needed for efficient, robust action and collaboration based on: learning programmatic skills, weakness-driven adaptive data/environment generation for skill improvement, and structured, selective world model discovery and inference; (3) Planning of long-horizon memory for multi-step reasoning and generation over continuously evolving, conflicting, and scattered information. We will cover diverse domains (math, commonsense, coding, tool-use, computer use, etc.), modalities (text, images, videos, audio, layouts, etc.), and real-world applications (early medical diagnosis and classroom education engagement).

Biography

Dr. Mohit Bansal is the John R. & Louise S. Parker Distinguished Professor, Director of the MURGe-Lab (UNC-AI Group), and Core AI Lead of the ENGAGE NSF-AI Institute in the Computer Science department at UNC Chapel Hill. He received his PhD from UC Berkeley and his BTech from IIT Kanpur. His research expertise is in multimodal generative models, reasoning and planning agents, faithful language generation, and interpretable, efficient, and generalizable deep learning. He is an ACL and AAAI Fellow and recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE), IIT Kanpur Young Alumnus Award, DARPA Director’s Fellowship, NSF CAREER Award, Google Focused Research Award, Microsoft Investigator Fellowship, Army Young Investigator Award (YIP), DARPA Young Faculty Award (YFA), and outstanding paper awards at ACL, CVPR, EACL, COLING, CoNLL, and TMLR. He has been a keynote speaker for the IEEE/CVF WACV 2027, IEEE MLSP 2026, ECAI 2025, ACM-CODS 2025, AACL-IJCNLP 2023, CoNLL 2023, and INLG 2022 conferences. His service includes EMNLP Program Co-Chair, Associate Editor-in-Chief for TPAMI, CoNLL Program Co-Chair, ACL Executive Committee, ACM Doctoral Dissertation Award Committee, ACL Doctoral Dissertation Award Co-Organizer, ACL Mentorship Program Co-Founder, and Associate Editor for ACM AI Letters, TACL, CL, IEEE/ACM TASLP, and CSL journals. Webpage: https://www.cs.unc.edu/~mbansal/