Keynotes
Pascale Fung
The Hong Kong University of Science & Technology, China
Abstract
AI has reached a turning point. Systems can now perceive,
generate, and act in language and image across digital platforms
at unprecedented scale. Yet as AI moves from tools to
collaborators—embedded in decision-making, institutions, and
everyday life—a new requirement becomes unavoidable: AI must
understand the world the way humans inhabit it. This talk
introduces Cognitive World Modeling as the next phase of AI
development. It unifies physical world modeling—time, space,
causality, action—with mental world modeling—goals, beliefs,
intentions, emotions, and social norms—into a single, persistent
representation of reality as experienced by humans. Together,
these models allow AI systems not only to predict outcomes, but
to reason about meaning, context, and consequence. Cognitive
World Modeling moves AI beyond reactive toward systems that can
plan, explain, adapt, and collaborate over time. Alignment and
trust emerge not as post hoc constraints, but as properties of
systems that maintain accurate, evolving models of both the
external world and the humans within it.
Bio
Pascale Fung is a Fellow of the ACL, AAAI, IEEE, and ISCA for
her significant contribution to human-machine interactions and
AI ethics. She is an expert on the Global Future Council of the
World Economic Forum since 2016. She is on the Expert Network of
the UN Advisory Body on AI. She is a Chair Professor at the
Department of Electronic & Computer Engineering at The Hong Kong
University of Science & Technology (HKUST), and a visiting
professor at the Central Academy of Fine Arts in Beijing. She
previously led the Embodied AI Agents research effort at
Meta-FAIR.
Mounia Lalmas
Spotify, UK
Abstract
AI is no longer just a component of Web systems; it is
increasingly shaping the experiences users have online. From
search and recommendation to conversational and generative
interfaces, AI is redefining how people interact with content at
Web scale. In this keynote, I reflect on how recent advances in
AI, including deep learning and generative models, are reshaping
the design space of Web technologies. Drawing on insights from
developing AI-driven systems at Spotify, I discuss how search
and recommendation are evolving into interactive, intent-aware
experiences that support exploration and discovery. The talk
highlights emerging system paradigms and research questions
around building such experiences at scale, and reflects on the
implications for the design of future Web systems and
interactions.
Bio
Mounia Lalmas is Senior Director of Research at Spotify, where
she leads Tech Research in Personalisation, focusing on search,
recommendation, and discovery systems at scale. Her work spans
information retrieval, recommender systems, user engagement, and
the application of modern AI techniques, including generative
models, to large-scale online platforms. She previously held
senior roles in industry and academia, including Director of
Research at Yahoo and Professor of Information Retrieval at
Queen Mary University of London, and has also held a research
professorship at the University of Glasgow. Mounia is an
Honorary Professor at University College London and a
Distinguished Research Fellow at the University of Amsterdam. An
active member of the web, search, and recommender systems
research communities, she has co-chaired major conferences
including SIGIR, WWW, WSDM, and CIKM, and authored over 260
publications. Her work bridges foundational research and
real-world deployment, with a current focus on how AI is
reshaping large-scale systems.
Katrina Ligett
The Hebrew University of Jerusalem, Israel
Abstract
We often treat data as an infinitely reusable resource: a single
dataset can support many analyses, train multiple models, and be
shared widely without apparent cost. This talk argues that in
important ways, data is not endlessly reusable. Instead, in
certain contexts, data behaves like a consumable resource that
degrades with use. The clearest example arises in the presence
of privacy concerns. Fundamental results show that any
informative public analysis of personal data inevitably leaks
some information about the underlying individuals, and that
these privacy losses accumulate across repeated uses of the same
or overlapping datasets. If some level of privacy is to be
preserved, this imposes intrinsic limits on how many times data
can be used. In joint work under submission, we connect this
perspective to the mosaic effect from legal scholarship, arguing
that privacy risks arise not only from combining data pieces,
but also from combining seemingly innocuous data uses. This view
suggests regulatory and technical approaches that treat data use
itself as a rival good. Data can also degrade with use even when
privacy is not at stake. A line of work on adaptive data
analysis shows that repeatedly querying the same dataset can
lead to overfitting: results that appear valid on the dataset
but fail to generalize to the underlying distribution, even when
the dataset is very large. In both privacy and generalization,
each interaction with a dataset consumes part of a limited
resource, constraining future computations. Recognizing data
degradation opens a range of research directions, including
systems for tracking and budgeting data use, algorithmic
techniques to mitigate degradation, the role of synthetic data
and data curators, and new models of non-worst-case adaptive
computation. Together, these directions work towards a data
ecosystem that explicitly accounts for data degradation.
Bio
Katrina Ligett is a Professor in the School of Computer Science
and Engineering at The Hebrew University of Jerusalem, where he
is also the director of the interdisciplinary Federmann Center
for the Study of Rationality, and an affiliated faculty member
and former head of the MATAR program on the Interfaces of
Technology, Society, and Networks. Before joining Hebrew
University, she was faculty in computer science and economics at
Caltech. Katrina’s primary research interests are in data
privacy, algorithmic fairness, machine learning theory, and
algorithmic game theory. She received her PhD in Computer
Science from Carnegie Mellon University in 2009 and did her
postdoc at Cornell University. She is a recipient of an ERC
grant, an NSF CAREER award, and a Microsoft Faculty Fellowship.
Alistair Moffat
University of Melbourne, Australia
Abstract
In information retrieval and web search we measure how good
search engines are at ordering answers to user queries, how
close two ranked lists are to each other, how good LLMs are at
re-ranking sets of candidate documents, and how close generated
answer sentences are to the ideal output. This talk begins by
motivating the top-weighted measurements that arise when ordered
sequences are involved, including reviewing the rank-biased
precision and rank-biased overlap measurements proposed in 2008
and 2010 respectively. The second part of the talk then presents
recent work unifying the two previous rank-biased approaches as
elements in a larger framework that exposes a third rank-biased
measurement, rank-biased recall, a dual of rank-biased
precision. Finally, in the third part of the talk, new ways in
which the degree of top-weighting bias can be controlled are
described, allowing practitioners and researchers to better
define their measurement goals, and hence more precisely target
their experiments.
Bio
Professor Alistair Moffat is now in his 40th year as a faculty
member at the University of Melbourne. Early work included
coauthorship of the book “Managing Gigabytes: Compressing and
Indexing Documents and Images” (1994, 1999), and development of
innovative mechanisms for implementing ranked queries via
compressed inverted indexes. Most recently Alistair has been
focused on IR evaluation, including models for user query
formulation and result perusal, and the material on rank-biased
measurement that is the subject of this talk. Alistair was
inducted into the SIGIR Academy in 2021, and recently became a
Fellow of the ACM for his contributions to the implementation
and evaluation of search systems.