[IDA ML Seminar] Machine Learning Seminar, 15/3 at 10:15: Flora Salim, "Learning Paradigms for Timeseries (TS) and SpatioTemporal (ST) Data (and Tasks): Towards Generative AI for TS and ST"

[Fredrik Lindsten]

Hi all,

The seminar by Flora Salim this Friday will start 15 min later than originally announced due to a change of travel plans. The new scheduled starting time is 10:30.

Best,
Fredrik



From: Fredrik Lindsten
Sent: den 5 mars 2024 15:22
To: ml-seminars at lists.liu.se; idaint at ida.liu.se; elliit at lists.liu.se; ai-academy at groups.liu.se; cvl.isy at lists.liu.se
Subject: RE: Machine Learning Seminar, 15/3 at 10:15: Flora Salim, "Learning Paradigms for Timeseries (TS) and SpatioTemporal (ST) Data (and Tasks): Towards Generative AI for TS and ST"

PS. The seminar will also be streamed over Zoom.

Join Zoom Meeting
https://liu-se.zoom.us/j/69750382806?pwd=ZCtTU0hDa0hoYzRRUnFKeE0vc0hwdz09

Meeting ID: 697 5038 2806
Passcode: 286687

---

One tap mobile
+46844682488,,69750382806#,,,,*286687# Sweden
+46850539728,,69750382806#,,,,*286687# Sweden

---

Dial by your location
* +46 8 4468 2488 Sweden
* +46 850 539 728 Sweden

Meeting ID: 697 5038 2806
Passcode: 286687

Find your local number: https://liu-se.zoom.us/u/cdqaHMGALi

---

Join by SIP
69750382806 at 109.105.112.236<mailto:69750382806 at 109.105.112.236>
* 69750382806 at 109.105.112.235<mailto:69750382806 at 109.105.112.235>

---

Join by H.323
* 109.105.112.236
* 109.105.112.235

Meeting ID: 697 5038 2806
Passcode: 286687





From: Fredrik Lindsten <fredrik.lindsten at liu.se<mailto:fredrik.lindsten at liu.se>>
Sent: den 5 mars 2024 11:42
To: ml-seminars at lists.liu.se<mailto:ml-seminars at lists.liu.se>; idaint at ida.liu.se<mailto:idaint at ida.liu.se>; elliit at lists.liu.se<mailto:elliit at lists.liu.se>; ai-academy at groups.liu.se<mailto:ai-academy at groups.liu.se>; cvl.isy at lists.liu.se<mailto:cvl.isy at lists.liu.se>
Subject: Machine Learning Seminar, 15/3 at 10:15: Flora Salim, "Learning Paradigms for Timeseries (TS) and SpatioTemporal (ST) Data (and Tasks): Towards Generative AI for TS and ST"

Welcome to a Machine Learning Seminar on Friday, March 15 at 10:15 in Alan Turing (note the time and place!)

Learning Paradigms for Timeseries (TS) and SpatioTemporal (ST) Data (and Tasks): Towards Generative AI for TS and ST
Flora Salim<https://fsalim.github.io/>, Professor in the School of Computer Science and Engineering, University of New South Wales (UNSW)

Abstract: The initial release of ChatGPT that has seen a worldwide uptake of over than 100 million in just two months after its launch in November 2022. There were several key milestones leading to the development of these foundation models, which underpin the ChatGPT technology, including the introduction of Transformers architecture and the self-supervised learning paradigm. How has the underpinning technologies been applied in the pervasive computing domain, such as for human behaviour modelling, and traffic and weather forecasting? Access to annotated human behaviour data has been expensive and often infeasible. This demands new ways for modelling behaviours at scale, moving away from discriminative, fully-supervised learning approaches, and from narrow tasks. The heterogeneity of both the data sources and the downstream tasks, as well as lack of annotations, makes self-supervised learning to be a compelling choice, as they require no labelled data and can be made compact and generalisable. I will present our self-supervised learning (SSL) pretraining approaches for multimodal sensor data, and also recent works on multimodal self-supervision. I will show why Transformer architecture, designed for sequence-to-sequence modelling, with multi-head attention mechanism, is a perfect fit for time-series data. When combined with Graph structure, it becomes a powerful combo for spatiotemporal prediction tasks. I will also present our works on leveraging Large Language Models (LLMs) for time-series modelling, such as for traffic forecasting and energy demand forecasting, using natural language prompts. Finally, I will discuss open issues around these models, including fairness and explainability, and present our ongoing projects to address them.

Bio: Flora Salim is a Professor in the School of Computer Science and Engineering (CSE), the inaugural Cisco Chair of Digital Transport & AI, University of New South Wales (UNSW) Sydney, and the Deputy Director (Engagement) of UNSW AI Institute. Her research is on machine learning for time-series and multimodal sensor data and on trustworthy AI. She has received several prestigious fellowships including Humboldt-Bayer Fellowship, Humboldt Fellowship, Victoria Fellowship, and ARC Australian Postdoctoral (Industry) Fellowship. She was a recipient of the Women in AI Awards 2022 Australia and New Zealand (Defence and Intelligence Category). She has worked with many industry and government partners, and managed large-scale research and innovation projects, leading to several patents and deployed systems. She is a member of the Australian Research Council (ARC) College of Experts. She has served as a Senior Area Chair / Area Chair of AAAI, WWW, NeurIPS, and many other top-tier conferences in AI and ubiquitous computing. She is an Editor of IMWUT, Associate-Editor-in-Chief of IEEE Pervasive Computing, Associate Editor of ACM Transactions on Spatial Algorithms and Systems, a Steering Committee member of ACM UbiComp, and an Associate of ELLIS Alicante.

Location: Alan Turing<http://www.ida.liu.se/department/location/search.en.shtml?keyword=alan>

------------------

The list of future seminars in the series is available at: http://www.ida.liu.se/research/machinelearning/seminars/
You can subscribe to the seminar series' calendar using this ics link: https://outlook.office365.com/owa/calendar/4d811ae47ce446f58d11a7c2f50a7ed8@ad.liu.se/0f5253d7bc7841248c71eb4c28eb2d668927992292494627279/calendar.ics





-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.liu.se/pipermail/ml-seminars/attachments/20240311/b07f3879/attachment-0001.htm>