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<p class="MsoNormal"><span lang="EN-US">Hi all,<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">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
<b><span style="color:red">10:30</span></b><span style="color:red">.</span><o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Best,<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Fredrik<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
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<p class="MsoNormal"><b><span lang="EN-GB" style="font-size:12.0pt;mso-fareast-language:SV">From:</span></b><span lang="EN-GB" style="font-size:12.0pt;mso-fareast-language:SV"> Fredrik Lindsten
<br>
<b>Sent:</b> den 5 mars 2024 15:22<br>
<b>To:</b> ml-seminars@lists.liu.se; idaint@ida.liu.se; elliit@lists.liu.se; ai-academy@groups.liu.se; cvl.isy@lists.liu.se<br>
<b>Subject:</b> 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"<o:p></o:p></span></p>
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<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">PS. The seminar will also be streamed over Zoom.<o:p></o:p></span></p>
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<p class="MsoNormal"><a name="WinZmBookmark_z201DE"><span lang="EN-US">Join Zoom Meeting<o:p></o:p></span></a></p>
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<p class="MsoNormal"><span style="mso-bookmark:WinZmBookmark_z201DE"><span lang="EN-US">Meeting ID: 697 5038 2806<o:p></o:p></span></span></p>
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<p class="MsoNormal"><span style="mso-bookmark:WinZmBookmark_z201DE"><span lang="EN-US">Meeting ID: 697 5038 2806<o:p></o:p></span></span></p>
<p class="MsoNormal"><span style="mso-bookmark:WinZmBookmark_z201DE"><span lang="EN-US">Passcode: 286687<o:p></o:p></span></span></p>
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<p class="MsoNormal"><span style="mso-bookmark:WinZmBookmark_z201DE"><span lang="EN-US">Meeting ID: 697 5038 2806<o:p></o:p></span></span></p>
<p class="MsoNormal"><span style="mso-bookmark:WinZmBookmark_z201DE"><span lang="EN-US">Passcode: 286687</span></span><a name="WinZmBookmarkEnd"></a><span lang="EN-US"><o:p></o:p></span></p>
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<p class="MsoNormal"><b><span lang="EN-GB" style="font-size:12.0pt;mso-fareast-language:SV">From:</span></b><span lang="EN-GB" style="font-size:12.0pt;mso-fareast-language:SV"> Fredrik Lindsten <</span><a href="mailto:fredrik.lindsten@liu.se"><span lang="EN-GB" style="font-size:12.0pt;mso-fareast-language:SV">fredrik.lindsten@liu.se</span></a><span lang="EN-GB" style="font-size:12.0pt;mso-fareast-language:SV">>
<br>
<b>Sent:</b> den 5 mars 2024 11:42<br>
<b>To:</b> </span><a href="mailto:ml-seminars@lists.liu.se"><span lang="EN-GB" style="font-size:12.0pt;mso-fareast-language:SV">ml-seminars@lists.liu.se</span></a><span lang="EN-GB" style="font-size:12.0pt;mso-fareast-language:SV">;
</span><a href="mailto:idaint@ida.liu.se"><span lang="EN-GB" style="font-size:12.0pt;mso-fareast-language:SV">idaint@ida.liu.se</span></a><span lang="EN-GB" style="font-size:12.0pt;mso-fareast-language:SV">;
</span><a href="mailto:elliit@lists.liu.se"><span lang="EN-GB" style="font-size:12.0pt;mso-fareast-language:SV">elliit@lists.liu.se</span></a><span lang="EN-GB" style="font-size:12.0pt;mso-fareast-language:SV">;
</span><a href="mailto:ai-academy@groups.liu.se"><span lang="EN-GB" style="font-size:12.0pt;mso-fareast-language:SV">ai-academy@groups.liu.se</span></a><span lang="EN-GB" style="font-size:12.0pt;mso-fareast-language:SV">;
</span><a href="mailto:cvl.isy@lists.liu.se"><span lang="EN-GB" style="font-size:12.0pt;mso-fareast-language:SV">cvl.isy@lists.liu.se</span></a><span lang="EN-GB" style="font-size:12.0pt;mso-fareast-language:SV"><br>
<b>Subject:</b> 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"<o:p></o:p></span></p>
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<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:16.0pt">Welcome to a Machine Learning Seminar on Friday, March 15 at 10:15 in Alan Turing (note the time and place!)<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span lang="EN-US" style="font-size:16.0pt;color:black">Learning Paradigms for Timeseries (TS) and SpatioTemporal (ST) Data (and Tasks): Towards Generative AI for TS and ST<br>
</span></b><a href="https://fsalim.github.io/"><b><span lang="EN-US" style="font-size:16.0pt;color:#225785">Flora Salim</span></b></a><b><span lang="EN-US" style="font-size:16.0pt;color:black">, Professor in the School of Computer Science and Engineering, University
of New South Wales (UNSW)<o:p></o:p></span></b></p>
<p class="MsoNormal"><b><span lang="EN-US" style="color:black"><o:p> </o:p></span></b></p>
<p class="MsoNormal"><i><span lang="EN-US" style="color:black">Abstract:</span></i><span lang="EN-US" style="color:black"> 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.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="color:black"><o:p> </o:p></span></p>
<p class="MsoNormal"><i><span lang="EN-US" style="color:black">Bio:</span></i><span lang="EN-US" style="color:black"> 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.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="color:black"><o:p> </o:p></span></p>
<p class="MsoNormal"><i><span lang="EN-US" style="color:black">Location: </span></i><a href="http://www.ida.liu.se/department/location/search.en.shtml?keyword=alan"><b><span lang="EN-US" style="color:#225785">Alan Turing</span></b></a><b><span lang="EN-US" style="color:black"><o:p></o:p></span></b></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
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<p class="MsoNormal"><span lang="EN-US"> <o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">The list of future seminars in the series is available at:
</span><a href="http://www.ida.liu.se/research/machinelearning/seminars/" target="_BLANK2"><span lang="EN-US">http://www.ida.liu.se/research/machinelearning/seminars/</span></a><span lang="EN-US"><o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">You can subscribe to the seminar series' calendar using this ics link:
</span><a href="https://outlook.office365.com/owa/calendar/4d811ae47ce446f58d11a7c2f50a7ed8@ad.liu.se/0f5253d7bc7841248c71eb4c28eb2d668927992292494627279/calendar.ics" target="_BLANK2"><span lang="EN-US">https://outlook.office365.com/owa/calendar/4d811ae47ce446f58d11a7c2f50a7ed8@ad.liu.se/0f5253d7bc7841248c71eb4c28eb2d668927992292494627279/calendar.ics</span></a><span lang="EN-US"><o:p></o:p></span></p>
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