BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Sydney Precision Data Science Centre - ECPv6.17.4.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://spds.sydney.edu.au
X-WR-CALDESC:Events for Sydney Precision Data Science Centre
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Australia/Sydney
BEGIN:STANDARD
TZOFFSETFROM:+1100
TZOFFSETTO:+1000
TZNAME:AEST
DTSTART:20250405T160000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+1000
TZOFFSETTO:+1100
TZNAME:AEDT
DTSTART:20251004T160000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+1100
TZOFFSETTO:+1000
TZNAME:AEST
DTSTART:20260404T160000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+1000
TZOFFSETTO:+1100
TZNAME:AEDT
DTSTART:20261003T160000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+1100
TZOFFSETTO:+1000
TZNAME:AEST
DTSTART:20270403T160000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+1000
TZOFFSETTO:+1100
TZNAME:AEDT
DTSTART:20271002T160000
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260921T130000
DTEND;TZID=Australia/Sydney:20260921T140000
DTSTAMP:20260914T031739Z
CREATED:20260914T031524Z
LAST-MODIFIED:20260914T031739Z
UID:5500-1789995600-1789999200@spds.sydney.edu.au
SUMMARY:STORM: Unified spatial\, temporal\, and multimodal data integration into biologically interpretable embeddings
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Xinyi Lisa Chen\, Yale University \n\n\n\nThis is an online event held via Zoom: https://uni-sydney.zoom.us/j/85114748391 \n\n\n\n\n\n\n\n\n\nSpatiotemporal multi-omic atlases are rapidly emerging and shifting tissue biology from static\, single-modality snapshots to dynamic\, multi-layer atlases across development and disease. Yet integrating space\, time\, and modality while preserving tissue geometry\, transient niches\, and real biological change remains difficult. Here we introduce STORM (Spatial Temporal multi-Omics Representation Model)\, among the first end-to-end frameworks to jointly model space–time–modality while grounding the representation in gene programs. STORM learns a unified cell embedding using a multimodal graph neural network variational autoencoder\, jointly aligning timepoints and modalities while correcting time-dependent technical variation. Leveraging curated gene programs\, STORM decomposes complex biology into interpretable\, program-level components across modalities and reveals time-evolving program dynamics. STORM extends to multiple timepoints\, sequencing platforms\, tissues\, and biological conditions\, as demonstrated in three paired spatial RNA–ATAC datasets spanning postnatal (P0–P22) and embryonic (E11.0–E18.5) mouse brain development and a lysolecithin-induced demyelination model. Across datasets\, STORM preserves stage-dependent anatomy\, resolves small anatomically meaningful populations (including the island of Calleja)\, tracks coordinated spatiotemporal program trajectories\, and enables program-level RNA–ATAC contrasts that expose regulatory priming not apparent from expression alone. Our results demonstrate that STORM provides a scalable\, interpretable route to dissect spatiotemporal molecular programs from time-course spatial multi-omic data. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nXinyi Lisa Chen\n\n\n\nXinyi Lisa Chen is a Ph.D. candidate in Computational Biology and Biomedical Informatics at Yale University\, advised by Professor Hongyu Zhao. Before Yale\, she earned her bachelor’s degree in Computer Science and Statistics from the University of Toronto. Her research develops interpretable machine learning methods for spatial and perturbational multi-omics data\, with the broader goal of building computational models of a “virtual cell in tissue.” She is the lead developer of STORM\, a graph-based generative framework that integrates spatial context\, developmental time\, and multi-modal measurements (e.g. RNA and ATAC) into biologically interpretable gene-program representations. She has also led multimodal single-cell analyses of cellular and regulatory changes across prodromal and early Parkinson’s disease. More recently\, Lisa has expanded her work to scientific AI agents\, studying reliable tool use\, long-horizon evaluation\, experience memory reuse\, and failure monitors for AI agents.  \n\n\n\nConnect with Xinyi Lisa:LinkedIn: www.linkedin.com/in/xinyi-lisa-chen-yale
URL:https://spds.sydney.edu.au/event/storm-unified-spatial-temporal-and-multimodal-data-integration-into-biologically-interpretable-embeddings/
ATTACH;FMTTYPE=image/jpeg:https://spds.sydney.edu.au/wp-content/uploads/2025/02/Complex-systems-1-edited-scaled.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260928T130000
DTEND;TZID=Australia/Sydney:20260928T140000
DTSTAMP:20260914T032727Z
CREATED:20260914T032605Z
LAST-MODIFIED:20260914T032727Z
UID:5507-1790600400-1790604000@spds.sydney.edu.au
SUMMARY:Powerful and accurate case-control analysis of spatial molecular data
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Yakir Reshef\, Harvard Medical School \n\n\n\nThis is an online event held via Zoom: https://uni-sydney.zoom.us/j/85114748391 \n\n\n\n\n\n\n\n\n\nAs spatial molecular data grow in scope\, there is a pressing need to identify disease-associated spatial structures. Current approaches typically make restrictive assumptions such as representing tissue regions by abundances of discrete cell types and samples by abundances of discrete niches; this risks overlooking important signals. I will discuss variational inference-based microniche analysis (VIMA)\, a method combining deep learning with principled statistics to discover disease-associated spatial features with greater flexibility and precision. VIMA trains an ensemble of variational autoencoders to summarize the contents of every small tissue patch in a dataset via numeric “fingerprints”. It uses these to define many data-dependent\, overlapping “microniches” and meta-analyzes them to identify microniches whose abundance correlates significantly with case-control status. After describing the method\, I’ll show how it performs in simulations designed to assess calibration\, power\, and spatial accuracy. I’ll then give some examples of how we have used VIMA on spatial datasets from diverse spatial modalities\, where it recapitulates known biology and identifies novel spatial features of disease. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr Yakir Reshef\n\n\n\nYakir Reshef completed his undergraduate training in mathematics at Harvard College and subsequently completed a Fulbright in probability theory followed by a PhD in computer science at Harvard University and an MD from Harvard Medical School. He is currently a faculty member at Harvard Medical School and Brigham and Women’s Hospital\, where his group focuses on developing and applying new methods to achieve a quantitative understanding of inflammation and auto-immunity from high-dimensional molecular data such as single-cell and spatial transcriptomics data. He is also clinically active\, treating patients with autoimmune disease as a rheumatologist. \n\n\n\nConnect with Yakir:X: @YakirReshefBluesky: @yakirreshef.bsky.social
URL:https://spds.sydney.edu.au/event/powerful-and-accurate-case-control-analysis-of-spatial-molecular-data/
ATTACH;FMTTYPE=image/jpeg:https://spds.sydney.edu.au/wp-content/uploads/2025/01/Complex-systems-1-scaled.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20261015T110000
DTEND;TZID=Australia/Sydney:20261015T120000
DTSTAMP:20260909T020338Z
CREATED:20260909T015737Z
LAST-MODIFIED:20260909T020338Z
UID:5483-1792062000-1792065600@spds.sydney.edu.au
SUMMARY:Charles Perkins Centre Data Science Hub - Introduction and Showcase
DESCRIPTION:This event is in-person only and requires registration. \n\n\n\n\n\n\n\nJoin us as we introduce the Charles Perkins Centre Data Science Hub and showcase our first projects.Discover how the Data Science Hub’s collaborative platform advances data-intensive research\, builds capacity and shares knowledge. The event will highlight key achievements from projects of varying scale and share our vision to support EMCRs and accelerate research translation.We invite you to meet our data scientists\, explore opportunities for collaboration\, and discover how to engage with us.  Refreshments will be provided. \n\n\n\n\n\n\n\nThe Data Science Hub is a collaborative partnership with the Charles Perkins Centre and Sydney Precision Data Science Centre\, co-funded by the Charles Perkins Centre’s Jennie Mackenzie Research Fund\, the Faculty of Medicine and Health and Faculty of Science.  The Data Science Hub advances data-intensive research with a direct translational impact in biomedical\, metabolomics health\, epidemiological research\, and beyond. We specialise in context-specific data analysis for high-throughput biomedical data generated by scientists.
URL:https://spds.sydney.edu.au/event/charles-perkins-centre-data-science-hub-introduction-and-showcase/
LOCATION:Jennie Mackenzie Room\, Level 6\, Charles Perkins Centre\, Johns Hopkins Drive\, University of Sydney\, Camperdown NSW 2006\, Sydney\, 2006\, Australia
ATTACH;FMTTYPE=image/jpeg:https://spds.sydney.edu.au/wp-content/uploads/2026/09/DASH-Event-banner_resized-scaled.jpg
END:VEVENT
END:VCALENDAR