BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Sydney Precision Data Science Centre - ECPv6.16.2//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Sydney Precision Data Science Centre
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:20260525T130000
DTEND;TZID=Australia/Sydney:20260525T140000
DTSTAMP:20260523T062659
CREATED:20260518T023913Z
LAST-MODIFIED:20260518T023915Z
UID:5129-1779714000-1779717600@spds.sydney.edu.au
SUMMARY:Generalized cell phenotyping for spatial proteomics with language-informed vision models
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Xuefei (Julie) Wang\, California Institute of Technology \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\nWe present DeepCell Types\, a novel approach to cell phenotyping for spatial proteomics that addresses the challenge of generalization across diverse datasets with varying marker panels collected across different platforms. Our approach utilizes a transformer with channel-wise attention to create a language-informed vision model; this model’s semantic understanding of the underlying marker panel enables it to learn from and adapt to heterogeneous datasets. Leveraging a curated\, diverse dataset named Expanded TissueNet with cell type labels spanning the literature and the NIH Human BioMolecular Atlas Program (HuBMAP) consortium\, our model demonstrates robust performance across various cell types\, tissues\, and imaging modalities. Comprehensive benchmarking shows superior accuracy and generalizability of our method compared to existing methods. This work significantly advances automated spatial proteomics analysis\, offering a generalizable and scalable solution for cell phenotyping that meets the demands of multiplexed imaging 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\nXuefei (Julie) Wang\n\n\n\nXuefei (Julie) Wang is a PhD student at Caltech co-advised by David Van Valen and Yisong Yue. Her research focuses on the fundamental principles of foundation models and agentic systems\, with a particular emphasis on spatial proteomics and transcriptomics. Julie’s work addresses the challenges of large-scale biological data through innovative modeling\, including the development of language-informed vision models for generalized cell phenotyping. Her goal is to build knowledge-centric agents that accelerate discovery by compounding insights from foundation tools and literature into a persistent knowledge base that grows more capable with use. In addition to her work at Caltech\, she has contributed to scientific AI efforts as a Student Researcher at Google Research with Michael P. Brenner.Connect with Xuefei:LinkedIn: xuefei-wangX: xuefei_whttps://xuefei-wang.github.io/
URL:https://spds.sydney.edu.au/event/generalized-cell-phenotyping-for-spatial-proteomics-with-language-informed-vision-models/
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:20260601T130000
DTEND;TZID=Australia/Sydney:20260601T140000
DTSTAMP:20260523T062659
CREATED:20260417T015751Z
LAST-MODIFIED:20260420T051802Z
UID:4869-1780318800-1780322400@spds.sydney.edu.au
SUMMARY:Through the Unlabeled Lens of Spatial Multi-Omics
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Anthony A. Fung\, 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\nSometimes it matters less what you look at\, and more what you see. Common practices in clinical pathology often involve multiple histological stains on serial sections of tissue biopsy to obtain the highest diagnostic power\, but this requires expertise\, reagent costs\, consumes tissue\, risks deformation\, and complicates co-registration\, potentially missing rare microstructures. Now there is a major push for spatial multi-omics integration\, but even adjacent tissue sections captured with different modalities decrease performance. Today’s seminar introduces a non-destructive label-free optical platform combining SRS\, SHG\, and TPF enables high-resolution molecular imaging to unravel the lipidomic\, metabolic\, and morphometric landscape of kidney disease\, and how these data types can augment your modalities. \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 Anthony A. Fung\n\n\n\nDr Anthony A. Fung is a T32 postdoctoral fellow in Professor Rong Fan’s group at Yale University. He received his PhD in Bioengineering at University of California San Diego from Professor Lingyan Shi’s group. Anthony has received several awards in the quantitative spatial biology field and is a collaborating investigator in both HuBMAP and SenNet consortia. His current work centers on the development and application of spatial multi-omics technologies in aging and immune senescence.Find out more on LinkedIn:https://www.linkedin.com/in/anthony-fung/
URL:https://spds.sydney.edu.au/event/through-the-unlabeled-lens-of-spatial-multi-omics/
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:20260728T140000
DTEND;TZID=Australia/Sydney:20260729T170000
DTSTAMP:20260523T062659
CREATED:20260507T235254Z
LAST-MODIFIED:20260507T235521Z
UID:4934-1785247200-1785344400@spds.sydney.edu.au
SUMMARY:Microcredential: Data Analysis for Precision Health
DESCRIPTION:As we enter the data revolution\, the scale and accessibility of health and medical data has reached unprecedented levels creating a growing need for expertise in extracting insights from this data. \n\n\n\nThis course will provide participants with essential statistical skills to analyse and interpret health and medical data. Key topics include linear models\, mixed effect models\, logistic regression and survival analysis. \n\n\n\nReal health and medical data will be utilised to explore common challenges\, practical workarounds\, and translate data into actionable insights. \n\n\n\nBy the end of this course\, you will be able to:\n\n\n\n\nformulate and interpret appropriate linear models to describe the relationships between multiple factors\n\n\n\ntrain and evaluate logistic regression models for binary data\n\n\n\nunderstand and apply linear mixed effect models for data with repeated measures\n\n\n\nvisualise survival data with Kaplan-Meier curves and perform inference with Cox proportional hazards models.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAims\n\n\n\nThis course will give participants the necessary understanding and skills to perform statistical analyses on health and medical data. \n\n\n\nParticipants will gain experience in working with real data and develop critical thinking skills to address common challenges. \n\n\n\nParticipants will learn to communicate their data and findings through graphical and statistical summaries. \n\n\n\nContent\n\n\n\nThe course covers four main topics: \n\n\n\n\nLinear models: Fit\, refine\, interpret and visualise regression models. We will discuss making predictions\, evaluating model fit and feature selection.\n\n\n\nLogistic regression: Interpret odds ratios to evaluate risk factors\, assess model performance when working with binary health outcomes.\n\n\n\nMixed effect models: Analyse repeated measures and hierarchical data to understand individual and group-level patterns in health and medical contexts.\n\n\n\nSurvival analysis: Model time-to-event data using Kaplan-Meier curves and Cox proportional hazards models\, and assess model accuracy with metrics like the C-index.
URL:https://spds.sydney.edu.au/event/microcredential-data-analysis-for-precision-health/
LOCATION:Room 4 & 5\, Level 16 – The University of Sydney Business School – CBD Campus\, Room 4 & 5\, Level 16 - The University of Sydney Business School - CBD Campus\, Sydney\, NSW\, 2000\, Australia
END:VEVENT
END:VCALENDAR