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X-WR-CALNAME:Sydney Precision Data Science Centre
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X-WR-CALDESC:Events for Sydney Precision Data Science Centre
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DTSTART;TZID=Australia/Sydney:20260511T130000
DTEND;TZID=Australia/Sydney:20260511T140000
DTSTAMP:20260515T012415Z
CREATED:20260424T020750Z
LAST-MODIFIED:20260515T012415Z
UID:4890-1778504400-1778508000@spds.sydney.edu.au
SUMMARY:Coupling Single-cell Genome & Epigenome to Study Functional Consequence of Somatic SVs
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Hyobin Jeong\, Yonsei University \n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nSomatic structural variants are widespread in cancer\, but their impact on disease evolution is understudied due to a lack of methods to directly characterize their functional consequences. We proposed a computational method\, scNOVA\, which utilizes Strand-seq to perform haplotype-aware integration of structural variant discovery and molecular phenotyping in single cells\, by using nucleosome occupancy to infer gene expression as a read-out. Application to leukemias and cell lines identifies local effects of copy-balanced rearrangements on gene deregulation\, and consequences of structural variants on aberrant signaling pathways in subclones. We discovered distinct SV subclones with dysregulated Wnt signaling in a chronic lymphocytic leukemia patient. We further uncovered the consequences of subclonal chromothripsis in T-cell acute lymphoblastic leukemia\, which revealed c-Myb activation\, enrichment of a primitive cell state and informed successful targeting of the subclone in cell culture\, using a Notch inhibitor. More recently\, scNOVA to complex karyotype acute myeloid leukemia (AML) revealed dynamic clonal evolution and targetable phenotypes. Not only cancer system\, we can apply this approach to study functional effect of somatic SVs in clonal hematopoiesis and aging. Also\, we are now extending scNOVA to develop scalable and broadly applicable bioinformatics methods which can link SVs to their functional effects\, to enable systematic single-cell multiomic studies of structural variation in heterogeneous cell populations. \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 Hyobin Jeong\n\n\n\nDr Hyobin Jeong is an assistant professor at Yonsei University in the Department of Systems Biology. She received her PhD in Interdisciplinary Bioscience and Biotechnology from Pohang University of Science and Technology (POSTECH). She has completed postdoctoral fellowships at the European Molecular Biology Laboratory (EMBL) and the Institute of Molecular Biology (IMB) in Germany\, and the Institute of Basic Science in Korea. Before her current role\, she was a Research Professor at the Hanyang Institute of Bioscience and Biotechnology.
URL:https://spds.sydney.edu.au/event/coupling-single-cell-genome-epigenome-to-study-functional-consequence-of-somatic-svs/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260518T130000
DTEND;TZID=Australia/Sydney:20260518T140000
DTSTAMP:20260522T023827Z
CREATED:20260424T022258Z
LAST-MODIFIED:20260522T023827Z
UID:4897-1779109200-1779112800@spds.sydney.edu.au
SUMMARY:Genetic regulation of human skeletal muscle: from bulk QTLs to single-nucleus resolution
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Wang Wenjing\, National University of Singapore \n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nAlthough lifestyle-induced weight loss reduces type 2 diabetes risk\, how genetic variation shapes gene expression and splicing responses to weight loss remains poorly understood\, particularly in Asian populations. In the first part of this talk\, I will present our recently published work (Cell Genomics\, 2025) in which we profiled skeletal muscle transcriptomes from 54 overweight/obese Asian individuals before and after a 16-week lifestyle intervention in the Singapore Adult Metabolism Study (SAMS). The intervention resulted in ~10% weight loss and ~30% improvement in insulin-stimulated glucose uptake. We identified 505 differentially expressed genes enriched in mitochondrial function and insulin sensitivity pathways\, mapped cis-eQTLs and cis-sQTLs that are shared or condition-specific\, and integrated these regulatory variants with GWAS signals for metabolic traits to pinpoint candidate causal genes and mechanisms. In the second part\, I will introduce our ongoing effort to build a comprehensive single-nucleus and spatial transcriptomic atlas of human skeletal muscle. Using snRNA-seq from over 300 biopsies spanning lean and obese individuals across three Asian ancestries\, we have profiled more than one million nuclei and annotated over 20 cell types capturing substantial cellular heterogeneity across ancestry\, adiposity\, and metabolic states. We are now extending this resource with spatial transcriptomics (MERFISH and Xenium) on paired pre- and post-intervention samples to spatially resolve cell-type-specific regulatory programs and microenvironment remodeling during weight loss. Together\, these efforts establish the first longitudinal\, ancestry-diverse single-nucleus and spatial regulatory reference for Asian human skeletal muscle. \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\nWenjing Wang\n\n\n\nWenjing Wang is a third-year PhD candidate in the lab of Prof. Liu Boxiang at the National University of Singapore\, working at the intersection of functional genomics and metabolic disease. Her work combines bulk and single-nucleus RNA sequencing with QTL mapping to understand how genetic variation and lifestyle interventions reshape gene regulation across cell types. Her recent study on gene expression and splicing responses to exercise- and diet-induced weight loss was published in Cell Genomics (2025). She is currently building a large-scale single-cell and spatial atlas of human skeletal muscle across diverse Asian populations. \n\n\n\nFind out more on LinkedIn: https://www.linkedin.com/in/wenjing-wang-40a67b18b/
URL:https://spds.sydney.edu.au/event/genetic-regulation-of-human-skeletal-muscle-from-bulk-qtls-to-single-nucleus-resolution/
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260525T130000
DTEND;TZID=Australia/Sydney:20260525T140000
DTSTAMP:20260529T064822Z
CREATED:20260518T023913Z
LAST-MODIFIED:20260529T064822Z
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 was an online event held via Zoom. \n\n\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/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260601T130000
DTEND;TZID=Australia/Sydney:20260601T140000
DTSTAMP:20260605T040625Z
CREATED:20260417T015751Z
LAST-MODIFIED:20260605T040625Z
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 was an online event held via Zoom. \n\n\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/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260615T130000
DTEND;TZID=Australia/Sydney:20260615T140000
DTSTAMP:20260619T105424Z
CREATED:20260601T020224Z
LAST-MODIFIED:20260619T105424Z
UID:5173-1781528400-1781532000@spds.sydney.edu.au
SUMMARY:Reconstructing biologically coherent cellular profiles from imaging-based spatial transcriptomics
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Long Yuan\, Johns Hopkins University \n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nIn imaging-based spatial transcriptomics\, transcript-to-cell assignment shapes downstream biological interpretation\, including cell typing\, ligand–receptor inference\, and niche characterization. However\, two-dimensional segmentation of volumetric tissue often yields mixed cellular profiles\, while cells without detected nuclei may be missed entirely\, affecting downstream analyses. We present TRACER\, a framework that refines cellular representations in imaging-based spatial transcriptomics by leveraging gene–gene coherence and spatial co-localization of transcripts observed directly in the data\, without requiring external annotations or reference atlases. TRACER resolves mixed cellular profiles and reconstructs partial cells whose nuclei are not detected\, enabling more complete representation of cells within tissue sections. We also introduce coherence-based metrics that quantify transcriptional purity and conflict\, enabling platform-agnostic benchmarking of segmentation quality. Across diverse platforms\, tissues\, and segmentation methodologies\, TRACER consistently improves the coherence of cellular profiles and the quality of downstream analyses. \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\nLong Yuan\n\n\n\nLong Yuan is a PhD candidate in Immunology and an M.S.E. candidate in Computer Science at Johns Hopkins University. His research focuses on developing scalable machine learning and statistical methods for spatial and single-cell omics\, with applications in cancer biology and immune-metabolic diseases. His work spans spatial multi-omics\, graph-based learning\, and multimodal data integration. As a member of the Break Through Cancer GBM and Data Science TeamLab\, he develops computational approaches for integrating and analyzing large-scale spatial omics datasets.Find out more on LinkedIn and X:https://www.linkedin.com/in/long-yuan-3a8b953aa/@Long_et_al
URL:https://spds.sydney.edu.au/event/reconstructing-biologically-coherent-cellular-profiles-from-imaging-based-spatial-transcriptomics/
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260622T130000
DTEND;TZID=Australia/Sydney:20260622T140000
DTSTAMP:20260629T042022Z
CREATED:20260616T235053Z
LAST-MODIFIED:20260629T042022Z
UID:5203-1782133200-1782136800@spds.sydney.edu.au
SUMMARY:Statistical Brain Network Analysis: Recent Developments and Future Directions
DESCRIPTION:Judith and David Coffey SeminarSpeaker: Prof Sean L. Simpson\, Wake Forest University \n\n\n\nThis was a hybrid event. In-person in the Mackenzie Seminar Room\, Level 6\, Charles Perkins Centre and online via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nThe recent fusion of network science and neuroscience has catalyzed a paradigm shift in how we study the brain and led to the field of brain network analysis. Brain network analyses hold great potential in helping us understand normal and abnormal brain function by providing profound clinical insight into links between system-level properties and health and behavioral outcomes. Nonetheless\, many statistical challenges remain to be able to fully realize the promise of this field. Here we touch on a few of these challenges\, briefly survey three complementary statistical frameworks that we have developed to attempt to address a subset of these needs—a mixed modeling framework\, a distance regression framework\, and a hidden semi-Markov modeling framework—and discuss potential future avenues of research. \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\nProf Sean L. Simpson\n\n\n\nProf Sean L. Simpson is a biostatistician in the Department of Biostatistics and Data Science\, with joint appointments in Biomedical Engineering and Neuroscience\, and an Affiliate appointment with the Maya Angelou Center for Healthy Communities (MARCH) at Wake Forest University School of Medicine. His main research focus has been on the development of novel fusions of statistical tools with network science methods for the analysis of whole-brain network data. Studying the brain as a whole and statistically accounting for the inherent complexity in the way various regions of the brain interact will engender a more biologically meaningful approach to understanding the root causes of a number of brain diseases and disorders. 
URL:https://spds.sydney.edu.au/event/statistical-brain-network-analysis-recent-developments-and-future-directions/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260728T140000
DTEND;TZID=Australia/Sydney:20260729T170000
DTSTAMP:20260507T235521Z
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
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260803T130000
DTEND;TZID=Australia/Sydney:20260803T140000
DTSTAMP:20260810T023032Z
CREATED:20260719T235602Z
LAST-MODIFIED:20260810T023032Z
UID:5282-1785762000-1785765600@spds.sydney.edu.au
SUMMARY:Interpretable\, flexible and spatially-aware integration of multiple spatial transcriptomics datasets from diverse sources
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Jia Zhao\, Yale University \n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nRecent advances in spatial transcriptomics have generated an expanding collection of heterogeneous datasets\, offering unprecedented opportunities to investigate tissue organizations and functions. However\, effective interpretation and integration of data originating from diverse sources and conditions remain a major challenge. In this talk\, we will present INSPIRE\, a deep-learning method for interpretable\, integrative analysis of multiple spatial transcriptomic datasets. INSPIRE adopts an adversarial learning strategy with graph neural networks to achieve spatially-informed and adaptive data integration. By incorporating non-negative matrix factorization\, INSPIRE identifies interpretable spatial factors and associated gene programs that characterize tissue architecture\, cell-type organization\, and biological processes. Across a broad range of applications\, INSPIRE demonstrates superior performance in resolving fine-grained biological signals\, integrating complementary strengths across technologies\, capturing condition-specific variation\, uncovering tumor microenvironment heterogeneity\, elucidating developmental dynamics\, and facilitating three-dimensional tissue reconstruction. INSPIRE also scales to extremely large datasets\, as demonstrated by applications to Xenium-profiled human breast cancer and Stereo-seq mouse organogenesis datasets. \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 Jia Zhao\n\n\n\nDr Jia Zhao received her PhD in Mathematics from the Hong Kong University of Science and Technology under the supervision of Prof. Can Yang. She completed her postdoctoral training in the Department of Biostatistics at Yale University\, mentored by Prof Hongyu Zhao and Prof Rui Chang. She will soon join the School of Mathematical Sciences at Shanghai Jiao Tong University as a faculty member. Her research focuses on developing computational methods to address analytical challenges arising from large-scale biomedical data. She has led the development of methods for integrative single-cell analysis\, interpretable modeling of spatial omics data\, and robust causal inference among complex traits. Her work has been published in leading journals\, including Nature Genetics\, Nature Machine Intelligence\, Nature Computational Science\, and PNAS.Find out more on X:@JiaZhao_stat
URL:https://spds.sydney.edu.au/event/interpretable-flexible-and-spatially-aware-integration-of-multiple-spatial-transcriptomics-datasets-from-diverse-sources/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260810T130000
DTEND;TZID=Australia/Sydney:20260810T140000
DTSTAMP:20260814T062437Z
CREATED:20260724T064257Z
LAST-MODIFIED:20260814T062437Z
UID:5294-1786366800-1786370400@spds.sydney.edu.au
SUMMARY:SPEAK: Spatial Prompting with Expert Aligned Knowledge for Tissue Domain Identification in Spatial Transcriptomics
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Xiting Yan\, Yale University \n\n\n\nThis was an online event held via Zoom. \n\n\n\n\n\n\n\n\n\n\n\nSpatially resolved transcriptomic (SRT) data requires spatial domain identification to enable tissue microenvironment-specific downstream analyses. Here we present SPEAK (Spatial Prompting with Expert-Aligned Knowledge)\, a large language model (LLM)-based method to identify spatial domains from SRT data by taking advantage of the prior knowledge from both LLM and human experts. SPEAK constructs a spatial context prompt for each cell/spot based on cell types and marker genes of its neighboring cells\, enabling zero-shot inference\, expert-guided fine-tuning\, and prototype updating through two-stage prompting. Applications to STARmap\, Visium\, MERFISH and Xenium datasets showed advantages of SPEAK over existing spatial domain identification methods in domain prediction accuracy\, robustness to limited prior knowledge\, biological interpretability\, and capacity for efficient expert-guided fine-tuning with generalizability to other tissue sections.  \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 Xiting Yan\n\n\n\nDr Yan is an Associate Professor from the Section of Pulmonary\, Critical Care and Sleep Medicine at Yale University School of Medicine\, with a secondary appointment at the Department of Biostatistics at Yale University School of Public Health. She is the Director of Data Analysis and Bioinformatics Hub at the Cent for Precision Pulmonary Medicine (P2MED). Dr Yan is a world known computational biologist\, bioinformatician and biostatistician\, with extensive research experiences in large scale multi-omic data analyses at both bulk and single-cell resolution. Her current research interest focus on two parts: (1) developing novel statistical and computational models to analyze large scale multi-omics and drug perturbation data to better understand disease pathogenesis and facilitate precision medicine development\, and (2) understanding the heterogeneity\, pathogenesis and progression of pulmonary diseases\, such as asthma\, idiopathic pulmonary fibrosis (IPF)\, sarcoidosis\, chronic obstructive pulmonary disease (COPD)\, pediatric cystic fibrosis and so on\, by tailoring statistical and computational methods based on existing biological knowledge of the diseases. She is specifically interested in development of novel analytical methods for single-cell RNA sequencing data\, spatial transcriptomic data\, drug perturbation data and integration of different omics data. In this talk\, she will introduce a recently developed method for spatial domain identification using large language model.
URL:https://spds.sydney.edu.au/event/speak-spatial-prompting-with-expert-aligned-knowledge-for-tissue-domain-identification-in-spatial-transcriptomics/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260817T130000
DTEND;TZID=Australia/Sydney:20260817T140000
DTSTAMP:20260817T054954Z
CREATED:20260731T004352Z
LAST-MODIFIED:20260817T054954Z
UID:5405-1786971600-1786975200@spds.sydney.edu.au
SUMMARY:Single-cell and spatial methods to dissect complex diseases
DESCRIPTION:Judith and David Coffey SeminarSpeaker: Dr Boxiang Liu\, National University of Singapore \n\n\n\nThis is a hybrid event. In-person at the Mackenzie Seminar Room\, Level 6\, Charles Perkins CentreOnline via Zoom: https://uni-sydney.zoom.us/j/85114748391 \n\n\n\n\n\n\n\n\n\n\n\nGenome-wide association studies have identified more than a million risk variants for complex diseases\, yet fewer than 5% of complex disease loci have validated target genes. Closing this gap requires resolving genetic regulation at the right cell types\, ancestries\, and cellular contexts. In this talk\, I will present three complementary efforts from my lab. First\, AIDA — a single-cell atlas of ~1 million PBMCs from ~500 donors of diverse Asian descent profiled with 5′ chemistry — captures 4.3-fold more splice junctions than prior 3′ libraries and reveals ancestry-biased splicing events\, including an Asian-specific TCHP variant that modulates Graves’ disease risk. Second\, we developed ISSAC to map cell-state-dependent sQTLs across millions of cells and uncovers Alzheimer’s-biased sQTLs in dorsolateral prefrontal cortex snRNA-seq. Third\, we developed DIRAC to use adversarial domain-invariant representations to harmonize spatial multi-omic datasets\, enabling a high-resolution T cell development atlas in the mouse thymus. Together\, these methods chart a path from population-scale genetics to cell-state- and tissue-resolved mechanisms underlying complex diseases. \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 Boxiang Liu\n\n\n\nAs a PI and the director of the Genomic Data Science Lab in the National University of Singapore (www.boxiangliulab.com)\, Dr Boxiang Liu advances the understanding of complex human diseases through innovative genetic\, single-cell\, and spatial transcriptomic analyses. He has published >90 publications\, including Nature\, Cell\, Nature Genetics\, and Nature Methods\, focusing on genomic and transcriptomic methodologies to dissect the genetic architecture of polygenic diseases. A significant facet of his academic endeavour includes a pivotal role in the Genotype-Tissue Expression (GTEx) Project\, contributing to a comprehensive understanding of genetic effects on molecular phenotype across diverse tissues. Additionally\, he spearheaded the single-cell splicing analysis within the Asian Immune Diversity Atlas project\, which collected >500 donors of diverse Asian ancestries. His group’s work provided the first cell-type-specific sQTL map using over 1 million PBMC single cells. He has been awarded the Presidential Young Professorship (Singapore)\, National Academy of Science Young Scientist Award (Singapore)\, National Research Foundation Fellow (Singapore)\, President’s Award in Natural Sciences and Mathematics (US)\, Charles B. Carrington Memorial Award (US)\, and the National Award for Outstanding Overseas PhD Students (China). \n\n\n\nConnect with Boxiang:X: @boxiangliuBluesky: @boxiangliu.bsky.social
URL:https://spds.sydney.edu.au/event/single-cell-and-spatial-methods-to-dissect-complex-diseases/
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260824T130000
DTEND;TZID=Australia/Sydney:20260824T140000
DTSTAMP:20260817T225949Z
CREATED:20260806T031950Z
LAST-MODIFIED:20260817T225949Z
UID:5413-1787576400-1787580000@spds.sydney.edu.au
SUMMARY:Modeling patient tissues at molecular resolution with Eva
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Yufan Liu\, HKU \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\nTissue structure is essential to function and homeostasis in all organs\, and disruptions to structure usually indicate disease. Modeling relationships between structural\, molecular\, and clinical aspects of tissues could advance new diagnostics and treatment strategies. Although profiling techniques like spatial proteomics can capture these relationships\, the data remain challenging to extract insight from. Here\, we present Eva\, a foundation model for tissue imaging data that learns multi-scale spatial representations of tissues at the molecular\, cellular\, and sample level. Eva uses a novel vision transformer architecture and is pre-trained on masked reconstruction of over 40 million matched spatial proteomics and histopathology images. We show that Eva excels at a variety of tasks\, including cross-modal inference from H&E to proteomics stains\, quality control\, data annotation\, zero-shot retrieval\, survival modeling\, and patient stratification. Extensive evaluations on held-out validation data demonstrate the versatility and generalizability of the learned embeddings. We anticipate that Eva will accelerate translational science by bridging basic research and clinical practice. \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\nYufan Liu\n\n\n\nYufan Liu is a PhD candidate in Computer Science at the School of Computing and Data Science\, The University of Hong Kong. His research interests include AI4Science\, bioinformatics\, and computational biology\, with a particular focus on spatial biology.
URL:https://spds.sydney.edu.au/event/modeling-patient-tissues-at-molecular-resolution-with-eva/
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260831T130000
DTEND;TZID=Australia/Sydney:20260831T140000
DTSTAMP:20260817T044136Z
CREATED:20260817T043448Z
LAST-MODIFIED:20260817T044136Z
UID:5447-1788181200-1788184800@spds.sydney.edu.au
SUMMARY:Illuminating the dark proteome: Insights from RNA translation in health and disease
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Sonia Chothani\, Genome Institute of Singapore \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\nRNA translation is a fundamental process in gene expression\, yet its extent\, role and regulation in human diseases remain unclear. In my talk\, I will describe how our data-driven approaches to study RNA translation have revealed a substantial portion of gene expression activity that has been overlooked when studying diseases. This has led to 1) a paradigm shift in that human transcripts often translate multiple distinct open reading frames (ORFs)\, 2) a 40% increase in translated ORFs encoded in the human genome including within lncRNAs and 3) detection of widespread gene expression changes that were previously missed in traditional RNA-based target discovery screens. These findings highlight a largely untapped search-space for new biomarkers\, therapeutic targets and antigens for targeted therapy. Additionally\, the algorithms developed provide a powerful platform to gain insights on the translatome in any given system\, as demonstrated by our work in fibrosis\, inflammation\, and cancer\, paving the way for future breakthroughs in RNA biology. \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 Sonia Chothani\n\n\n\nDr Sonia Chothani is a Principal Investigator at the Genome Institute of Singapore\, A*STAR\, Singapore. She holds a B.Tech in Biotechnology from IIT Madras and an M.S. in Computational Biology from Carnegie Mellon University and a Ph.D. in Integrated Biology and Medicine at Duke-NUS medical school\, where she specialized in RNA translation and its regulation in cardiac fibrosis. She holds 3 patents and has published >35 papers (h-index 18) in journals such as Circulation\, Molecular Cell\, Nature\, and Science Translational Medicine. Through an interdisciplinary approach that combines computational and experimental biology\, her lab aims to translate fundamental discoveries in RNA translation biology into new opportunities for cancer treatment. \n\n\n\nConnect with Sonia:https://www.linkedin.com/in/sonia-chothani-b192b515/
URL:https://spds.sydney.edu.au/event/illuminating-the-dark-proteome-insights-from-rna-translation-in-health-and-disease/
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260907T130000
DTEND;TZID=Australia/Sydney:20260907T140000
DTSTAMP:20260824T002437Z
CREATED:20260823T235037Z
LAST-MODIFIED:20260824T002437Z
UID:5463-1788786000-1788789600@spds.sydney.edu.au
SUMMARY:Single-cell spatial pharmacobiology identifies conserved stromal barriers to therapeutic antibody delivery in human solid tumors
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Guolan Lu\, Stanford 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\nThe development of effective antibody therapeutics has been hampered by a lack of methods to measure drug delivery and activity within tumors at single-cell resolution. Here we introduce single-cell spatial pharmacobiology (SSP)\, an experimental and analytical framework that integrates in situ imaging of a systemically infused\, fluorescently labeled therapeutic antibody with high-plex spatial proteomics to quantify antibody distribution\, target engagement and tumor microenvironment (TME) architecture. We applied SSP to tumor tissues from participants with head and neck squamous cell carcinoma and pancreatic ductal adenocarcinoma who received the antibody panitumumab-IRDye800 in phase 1 trials. SSP identified pronounced spatial heterogeneity in single-cell drug delivery and target engagement\, shaped by conserved stromal barriers\, including periostin-rich extracellular matrix assemblies and fibroblast-activation-protein-positive cancer-associated fibroblast neighborhoods\, which were associated with reduced antibody delivery in both tumor types. SSP measures drug–target–TME interactions in human tumors and can support studies of resistance mechanisms\, dosing strategies and discovery of spatial biomarkers for precision oncology. \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 Guolan Lu\n\n\n\nGuolan Lu is a tenure-track Assistant Professor at Stanford University. Her laboratory develops spatial multi-omics and artificial intelligence technologies to determine how tissue organization shapes disease progression and therapeutic response. During her postdoctoral training at Stanford with Eben Rosenthal and Garry Nolan\, she designed and led a first-in-human fluorescence-guided surgery study in pancreatic cancer and pioneered single-cell spatial pharmacobiology in human tumors. She received her PhD through the joint Biomedical Engineering program at Georgia Tech and Emory University. \n\n\n\nConnect with Guolan:X: @GuolanLu
URL:https://spds.sydney.edu.au/event/single-cell-spatial-pharmacobiology-identifies-conserved-stromal-barriers-to-therapeutic-antibody-delivery-in-human-solid-tumors/
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20260914T130000
DTEND;TZID=Australia/Sydney:20260914T140000
DTSTAMP:20260903T020427Z
CREATED:20260902T015611Z
LAST-MODIFIED:20260903T020427Z
UID:5473-1789390800-1789394400@spds.sydney.edu.au
SUMMARY:Joint PhD student presentations
DESCRIPTION:This is a hybrid event with two presentations. In person venue: Carslaw Lecture Theatre 373Zoom link: https://uni-sydney.zoom.us/j/85114748391 \n\n\n\n\n\n\n\n\n\nInterpretable analytical methods for characterising disease-associated phenotypic modulation of cellsSpeaker: Elijah Willie\, University of Sydney\n\n\n\nRealising the analytical potential of single-cell technologies requires computational frameworks that address fundamental challenges in characterising cellular heterogeneity and disease-associated phenotypes. High-throughput cytometry and spatial omics platforms have advanced substantially in their resolution and throughput\, enabling simultaneous measurement of dozens of parameters across millions of cells. However\, these technologies present analytical obstacles including technical artifacts\, batch effects\, and the complexity of spatial interactions that limit biological discovery and clinical application. Addressing challenges such as robust cell type identification\, cross-cohort transferability\, and spatial variance decomposition necessitates the development of innovative computational approaches. \n\n\n\nTo this end\, this work contributes to single-cell computational biology by (1) establishing a multiview framework that harmonizes correlation and distance metrics through ensemble learning\, overcoming similarity metric limitations in imaging cytometry to enable robust identification of cellular phenotypes\, (2) developing transferable deep learning approaches combining batch-agnostic normalization with hierarchically-structured feature selection to enable cross-institutional validation of cytometry-based biomarkers\, and (3) creating variance decomposition methods for spatial transcriptomics that quantify cell type-specific interactions while correcting for lateral spillover\, revealing how proximity modulates gene expression programs in breast cancer and melanoma. The frameworks presented here provide validated computational tools that advance single-cell analysis and establish foundations for future development of interpretable methods that characterise disease-associated cellular modulation. \n\n\n\n\n\n\n\nMultimodal learning for organ transplant diagnosticsSpeaker: Harry Robertson\, University of Sydney\n\n\n\nKidney transplantation remains the only solution for end-stage renal failure\, yet chronic immune injury and reactive monitoring limit long-term graft survival and patient quality of life. Current strategies to monitor patients are reactive and require invasive biopsies to diagnose allograft dysfunction. These biopsies are assessed by semi-quantitative criteria with known between-reader variability. This thesis develops 1) a blood test for allograft rejection and 2) a co-pilot for renal biopsy assessment. To develop our blood test\, we first assembled PROMAD\, the largest transcriptomic resource in the field\, spanning more than 150 studies and 12\,000 samples across kidney\, heart\, liver and lung transplantation. By developing TOP\, a novel transfer learning framework\, we produced an organ-agnostic blood test that predicts biopsy-proven rejection (AUC=0.81)\, outperforming the current standard-of-care. Next\, we developed RenalAgent an AI co-pilot that reads kidney biopsies\, scores lesions and produces evidence-linked reports. Across 15\,082 biopsies from Mass General Brigham (MGB)\, RenalAgent achieved high concordance with expert pathologists (mean kappa=0.73 across 16 tasks). In external cohorts from Australia\, Europe\, and Mt Sinai (US)\, RenalAgent demonstrated prognostic utility through longitudinal assessment\, predicting graft loss better than the current standard-of-care. Together\, these contributions aim to advance precision monitoring for transplant recipients. \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\nElijah Willie\n\n\n\nElijah studied computer science and molecular biology as an undergraduate at Simon Fraser University\, then completed a master’s in bioinformatics at the University of British Columbia. After a few years working as a bioinformatician\, he began his PhD at the University of Sydney in 2022 and completed it in 2025. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHarry Robertson\n\n\n\nHarry completed his undergraduate degree in applied medical science at the University of Sydney in 2021. During his PhD he received a Westpac Future Leaders Scholarship\, and in 2024 a Fulbright Future Scholarship to study under Prof. Faisal Mahmood at Harvard. His work has been published in Nature Medicine.
URL:https://spds.sydney.edu.au/event/statistical-bioinformatics-seminar/
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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/
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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/
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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
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