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DTSTART;TZID=Australia/Sydney:20260810T130000
DTEND;TZID=Australia/Sydney:20260810T140000
DTSTAMP:20260814T062437Z
CREATED:20260724T064257Z
LAST-MODIFIED:20260814T062437Z
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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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END:VEVENT
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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END:VEVENT
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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END:VEVENT
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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END:VEVENT
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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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/
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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
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