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DTSTART;TZID=Australia/Sydney:20260914T130000
DTEND;TZID=Australia/Sydney:20260914T140000
DTSTAMP:20260918T012136Z
CREATED:20260902T015611Z
LAST-MODIFIED:20260918T012136Z
UID:5473-1789390800-1789394400@spds.sydney.edu.au
SUMMARY:Joint PhD student presentations
DESCRIPTION:This was a hybrid event with two presentations.  \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:20260925T005653Z
CREATED:20260914T031524Z
LAST-MODIFIED:20260925T005653Z
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 was an online event held via Zoom. \n\n\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:20261009T052231Z
CREATED:20260930T003331Z
LAST-MODIFIED:20261009T052231Z
UID:5541-1790600400-1790604000@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 was an online event held via Zoom. \n\n\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-2/
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20261012T130000
DTEND;TZID=Australia/Sydney:20261012T140000
DTSTAMP:20261019T051733Z
CREATED:20261019T051703Z
LAST-MODIFIED:20261019T051733Z
UID:5573-1791810000-1791813600@spds.sydney.edu.au
SUMMARY:Predicting Cellular Perturbations: Biological Benchmarks and AI-Assisted Model Discovery
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Yiqun Chen\, Johns Hopkins University \n\n\n\nThis was an online event held via Zoom.Zoom link: https://uni-sydney.zoom.us/j/85114748391 \n\n\n\n\n\n\n\n\n\nLarge-scale genetic perturbation screens have enabled researchers to probe gene function and regulatory networks\, but measuring changes in gene expression for every perturbation in a new cellular context remains experimentally costly\, if not infeasible. This limitation has motivated substantial interest in machine learning (ML) and artificial intelligence (AI) models that predict transcriptional responses to unmeasured perturbations and cellular contexts. \n\n\n\nIn this talk\, I will give an overview of a simplified framing of the problem and present some recent work addressing these questions through statistical modeling\, biological benchmarking\, and AI-assisted model development. We begin with a prediction benchmarking study that compares popular deep learning methods versus simplified statistical models built on top of pretrained embeddings (vectors summarizing biological information) for classifying whether perturbation–gene pairs lead to up\, down\, or no changes in differential expression (DE). We find that models with strong expression-level accuracy do not necessarily identify the genes that respond to a perturbation. In particular\, discrepancies between predicted and observed expression distributions can produce substantial disagreement in differential expression calls. \n\n\n\nI will also discuss our preliminary experiments using AI agents to develop perturbation-prediction methods. Some agent-developed models outperform established baselines on expression prediction\, and inspection of their analysis workflows reveals recurring uses of biological knowledge and statistical adjustments that are conceptually simple\, yet elaborate and informative. These results highlight opportunities for agents to help discover useful modeling strategies\, while raising questions about reproducibility\, evaluation\, and the translation and generalization of predictive gains to other related problems. Our work explores how biological knowledge\, statistical structure\, and increasingly automated model development can improve the prediction and interpretation of cellular perturbations. \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 Yiqun Chen\n\n\n\nYiqun Chen (he/him) is an Assistant Professor of Biostatistics with a courtesy appointment in Computer Science at Johns Hopkins University. He was previously a data science postdoctoral fellow at Stanford and received his PhD from the University of Washington\, Seattle. His research focuses on statistical inference and evaluation frameworks for modern AI agents and biomedical data science practice\, with recent work exploring creative ways to leverage large language models for scientific data analysis and discovery.  \n\n\n\nConnect with Yiqun:X: @yc_yc_yc_yc
URL:https://spds.sydney.edu.au/event/predicting-cellular-perturbations-biological-benchmarks-and-ai-assisted-model-discovery-2/
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20261015T110000
DTEND;TZID=Australia/Sydney:20261015T120000
DTSTAMP:20260923T010945Z
CREATED:20260909T015737Z
LAST-MODIFIED:20260923T010945Z
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\nFor more information\, please visit this page.\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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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20261019T130000
DTEND;TZID=Australia/Sydney:20261019T140000
DTSTAMP:20261009T031704Z
CREATED:20260914T032605Z
LAST-MODIFIED:20261009T031704Z
UID:5507-1792414800-1792418400@spds.sydney.edu.au
SUMMARY:Genome-wide genetic models for association\, prediction and heritability analyses 
DESCRIPTION:Judith and David Coffey SeminarSpeaker: Prof David Balding\, University of Melbourne \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\nGenome-wide genetic models have been widely used\, and have led to big advances in understanding the genomic architecture of complex traits\, and in predicting these traits. However\, the statistical models are fundamentally ill-posed due to both “too many predictors” and correlation among the predictors. Different approaches to regularisation to overcome these problems can lead to important differences in results\, and there has been confusion and controversy over model choices. I present a framework for resolving these problems that leads to important gains in the power of statistical tests and accuracy of prediction. Our framework uses shrinkage regression with a distinct shrinkage parameter for each genetic predictor (SNP)\, determined by fitting a low-dimensional “heritability model” with independent variables that can include genome annotation features as well as LD and MAF.  Applying our modelling framework to the UK Biobank and other datasets\, I will discuss advances in prediction accuracy and in understanding how causal effects are distributed across the genome. If time permits I might briefly speak on other recent work\, including assessing the weight of evidence for Y-chromosome DNA profiles. \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 David Balding\n\n\n\nDavid Balding is Honorary Professor of Statistical Genetics at the University of Melbourne\, and a former Director of Melbourne Integrative Genomics. He was educated at the University of Newcastle\, NSW (B Math) and the University of Oxford\, UK (D Phil). He is a fellow of the Australian Academy of Science\, and was recently President of the International Genetic Epidemiology Society (IGES)\, Section Editor for Methods at PLOS Genetics and lead editor of the Handbook of Statistical Genomics.  In the 2025 ScholarGPS Highly Ranked Scholars\, he was ranked 1st in the world for DNA profiling.
URL:https://spds.sydney.edu.au/event/powerful-and-accurate-case-control-analysis-of-spatial-molecular-data/
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DTSTART;TZID=Australia/Sydney:20261026T130000
DTEND;TZID=Australia/Sydney:20261026T140000
DTSTAMP:20261023T042619Z
CREATED:20261023T042617Z
LAST-MODIFIED:20261023T042619Z
UID:5582-1793019600-1793023200@spds.sydney.edu.au
SUMMARY:Foundation Models for Genomics: From Epigenomic Prediction to Biological Reasoning
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Dr Wenpin Hou\, Duke University \n\n\n\nThis is an online event held via Zoom.Zoom link: https://uni-sydney.zoom.us/j/85114748391 \n\n\n\n\n\n\n\n\n\nRecent advances in foundation models and large language models (LLMs) are creating new opportunities for analyzing complex epigenomic data and accelerating biomedical discovery. However\, important challenges remain in learning meaningful biological representations and ensuring that model predictions and reasoning are accurate\, reliable\, and biologically grounded. In this talk\, I will present our research in two complementary directions. First\, I will discuss the development of foundation models for genomic and epigenomic data\, highlighting how deep learning can capture complex relationships between genomic context and DNA methylation to enable genome-wide epigenomic prediction without requiring direct methylation measurements. Second\, I will discuss our efforts to apply and systematically evaluate LLMs for biological reasoning and computational analysis\, including cell-type annotation\, biological knowledge assessment\, and biomedical image classification. I will highlight both the capabilities and limitations of current models\, as well as approaches to improve their reliability through biological knowledge and ontology grounding. \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 Wenpin Hou\n\n\n\nDr Wenpin Hou is an Assistant Professor in the Department of Biostatistics and Bioinformatics at Duke University School of Medicine\, affiliated with New York Genome Center. Her research lies at the intersection of computational biology\, artificial intelligence\, and statistical genomics\, with a focus on developing computational methods to understand gene regulation using single-cell\, spatial\, and epigenomic data. More recently\, her group has focused on foundation models and AI-driven approaches for genomics\, aiming to build predictive models of cellular states and regulatory programs across diverse biological contexts. Prior to joining Duke\, Dr Hou was an Assistant Professor in the Department of Biostatistics at Columbia University and an affiliated member of the Data Science Institute. She completed her postdoctoral training in Biostatistics and Computer Science at Johns Hopkins University and received her Ph.D. in Mathematics from The University of Hong Kong. Her work has been recognized through NIH Pathway to Independence Award (K99/R00) and Maximizing Investigators’ Research Award (MIRA) for Early Stage Investigators. She is also a recipient of the 2025 Mentor of the Year Award at Columbia University Irving Medical Center.
URL:https://spds.sydney.edu.au/event/foundation-models-for-genomics-from-epigenomic-prediction-to-biological-reasoning/
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20261109T130000
DTEND;TZID=Australia/Sydney:20261109T140000
DTSTAMP:20261021T003844Z
CREATED:20261009T035522Z
LAST-MODIFIED:20261021T003844Z
UID:5561-1794229200-1794232800@spds.sydney.edu.au
SUMMARY:Large-scale GWAS integrated with fine-mapping and single-cell analyses prioritizes effector genes\, cell-contexts of glaucoma risk
DESCRIPTION:Statistical Bioinformatics SeminarSpeaker: Han Wang\, Peking University \n\n\n\nThis is an online event held via Zoom.Zoom link: https://uni-sydney.zoom.us/j/85114748391 \n\n\n\n\n\n\n\n\n\nPrimary open-angle glaucoma (POAG) is the leading cause of irreversible blindness worldwide\, and the genetic architecture of POAG at cellular level remains incompletely understood. In this study\, we conducted a large-scale genome-wide association study of POAG involving approximately 3 million individuals (97\,229 cases) and identified 495 loci (198 novel). External evaluation in 23andMe Research Institute (114\,468 cases and 3\,525\,660 controls) showed that 412 (84.6%) had P < 0.0001 after Bonferroni correction. Integrating multi-omics datasets with advanced fine-mapping approaches\, we developed a glaucoma gene prioritization framework and identified 285 effector genes. Single-cell RNA-seq analysis revealed retinal cell types including astrocytes\, Müller glia\, retinal pigment epithelial cells\, and retinal ganglion cells. Using our new scRNA-seq in primary trabecular meshwork cells (TMCs)\, we further prioritized TMC-context putative genes\, including ADAM15 and GAS7. Finally\, drug-repurposing analyses identified therapeutic target hypotheses for POAG. Together\, these findings provide a comprehensive genetic architecture for POAG\, highlighting novel effector genes and cellular contexts\, and offering potential drug repurposing opportunities. \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\nHan Wang\n\n\n\nHan Wang is a PhD student (work with Dr Xikun Han) in Epidemiology and Biostatistics at Peking University. His research lies at the intersection of statistical genetics and ophthalmology genetics\, focusing on dissecting the genetic architecture of glaucoma and retinal diseases. He integrates large-scale GWAS\, single-cell RNA/ATAC sequencing\, and gene prioritization to identify effector genes and to repurpose existing drugs for glaucoma.
URL:https://spds.sydney.edu.au/event/predicting-cellular-perturbations-biological-benchmarks-and-ai-assisted-model-discovery/
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