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X-WR-CALDESC:Events for Harvard T.H. Chan School of Public Health
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240417T160000
DTEND;TZID=America/New_York:20240417T170000
DTSTAMP:20260512T180900
CREATED:20240415T180824Z
LAST-MODIFIED:20241122T064634Z
UID:111360004403-1713369600-1713373200@hsph.harvard.edu
SUMMARY:Quantitative Issues in Cancer Research Working Group Seminar
DESCRIPTION:Home / Building 2\, Room 426\n\n\n\n\n\n\nBuilding 2\, Room 426\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTime \n\n\n\n\n\n\n\nEvent Type \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nKimberly Greco\, PhD Student\, Department of Biostatistics\, Harvard T.H. Chan School of Public Health \n\n\nGraph Attention Framework to Enhance Rare Disease Sub-Phenotyping from EHR\n \n\n\nAbstract: Accurately sub-phenotyping patients according to their risk for an adverse clinical outcome can significantly enhance clinical decision-making. Recent advances in patient representation learning have enabled the development of sophisticated clustering algorithms designed to accurately sub-phenotype patients in ways that are predictive of these outcomes. To optimize data for clustering\, we introduce a methodology utilizing a Graph Attention Network (GAT) to enhance Electronic Health Record (EHR) code-level embeddings. This approach facilitates the generation of rich patient-level embeddings\, which are then leveraged in downstream clustering tasks aimed at sub-phenotyping patients based on their risk of experiencing a particular outcome. Building on this foundation\, we explore ongoing work focused on advancing personalized medicine for patients with rare diseases. \n\n\n\n\n\n\n\n\n	\n		\n		\n			\n				\n					\n						Unleash your potential at Harvard Chan School.					\n					In addition to our degree programs\, we offer highly targeted programs through our Advanced Learning Academy\, directed and taught by Harvard faculty. \n											\n																															\n									\n										Degree Programs									\n								\n																															\n									\n										How to Apply									\n								\n																															\n									\n										Advanced Learning Academy
URL:https://hsph.harvard.edu/biostatistics/events/quantitative-issues-in-cancer-research-working-group-seminar-111/
LOCATION:Building 2\, Room 426
ATTACH;FMTTYPE=application/pdf:https://hsph.harvard.edu/wp-content/uploads/2024/04/Cancer-Working-Group-Seminar-4-17-2024.pdf
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240410T160000
DTEND;TZID=America/New_York:20240410T170000
DTSTAMP:20260512T180900
CREATED:20240408T163735Z
LAST-MODIFIED:20241122T064621Z
UID:111360004399-1712764800-1712768400@hsph.harvard.edu
SUMMARY:Quantitative Issues in Cancer Research Working Group Seminar
DESCRIPTION:Home / Building 2\, Room 426\n\n\n\n\n\n\nBuilding 2\, Room 426\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTime \n\n\n\n\n\n\n\nEvent Type \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nElizabeth Graff\, PhD Student\, Department of Biostatistics\, Harvard T.H. Chan School of Public Health \n\n\nApplications of Deep Learning for Graph-Structured Data: From Disease Spread to Social Networks \n\n\nAbstract: How can we apply deep learning to solve problems in modeling the spread of disease? In this talk\, we will explore the components and applications of Graph Neural Networks (GNNs)\, a class of neural networks that are specifically designed to learn from graph-structured data. We will discuss the versatility of graphs in representing complex relationships across various domains from molecular structures to social networks\, which necessitates models like GNNs that can capture both the graph topology and node-level information. We will examine examples of studies that leverage GNNs to achieve machine learning tasks on graphs\, including node and edge predictions and whole graph classifications. \n\n\n\n\n\n\n\n\n	\n		\n		\n			\n				\n					\n						Unleash your potential at Harvard Chan School.					\n					In addition to our degree programs\, we offer highly targeted programs through our Advanced Learning Academy\, directed and taught by Harvard faculty. \n											\n																															\n									\n										Degree Programs									\n								\n																															\n									\n										How to Apply									\n								\n																															\n									\n										Advanced Learning Academy
URL:https://hsph.harvard.edu/biostatistics/events/quantitative-issues-in-cancer-research-working-group-seminar-110/
LOCATION:Building 2\, Room 426
ATTACH;FMTTYPE=application/pdf:https://hsph.harvard.edu/wp-content/uploads/2024/04/Cancer-Working-Group-Seminar-4-10-2024.pdf
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240409T130000
DTEND;TZID=America/New_York:20240409T140000
DTSTAMP:20260512T180900
CREATED:20240405T160853Z
LAST-MODIFIED:20241122T064619Z
UID:111360004398-1712667600-1712671200@hsph.harvard.edu
SUMMARY:PQG Seminar
DESCRIPTION:Home / Building 2\, Room 426\n\n\n\n\n\n\nBuilding 2\, Room 426\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTime \n\n\n\n\n\n\n\nEvent Type \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nRuben Dries \n\nAssistant Professor\, Medicine\nBoston University \n\nTowards Solutions for Large-Scale Multi-Modal Spatial Data Analysis\n\n\n\nIn the burgeoning field of spatial biology\, the integration of multi-modal spatial omics technologies presents both a formidable challenge and a tremendous opportunity for advancing clinical research and diagnostics. I will discuss the concerted efforts of our laboratory to address the complexities inherent in large-scale multi-modal spatial data analysis\, with a specific focus on making spatial biology more accessible for clinical projects. Our approach is threefold: firstly\, we focus on implementing the latest spatial omics technologies with the goal to integrate their functional outputs and as such harness the full potential of spatially resolved molecular data.  Secondly\, we develop robust data structures tailored for the efficient storage\, retrieval\, and manipulation of large volumes of multi-modal spatial data\, ensuring that our solutions are scalable and adaptable to the ever-evolving landscape of spatial biology. Finally\, we prioritize the usability of our analytical tools and strategies\, offering a user-friendly interface that empowers clinicians and researchers with minimal computational background to engage in sophisticated spatial data analysis. By addressing these key areas\, our laboratory not only aims to advance the methodological framework for spatial data analysis but also to foster the integration of spatial omics data into routine clinical practice\, thereby opening new avenues for personalized medicine and biomarker discovery. Through this integrated approach\, we contribute to the establishment of a more accessible\, efficient\, and comprehensive ecosystem for the analysis of spatial biology data\, ultimately facilitating the translation of complex spatial omics data into actionable clinical insights.
URL:https://hsph.harvard.edu/biostatistics/events/pqg-seminar-32/
LOCATION:Building 2\, Room 426
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240403T160000
DTEND;TZID=America/New_York:20240403T170000
DTSTAMP:20260512T180900
CREATED:20240403T174248Z
LAST-MODIFIED:20241122T064610Z
UID:111360004396-1712160000-1712163600@hsph.harvard.edu
SUMMARY:Quantitative Issues in Cancer Research Working Group Seminar
DESCRIPTION:Home / Building 2\, Room 426\n\n\n\n\n\n\nBuilding 2\, Room 426\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTime \n\n\n\n\n\n\n\nEvent Type \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChristian Covington\, PhD Student\, Department of Biostatistics\, Harvard T.H. Chan School of Public Health \n\n\nStatistical theory and the practice of data analysis: A brief and biased history \n\n\nAbstract: This talk gives an account of the replication crisis and how different disciplines– namely applied sciences\, statistics\, and theoretical computer science (TCS)\, have developed their own research agendas in order to address it. I distinguish between two tracks in the history of methodological development: one regarding adaptivity in data analysis\, the other regarding “methodological uncertainty” in model selection\, data processing choices\, etc. \n\n\nI provide an overview of a few different methodological approaches\, developed in the statistics and TCS communities\, for achieving valid inference under adaptivity. Then I describe two increasingly popular frameworks developed primarily by psychologists for incorporating methodological uncertainty into a data analysis pipeline: multiverse analysis and specification curve analysis. Through examples\, I explore confusion and disagreement about how these ideas ought to be used. Finally\, I argue that more work is needed to understand what these methods can and can’t provide\, both philosophically and statistically\, and provide some preliminary ideas to this end. \n\n\n\n\n\n\n\n\n	\n		\n		\n			\n				\n					\n						Unleash your potential at Harvard Chan School.					\n					In addition to our degree programs\, we offer highly targeted programs through our Advanced Learning Academy\, directed and taught by Harvard faculty. \n											\n																															\n									\n										Degree Programs									\n								\n																															\n									\n										How to Apply									\n								\n																															\n									\n										Advanced Learning Academy
URL:https://hsph.harvard.edu/biostatistics/events/quantitative-issues-in-cancer-research-working-group-seminar-109/
LOCATION:Building 2\, Room 426
ATTACH;FMTTYPE=application/pdf:https://hsph.harvard.edu/wp-content/uploads/2024/04/Cancer-Working-Group-Seminar-4-3-2024.pdf
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240327T160000
DTEND;TZID=America/New_York:20240327T170000
DTSTAMP:20260512T180900
CREATED:20240322T203054Z
LAST-MODIFIED:20241122T064555Z
UID:111360004393-1711555200-1711558800@hsph.harvard.edu
SUMMARY:Quantitative Issues in Cancer Research Working Group Seminar
DESCRIPTION:Home / Building 2\, Room 426\n\n\n\n\n\n\nBuilding 2\, Room 426\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTime \n\n\n\n\n\n\n\nEvent Type \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMónica Robles Fontán\, PhD Student\, Department of Biostatistics\, Harvard T.H. Chan School of Public Health \n\n\nLeveraging Record Linkage To Enhance Public Health Research \n\n\nAbstract: Record linkage is the task of combining records from different populations that belong to a single entity to create a new single population. This task allows researchers to take advantage of existing data sources to answer scientific questions that otherwise would be difficult to assess\, such as studies requiring large sample sizes. There are two main approaches to performing record linkage tasks: deterministically and probabilistically\, although most implementations combine both approaches. In this talk\, we will explore the problem of record linkage and discuss the theoretical framework as developed by Fellegi & Sunter (1969). We will discuss practical issues that arise in the task of record linkage\, as well as a real-world example in the context of observational data for COVID-19 vaccination and outcomes from Puerto Rico. \n\n\n\n\n\n\n\n\n	\n		\n		\n			\n				\n					\n						Unleash your potential at Harvard Chan School.					\n					In addition to our degree programs\, we offer highly targeted programs through our Advanced Learning Academy\, directed and taught by Harvard faculty. \n											\n																															\n									\n										Degree Programs									\n								\n																															\n									\n										How to Apply									\n								\n																															\n									\n										Advanced Learning Academy
URL:https://hsph.harvard.edu/biostatistics/events/quantitative-issues-in-cancer-research-working-group-seminar-108/
LOCATION:Building 2\, Room 426
ATTACH;FMTTYPE=application/pdf:https://hsph.harvard.edu/wp-content/uploads/2024/03/Cancer-Working-Group-Seminar-3-27-2024.pdf
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240322T130000
DTEND;TZID=America/New_York:20240322T135000
DTSTAMP:20260512T180900
CREATED:20240319T210255Z
LAST-MODIFIED:20241122T064553Z
UID:111360004392-1711112400-1711115400@hsph.harvard.edu
SUMMARY:HIV Working Group Seminar
DESCRIPTION:Home / Building 2\, Room 426\n\n\n\n\n\n\nBuilding 2\, Room 426\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTime \n\n\n\n\n\n\n\nEvent Type \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTanayott (Tony) Thaweethai\nAssociate Director for Biostatistics Research and Engagement\, Massachusetts General Hospital Biostatistics \n\n\nDevelopment of a symptom-based definition of long COVID using negative-unlabeled data\nAbstract: While most people infected with COVID-19 recover after the acute phase of infection\, some people continue to experience persistent symptoms months and even years after infection. These symptoms\, also known as post-acute sequelae of SARS- CoV-2 infection (PASC)\, or long COVID\, present a significant public health concern. However\, long COVID remains poorly understood\, as there is no generally agreed upon definition of PASC in the medical community. This has made it difficult to characterize population-level burden of the disease or study pathophysiological mechanisms. Because only those who are infected can develop PASC\, we are in the world of negative-unlabeled data\, where uninfected individuals are PASC-negative but infected individuals are a mixture of PASC-positive and PASC-negative. In this talk\, I will present a novel statistical approach that we used to develop the first data-driven\, symptom- based definition of long COVID from RECOVER\, the largest observational cohort study of long COVID in adults and children. \n\n\n\n\n\n\n\n\n	\n		\n		\n			\n				\n					\n						Unleash your potential at Harvard Chan School.					\n					In addition to our degree programs\, we offer highly targeted programs through our Advanced Learning Academy\, directed and taught by Harvard faculty. \n											\n																															\n									\n										Degree Programs									\n								\n																															\n									\n										How to Apply									\n								\n																															\n									\n										Advanced Learning Academy
URL:https://hsph.harvard.edu/biostatistics/events/hiv-working-group-seminar-49/
LOCATION:Building 2\, Room 426
ATTACH;FMTTYPE=application/pdf:https://hsph.harvard.edu/wp-content/uploads/2024/03/HIV-Working-Group-3-22-2024.pdf
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240320T160000
DTEND;TZID=America/New_York:20240320T170000
DTSTAMP:20260512T180900
CREATED:20240318T180338Z
LAST-MODIFIED:20241122T064537Z
UID:111360004391-1710950400-1710954000@hsph.harvard.edu
SUMMARY:Quantitative Issues in Cancer Research Working Group Seminar
DESCRIPTION:Home / Building 2\, Room 426\n\n\n\n\n\n\nBuilding 2\, Room 426\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTime \n\n\n\n\n\n\n\nEvent Type \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSajia Darwish\, PhD Student\, Department of Biostatistics\, Harvard T.H. Chan School of Public Health \n\n\nDiscussion of “What is the probability of replicating a statistically significant effect?” (Miller 2009) \n\n\nAbstract: If an initial experiment produces a statistically significant effect\, what is the probability that this effect will be replicated in a follow-up experiment? [This paper] argues that this seemingly fundamental question can be interpreted in two very different ways and that its answer is\, in practice\, virtually unknowable under either interpretation. Although the data from an initial experiment can be used to estimate one type of replication probability\, this estimate will rarely be precise enough to be of any use. The other type of replication probability is also unknowable\, because it depends on unknown aspects of the research context. Thus\, although it would be nice to know the probability of replicating a significant effect\, researchers must accept the fact that they generally cannot determine this information\, whichever type of replication probability they seek. \n\n\n\n\n\n\n\n\n	\n		\n		\n			\n				\n					\n						Unleash your potential at Harvard Chan School.					\n					In addition to our degree programs\, we offer highly targeted programs through our Advanced Learning Academy\, directed and taught by Harvard faculty. \n											\n																															\n									\n										Degree Programs									\n								\n																															\n									\n										How to Apply									\n								\n																															\n									\n										Advanced Learning Academy
URL:https://hsph.harvard.edu/biostatistics/events/quantitative-issues-in-cancer-research-working-group-seminar-107/
LOCATION:Building 2\, Room 426
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240319T130000
DTEND;TZID=America/New_York:20240319T140000
DTSTAMP:20260512T180900
CREATED:20240307T224115Z
LAST-MODIFIED:20241122T064524Z
UID:111360004388-1710853200-1710856800@hsph.harvard.edu
SUMMARY:PQG Student and Postdoc Seminar
DESCRIPTION:Home / Building 2\, Room 426\n\n\n\n\n\n\nBuilding 2\, Room 426\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTime \n\n\n\n\n\n\n\nEvent Type \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nKodi Taraszka \n\n\n\nResearch Fellow in Medicine\nDana-Farber Cancer Institute\n\nCOX proportional hazards Mixed Model (COXMM) accurately estimates the heritability of time-to-event traits\n\nWith large biobanks connecting electronic health records with genetic sequencing\, our understanding of the genetic architecture of time-to-event (TTE) traits such as age-of-onset\, treatment response\, and disease progression has grown. As a result\, several genome-wide association study (GWAS) methods have been developed based on the TTE phenotypic generative model (PGM); however\, all existing heritability methods still model a linear relationship between the trait and genetics. Here\, we propose a new heritability method\, COXMM\, a COX proportional hazard Mixed Model designed to estimate the heritability of traits which follow a TTE PGM. We demonstrate the efficacy of COXMM for TTE heritability estimation\, both in simulations and in the UK Biobank.\n\n\n\n\n\n\n\n	\n		\n		\n			\n				\n					\n						Unleash your potential at Harvard Chan School.					\n					In addition to our degree programs\, we offer highly targeted programs through our Advanced Learning Academy\, directed and taught by Harvard faculty. \n											\n																															\n									\n										Degree Programs									\n								\n																															\n									\n										How to Apply									\n								\n																															\n									\n										Advanced Learning Academy
URL:https://hsph.harvard.edu/biostatistics/events/pqg-student-and-postdoc-seminar/
LOCATION:Building 2\, Room 426
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240306T160000
DTEND;TZID=America/New_York:20240306T170000
DTSTAMP:20260512T180900
CREATED:20240305T194848Z
LAST-MODIFIED:20241122T064522Z
UID:111360004387-1709740800-1709744400@hsph.harvard.edu
SUMMARY:Quantitative Issues in Cancer Research Working Group Seminar
DESCRIPTION:Home / Building 2\, Room 426\n\n\n\n\n\n\nBuilding 2\, Room 426\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTime \n\n\n\n\n\n\n\nEvent Type \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPhillip Nicol\, PhD Student\, Department of Biostatistics\, Harvard T.H. Chan School of Public Health \n\n\nEstimation in Poisson log-bilinear models \n\n\nAbstract: The Poisson log-bilinear model\, also known as GLM-PCA\, is a commonly used approach for dimension reduction in single-cell RNA-seq data. Model parameters are usually estimated via maximum likelihood. However\, we show that the MLE can be undefined for some realistic single-cell datasets. In this talk\, we show how this issue can be resolved by adding appropriate priors to the model parameters. Importantly\, the prior information can be incorporated with minor adjustments to existing estimation algorithm. We demonstrate the approach on real and simulated single cell data and discuss extensions to spatial transcriptomics.  \n\n\n\n\n\n\n\n\n	\n		\n		\n			\n				\n					\n						Unleash your potential at Harvard Chan School.					\n					In addition to our degree programs\, we offer highly targeted programs through our Advanced Learning Academy\, directed and taught by Harvard faculty. \n											\n																															\n									\n										Degree Programs									\n								\n																															\n									\n										How to Apply									\n								\n																															\n									\n										Advanced Learning Academy
URL:https://hsph.harvard.edu/biostatistics/events/quantitative-issues-in-cancer-research-working-group-seminar-106/
LOCATION:Building 2\, Room 426
ATTACH;FMTTYPE=application/pdf:https://hsph.harvard.edu/wp-content/uploads/2024/03/Cancer-Working-Group-Seminar-3-6-2024.pdf
END:VEVENT
END:VCALENDAR