BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Harvard T.H. Chan School of Public Health - ECPv6.11.2.1//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Harvard T.H. Chan School of Public Health X-ORIGINAL-URL:https://hsph.harvard.edu X-WR-CALDESC:Events for Harvard T.H. Chan School of Public Health REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20240310T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20241103T060000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/New_York:20240911T160000 DTEND;TZID=America/New_York:20240911T170000 DTSTAMP:20260512T171454 CREATED:20240909T194745Z LAST-MODIFIED:20241122T064950Z UID:111360004718-1726070400-1726074000@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\nAnuraag Gopaluni\, PhD Candidate\, Department of Biostatistics\, Harvard University \n\n\nMethods for accurate real-time estimates of death in the context of reporting delays\n \n\n\nAbstract: State-level mortality data in the United States is subject to reporting delays of up to 18 weeks\, causing gaps between reported and true mortality in the short-term. Existing methods for correcting gaps from reporting delays do not appropriately account for seasonality or uncertainty in prior lags. We use Massachusetts DPH data to develop a model based on discrete-time survival modeling combined with smoothing that accurately predicts the true death count on a daily basis with appropriate measures of uncertainty. \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-115/ LOCATION:Building 2\, Room 426 ATTACH;FMTTYPE=application/pdf:https://hsph.harvard.edu/wp-content/uploads/2024/09/Cancer-Working-Group-Seminar-9-11-2024.pdf END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20240906T130000 DTEND;TZID=America/New_York:20240906T135000 DTSTAMP:20260512T171454 CREATED:20240829T205629Z LAST-MODIFIED:20241122T064930Z UID:111360004716-1725627600-1725630600@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\nHIV Training Grant \n\n\nLightning Talks \n\n\nAbstract: Join us in learning about the important work being conducted by the PhD and Postdoctoral researchers on the HIV Training Grant! Over two sessions\, all 10 trainees will present 5-minute lightning talks about their research projects shaping the future of infectious disease and adjacent areas. \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-53/ LOCATION:Building 2\, Room 426 ATTACH;FMTTYPE=application/pdf:https://hsph.harvard.edu/wp-content/uploads/2024/08/HIV-Working-Group-9-6-2024.pdf END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20240904T110000 DTEND;TZID=America/New_York:20240904T130000 DTSTAMP:20260512T171454 CREATED:20240725T170622Z LAST-MODIFIED:20241122T064831Z UID:111360004581-1725447600-1725454800@hsph.harvard.edu SUMMARY:Thesis Defense - Peyton Smith 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\nPeyton will present the thesis entitled “Development of predictive tools for Alzheimer’s disease using multiomic platforms through application of penalized regression techniques”. The thesis committee is chaired by Dr. Christoph Lange\, and includes Dr. Georg Hahn and Dr. Erin Lake. \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/thesis-defense-peyton-smith/ LOCATION:Building 2\, Room 426 ATTACH;FMTTYPE=image/png:https://hsph.harvard.edu/wp-content/uploads/2024/07/09-04-2024_Thesis-Defense-Smith-Peyton.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20240514T130000 DTEND;TZID=America/New_York:20240514T140000 DTSTAMP:20260512T171454 CREATED:20240506T222728Z LAST-MODIFIED:20241122T064742Z UID:111360004423-1715691600-1715695200@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\nXiuwei Zhang \n\n Assistant Professor\, School of Computational Science and Engineering\nGeorgia Institute of Technology \n\n\n\nCell dynamics across various time scales and spatial coordinates \n\n\nIn biological processes like development and disease progression\, cells differentiate into various cell types. At the time scale of cell divisions\, lineage tracing technologies make it possible to reconstruct the cell division history\, and learn how cell types are formed through generations of cell divisions for one individual. At a larger time scale\, scRNA-seq data of a number of individuals under different conditions or developmental time points are obtained\, which allows for systematic analysis of gene expression changes across conditions and the prediction of data under unseen conditions. Cell temporal processes are closely coupled with spatial organization of cells\, and thus the two aspects are best considered in the same picture. After cell division\, cells can migrate to different locations in the tissue. We leverage temporal information in tackling current challenges in spatial data analysis\, including cell type deconvolution and mapping between scRNA-seq and spatial data. Finally\, we have developed a series of de novo simulation tools which help with evaluating computational methods for the problems above and beyond. I will introduce these simulation tools which can be useful for a wide range of computational tasks for single cell omics. URL:https://hsph.harvard.edu/biostatistics/events/pqg-seminar-34/ LOCATION:Building 2\, Room 426 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20240508T160000 DTEND;TZID=America/New_York:20240508T170000 DTSTAMP:20260512T171454 CREATED:20240503T220806Z LAST-MODIFIED:20241122T064740Z UID:111360004422-1715184000-1715187600@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\nDaniel Schwartz\, Postdoctoral Research Fellow\, Department of Biostatistics\, Harvard University \n\n\nDynamic Latent Factor Models To Infer Dietary Patterns From Nutrition Survey Data \n\n\nAbstract: A growing body of research has shown that poor diet is a leading risk factor for death\, especially in connection with chronic diseases such as cardiovascular disease. However\, these studies provide limited insights because they use simplistic measures of diet measured at a single timepoint. To address this issue\, we develop a Bayesian dynamic latent factor model to succinctly describe multivariate dietary patterns. Our approach flexibly incorporates multivariate\, longitudinal nutrition survey data such as food frequency questionnaires with multiple outcome types (e.g. ordinal\, continuous\, etc.). A truncated multiplicative gamma process prior is placed on the factor loadings to adaptively estimate low-dimensional dietary patterns. Importantly\, our model also incorporates covariates such as demographics to assess how dietary patterns differ across subpopulations of interest. As a motivating application we consider the Black Women’s Health Study\, where we construct dynamic measures of diet that will be used in downstream analyses to better understand cardiovascular disease risk among black women in the United States. \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-114/ LOCATION:Building 2\, Room 426 ATTACH;FMTTYPE=application/pdf:https://hsph.harvard.edu/wp-content/uploads/2024/05/Cancer-Working-Group-Seminar-5-8-2024.pdf END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20240508T130000 DTEND;TZID=America/New_York:20240508T143000 DTSTAMP:20260512T171454 CREATED:20240412T173815Z LAST-MODIFIED:20241122T064630Z UID:111360004401-1715173200-1715178600@hsph.harvard.edu SUMMARY:Thesis Defense - Jiaxin Shen 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\nJiaxin will present the thesis entitled “Unsupervised Model Aggregation Methods to Integrate Pre-trained Polygenic Risk Prediction Models”. The thesis committee is chaired by Dr. Rui Duan\, and includes Dr. Georg Hahn and Dr. Erin Lake. \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/thesis-defense-jiaxin-shen/ LOCATION:Building 2\, Room 426 ATTACH;FMTTYPE=image/png:https://hsph.harvard.edu/wp-content/uploads/2024/04/05-08-2024-Thesis-Defense-Shen-Jiaxin.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20240503T123000 DTEND;TZID=America/New_York:20240503T143000 DTSTAMP:20260512T171454 CREATED:20240326T212722Z LAST-MODIFIED:20241122T064557Z UID:111360004394-1714739400-1714746600@hsph.harvard.edu SUMMARY:Dissertation Defense - Intekhab Hossain 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\nIntekhab will present the dissertation entitled “Biologically motivated artificial intelligence for explainable gene regulatory dynamics”. The dissertation committee is chaired by Dr. John Quackenbush\, and includes Dr. Rebekka Burkholz\, Dr. Rong Ma\, and Dr. Kimberly Glass. \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/dissertation-defense-intekhab-hossain/ LOCATION:Building 2\, Room 426 ATTACH;FMTTYPE=image/png:https://hsph.harvard.edu/wp-content/uploads/2024/03/05-03-2024-Dissertation-Defense-Hossain-Intekhab.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20240503T100000 DTEND;TZID=America/New_York:20240503T120000 DTSTAMP:20260512T171454 CREATED:20240214T175559Z LAST-MODIFIED:20241122T064448Z UID:111360004385-1714730400-1714737600@hsph.harvard.edu SUMMARY:Dissertation Defense - Eric Cohn 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\nEric will present the dissertation entitled “Statistical Methods for the Study of Effect Modification and Spatial Causal Inference: Theory and Applications”. The dissertation committee is chaired by Dr. José Zubizarreta\, and includes Dr. Rajarshi Mukherjee and Dr. Andrea Rotnitzky. \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/dissertation-defense-eric-cohn/ LOCATION:Building 2\, Room 426 ATTACH;FMTTYPE=application/pdf:https://hsph.harvard.edu/wp-content/uploads/2024/02/Dissertation-Defense-Cohn-Eric-1.pdf END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20240501T160000 DTEND;TZID=America/New_York:20240501T170000 DTSTAMP:20260512T171454 CREATED:20240426T223338Z LAST-MODIFIED:20241122T064708Z UID:111360004410-1714579200-1714582800@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\nAmy Zhou\, PhD Student\, Department of Biostatistics\, Harvard University \n\n\nComparison of Outcome-Dependent Sampling for Semi-Competing Risks \n\n\nAbstract: Outcome-dependent sampling is a commonly used design tool to collect otherwise unavailable information on a subset of participants rather than all participants. This is particularly useful in research settings where one or more covariates of interest may not be readily available\, whether cost-prohibitive\, time-consuming\, or difficult to obtain in a resource-limited setting. Two common outcome-dependent sampling methods used in time-to-event settings are nested case-control and case-cohort. Classes of designs for both nested case-control and case-cohort were developed to extend their use to analysis of semi-competing risks. Semi-competing risks refers to the setting where interest lies in some non-terminal event\, the occurrence of which is subject to some terminal event (typically\, but not always\, death). We compare the efficiency of these two designs for semi-competing risks through simulation to show the effect of censoring\, type of risk factor\, subcohort size\, and more and illustrate the flexibility of these two designs to tailor resource allocation that best suit the disease context and study goals. \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-2/ LOCATION:Building 2\, Room 426 ATTACH;FMTTYPE=image/png:https://hsph.harvard.edu/wp-content/uploads/2024/04/05-01-2024-Cancer-Working-Group-Seminar.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20240424T160000 DTEND;TZID=America/New_York:20240424T170000 DTSTAMP:20260512T171454 CREATED:20240422T180246Z LAST-MODIFIED:20241122T064644Z UID:111360004405-1713974400-1713978000@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\nCarmen B. Rodriguez\, PhD Student\, Department of Biostatistics\, Harvard T.H. Chan School of Public Health \n\n\nA Bayesian Mixture Model Approach to Examining Socioeconomic Disparities in Endometrial Cancer Care in Massachusetts. \n\n\nAbstract: Endometrial cancer (EC) is the most common gynecologic cancer in the United States. On average\, African American women have 55% higher 5-year mortality risk compared to white women\, and like other minority groups\, they are vulnerable to receiving care that is not concordant with evidence-based treatment guidelines. These differences are linked to systemic and structural factors relating to difficulties in accessing care and the socioeconomic environments in which individuals reside. Previous research has examined socioeconomic factors (e.g.\, education\, income) individually/independently\, but these often interact as social determinants of health. In this project\, we took a multifactorial approach in how we examine racial-ethnic and socioeconomic factors leading to bias and disparities in EC care. We follow a social determinants of health framework to describe neighborhood socioeconomic status (NSES) profiles/clusters. We identified NSES profiles through the application of a multivariate Bernoulli mixture model. Using census tract aggregate level data and patient-level information from 9318 patients collected in the 2006-2017 Massachusetts Cancer Registry\, we examined differences in receipt of optimal care for EC patients in Massachusetts by NSES profiles. We compared the stability of our cluster profiles across three waves of the American Community Survey 5-year estimates (2006-2010\,2011-2015 and 2015-2019)\, and compared these results to other aggregate measures used in cancer surveillance datasets. \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-112/ LOCATION:Building 2\, Room 426 ATTACH;FMTTYPE=application/pdf:https://hsph.harvard.edu/wp-content/uploads/2024/04/Cancer-Working-Group-Seminar-4-24-2024.pdf END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20240423T130000 DTEND;TZID=America/New_York:20240423T140000 DTSTAMP:20260512T171454 CREATED:20240411T155001Z LAST-MODIFIED:20241122T064623Z UID:111360004400-1713877200-1713880800@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\nSeunggeun ‘Shawn’ Lee \n\nAdjunct Professor\, Biostatistics\nUniversity of Michigan \n\nRare variant association analysis\n\n\n\nRare variants significantly impact complex diseases. This presentation will first introduce SAIGE-GENE and SAIGE-GENE+\, methodologies extending SAIGE to gene/region-based rare variant tests. These methods efficiently utilize mixed effects models to adjust for sample relatedness and saddlepoint approximations to account for case-control imbalance. SAIGE-GENE+ additionally incorporates functional annotations and collapsing of ultra-rare variants that can help to improve type I error control and power. In the second part of the talk\, I will introduce our recent work to estimate effect sizes of rare variants. The method\, RareEffect\, uses an empirical Bayesian approach that estimates gene/region-level heritability and then an effect size of each variant. We also show the effect sizes obtained from our model can be leveraged to improve the performance of polygenic scores. \n\n\n  URL:https://hsph.harvard.edu/biostatistics/events/pqg-seminar-33/ LOCATION:Building 2\, Room 426 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20240419T130000 DTEND;TZID=America/New_York:20240419T135000 DTSTAMP:20260512T171454 CREATED:20240416T154956Z LAST-MODIFIED:20241122T064636Z UID:111360004404-1713531600-1713534600@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\nPaige Williams\nSenior Lecturer on Biostatistics\, Harvard T.H. Chan School of Public Health\, Department of Biostatistics \n\n\nThe Pediatric HIV/AIDS Cohort Study (PHACS) \n\n\nAbstract: The Pediatric HIV/AIDS Cohort Study (PHACS) network conducts multiple longitudinal cohort studies investigating the long-term effects of HIV and antiretroviral (ARV) medications in children and young adults who were born with HIV or born exposed to HIV. The network is funded by a grant from NICHD to the Harvard Chan School of Public Health\, and includes 4 support cores based at the Harvard Chan School\, including a Scientific Core and an Epidemiological and Statistical Methods Core. One of the studies (SMARTT) investigates the safety of antiretroviral medications used by pregnant women to prevent transmission of HIV in their infants\, and has enrolled and followed over 5000 children in the US. Another set of studies (AMP Up Series) has enrolled over 1000 young adults who were born with HIV\, and evaluates their physical and mental health as they transition to adult care. In this talk I will summarize the ongoing PHACS research projects\, highlight some interesting statistical projects that have been addressed\, and describe how students can get involved in applied or methodological work related to this network. \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-51/ LOCATION:Building 2\, Room 426 ATTACH;FMTTYPE=application/pdf:https://hsph.harvard.edu/wp-content/uploads/2024/04/HIV-Working-Group-4-19-2024.pdf END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20240417T160000 DTEND;TZID=America/New_York:20240417T170000 DTSTAMP:20260512T171454 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:20260512T171454 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:20260512T171454 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:20260512T171454 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:20260512T171454 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:20260512T171454 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:20260512T171454 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:20260512T171454 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:20260512T171454 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