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DTSTART;TZID=America/New_York:20220307T130000
DTEND;TZID=America/New_York:20220307T140000
DTSTAMP:20260412T074255
CREATED:20220301T215910Z
LAST-MODIFIED:20220307T192958Z
UID:3390-1646658000-1646661600@ds.dfci.harvard.edu
SUMMARY:Data Science Seminar: Engineering Protease Activity Sensors For Personalized Detection and Profiling of Cancer
DESCRIPTION:Monday March 7th\, 2022\n1:00PM Eastern Time \nAva Soleimany\, PhD\nSenior Researcher\, Biomedical Machine Learning Group at Microsoft Research\, New England \nAbstract: Precision cancer medicine envisions a world where diagnostic and therapeutic opportunities are intelligently tailored to individual patient needs. Achieving this vision necessitates access to high quality\, accurate\, and individualized information about disease state. Engineered probes that sense disease activity — dynamically and directly within the body — could provide this information by generating signals that functionally measure the state of one’s disease. \nIn this talk\, I will discuss my work in engineering a novel class of nanoscale sensors that directly query tumor microenvironments by measuring the activity of proteases\, enzymes directly involved in all functional hallmarks of cancer. I will share how we can leverage the rich\, functional data generated by these sensors to design and deploy novel\, expressive\, and high-fidelity machine learning models to power individualized diagnostic decision-making and noninvasive monitoring. Finally\, I show how we can engineer sensors to spatially profile protease activity in situ\, demonstrating their ability to discover personalized insights into mechanisms of tumor progression and treatment response and encouraging their translation for precision cancer medicine.
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-engineering-protease-activity-sensors-for-personalized-detection-and-profiling-of-cancer/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2022/03/Ava_Soleimany_Background_small-e1646171916155.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220304T120000
DTEND;TZID=America/New_York:20220304T130000
DTSTAMP:20260412T074255
CREATED:20220223T191012Z
LAST-MODIFIED:20220304T195647Z
UID:3365-1646395200-1646398800@ds.dfci.harvard.edu
SUMMARY:Data Science Seminar: Radiomics for Feature Extraction from Radiological Images
DESCRIPTION:Friday\, March 4\, 2022\n12:00PM Eastern Time \nAni Eloyan\, PhD\nAssistant Professor\nDepartment of Biostatistics\, Brown University \nRegister. \nAbstract: Cancer patients routinely undergo radiological evaluations where images of various modalities including computed tomography\, positron emission tomography\, and magnetic resonance images are collected for diagnosis and for evaluation of disease progression. Tumor characteristics\, often referred to as measures of tumor heterogeneity\, can be computed using these clinical images and used as predictors of disease progression and patient survival. Several approaches to quantifying tumor heterogeneity have been proposed including simple intensity histogram-based measures\, metrics attempting to quantify average distance from a homogeneous surface\, and texture analysis-based methods. I will present a statistical framework for principal manifold estimation for obtaining a smooth estimate of tumor surface. I will describe an estimation procedure for tumor heterogeneity using clustering methods considering the neighborhood topology of the tumors. The proposed approach incorporates the spatial structure of the tumor image using neighborhood summary measures. The results of using the proposed methods to estimate cancer survival using machine learning algorithms will be discussed.
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-radiomics-for-feature-extraction-from-radiological-images/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2022/02/aeloyan_photo.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220301T130000
DTEND;TZID=America/New_York:20220301T140000
DTSTAMP:20260412T074255
CREATED:20220105T134746Z
LAST-MODIFIED:20220304T192258Z
UID:3245-1646139600-1646143200@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Considerations for Extracting Real-World Evidence from Real-World Data
DESCRIPTION:Tuesday\, March 1\, 2022\n1:00pm Eastern Time \nRebecca A. Hubbard\, PhD\nProfessor of Biostatistics\nUniversity of Pennsylvania Perlman School of Medicine \nYouTube Video \nAbstract: Opportunities to use real-world data (RWD)\, including electronic health records (EHR) and medical claims data\, have exploded over the past decade. The Covid-19 pandemic has provided a particularly dramatic illustration of the potential value of RWD for advancing medical research as well as the high-risk of bias inherent to many such studies. Using data sources that were not collected for research purposes comes at a cost\, and naïve use of these data without considering their complexity and imperfect quality can lead to biased inference. While RWD offer the opportunity to generate timely evidence grounded in real-world populations and clinical practice\, issues of information bias and confounding create serious threats to the validity of these studies. The statistician is faced with a quandary: how to effectively utilize RWD to advance research without compromising best practices for principled data analysis. In this talk I will use examples from my research on methods for the analysis of EHR derived-data to illustrate how an understanding of the EHR data generating mechanism can inform selection of appropriate study questions and development and application of statistical methods that minimize the risk of bias. The overarching goal of this presentation is to raise awareness of challenges associated with the analysis of RWD and demonstrate that valid evidence generation can be grounded in an understanding of the scientific context and data generating process. \n  \nDr. Hubbard’s research focuses on the development and application of methods to improve analyses using real world data sources including electronic health records (EHR) and claims data. The data science era demands novel analytic methods to transform the wealth of data created as a byproduct of our digital interactions into valid and generalizable knowledge. Dr. Hubbard’s research emphasizes statistical methods designed to meet this challenge by addressing the messiness and complexity of real world data including informative observation schemes\, phenotyping error\, and error and missingness in confounders. Her methods have been applied to support the advancement of a broad range of research areas through use of EHR and claims data including health services research\, cancer epidemiology\, aging and dementia\, and pharmacoepidemiology. \n 
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-using-ehr-to-accelerate-cancer-outcomes-research-without-leaving-valid-inference-behind/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2022/01/1154_3.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220228T130000
DTEND;TZID=America/New_York:20220228T140000
DTSTAMP:20260412T074255
CREATED:20220217T180736Z
LAST-MODIFIED:20220218T174651Z
UID:3347-1646053200-1646056800@ds.dfci.harvard.edu
SUMMARY:Data Science Seminar: From descriptive to predictive biology via single-cell multiomics
DESCRIPTION:Monday February 28\, 2022\n1:00PM Eastern Time \nGenevieve Stein-O’Brien\nInstructor\, Johns Hopkins University\, School of Medicine\nDepartment of Oncology\, Division of Biostatistics and Bioinformatics;\nDepartment of Neuroscience; and McKusick-Nathans Department of Genomic Medicine\nAssistant Director\, Johns Hopkins University Single Cell Consortium \nRegister. \nAbstract: As the single-cell field races to characterize each cell type\, state\, and behavior\, the complexity of the computational analysis approaches the complexity of the biological systems. Single cell and imaging technologies now enable unprecedented measurements of state transitions in biological systems\, providing high-throughput data that capture tens-of-thousands of measurements on hundreds-of-thousands of samples. Thus\, the definition of cell type and state is evolving to encompass the broad range of biological questions now attainable. To answer these questions requires the development of computational tools for integrated multiomics analysis. Merged with statistical and mathematical models\, these algorithms will be able to forecast future states of biological systems\, going from statistical inferences of phenotypes to time course predictions of the biological systems with dynamic maps. Thus\, systems biology for forecasting biological system dynamics from multiomic data represents the future of cell biology empowering a new generation of technology-driven predictive medicine.
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-from-descriptive-to-predictive-biology-via-single-cell-multiomics/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2022/02/1609946354584.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220224T130000
DTEND;TZID=America/New_York:20220224T140000
DTSTAMP:20260412T074255
CREATED:20220218T134643Z
LAST-MODIFIED:20220218T175729Z
UID:3355-1645707600-1645711200@ds.dfci.harvard.edu
SUMMARY:Data Science Seminar: Spatial meshing for general Bayesian multivariate models
DESCRIPTION:Thursday February 24\, 2022\n1:00PM Eastern Time \nMichele Peruzzi\, PhD\nPostdoctoral Associate\, Department of Statistical Science\, Duke University \nRegister. \nAbstract: In this talk\, I will consider the problem of fitting Bayesian models with spatial random effects to large scale multivariate multi-type data from satellite imaging\, land-based weather and air quality sensors\, and citizen science\, with diverse applications in the environmental sciences\, ecology\, and public health. \nIn these contexts\, the goal of quantifying spatial associations via random effects in Bayesian hierarchical models can be achieved by letting a Gaussian process (GP) characterize dependence in space\, time\, and across outcomes. GPs have desirable properties and lead to extremely flexible models\, which are able to accurately quantify uncertainties. Unfortunately\, GPs are notoriously poor performers in large data settings. While the literature on scalable GPs has primarily focused on their well-known bottlenecks in models of univariate continuous outcomes\, I consider the more challenging hurdles to efficient computations facing latent models of multivariate non-Gaussian outcomes.\nI introduce spatial meshing and manifold preconditioning as tools for efficient computations of multivariate Bayesian models of spatially referenced non-Gaussian data. First\, I outline spatial meshing as a tool for building scalable processes using patterned directed acyclic graphs on partitioned spatial domains. Then\, I present manifold preconditioning as a novel Langevin method for superior sampling performance with non-Gaussian multivariate data that are common in studying species’ communities. \nIn addition to these main topics\, I discuss additional strategies for improving Markov-chain Monte Carlo performance\, concluding with applications showcasing the flexibility of the proposed methodologies. All presented methods are implemented in the high performance R package ‘meshed’\, available on CRAN.
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-spatial-meshing-for-general-bayesian-multivariate-models/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2022/02/thumb_image_1811412.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220215T130000
DTEND;TZID=America/New_York:20220215T140000
DTSTAMP:20260412T074255
CREATED:20220208T194759Z
LAST-MODIFIED:20220215T200828Z
UID:3322-1644930000-1644933600@ds.dfci.harvard.edu
SUMMARY:Data Science Seminar: End-to-end AI for Screening Mammography
DESCRIPTION:Tuesday February 15\, 2022\n1:00PM Eastern Time \nWilliam Lotter\, PhD\nVice President of Machine Learning\, RadNet\, Inc.\nChief Technology Officer & Co-Founder\, DeepHealth\, Inc. \nRegister. \nScreening mammography has been estimated to reduce breast cancer mortality by 20-40%\, but significant opportunities remain for improving access and overall quality. Artificial intelligence (AI) has the potential to deliver these improvements\, but developing clinically-effective AI presents additional challenges spanning development through clinical integration. In this talk\, I will present an AI algorithm that addresses the “needle-in-a-haystack” nature of mammography while efficiently using labeled training data and enabling localization-based explainability. I will then detail the algorithm’s performance\, providing evidence of generalization across populations and an ability to aid in earlier cancer detection. Finally\, I will discuss our efforts in achieving regulatory clearance and large-scale clinical deployment.
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-end-to-end-ai-for-screening-mammography/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2022/02/bill-lotter.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220208T130000
DTEND;TZID=America/New_York:20220208T140000
DTSTAMP:20260412T074255
CREATED:20210614T165138Z
LAST-MODIFIED:20220215T201001Z
UID:2802-1644325200-1644328800@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Early Phase Design Considerations for Oncology Drug Development in the Era of Immunotherapy and Targeted Agents
DESCRIPTION:Tuesday\, February 8\, 2022\n1:00pm Eastern Time \nYouTube Video \nElizabeth Garrett-Mayer\, PhD\, FSCT\nVice President\nCenter for Research and Analytics (CENTRA) \nDr. Garrett-Mayer joined ASCO in 2017 as CENTRA’s Division Director for Biostatistics and Research Data Governance and became CENTRA’s first Vice President in 2022. CENTRA leads ASCO’s research efforts\, including the TAPUR Study\, ASCO’s COVID-19 Registry\, and research projects aimed at increasing minority enrollment and expanding eligibility criteria in clinical trials. Prior to joining ASCO\, she served on the faculty in the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins in the Department of Oncology\, and then joined the faculty of the Medical University of South Carolina (MUSC) and established the Biostatistics Shared Resource at the Hollings Cancer Center (HCC). \nShe earned her bachelor’s degree from Bowdoin College in Math and Economics\, and a PhD in Biostatistics from Johns Hopkins Bloomberg School of Public Health. Her publication record includes more than 280 peer-reviewed publications\, primarily in cancer research and research methods. She has also been a member of numerous NIH grant review committees\, Data Safety Monitoring Boards for NIH-supported clinical trials and has served on the editorial board of three peer-reviewed journals (Clinical Trials\, Cancer\, and the Journal of the National Cancer Institute)\, and as faculty on the ASCO/AACR Methods in Clinical Cancer Research Workshop for over a decade. \n  \nAbstract: \nUncertainty about optimality of doses of anti-cancer agents approved in recent years\, including PD-L1 blockade agents and targeted agents\, has led to a movement in the oncology drug development field to reconsider the traditional approach for drug development.  Following in the path of approvals for nivolumab and pembrolizumab\, trial designs for dose finding trials have ballooned from sample sizes in the range of 12-50 to the hundreds or more.  In addition\, post-marketing trials have demonstrated that lower doses of certain drugs may be as efficacious as approved doses.  The has contributed to a renewed focus on ‘dose optimization\,’ causing statisticians\, researchers\, patient advocates and regulators to realize the profound inadequacies of traditional phase I dose finding designs. Trial approaches need to incorporate the adaptive nature of a seamless toxicity-efficacy paradigm\, while maintaining practical aspects of trial implementation\, statistical properties\, endpoint measurement and safety and well-being of patients. This talk will discuss these aspects and provide recent context of drug approvals in oncology.
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-elizabeth-garrett-meyer/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220204T130000
DTEND;TZID=America/New_York:20220204T140000
DTSTAMP:20260412T074255
CREATED:20220202T172751Z
LAST-MODIFIED:20220202T172751Z
UID:3303-1643979600-1643983200@ds.dfci.harvard.edu
SUMMARY:Data Science Seminar: Deciphering Tissue Microenvironment from Next Generation Sequencing Data
DESCRIPTION:Friday February 4\, 2022\n1:00PM Eastern Time \nRegister. \nJian Hu\nPhD Candidate\, Department of Biostatistics\, Epidemiology and Informatics\nUniversity of Pennsylvania \nABSTRACT: The advent of high-throughput next-generation sequencing (NGS) technologies has transformed our understanding of cell biology and human disease. As NGS has been adopted earliest by the scientific community\, its use has now become widespread\, and the technology has improved rapidly. At present\, it is now common for laboratories to assay genome-wide transcriptomes of thousands of cells in a single scRNA-seq experiment. In addition\, technologies that enable the measurement of new information\, for example\, chromatin accessibility\, protein quantification\, and spatial location\, have been developed. In order to take full advantage of the multi-modality information when analyzing NGS data\, new methods are demanded. This seminar will introduce several machine learning algorithms for NGS data analysis with different aims\, including cell type classification\, spatial domain detection\, and tumor microenvironment annotation. \nParts of the talk are based on the following two papers: \n\nhttps://www.nature.com/articles/s42256-020-00233-7 \n\nhttps://www.nature.com/articles/s41592-021-01255-8 \nKEYWORDS: single cell RNA sequencing (scRNA-seq)\, Spatial transcriptomics (ST)\, tumor microenvironment\, machine learning
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-deciphering-tissue-microenvironment-from-next-generation-sequencing-data/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220119T130000
DTEND;TZID=America/New_York:20220119T160000
DTSTAMP:20260412T074255
CREATED:20211112T192533Z
LAST-MODIFIED:20220120T121922Z
UID:3164-1642597200-1642608000@ds.dfci.harvard.edu
SUMMARY:2022 DF/HCC Celebration of Early Career Investigators in Cancer Research
DESCRIPTION:January 19 | 2022 | 1-4PM Eastern Time \nThis annual symposium showcases the talent of early career investigators at the Dana-Farber/Harvard Cancer Center who work in population science\, including epidemiology\, biostatistics\, outcomes\, diversity\, and cancer care delivery research\, and early detection. \nThis year\, Ann Partridge\, MD\, MPH will be our keynote speaker. Dr. Partridge is a Professor of Medicine at Harvard Medical School\, and Vice Chair of Medical Oncology at Dana-Farber Cancer Institute\, where she also serves as Director of the Adult Survivorship Program and leads the Program for Young Women with Breast Cancer. \nTalk title: Improving Care and Outcomes for Young Women with Breast Cancer: From Observation to Intervention \nFollowing Dr. Partridge’s talk will be a panel discussion with Tal Sella\, Tamryn Gray\, and Peter Miller\, and then short talks and posters by our early career investigators. \nAgenda and video: https://www.dfhcc.harvard.edu/ecis \n\n\n\n\nThis symposium is scheduled as a virtual event. 
URL:https://ds.dfci.harvard.edu/event/2022-df-hcc-celebration-of-early-career-investigators-in-cancer-research/
CATEGORIES:Conference
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2021/11/ECIS2022-Abstracts-DS-Website.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220111T130000
DTEND;TZID=America/New_York:20220111T140000
DTSTAMP:20260412T074255
CREATED:20211130T233743Z
LAST-MODIFIED:20220111T182225Z
UID:3204-1641906000-1641909600@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Studies on COVID-19 and Cancer using National Real-World VA Data
DESCRIPTION:Nathanael Fillmore is the Associate Director for Machine Learning and Advanced Analytics at the VA Boston Healthcare System’s Cooperative Studies Program Informatics Center. He leads a data science team focused on using machine learning and data science methods\, in combination with the VA’s large clinical\, genomic\, and imaging databases\, to generate knowledge and resources that will improve care for Veterans with cancer. He also holds a faculty position at Harvard Medical School and is site director for the VA/NCI Big Data-Scientist Training Enhancement Program. Previously\, he was a post-doctoral fellow at Dana-Farber Cancer Institute and Harvard School of Public Health under the direction of Giovanni Parmigiani and Nikhil Munshi. \nThis seminar was not recorded. 
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-studies-on-covid-19-and-cancer-using-national-real-world-va-data/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211207T130000
DTEND;TZID=America/New_York:20211207T140000
DTSTAMP:20260412T074255
CREATED:20210928T184004Z
LAST-MODIFIED:20211209T121847Z
UID:3064-1638882000-1638885600@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Cancer on the Way to Mars
DESCRIPTION:Tuesday\, December 7\, 2021\n1:00pm Eastern Time \nGiovanni Parmigiani\, PhD\nProfessor\nHarvard T.H. Chan School of Public Health \nLink to YouTube Video \nCancer on the Way to Mars Links: \nhttps://www.nap.edu/download/26155 \nhttps://www.env.go.jp/en/chemi/rhm/basic-info/index.html \nhttps://www.nasa.gov/feature/goddard/real-martians-how-to-protect-astronauts-from-space-radiation-on-mars \nhttps://www.nasa.gov/sites/default/files/atoms/files/space_radiation_ebook.pdf \nhttps://standards.nasa.gov/standard/nasa/nasa-std-3001-vol-1 \nhttps://spaceradiation.larc.nasa.gov/nasapapers/2020/5008710.pdf
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-cancer-on-the-way-to-mars/
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2021/09/Parmigiani_Giovanni_sq.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211109T130000
DTEND;TZID=America/New_York:20211109T140000
DTSTAMP:20260412T074255
CREATED:20210917T160314Z
LAST-MODIFIED:20211116T141230Z
UID:3016-1636462800-1636466400@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: A Bayesian Phase I/II Trial Design for Immunotherapy
DESCRIPTION:Tuesday\, November 9\, 2021\n1:00pm Eastern Time \nSuyu Liu\, PhD\nAssociate Professor\nDepartment of Biostatistics\nThe University of Texas MD Anderson Cancer Center \nA Bayesian Phase I/II Trial Design for Immunotherapy \nImmunotherapy is an innovative treatment approach that stimulates a patient’s immune system to fight cancer. It demonstrates characteristics distinct from conventional chemotherapy and stands to revolutionize cancer treatment. We propose a Bayesian phase I/II dose-finding design that incorporates the unique features of immunotherapy by simultaneously considering three outcomes: immune response\, toxicity and efficacy. The objective is to identify the biologically optimal dose\, defined as the dose with the highest desirability in the risk-benefit tradeoff. An Emax model is utilized to describe the marginal distribution of the immune response. Conditional on the immune response\, we jointly model toxicity and efficacy using a latent variable approach. Using the accumulating data\, we adaptively randomize patients to experimental doses based on the continuously updated model estimates. A simulation study shows that our proposed design has good operating characteristics in terms of selecting the target dose and allocating patients to the target dose. \nYouTube Link
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-suyu-liu/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211103T130000
DTEND;TZID=America/New_York:20211103T150000
DTSTAMP:20260412T074255
CREATED:20210928T135710Z
LAST-MODIFIED:20211029T115457Z
UID:3043-1635944400-1635951600@ds.dfci.harvard.edu
SUMMARY:Postdoc Recruitment Day
DESCRIPTION:The Dana-Farber Cancer Institute Department of Data Science announces its second annual Postdoc Recruitment Day to be held on Wednesday\, November 3rd from 1-3pm EST. \nIf you are interested in learning more about postdoctoral opportunities at Dana-Farber Cancer Institute and would like to learn about the research our faculty are conducting\, please sign up for this free event. The faculty participating this year are: \n· Sahand Hormoz\, Assistant Professor \n· Rafael Irizarry\, Professor and Department Chair \n· Heng Li\, Assistant Professor \n· Giovanni Parmigiani\, Professor \n· Mehmet Samur\, Senior Research Scientist \n· Nabihah Tayob\, Assistant Professor \nThe faculty will give brief overviews of their current research. You will also hear from current and former postdocs\, and hear about the resources available in the department\, at Dana-Farber and throughout the Boston area. \nSpace is limited and we request that you are actively looking for a postdoctoral position. Please complete this form to request admission. The deadline to apply is October 29th. \nYou can see our open postdoctoral positions on our website: https://ds.dfci.harvard.edu/careers/
URL:https://ds.dfci.harvard.edu/event/postdoc-recruitment-day/
CATEGORIES:Recruitment
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211028T110000
DTEND;TZID=America/New_York:20211028T120000
DTSTAMP:20260412T074255
CREATED:20211021T234121Z
LAST-MODIFIED:20211029T115538Z
UID:3129-1635418800-1635422400@ds.dfci.harvard.edu
SUMMARY:Data Science Seminar: Causal Inference Methods for Measures of Health Disparities
DESCRIPTION:Thursday\, October 28\, 2021\n11:00am Eastern Time \nTengfei Li\nGeorgetown University \nCausal Inference Methods for Measures of Health Disparities \nThere is increased interest in the evaluation of health disparities between different socioeconomic groups using data from observational studies. However\, in the absence of randomization\, the results and conclusions may be limited to associations rather than causal effects. The causal inference framework allows us to estimate causal measures for such situations. We present inverse probability weighting (IPW) and doubly robust (DR) estimators for the marginal means of the distributions of the potential outcomes for multiple socioeconomic groups using generalized propensity scores. We estimate the variance of the vector of IPW and DR estimators for the marginal means by using an M-estimation approach. The variances of the estimators for causal measures of health disparities are subsequently estimated using the multivariate delta method. In simulation studies\, the new methods provide coverage probabilities that are close to the nominal level when used to construct 95% confidence intervals for the causal measures of health disparities. \nKeywords: health disparities\, inverse probability weighting estimator\, doubly robust estimator\, generalized propensity score\, M-estimation.
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-causal-inference-methods-for-measures-of-health-disparities/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211019T130000
DTEND;TZID=America/New_York:20211019T140000
DTSTAMP:20260412T074255
CREATED:20210928T181448Z
LAST-MODIFIED:20211201T165250Z
UID:3058-1634648400-1634652000@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Treatment-free Survival as a Novel Outcome Measure of Immuno-oncology-based Therapy
DESCRIPTION:Tuesday\, October 19\, 2021\n1:00pm Eastern Time \nMeredith Regan\, ScD\nAssociate Professor\nDepartment of Data Science\, Dana-Farber Cancer Institute\,\nHarvard Medical School \nTreatment-free Survival as a Novel Outcome Measure of Immuno-oncology-based Therapy \nYouTube Link
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-meredith-regan/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2021/09/preferred_meredith_regan_square.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210914T130000
DTEND;TZID=America/New_York:20210914T140000
DTSTAMP:20260412T074255
CREATED:20210614T164652Z
LAST-MODIFIED:20211021T134349Z
UID:2797-1631624400-1631628000@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: COVID Vaccine Efficacy Trial Designs\, Open Questions and Statistical Challenges
DESCRIPTION:Tuesday September 14\, 2021\n1:00PM Eastern Time \nHolly Janes\, Ph.D.\nProfessor\, Vaccine and Infectious Disease Division\, Fred Hutchinson Cancer Research Center\nProfessor\, Public Health Sciences Division\, Fred Hutchinson Cancer Research Center \nYouTube video now available.
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-holly-janes/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2021/06/holly-janes-sq.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210616T100000
DTEND;TZID=America/New_York:20210616T120000
DTSTAMP:20260412T074255
CREATED:20210608T184016Z
LAST-MODIFIED:20210630T184005Z
UID:2775-1623837600-1623844800@ds.dfci.harvard.edu
SUMMARY:Training Session: Survival Analysis and Competing Risks Data Analysis
DESCRIPTION:June 16\, 23\, and 30\, 2021\n10:00am-12:00PM Eastern Time \nHaesook Kim\, PhD\nPrincipal Research Scientist\nDepartment of Data Science\, Dana-Farber Cancer Institute\nDepartment of Biostatistics\, Harvard T.H. Chan School of Public Health \nRSVP: https://bit.ly/DSTSJune16
URL:https://ds.dfci.harvard.edu/event/training-session-survival-analysis-and-competing-risks-data-analysis/2021-06-16/
CATEGORIES:Training Session
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2021/06/kim_headshot.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210608T130000
DTEND;TZID=America/New_York:20210608T140000
DTSTAMP:20260412T074255
CREATED:20210108T131835Z
LAST-MODIFIED:20211021T134536Z
UID:2398-1623157200-1623160800@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: The Use of External Control Data for Predictions and Interim Analyses in Clinical Trials
DESCRIPTION:June 8\, 2021\n1:00PM \nLorenzo Trippa\, PhD\nAssociate Professor\nDepartment of Biostatistics\, Harvard T.H. Chan School of Public Health\nDepartment of Data Science\, Dana-Farber Cancer Institute \nThe Use of External Control Data for Predictions and Interim Analyses in Clinical Trials \nYouTube video now available.
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-lorenzo-trippa/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2021/01/trippa.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210525T130000
DTEND;TZID=America/New_York:20210525T140000
DTSTAMP:20260412T074255
CREATED:20210506T120012Z
LAST-MODIFIED:20211021T134757Z
UID:2724-1621947600-1621951200@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: Modeling Cancer Etiology and Evolution\, and its Implications for Prevention
DESCRIPTION:Tuesday May 25\, 2021\n1:00PM ET \nA conversation with Cristian Tomasetti\nAssociate Professor\, Department of Oncology\, Johns Hopkins Medicine\, Sidney Kimmel Comprehensive Cancer Center \nModerator: Giovanni Parmigiani \nYouTube Video now available. 
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-mathematical-modeling-and-cancer-prevention/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2021/05/cristian-head-shot.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210519T100000
DTEND;TZID=America/New_York:20210519T120000
DTSTAMP:20260412T074255
CREATED:20210415T135645Z
LAST-MODIFIED:20210520T125744Z
UID:2703-1621418400-1621425600@ds.dfci.harvard.edu
SUMMARY:Training Session: Efficient Phase I Clinical Trial Design
DESCRIPTION:May 19\, 2021\n10:00am-12:00PM Eastern Time \nFangxin Hong\, PhD\nSenior Research Scientist\nDepartment of Data Science\, Dana-Farber Cancer Institute\nDepartment of Biostatistics\, Harvard T.H. Chan School of Public Health \n  \n 
URL:https://ds.dfci.harvard.edu/event/training-session-efficient-phase-i-clinical-trial-design/
CATEGORIES:Training Session
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2021/04/FH.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210511T130000
DTEND;TZID=America/New_York:20210511T140000
DTSTAMP:20260412T074255
CREATED:20200921T180944Z
LAST-MODIFIED:20210511T180948Z
UID:2138-1620738000-1620741600@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Single-Cell RNA-Seq Data Analysis Via a Regularized Zero-Inflated Mixture Model Framework
DESCRIPTION:Frontiers in Biostatistics Seminar\nMay 11\, 2021\n1:00PM \nJianhua Hu\, PhD\nProfessor\, Biostatistics (in Medicine and in the Herbert Irving Comprehensive Cancer Center)\nDirector\, Cancer Biostatistics Program\nColumbia University \nRegister at: http://bit.ly/FIBMay21 \nAbstract: Applications of single-cell RNA sequencing in various biomedical research areas have been blooming. This new technology provides unprecedented opportunities to study disease heterogeneity at the cellular level. However\, unique characteristics of scRNA-seq data\, including large dimensionality\, high dropout rates\, and possibly batch effects\, bring great difficulty into the analysis of such data. Not appropriately addressing these issues obstructs true scientific discovery. Herein\, we propose a unified Regularized Zero-inflated Mixture Model framework designed for scRNA-seq data (RZiMM-scRNA) to simultaneously detect cell subgroups and identify gene differential expression based on a developed importance score\, accounting for both dropouts and batch effects. We conduct extensive empirical investigation to demonstrate the promise of RZiMM-scRNA in comparison to several popular methods\, including K-means and Hierarchical clustering. \nThis seminar was not recorded as the research presented has not been published yet. 
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-jianhua-hu/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/09/Hu_4x4.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210323T130000
DTEND;TZID=America/New_York:20210323T140000
DTSTAMP:20260412T074255
CREATED:20210205T151853Z
LAST-MODIFIED:20210511T181115Z
UID:2447-1616504400-1616508000@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: The Importance of Representative Samples in Clinical Trials
DESCRIPTION:Tuesday March 23\, 2021\n1:00PM Eastern Time \nA conversation with Timothy Rebbeck\nVincent L. Gregory\, Jr. Professor of Cancer Prevention\, Epidemiology\, Harvard T.H. Chan School Of Public Health\nProfessor\, Medical Oncology\, Dana-Farber Cancer Institute \nModerator: Rafael Irizarry \nYouTube Link: https://www.youtube.com/watch?v=Q4uBibxGf20
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-the-importance-of-representative-samples-in-clinical-trials/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2021/02/rebbeck_sq.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210309T130000
DTEND;TZID=America/New_York:20210309T140000
DTSTAMP:20260412T074255
CREATED:20200921T180427Z
LAST-MODIFIED:20210511T181203Z
UID:2133-1615294800-1615298400@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Distributed Statistical Learning and Inference in EHR and Other Healthcare Datasets
DESCRIPTION:Frontiers in Biostatistics Seminar\nMarch 9\, 2021\n1:00PM \nRui Duan\, PhD\nAssistant Professor of Biostatistics\nHarvard TH Chan School of Public Health \nDistributed Statistical Learning and Inference in EHR and Other Healthcare Datasets  \nAbstract: The growth of availability and variety of healthcare data sources has provided unique opportunities for data integration and evidence synthesis\, which can potentially accelerate knowledge discovery and enable better clinical decision making.  However\, many practical and technical challenges\, such as data privacy\, high-dimensionality and heterogeneity across different datasets\, remain to be addressed. In this talk\, I will introduce several methods for effective and efficient integration of electronic health records and other healthcare datasets. Specifically\, we develop communication-efficient distributed algorithms for jointly analyzing multiple datasets without the need of sharing patient-level data. Our algorithms are able to account for heterogeneity across different datasets. We provide theoretical guarantees for the performance of our algorithms\, and examples of implementing the algorithms to real-world clinical research networks. \nYouTube Link: https://www.youtube.com/watch?v=IscQ3ruxl1o
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-rui-duan/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/09/duan4x4.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210223T130000
DTEND;TZID=America/New_York:20210223T140000
DTSTAMP:20260412T074255
CREATED:20210205T145646Z
LAST-MODIFIED:20210511T181245Z
UID:2441-1614085200-1614088800@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: Diversity and Ethics in Genomics
DESCRIPTION:Tuesday February 23\, 2021\n1:00PM Eastern Time \nA conversation with Keolu Fox\, PhD\nAssistant Professor\, Department of Anthropology\, University of California\, San Diego \nModerator: Aedin Culhane \nYouTube Link: https://www.youtube.com/watch?v=VANlStOnFPY
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-diversity-and-ethics-in-genomics/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2021/02/fox3.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210209T130000
DTEND;TZID=America/New_York:20210209T140000
DTSTAMP:20260412T074255
CREATED:20200921T175912Z
LAST-MODIFIED:20210511T181321Z
UID:2129-1612875600-1612879200@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Group Sequential Design Assuming Delayed Benefit
DESCRIPTION:February 9\, 2021\n1:00PM ET \nKeaven Anderson\, PhD\nScientific AVP\, Methodology Research\, Biostatistics at Merck \nGroup Sequential Design Assuming Delayed Benefit \nAbstract: We consider an asymptotic approach to design of group sequential trials with a potentially delayed effects. Logrank\, weighted logrank tests and combination tests are of primary interest\, but we also consider restricted mean survival. The asymptotic approach allows both quick derivation of study design properties that are also easily verified using simulation. We rely heavily on work done by Tsiatis\, 1981 published while he was at the HSPH. The impact of a potential delay in treatment effect on timing of analyses\, study boundaries and sample size are demonstrated. The value of a robust design that is well powered under a variety of assumptions is emphasized. Open source software is provided for implementation. Given the current regulatory climate\, logrank testing may still be preferred for these trials\, but we hope that efficiencies and Type I error control associated with alternatives may make other options more acceptable for future consideration. \nYouTube Link: https://www.youtube.com/watch?v=DhwZOX5uMKU
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-keaven-anderson/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/09/anderson1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210126T130000
DTEND;TZID=America/New_York:20210126T140000
DTSTAMP:20260412T074255
CREATED:20210119T133552Z
LAST-MODIFIED:20210511T181357Z
UID:2413-1611666000-1611669600@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: Vaccine Prioritization Strategies
DESCRIPTION:Tuesday January 26\, 2021\n1:00PM ET \nA conversation with Daniel Larremore\nAssistant Professor\, Department of Computer Science at University of Colorado-Boulder and BioFrontiers Institute \nand \nKate Bubar\nStudent\, Department of Applied Mathematics\, University of Colorado-Boulder \nModerator: Rafael Irizarry \nRSVP at https://bit.ly/DSJan26 \nYouTube Link: https://www.youtube.com/watch?v=fJuHNNP8TLg
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-vaccine-prioritization-strategies/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2021/01/joint_pix.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210112T130000
DTEND;TZID=America/New_York:20210112T140000
DTSTAMP:20260412T074255
CREATED:20200921T175311Z
LAST-MODIFIED:20210112T190625Z
UID:2123-1610456400-1610460000@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Statistical Modeling and Adjustment for Sampling Biases
DESCRIPTION:Frontiers in Biostatistics Seminar\nJanuary 12\, 2021\n1:00PM \nJing Ning\, PhD\nAssociate Professor. Department of Biostatistics\nDivision of Quantitative Sciences\nThe University of Texas M.D. Anderson Cancer Center \nRegister at: https://bit.ly/FIBJan12 \nAbstract: Bias sampling mechanisms are commonly encountered in applications where the subjects in a target population are not given an equal chance to be selected\, either accidentally\, by natural circumstances\, or intentionally by design. Statistical methods not properly accounting for such a challenge often lead to invalid inferences. For example\, evidence combined from published studies may lead to overly optimistic conclusions due to publication bias\, and the well-known length bias can cause the screening to appear to be more successful than it really is. In this talk\, I will present our recent work to adjust the sampling biases in diverse applications such as the survivorship bias in prevalent cohort\, the self-reporting bias in longitudinal analysis and the publication bias in meta-analysis.
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-jing-ning/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/09/ning.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201221T140000
DTEND;TZID=America/New_York:20201221T150000
DTSTAMP:20260412T074255
CREATED:20201211T185808Z
LAST-MODIFIED:20201222T214128Z
UID:2361-1608559200-1608562800@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: Understanding COVID-19 Vaccine Trial Designs and Current Results
DESCRIPTION:Monday December 21\, 2020\n2:00PM ET \nUnderstanding COVID-19 Vaccine Trial Designs and Current Results \nA conversation with David Benkeser\nAssistant Professor\, Biostatistics and Bioinformatics\nRollins School of Public Health\, Emory University \nModerator: Rafael Irizarry \nRSVP at https://bit.ly/DSDec21
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-understanding-covid-19-vaccine-trial-designs-and-current-results/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/12/benkeser_sq.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201215T130000
DTEND;TZID=America/New_York:20201215T140000
DTSTAMP:20260412T074255
CREATED:20201119T201351Z
LAST-MODIFIED:20201215T204139Z
UID:2324-1608037200-1608040800@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: Data-Driven Policy in Puerto Rico
DESCRIPTION:Tuesday December 15\, 2020\n1:00PM ET \nData-Driven Policy in Puerto Rico \nA conversation with Arnaldo Cruz\nDirector of Research and Policy of the Financial\nOversight and Management Board of Puerto Rico\nCo-founder of ABRE Puerto Rico \nModerator: Rafael Irizarry \nRSVP at https://bit.ly/DSDec15
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-data-driven-policy-in-puerto-rico/
CATEGORIES:Zoominar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/11/cruz.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201214T150000
DTEND;TZID=America/New_York:20201214T163000
DTSTAMP:20260412T074255
CREATED:20201130T190048Z
LAST-MODIFIED:20201215T114236Z
UID:2341-1607958000-1607963400@ds.dfci.harvard.edu
SUMMARY:DF/HCC Cancer Data Science Program &Harvard Chan Bioinformatics CoreJoint Symposium on scRNAseq Methodology
DESCRIPTION:Monday\, December 14\, 3:00-4:30 PM ET  \nRSVP https://bit.ly/CDSBioDec14 \nSpeakers: \n\nAedin Culhane\, Senior Research Scientist\, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health\nIsabella Grabski\, PhD Student in Biostatistics\, Harvard University\nProbabilistic gene barcodes identify cell-types in single-cell RNA-sequencing data\nShannan Ho Sui\, Senior Research Scientist\, Harvard T.H. Chan School of Public Health\nRadhika S Khetani\, Research Scientist\, Harvard T.H. Chan School of Public Health\nPeter Kharchenko\, Associate Professor of Biomedical Informatics\, Harvard Medical School\nX. Shirley Liu\, Professor\, Harvard T.H. Chan School of Public Health\nAnalysis of Single-Cell Data to Model Tumor and Immune Gene Regulation\nLuca Pinello\, Associate Professor of Pathology\, Harvard Medical School and Mass General Cancer Center\nTrajectory inference and visualization of single cell data
URL:https://ds.dfci.harvard.edu/event/df-hcc-cancer-data-science-program-harvard-chan-bioinformatics-corejoint-symposium-on-scrnaseq-methodology/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/11/dfhhc-joint-symp-icon.png
END:VEVENT
END:VCALENDAR