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DTSTART;TZID=America/New_York:20200519T130000
DTEND;TZID=America/New_York:20200519T140000
DTSTAMP:20260413T153653
CREATED:20200429T173724Z
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SUMMARY:Streamlined empirical Bayes estimation for contextual bandits with applications in mobile health
DESCRIPTION:Frontiers in Biostatistics Webinar\nMarianne Menictas\nPostdoctoral Fellow\, Department of Statistics\nHarvard University \nMobile health (mHealth) technologies are increasingly being employed to deliver interventions to users in their natural environments. With the advent of increasingly sophisticated sensing devices (e.g.\, GPS) and phone-based EMA\, it is becoming possible to deliver interventions at moments when they can most readily influence a person’s behavior. For example\, for someone trying to increase physical activity\, moments when the person can be active are critical decision points when a well-timed intervention could make a difference. The promise of mHealth hinges on the ability to provide interventions at times when users need the support and are receptive to it. Thus\, our goal is to learn the optimal time and intervention for a given user and context. A significant challenge to learning is that there are often only a few opportunities per day to provide treatment. Additionally\, when there is limited time to engage users\, a slow learning rate can pose problems\, potentially raising the risk that users will abandon the intervention. To prevent disengagement\, a learning algorithm should learn quickly in spite of noisy measurements. To accelerate learning\, information may be pooled across users and time in a dynamic manner\, combining a contextual bandit algorithm with a Bayesian random effects model for the reward function. As information accumulates\, however\, tuning user and time specific hyperparameters becomes computationally intractable. In this talk\, we focus on solving this computational bottleneck.
URL:https://ds.dfci.harvard.edu/event/streamlined-hyper-parameter-tuning-in-mobile-health/
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200428T130000
DTEND;TZID=America/New_York:20200428T140000
DTSTAMP:20260413T153653
CREATED:20200428T180127Z
LAST-MODIFIED:20220525T113511Z
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SUMMARY:Reframing proportional-hazards modeling for large time-to-event datasets with applications to deep learning
DESCRIPTION:Frontiers in Biostatistics Seminar\nNoah Simon\, Ph.D.\nAssociate Professor\nDepartment of Biostatistics\nUniversity of Washington\n\nTo build inferential or predictive survival models\, it is common to assume proportionality of hazards and fit a model by maximizing the partial likelihood. This has been combined with non-parametric and high dimensional techniques\, eg. spline expansions and penalties\, to flexibly build survival models. \nNew challenges require extension and modification of that approach. In a number of modern applications there is interest in using complex features such as images to predict survival. In these cases\, it is necessary to connect more modern backends to the partial likelihood (such as deep learning infrastructures based on eg. convolutional/recurrent neural networks). In such scenarios\, large numbers of observations are needed to train the model. However\, in cases where those observations are available\, the structure of the partial likelihood makes optimization difficult (if not completely intractable). \nIn this talk we show how the partial likelihood can be simply modified to easily deal with large amounts of data. In particular\, with this modification\, stochastic gradient-based methods\, commonly applied in deep learning\, are simple to employ. This simplicity holds even in the presence of left truncation/right censoring\, and time-varying covariates. This can also be applied relatively simply with data stored in a distributed manner.
URL:https://ds.dfci.harvard.edu/event/reframing-proportional-hazards-modeling-for-large-time-to-event-datasets-with-applications-to-deep-learning/
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200402T130000
DTEND;TZID=America/New_York:20200402T140000
DTSTAMP:20260413T153653
CREATED:20200612T115313Z
LAST-MODIFIED:20200612T122256Z
UID:1618-1585832400-1585836000@ds.dfci.harvard.edu
SUMMARY:COVID-19 Data Science Zoomposium
DESCRIPTION:Caroline Buckee\, Department of Biostatistics\nHarvard TH Chan School of Public Health\nHow do we predict the pandemic? \nMichael Mina\, Department of Epidemiology\nHarvard TH Chan School of Public Health\nThe importance and challenges of testing for COVID-19 \nNatalie Dean\, Department of Biostatistics\, University of Florida\nHow we evaluate the efficacy of potential therapies and vaccines  \nAlexis Madrigal\, The Atlantic\nJournalism in the time of COVID-19 \nModerated by Rafael Irizarry \nSponsored by the Department of Data Sciences\, Dana-Farber Cancer Institute\nand the Brown Institute for Media Innovation\, Columbia University
URL:https://ds.dfci.harvard.edu/event/covid-19-data-science-zoomposium/
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200226T100000
DTEND;TZID=America/New_York:20200226T120000
DTSTAMP:20260413T153653
CREATED:20200429T162749Z
LAST-MODIFIED:20200511T121722Z
UID:651-1582711200-1582718400@ds.dfci.harvard.edu
SUMMARY:Introduction to single cell RNA-seq data analysis for statisticians
DESCRIPTION:Data Science Training Session\nKelly Street\, Research Fellow\nEtai Jacob\, Research Fellow \nIn this short course\, we will introduce some of the most widely used tools for single cell analysis. We will describe common experimental methods utilized in the community to generate single cell RNA-seq data and demonstrate modern pre-processing and analysis pipelines. In addition\, we will discuss potential problems which frequently show up in analysis and ways to deal with them. After our first meeting\, participants will be encouraged to practice some of the workflows we will share with them in order to discuss issues they encountered during the second meeting.
URL:https://ds.dfci.harvard.edu/event/introduction-to-single-cell-rna-seq-data-analysis-for-statisticians/2020-02-26/
LOCATION:Center of Life Sciences\, Room 11081\, 3 Blackfan Circle\, Boston\, MA\, 02215\, United States
CATEGORIES:Training Session
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200219T100000
DTEND;TZID=America/New_York:20200219T120000
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CREATED:20200429T161423Z
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UID:644-1582106400-1582113600@ds.dfci.harvard.edu
SUMMARY:Alternatives to Hazard Ratio for Quantifying Treatment Effect on Time-to-Event Outcomes
DESCRIPTION:Data Science Training Session\nHajime Uno\nAssistant Professor\, Department of Data Science\nDana-Farber Cancer Institute
URL:https://ds.dfci.harvard.edu/event/alternatives-to-hazard-ratio-for-quantifying-treatment-effect-on-time-to-event-outcomes/
LOCATION:Center of Life Sciences\, Room 11081\, 3 Blackfan Circle\, Boston\, MA\, 02215\, United States
CATEGORIES:Training Session
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200207T160000
DTEND;TZID=America/New_York:20200207T170000
DTSTAMP:20260413T153653
CREATED:20200429T163321Z
LAST-MODIFIED:20200612T115603Z
UID:656-1581091200-1581094800@ds.dfci.harvard.edu
SUMMARY:The Clinical Impact of Genomics in the Pediatric Oncology
DESCRIPTION:DFCI Genomics Meetup\nKatherine Janeway\, MD\nPediatric Oncology\nDana-Farber Cancer Institute \nPizza is provided. \n 
URL:https://ds.dfci.harvard.edu/event/the-clinical-impact-of-genomics-in-the-pediatric-oncology/
LOCATION:Center of Life Sciences\, Room 11081\, 3 Blackfan Circle\, Boston\, MA\, 02215\, United States
CATEGORIES:Meetup
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/04/katherine-janeway-1.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200122T100000
DTEND;TZID=America/New_York:20200122T120000
DTSTAMP:20260413T153653
CREATED:20200429T131206Z
LAST-MODIFIED:20200511T121722Z
UID:632-1579687200-1579694400@ds.dfci.harvard.edu
SUMMARY:Collaborative Grant Writing and Statistical Methods for Grants
DESCRIPTION:Data Sciences Training Session\nRebecca Gelman\, PhD\nAssociate Professor\, Department of Data Sciences\nDana-Farber Cancer Institute
URL:https://ds.dfci.harvard.edu/event/collaborative-grant-writing-and-statistical-methods-for-grants/
LOCATION:Center of Life Sciences\, Room 11081\, 3 Blackfan Circle\, Boston\, MA\, 02215\, United States
CATEGORIES:Training Session
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200121T130000
DTEND;TZID=America/New_York:20200121T140000
DTSTAMP:20260413T153653
CREATED:20200429T130831Z
LAST-MODIFIED:20200511T121722Z
UID:622-1579611600-1579615200@ds.dfci.harvard.edu
SUMMARY:NEJM Statistical Guidelines for Authors: Under the Hood
DESCRIPTION:Data Sciences Training Session\nDavid Harrington\, PhD\nProfessor\, Department of Data Sciences\nDana-Farber Cancer Institute
URL:https://ds.dfci.harvard.edu/event/david-harrington-presents-nejm-statistical-guidelines-for-authors/
LOCATION:Center of Life Sciences\, Room 11081\, 3 Blackfan Circle\, Boston\, MA\, 02215\, United States
CATEGORIES:Training Session
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/04/harrington_TS.png
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20191204T050000
DTEND;TZID=America/New_York:20191204T070000
DTSTAMP:20260413T153653
CREATED:20200408T213229Z
LAST-MODIFIED:20200511T121722Z
UID:94-1575435600-1575442800@ds.dfci.harvard.edu
SUMMARY:Design of Phase III Studies
DESCRIPTION:Data Sciences Training Session\nDesign of Phase III Studies\, Part II \nRobert Gray\, PhD\nProfessor\, Department of Data Science\nDana-Farber Cancer Institute
URL:https://ds.dfci.harvard.edu/event/robert-gray-phd-leads-training-on-design-of-phase-iii-studies/
LOCATION:Center of Life Sciences\, Room 11081\, 3 Blackfan Circle\, Boston\, MA\, 02215\, United States
CATEGORIES:Training Session
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