Boston Obesity Nutrition Datathon
The Boston Obesity Nutrition Datathon is an event to spark curiosity and foster the development of collaborations among the Boston community working in the data science space (engineers, data scientists), with the nutrition and obesity scientific community (researchers, clinicians, epidemiologists, biologists). Registered teams of up to five will be provided datasets from researchers in the field of obesity, nutrition, and public health on October 19. During the week after data sets become available, teams will work together to create compelling visualizations, identify patterns, and discover novel insights. A group of mentor volunteers with expertise in the fields of Nutrition and Obesity will be available for teams to consult. At the end of the week and 24 hours before the event, teams will each submit a 3-5 page report with conclusions to the judging panel. Teams will have 5 minutes to present their findings at the event. The contest features $3000 in prizes for the best and most creative ideas and data visualizations! For more details, visit bondatathon.org
[TIE] Sept 25, 2018: Environmental Brunch sponsored by Tufts Institute for the Environment
[BCASA] Sept 25, 2018: Boston Chapter of the ASA Event to Celebrate Outstanding Undergraduate Teaching of Statistics
Speaker: Professor Nick Horton, Amherst College
Presentation Title: “Introductory Statistics in a World of Data Science: Where We Are and Where We Need to Head”
Date and Time: Tuesday, September 25, 2018; 4:30 – 6:30 PM
Location: Brown University School of Public Health, 121 South Main Street, Providence, RI 02903.
Cost: $6 for students; $12 for non-students.
Registration: Requested by September 21. Registration Link
Abstract: This is an exciting time for the broader statistics profession, with a flood of data available in myriad domains and an increased focus on the importance of data literacy and data science. There continues to be growth in the number of students taking statistics courses and the development of many innovative data science courses. This talk will survey the landscape of introductory statistics and biostatistics courses in an era of data science, and address questions regarding the role of computation, how to balance the needs of general education students and future statistics and data science students, the role of the statistical analysis cycle, pathways to support student success, faculty development, and the relationships between high school and college preparation. These developments have important implications not just for educators but for practitioners and researchers as well.
[BCASA] Oct 19, 2018: An Introduction to the Analysis of Incomplete Data
Instructor: Professor Ofer Harel, University of Connecticut
Organizer: American Statistical Association Council of Chapters
Co-sponsors: Boston Chapter of the ASA (BCASA) & BU student Chapter of the ASA (BUSCASA)
Location: Boston University, Sargent College Room 101, 635 Commonwealth Avenue, Boston.
Date & Time: October 19, 2018. Sign-in 8:30-9:00 AM, Presentation 9:15 AM-4:30 PM
Registration: Requested by October 15. Registration Link
Cost: Boston ASA members: $70; Non-members: $85; Students: $30
Abstract: Missing data is a common complication in applied research. Although most practitioners are still ignoring the missing data problem, numerous books and research articles demonstrate that dealing with it correctly is very important. Biased results and inefficient estimates are just some of the risks of incorrectly dealing with incomplete data. In this course, we will introduce incomplete data vocabulary and present problems and solutions to the missing data issue. We will emphasize practical implementation of the proposed strategies including discussion of software to implement procedures for incomplete data.
[BCASA] Nov 14, 2018: Presentation by Professor Susan Murphy
Title: Stratified Micro-Randomized Trials with Applications in Mobile Health
Presenter: Susan Murphy, Harvard University
Co-Sponsors: Boston Chapter of the ASA, Department of Mathematics & Statistics at Boston University, and the IDSS Institute at MIT
Date & Time: Wednesday, November 14, 2018. Social and light Dinner 6:15 pm; Presentation 7:00 pm
Location: Room E51-149; MIT Tang Center; 70 Memorial Drive, Cambridge
Registration: Requested by 10 am, November 12, 2018. Registration Link
Cost: Dinner $7 for students; $12 for non-students.
Abstract: Technological advancements in the field of mobile devices and wearable sensors make it possible to deliver treatments anytime and anywhere to users like you and me. Increasingly the delivery of these treatments is triggered by detections/predictions of vulnerability and receptivity. These observations are likely to have been impacted by prior treatments. Furthermore, the treatments are often designed to have an impact on users over a span of time during which subsequent treatments may be provided. Here we discuss our work on the design of a mobile health smoking cessation study in which the above two challenges arose. This work involves the use of multiple online data analysis algorithms. Online algorithms are used in the detection, for example, of physiological stress. Other algorithms are used to forecast at each vulnerable time, the remaining number of vulnerable times in the day. These algorithms are then inputs into a randomization algorithm that ensures that each user is randomized to each treatment an appropriate number of times per day. We develop the stratified micro-randomized trial which involves not only the randomization algorithm but a precise statement of the meaning of the treatment effects and the primary scientific hypotheses along with primary analyses and sample size calculations. Considerations of causal inference and potential causal bias incurred by inappropriate data analyses play a large role throughout.