Events

The Data Science Transdisciplinary Area of Excellence (TAE) organizes and sponsors different kinds of events, which are described below. Explore events in the menu on the right hand side of the page. You can find webcasts of some talks on the "Webcasting" page. Subscribe to our Google Calendar to keep in touch.

Data Salon

The Data Salon is our signature event. It is an informal gathering where researchers on campus share ongoing data-related work with the data science community, with the objective of communicating with those outside of their immediate field. We then open discussion to all attendees with the goal of developing and identifying strategies, methods and related questions of interest to the researcher and data scientists. The concept of a "salon" is for a researcher to present problems and research opportunities in a domain-independent fashion that invites contributions and collaborations from different disciplines, rather than an evaluation of the merits of a specific result, as would be the case for a conventional research seminar.

Invited Speaker Series

For our Invited Speaker Series, we invite leaders in data science from off campus, including researchers, administrators, executives and policy makers from universities, institutes and organizations, to share their recent research developments or their insights on the data science movement.

Other Events

We organize events such as faculty-student mixers, workshops, lectures and data competitions.

We also sponsor/endorse seminars and colloquiums organized by various departments and units on campus. These events are often not initiated by the Data Science TAE, but by the individual departments or units.

Contact us if you would like to lead a discussion in a Data Salon event, nominate an invited speaker or request our sponsorship of a seminar that your department/unit is organizing.


All Events

Oct
7
Mon
12:00pm - 1:30pm
Anderson Center Reception Room
The Data Science Transdisciplinary Area of Excellence (TAE) is hosting an open house luncheon for any faculty member or researcher interested in learning more about the TAE and its new initiatives, the data science research on campus and possible opportunities for collaboration.

Register at http://bit.ly/DS-TAE-RSVP

In addition, send a one-page PowerPoint slide to Mike Jacobson at mjacobso@binghamton.edu that summarizes your research and interests. These slides will be shown in rotation during the luncheon.
Oct
9
Wed
11:00am - 12:00pm
AM -189
Joseph Hogan, professor of biostatistics, Carole and Lawrence Sirovich Professor of Public Health and deputy director of the Data Science Initiative at Brown University, will speak on “Using Electronic Health Records Data for Predictive and Causal Inference About the HIV Care Cascade.”


The HIV care cascade is a conceptual model describing essential steps in the continuum of HIV care. The cascade framework has been widely applied to define population-level metrics and milestones for monitoring and assessing strategies designed to identify new HIV cases, link individuals to care, initiate antiviral treatment and ultimately suppress viral load. Comprehensive modeling of the entire cascade is challenging because data on key stages of the cascade are sparse. Many approaches rely on simulations of assumed dynamical systems, frequently using data from disparate sources as inputs. However, growing availability of large-scale longitudinal cohorts of individuals in HIV care affords an opportunity to develop and fit coherent statistical models using single sources of data, and to use these models for both predictive and causal inferences. Using data from 90,000 individuals in HIV care in Kenya, we model progression through the cascade using a multistate transition model fitted using Bayesian Additive Regression Trees (BART), which allows considerable flexibility for the predictive component of the model. We show how to use the fitted model for predictive inference about important milestones and causal inference for comparing treatment policies. Connections to agent-based mathematical modeling are made. This is joint work with Yizhen Xu, Tao Liu, Rami Kantor and Ann Mwangi.


This seminar is partially supported by the Data Science TAE. For more question, contact Changqing Cheng at ccheng@binghamton.edu or Xingye Qiao at xqiao@binghamton.edu.
Oct
18
Fri
3:00pm - 4:15pm
FA 143
Dear Colleagues,

You are invited to join the DataViz Interest Group, which is sponsored by the Data Science TAE and the Digital Scholarship Center of the Binghamton University Libraries. Membership is open to anyone on campus who wishes to discuss and learn about data visualization, in conversation with the campus community. The group will focus, however, on the activities of faculty, staff, and graduate students.

Please follow the directions below to join, via the B-Engaged platform, which we intend to use for communication, event planning, and organization. If you know anyone else who wishes to join, feel free to convey this message to them.

Our first event, a series of short and informal presentations about ongoing campus data viz projects, has been scheduled for Friday, October 18 at 3:30 pm in Fine Arts 143. Refreshments will be provided. Please RSVP for the event via B-Engaged.

All best,
Nancy

Join DataViz on B-Engaged today!
Follow our group's Join Link: https://bengaged.binghamton.edu/dataviz/club_signupClick the blue "Join" button below the DataViz tileUnder the "Sign in" box, click the green "BU Login" buttonAt the CAS screen enter your PODS credentials and click the green "LOGIN" button
Nancy Um
Professor, Art History
Associate Dean for Faculty Development and Inclusion, Harpur College
Binghamton University

607.777.5288 | LN 2430
nancyum@binghamton.edu | http://nancyum.com
Preferred pronouns: she, her, hers
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