HDSI Annual Conference 2019 Panel on Data-Driven Scientific Discovery featuring Chris Stubbs, Cora Dvorkin, Galit Lahav, Venkatesh Murthy, and Matthew Schwartz.
Dear Friends,
Save the date for the upcoming HDSI Annual Conference 2022 taking place on November 15 and 16 at the SEC and Klarman Hall! Visit the conference website for details and registration. This event is free and open to the public!
Join us in continuing to celebrate National Hispanic Heritage Month by viewing our collection of data science-related reads and resources below.
Applications are now open for the 2023-2024 Harvard Data Science Initiative Postdoctoral Fellows Program! Apply now.
LatinX in AI (LXAI) bridges communities, academics, industry, and politicians working to further AI innovation and resources for LatinX individuals globally.
Harvard Data Science Initiative Annual Conference 2022
Tuesday, November 15
9:00 AM – 5:00 PM EST Science + Engineering Complex, Harvard SEAS
Wednesday, November 16, 2022
8:00 AM – 6:30 PM EST
Klarman Hall,
Harvard Business School
Two days of in-person workshops, tutorials, + plenary sessions
The Harvard Data Science Initiative Conference is a two-day event in Boston, MA that showcases data science in research and education through panels, keynotes, workshops, and tutorials featuring speakers from across Harvard, academia, and industry.
The Conference connects expert methodologists, data science professionals and educators across disciplines to ignite new discoveries with impacts on health, education, economics, social policy, business and the humanities.
This event is free and open to the public. Ticket required for admission. Please RSVP to reserve your spot.
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
Thursday, October 20, 2022
3:30 AM – 5:30 PM EST
Hawes Hall, Classroom 203, Harvard Business School
HDSI Causal Seminar: Edward McFowland III, Harvard University
Speaker:
Edward McFowland III, Assistant Professor, Technology and Operations Management Unit, Harvard Business School
Abstract:
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy uses predictive modeling techniques to "mine" variables of interest from available data, then includes those variables into an econometric framework to estimate causal effects. However, because the predictions from machine learning models are inevitably imperfect, econometric analyses based on the predicted variables likely suffer from bias due to measurement error. Read more.
Optimal nonparametric estimation of heterogeneous
causal effects
Thursday, November 3, 2022
3:30 AM – 5:30 PM EST
Hawes Hall, Classroom 203, Harvard Business School
HDSI Causal Seminar: Edward Kennedy, Carnegie Mellon
Speaker:
Edward Kennedy, Associate Professor of Statistics and Data Science, Carnegie Mellon University
Abstract:
Estimation of heterogeneous causal effects -- i.e., how effects of policies and treatments vary across units -- is fundamental to medical, social, and other sciences, and plays a crucial role in optimal treatment allocation, generalizability, subgroup effects, and more. Many methods for estimating conditional average treatment effects (CATEs) have been proposed in recent years, but there have remained important theoretical gaps in understanding if and when such methods make optimally efficient use of the data at hand. This is especially true when the CATE has nontrivial structure (e.g., smoothness or sparsity). Read more.
Thursday, November 10, 2022
1:30 PM – 2:30 PM EST
Virtual (Zoom)
HDSI Industry Seminar: Tammy Levy, Captain.tv
Speaker:
Tammy Levy, Chief Games Officer, Captain.tv
Abstract:
Underneath the fun of games we can find complex economies. In the last 15 years, with the rise of accessible broadband internet, video game developers have been able to regularly release game updates or "patches" through a process called live servicing. In addition to new content, game designers often add, remove, and rebalance the resources in the game– effectively manipulating the game's economy on a regular basis. In this talk, I will cover the basic principles of game economies and the core business KPIs used to monitor a game's performance. Then I'll walk through real examples behind the data-driven decisions for game optimization.
Towards Life 3.0 Talk Series: The Coming AI Hackers
Thursday, September 29, 2022
4:00 PM – 5:00 PM EST
Location: Virtual
Hosted by the Carr Center for Human Rights Policy
Speaker:
Bruce Schneier, Adjunct Lecturer in Public Policy, Harvard Kennedy School
About:
Towards Life 3.0: Ethics and Technology in the 21st Century is a talk series that draws upon a range of scholars, technology leaders, and public interest technologists to address the ethical aspects of the long-term impact of artificial intelligence on society and human life.
Harvard Data Science Initiative Postdoctoral Fellowship Program
Deadline: Monday, November 14th, 11:59 PM EST
The Harvard University Data Science Initiative is seeking applications for itsHarvard Data Science Initiative Postdoctoral Fellows Programfor the 2023-2024 academic year. The normal duration of the Fellowship is two years. Fellows will receive a generous salary as well as an annual allocation for research and travel expenses.
We are looking for researchers whose interests are in data science, broadly construed, and including researchers with a primarily methodological focus as well as researchers who advance both methodology and application. Fellows will be provided with the opportunity to pursue their research agenda in an intellectually vibrant environment with ample mentorship. We are looking for independent researchers who will seek out collaborations with other fellows and with faculty across all schools of Harvard University.
We recognize that strength comes through diversity and actively seek and welcome people with diverse backgrounds, experiences, and identities.
Interested in reading more about data science projects and news at Harvard? Check out our blog for features, top stories, and what we are learning now in the world of data.
Interested in engaging more with the Data Science community at Harvard? Join our Slack! The Slack is currently Harvard only, so if you are interested simply click the button below and send us an email from your Harvard email address.
