Research team at University of California, Berkeley, which included 2022 HDSI Postdoctoral Fellow Esther Rolf, developed new system that uses machine learning to drive low-cost, easy-to-use technology that one person could run on a laptop, without advanced training, to address their local problems.
Justin Fletcher joins the show to talk about how the US Space Force is using deep learning with telescope data to monitor satellites, potentially lethal space debris, and identify and prevent catastrophic collisions.
Learn about the connection between astronomy and data science and the programming languages to learn if you want to get into the field.
Data from NASA’s Chandra X-ray Observatory and James Webb Space Telescope have been combined. These images are from some of the earliest observations made by Webb. Chandra had previously observed these objects in X-ray light. These composites demonstrate how these two telescopes can work together. Read more via Chandra X-ray Observatory.
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
Harvard Business School
Two days of in-person workshops, tutorials, + plenary sessions
From content moderation to school assignment: What do theories of justice teach us about design?
Monday, October 17, 2022
11:00 AM – 12:00 PM EST
Science and Engineering Complex, Harvard SEAS
HCRCS Social Impact Seminar Series: Niloufar Salehi, University of California, Berkeley
Niloufar Salehi, Assistant Professor, School of Information, University of California, Berkeley
The Harvard Center for Research on Computation and Society (HCRCS) Social Impact Seminar Series explores how artificial intelligence can equitably solve social problems.
Computational systems have a complex relationship with justice: they may be designed with the intent to promote justice, tasked to resolve injustices, or actively contribute to injustice itself. In this talk I will take two theories of justice, restorative and distributive justice, as frameworks to analyze and imagine alternatives to two real-world systems. Read more.
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
Thursday, October 20, 2022
3:30 PM – 5:30 PM EST
Hawes Hall, Classroom 203, Harvard Business School
HDSI Causal Seminar: Edward McFowland III, Harvard University
Edward McFowland III is an Assistant Professor in the Technology and Operations Management Unit at Harvard Business School. He teaches the first-year TOM course in the required curriculum.
As a data and computational social scientist, Professor McFowland aims to bridge the gap between machine learning and the social sciences. Read more.
Edward McFowland III, Assistant Professor, Technology and Operations Management Unit, Harvard Business School
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
Thursday, November 3, 2022
3:30 PM – 5:30 PM EST
Hawes Hall, Classroom 203, Harvard Business School
HDSI Causal Seminar: Edward Kennedy, Carnegie Mellon
Edward Kennedy, Associate Professor of Statistics and Data Science, Carnegie Mellon University
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.
Symposium on Science, Technology, + the Human Future
November 3 – 5, 2022
Hosted by the Program on Science, Technology + Society at Harvard University in celebration of its 20th Anniversary
The Program on Science, Technology & Society is celebrating its 20th anniversary with a Symposium on Science, Technology and the Human Future, to be held at Harvard from November 3-5, 2022. This major event will feature a wide range of high profile speakers across political, academic, and broader society.
The Symposium begins at 5pm on Thursday, November 3 with a keynote lecture by novelist Arundhati Roy, including performances of original music and fiction written by Harvard students. We continue on Friday with panels on the role of science and technology in shaping the human future, including the future of knowledge, life, policy, and cities. Saturday includes open discussions on how STS can position us to better understand and govern ourselves, our societies, and our Earth.
Thursday, November 10, 2022
1:30 PM – 2:30 PM EST
HDSI Industry Seminar: Tammy Levy, Captain.tv
Tammy Levy, Chief Games Officer, Captain.tv
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.
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 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.