Data Science Initiative Seminar Series—Fall 2021
Please note: Spring 2022 sessions will be conducted via Zoom, usually on Wednesdays from 4:00-5:00 PM. A list of upcoming seminars can be viewed on the new DSI website. If you are interested in attending the seminars or potentially getting on the schedule for Spring, or the wait list for Fall, please email Blake LeBaron.
The Brandeis Data Science Initiative launched a new seminar series that began on March 16, 2021. The series provides opportunities for researchers at Brandeis who know little to nothing about data science to learn from faculty members who utilize these methods in their own research and become inspired to adapt data science to their own projects. It includes speakers across the School of Arts and Sciences, the Heller School, International Business School and the Rabb School.
This series reveals how data science has been used across multiple disciplines at Brandeis and beyond. It can also be used as a force for social good, especially during the pandemic. In addition to learning how to teach data science to the uninitiated, we will discuss examples that demonstrate how it can impact global health, combat misinformation campaigns and be applied to medicine, social science, music and archeology. You can register for upcoming events and watch videos of the previous seminars below.
Data science and Law: Exploring How Algorithms Can Give Insight into Legal Reasoning, Help Predict Legal Outcomes, and Social Perspectives for Access to Justice for All
Jonathan Touboul (Associate Professor of Mathematics) and Samuel Dahan (Queen’s University, Canada)
Data Science at Work in Higher Ed: The View Through Job Posting Analytics
Jessica Liebowitz (Visiting Research Scholar in Computer Science), Matt Ekins '20 (Analytic Consultant in the Office of Finance and Administration, Brandeis), and Tim Hickey (Professor, Department of Computer Science)
This talk will explore trends and occupational details of data scientific capacity in higher ed over the past decade. The analysis of these trends begins in computer science faculty jobs, and ends by illuminating the relevance of data science, machine learning, and artificial intelligence across the non-faculty workforce. The standardized nature of job posting analytics makes it possible to study faculty and non-faculty jobs through the same skills taxonomy, which results in a clear view of the distinctiveness of research universities as headquarters of data scientific talent. This clarification, in turn, points to untapped resources in the higher ed sector that can support innovation in data science education and in university operations.
Electoral Redistricting, Data Visualization, and Tidyverse Fundamentals with R
Alejandro Trelles (Assistant Professor of Politics) and Brandon Stanaway (IBS Graduate and Research Assistant at the Boston Planning and Development Agency)
Redistricting is one of the most politicized procedures within electoral management. Mexico’s approach is unique—since 1996 an independent board has been creating plans algorithmically. Parties, however, are able to formulate hundreds of counter-proposals in a closed-door environment. This session will offer context on how redistricting processes work outside of the U.S. and explain how R´s Tydeverse programming language can be used to analyze hundreds of partisan electoral maps to answer the following questions: What have been the political consequences of redistricting in Mexico? Why and when do political parties engage in the process? Were the most recent redistricting processes compliant with the law? Have discretionary rules been applied consistently within and across processes? Are the observed outputs consistent with how the process was officially portrayed?
From Point Clouds to People: Archaeological Applications of Airborne and Drone Lidar on the Mexico-Guatemala Border
Charles Golden (Associate Professor and Department Chair of Anthropology), G. Van Kollias III (Doctoral candidate in Anthropology), and Alex Bazarsky (Anthropology Major)
Archaeological research in the neo-tropics has been transformed over the past decade by lidar—the first remote sensing methodology that allows us to peek beneath the often dense forest canopy, to virtually peel away the trees and see the bare earth beneath, with its ancient cityscapes and agrarian infrastructure. Yet, for all the research potential of the vast and growing body of digital data produced, we still face the interpretive challenge of getting from point clouds to human behavior and meaning. In this presentation we'll explore some of the results produced, and questions raised, by recent airborne and drone-based lidar surveys of ancient Maya urban and rural landscapes carried out by the Proyecto Arqueológico Busiljá - Chocoljá in the Usumacinta River region, along the modern border of Mexico and Guatemala.
Using Data Science to Track Vaccine Misinformation Campaigns
Steven L. Wilson (Politics) and Chuxu Zhang (Computer Science)
Many complex systems can be modeled as network graphs, with nodes and edges representing objects and the relationships between them. Deep-learning based graph analytics (mainly graph representation learning) is one of the most important techniques in the modern artificial intelligence community. It has found valuable application in the social sciences in the development of neural networks that can classify text and images. Read Steven’s recent article in The Conversation about vaccine misinformation on social media and how disinformation gets in the way of countering the pandemic.
Teaching Data Science to the Uninitiated
Dustin Tingley (Professor of Government and Deputy Vice Provost for Advances in Learning at Harvard University)
A discussion of experiences with data science pedagogy and the teaching of applied data science to students and researchers in social science and the humanities. Tingley teaches “Data Science Ready,” a course focused on providing nontechnical professionals and students the skills required to interpret and implement data in their fields of work.
Big Data and Practical Machine-learning Techniques in Medicine and Social Science
William Crown (Heller) and Mark Coleman (Rabb)
This talk will present an introduction to common machine learning techniques, analytical tool kits, and big data technologies. Our focus will be applications of these methods in the social sciences, along with some of the unique research and computational challenges that social data presents. Examples using natural language processing, computer vision, prediction, inference, and classification will be discussed. We will also touch upon some of the recent and growing controversy around "ethical AI" and the pitfalls of using "biased" data.
Making Music with Data Science
Karen Desmond (Music), Sarah Mead (Music), and Timothy Hickey (Computer Science)
This session will focus on digitalization and digital visualization of music scores and music production. Karen, an expert in Medieval polyphony, will show how digital transcriptions can use the original shapes of music notation in medieval manuscripts to create modern scores that can be performed or analyzed by musicians, students and researchers. Timothy and Sarah will demonstrate how multi-camera interfaces can be used to enable viewers of a musical performance to have a more interactive experience by switching between views of different instrumentalists and seeing where they are in the music.
Robots on the Dig: Visualizing Material Culture
Ian Roy (Director For Research, Technology and Innovation, Founding Head of Brandeis MakerLab)
and Alexandra Ratzlaff (Classics)
How do you record the shape of something that will likely break as you excavate it? How do you record something shiny or reflective in-situ as it is removed from the dirt? We seek to develop new ways of analyzing the material culture of the ancient and historical world. The initial goal of our project is to prototype a ‘Single Camera Automated Photogrammetry Platform’ (SCAPP) with the final designs available open-source. The SCAPP is intended to be a relatively low-cost and easily reproduced alternative to expensive digital imaging equipment, that often exceeds them in quality or covers gaps they cannot image.