DCII Data Science Clusters

The Dedman College Interdisciplinary Institute sponsors research clusters convened by various faculty across campus. These clusters are open to participants from any and all disciplines and departments.  Clusters typically meet a few times each semester to discuss common interests and collaborate in shared activities. 

This year, five of the clusters address data science topics. These topics are provided below. If you are interested in participating in one of these clusters,  contact a convener of the cluster. Upcoming meetings for the clusters below are included on the Events page.

  1. GIS at SMU

    This Cluster brings together faculty, graduate students, and staff who are interested in GIS (Geographic Information Systems, for mapping and spatial analysis). In recent years the greater availability of spatial data has led to a growing interest in GIS across a variety of fields, including anthropology, art, earth sciences, economics, engineering, human rights and the humanities. The goals of the cluster include 1) connecting SMU faculty and students who may be working independently in the area of spatial analysis, and sharing the different uses and potentials of GIS across their fields; 2) identifying specific needs for SMU faculty training in GIS tools; and 3) helping the library and the Ford Building in setting up facilities and support strategies for GIS at SMU

    Conveners:Klaus Desmet, Economics; Mark McCoy, Anthropology; Jessie Zarazaga, Lyle School of Engineering


  2. Machine Learning and Control Theory

    Multilayer neural networks have been shown to be the most powerful models in machine learning. However, the fundamental reasons for this success remains not well understood and for that it will require mathematical tools and expertise. One of the most important mathematical tools that can be used to study neural networks is Control/Mean Field theory that deals with the behavior of dynamical systems with inputs, and how their behavior is modified by feedback. By this cluster, we would like to create a forum for faculties and researchers at  regional universities and companies working on this important topic.

    Conveners: Alejandro Aceves, Mathematics; Chul Moon, Statistics; Minh-Binh Tran, Mathematics 


  3. Political Decision Making

    In recent years, the study of political decision‐making has received increasing attention from mathematicians. This interest is driven by several factors, including the availability of computational resources that have enabled new algorithms for sampling high‐dimensional probability spaces, as well as a broadly felt urgency to contribute to civic life among members of a discipline that has historically viewed itself as apolitical. These factors align with SMU’s strategic interests in high‐performance computing and interdisciplinary research. We propose to organize a research cluster in Political Decision‐Making with three focus areas: the mathematics of redistricting, social choice theory, and mathematical modeling of polarization.

    'Conveners: Andrea Barreiro, Mathematics; Matthew Lockard, Philosophy; Scott Norris, Mathematics

  4. Technology, Society, and Value

    The DCII “Technology, Society and Value” Research Cluster provides a forum for interdisciplinary collaboration on ethical issues raised by emerging technologies.  The past several years have made it painfully clear that new technologies—from social media to artificial intelligence—will change the way we interact as a society and will raise new ethical issues in the process.  This research cluster will provide an opportunity for scholars and industry professionals across various domains to connect and learn from each other’s perspective on these issues, with a goal of determining some of the more promising avenues for future research.

    Conveners:  Ken Daley, Philosophy; Robert Howell,Philosophy; Suku Nair, Computer Science and Engineering

  5. Using New Data Sources

As technology has developed, new data types for research have become available. Examples of these are location data from cellphones, light data from satellites, and social media data from everyone! Though these sources produce voluminous data, it requires expertise, and sometimes money, to retrieve and use it. One purpose of this research cluster is to build capacity among researchers here at SMU in how to retrieve and make use of these data. A second goal is to identify applications and teams that will use these data sources to pursue new research avenues not previously available. 

Conveners:  Lynne Stokes, Data Science Institute; Nicos Makris, Civil Engineering;