TAMIDS/GEOSAT Operational Data Science Lab

Texas A&M as a Living Laboratory for Data Science

Texas A&M University is a large and complex organization, with over 69,000 students enrolled in programs over 19 Colleges and Schools in which research and instruction is led by roughly 4,000 faculty. The main College Station campus occupies 5,200 acres. Many functions of University infrastructure—including transportation, facilities, utilities, libraries and administration—routinely collect data during their operation. The resulting data are used to manage these functions across a range of timescales—ranging from planning, through daily operations, to troubleshooting and event response—and over the large and varied campus. Due to the constraints and demands of daily operations management, the potential for such data to improve the operations of the Texas A&M Infrastructure has not been fully realized.

The Operational Data Science Lab is a joint enterprise between the Texas A&M Institute of Data Science (TAMIDS) and the Texas A&M Center for Geospatial Science, Applications, and Technology (GEOSAT) to develop partnerships with operational organizations in Texas A&M that capitalize on institutional data investments to improve campus operations and achieve research and academic goals, and support Texas A&M researchers working on operational problems.

1. Identify new data-driven research problems arising from operational contexts;
2. Derive new insights and analyses from data and embody these in software tools;
3. Engage the broader community of researchers with operational data challenges;
4. Provide hands-on opportunities for students to work with real-world data;
5. Leverage the results for funding proposals and outreach.

Achieving these goals has the potential to transform Texas A&M into a living laboratory for Data Science, accelerating the pace and effectiveness of Texas A&M’s teaching, research, and outreach mission.

Lab Leadership

• Director: Yalong Pi, TAMIDS Associate Research Scientist, Operational Data Science
• Associate Director: Xinyue Ye, Texas A&M GEOSAT & Department of Landscape Architecture & Urban Planning

Lab Collaborators

• Jack Baldauf, Division of Research & Oceanography; Amir Behzadan, Construction Science; Costas Georghiades, Department of Electrical & Computer engineering, Dan Goldberg, Department of Geography;; Bruce Herbert, University Libraries & Geology/Geophysics; Tracy Hammond, IEEI & CSE; Sai Brindha Kapalayam V S, Utilities & Energy Services; Tim Lomax, Texas A&M Transportation Institute; Byron Prestridge, Transportation Services; Yasuko Sakurai, Utilities & Energy Services; Ron Steedly, Transportation Service;, Shawn Turner, TTI; Les Williams, Utilities and Energy Services; Jian Tao, Assistant Professor in Visualization.

Partner OPERATIONAL Organizations

• Texas A&M Transportation Services; Texas A&M Transportation Institute (TTI); University Libraries; Utilities and Energy Services; Division of Research

Events and Community

• TAMIDS Workshop on Operational Data Science, February 2019
• GIS Day Workshop on Geospatial Data Science and Campus Operations, November 2019

How to Get Involved

• As an operational unit with data or problems, or
• As a researcher with creative ideas, or
• As a student who wishes to join new data projects:

>>> Send email to the Operational Data Science Lab leaders

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Projects

Energy Consumption Analysis

Project: In collaboration with the Texas A&M Utilities & Energy Services (UES) and the Department of Statistics, this project is to analyze the time series data from the measurement of energy consumption of a building on campus and automate the process to detect anomalies. The framework created in this project is able to help the UES detect outliers and help them with their resource allocation decisions as well as maintenance schedules. This work is based on Prophet, an open source project by Facebook to forecast time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.

Research Areas: time series analysis; time series forecasting; outlier detection 

Data types: time series, csv files, SQL database

AI-based Traffic Measurement from Video Imaging

Research Organizational Intelligence

Partners: Division of Research, University Libraries

Project: This work uses information concerning faculty publications, grant proposals, and other data to profile Texas A&M’s research in thematic research areas and to identify unexplored opportunities for future research. This project will develop and use software tools that enable exploration and characterization of current and trending research, visualized research collaborations through coauthorship networks and identify latent and emerging opportunities for collaboration through subgraph prediction.

Research Areas: graph analysis, graph clustering, link prediction, keyword analysis.

Data types: text, bibliographic, publications, citations

Bikeshare Effectiveness and Operations

Project: This work uses the real-time locations of TAMU rideshare bikes (starting with Ofo and now using VeoRide) and their paths to measure the effectiveness of the bikeshare program and assist with the operational rebalancing efforts to ensure that bikes are where users need them, when they need them. This project will develop software tools for data visualization and exploration, automated processes and database structures for data ingestion, storage, and retrieval, and geospatial machine learning models and approaches for predictive analytics to guide operational efforts.

Research Areas: graph analysis, big data, machine learning, visualization

Data types: geospatial, demographic