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. Advances in these areas transfer to innovative applications in cognate areas. For example, TAMIDS work in Computer Vision in Transportation Systems has seeded new collaborations in disaster management, animal science, and in bioimage analysis.


Lab Members

  • Director: Yalong Pi, TAMIDS Associate Research Scientist, Operational Data Science
  • Associate Director: Xinyue Ye, Texas A&M GEOSAT & Department of Landscape Architecture & Urban Planning
  • Nick Duffield, TAMIDS and Department of Electrical & Computer Engineering
  • Snehashis Chakraborty, TAMIDS and Department of Electrical & Computer Engineering
  • Connor Gooch, MS in Geography
  • Jian Tao, TAMIDS and School of Performance, Visualization & Fine Arts

Current Labs Collaborators

  • Vivek Gupta, TTI; Michael Fox, ITEC; Walter R. Magnussen, ITEC; Patrick Newman, ITEC; David Retchless, Galveston; Luis Tedeschi, Animal Science; Karun Kaniyamattam, Animal Science; Daniel Paredes-Sabja, Biology; Zhe Zhang, Geography

Former Lab Collaborators

Partner Operational Organizations

Papers & Talks

  • Yalong Pi, Egleu Mendes, Luis Tedeschi, Jian Tao, Siddhanth Reddy, Nick Duffield (2023), Using Computer Vision Pixel Segmentation to Detect Beef Cattle Feeding Behavior, submitted
  • Snehashis Chakraborty, Hernan Santos Barrera, Nick Duffield, Michael Fox, Walter Magnussen, Patrick Newman, Anjuli Renold, Jian Tao, Valerie Vetrone (2024), Creating a Shared Public Safety Radio Dataset, 2024 IEEE World Forum on Public Safety Technology (WF-PST), to appear
  • H. Raghukumar et. al., (2024), Smart Communities, Smart Responders – Artificial Intelligence for Internet of Things Competition: A Case Study in Organizing a Public Safety Innovation Challenge, 2024 IEEE World Forum on Public Safety Technology (WF-PST), to appear
  • Diya Li, Zhe Zhang, Bahareh Alizadeh, Ziyi Zhang, Nick Duffield, Michelle A. Meyer, Courtney M.Thompson, Huilin Gao & Amir H. Behzadan (2023) A reinforcement learning-based routing algorithm for large street networks, International Journal of Geographical Information Science, DOI: 10.1080/13658816.2023.2279975
  • Ziyi Zhang, Diya Li, Zhenlei Song, Nick Duffield, and Zhe Zhang (2023). Location-Aware Social Network Recommendation via Temporal Graph Networks. In Proceedings of the 7th ACM SIGSPATIAL Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising (LocalRec ’23). Association for Computing Machinery, New York, NY, USA, 58–61. https://doi.org/10.1145/3615896.3628342
  • Farinoush Sharifi, Mark Burris, Luca Quadrifoglio, Nick Duffield, Xiaodan Xu, Alexander Meitiv, Yanzhi Xu (2023), Heavy-duty Battery Electric Trucks for Port Drayage Operations: A Feasibility Study, Transportation Research Board 2023 Annual Meeting
  • Ziyi Zhang, Diya Li, Zhe Zhang, Nick Duffield (2023), Deep Time-Series Spatial Clustering: A Robust and Efficient Approach to Map Covid-19 Spatiotemporal Mobility Patterns, International Symposium on Location-Based Big Data and GeoAI 2023 (LocBigDataAI 2023)
  • Yalong Pi, Nick Duffield, Amir Behzadan, Tim Lomax (2023), Lane-specific speed analysis in urban work zones with computer vision, Traffic Injury Prevention, DOI: 10.1080/15389588.2023.2173522
  • Nick Duffield (2022), Inspiring Data Science Research through Collaboration with University Operations, Keynote Talk, ACM Sigmetrics / IFIP Performance Conference, 2022
  • Yalong Pi, Xinyue Ye, Nick Duffield (2022), Rapid Damage Estimation of Texas Winter Storm Uri from Social Media Using Deep Neural Networks, Urban Sci. 20226(3), 62; https://doi.org/10.3390/urbansci6030062 
  • Pi Y., Lomax T., Duffield N., Behzadan A. (2022), Visual Recognition for Traffic Information Retrieval and Analysis in Major Events Using Convolutional Neural Networks, Comp. Urban Sci. 2, 2,.
  • B. Lin, L. Zou, N. Duffield, A. Mostafavi, H. Cai, B. Zhou, et al., (2022), Revealing the linguistic and geographical disparities of public awareness to Covid-19 outbreak through social media, International Journal of Digital Earth 2022 Vol. 15 Issue 1 Pages 868-889, https://doi.org/10.1080/17538947.2022.2070677
  • Ziyi Zhang, Diya Li, Zhe Zhang, N. Duffield (2021), A time-series clustering algorithm for analyzing the changes of mobility pattern caused by COVID-19, HANIMOB ’21: Proc. 1st ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility, November 2021


  • [OneGulf 2023] Towards Targeted Risk Mitigation: Community Engaged, Fast Impact Estimation of Extreme Weather using Big Social and Climate Data, PI D. Retchless, Co-PIs, N. Duffield, Xinyue Ye, Yalong Pi, 6/01/2023-5/31/2025, Harte Institute / Texas OneGulf, $532,988
  • [USDA 2023] Harnessing Precision Livestock Farming to Support Smart Agriculture for Sustainable Beef Cattle Production, USDA-NIFA, PI L. Tedeschi, Co-PIs N. Duffield, Y. PI, J. Tao, 09/01/2023-08/31/2024, $103,563
  • [NIST 2022] A Digital-Twin Enabled Testbed for Public Safety Communication Technologies, National Institute of Standards and Technology, PI. J. Tao, Co-PIs N. Duffield, W. Magnussen, A. Thomas, N. Reddy, 06/01/2022-05/31/2024, $1,200,000.
  • [NIST 2021] Creating a Shared Public Safety Radio Data Set for Sharing and Analysis, National Institute of Standards and Technology, 09/01/2021-08/31/2023, PI W. Magnussen, Co-I N. Duffield, J. Tao. Total $904,806 / Duffield $309,566
  • [MS 2021] Rapid damage prediction from social media using historical big data and deep learning. Microsoft AI for Humanitarian Action Program, PI Y. Pi, Co-PIs N. Duffield, X. Ye, $150,000 Azure Computing Credits

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:

Current Projects

Computer Vision for Detecting Clostridium Difficile Spores


  • Nick Duffield, TAMIDS & ECE
  • Isaias Negassi, MS Data Science
  • Daniel Paredes-Sabja, Biology
  • Yalong Pi, TAMIDS
  • Jian Tao, TAMIDS & PVFA

Partners: Texas A&M Department of Biology

Project: Tackling challenges in the rapid identification of C.difficile spores, which is essential for understanding the infection process, our lab is developing a point based deep learning computer vision system. This system is geared towards automating the 3D localization (adhered and internalized) process of the spores in confocal microscopy, especially small objects (500-1000 nm). This tool will significantly speed up the research processes of devising targeted treatments, monitoring treatment effectiveness, and preventing recurrent infections.

Research Areas: Computer vision, image segmentation, machine learning, image classification

Data Types: Imaging data

Applications of Computer Vision in Precision Livestock Farming


  • Nick Duffield, TAMIDS & ECE
  • Karun Kaniyamattam, Animal Science
  • Yalong Pi, TAMIDS
  • Jian Tao, TAMIDS & Performance, Visualization & Fine Arts
  • Luis Tedeschi, Department of Animal Science
  • Jack Wooley, MS Data Science
  • Siddhanth Reddy Sirgapoor, MS Data Science

Partners: Texas A&M AgriLife & Beef Center

Project: This work is pioneering the use of computer vision to transform data collection and analysis in livestock management. The project focuses on developing and refining algorithms to accurately identify and monitor animal behavior and health indicators. Using supervised, unsupervised, and statistical tools, the project has successfully demonstrated high performance in cattle behavior analysis through video monitoring, indicating a significant step forward in precision livestock farming.

Research Areas: Computer vision, Image classification, object tracking, motion detection

Data Types: Video, audio.

AI-based Vehicle and Pedestrian Traffic Measurement from Video Imaging


Former Members

Partners: Transportation Services, TTI

Project: Traffic and Pedestrian Data Analysis via Video Monitoring: Central to this project is the utilization of advanced computer vision techniques to analyze vehicle (boxes) and pedestrian (points) flow. We employ Convolutional Neural Networks (CNNs) for object/point detection and tracking algorithms for assigning detection IDs and traces. These methods were instrumental in analyzing traffic patterns around Texas A&M football games, offering insights for real-world traffic analysis to TTI and TAMIDS Transportation Service.

Research Areas: AI-based object classification; motion tracking; transportation;

Data Types: video, geospatial

Disaster Damage Assessment Using Social Media and Deep Learning


Partners: Texas A&M University at Galveston

Project: This project integrates deep learning with social media data analysis for prompt damage estimation in disaster scenarios. By employing a deep neural network (DNN) that processes OpenFEMA data and geotagged social media content, we’ve developed a model capable of performing feature extraction through natural language processing and computer vision. This model was effectively tested during Winter Storm Uri, demonstrating the potential of machine learning in enhancing disaster response and resource allocation strategies. 

Research Areas: Social media analysis, geospatial data analysis, image classification

Data Types: text, images, social media

Data & Load Modeling for Public Safety Radio Transition to LTE


  • Snehashis Chakraborty, TAMIDS & ECE
  • Nick Duffield, TAMIDS & ECE
  • Michael Fox, ITEC
  • Connor Gooch, MS in Geography
  • Walter R. Magnussen, ITEC
  • Patrick Newman, ITEC
  • Hernan D. Santos, ITEC
  • Jian Tao, TAMIDS & PVFA

Partners: Internet2 Technology Evaluation Center

Project: This project aims to gather Land Mobile Radio (LMR) data from a wide range of public safety organizations (PSOs) across the country. The primary objective is to conduct research and analysis to gain insights into the potential impacts of transitioning from LMR to LTE technology. Our project team is collaborating with ten different PSOs nationwide, acquiring not only LMR data but also relevant geographical details, including weather and event information. These data sets will serve as resources for in-depth research and analysis, allowing us to better understand LMR usage patterns and to prepare for a successful transition to LTE.

Research Areas: Mobile Radio Technology, GIS, Public Safety,

Data Types: Call records, geospatial data, infrastructure & facilities configuration

RDASH: Research Organizational Intelligence


  • Snehashis Chakraboty, TAMIDS & ECE
  • Jiaxin Du, PhD Candidate LAUP
  • Nick Duffield, TAMIDS & ECE
  • Suphanut Jamonnak, TAMIDS
  • Jian Tao, TAMIDS / VIZ / ECE / MXET
  • Xinyue Ye, LAUP, TAMIDS, & VPR

Former Members

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

Completed Projects

Lighthouse: A Red Flag AI Tool for Research Compliance


  • Nick Duffield, TAMIDS & ECE
  • Jian Tao, TAMIDS / VIZ / ECE / MXET
  • Tiffany Inbody, Research Compliance
  • Katherine Rojo del Busto, Research Compliance
  • Revanth Reddy Male, MS Computer Science
  • Sree Kiran Prasad Vadaga, MS Data Science


  • TAMU Division of Research, Research Compliance

Project: Universities have responsibility to identify and regulate research in areas such as biosafety, animal subjects, human subjects. This project developed a tool to assist research compliance officers by alerting “red-flag” areas at the proposal stage. The tool used Natural Language Processing (NLP) to enhance proposal review by generating flag categories from proposal documents, and Indicating text associated with specific red-flag catrgories.

Research Areas: Natural Language Processing, Text Processing, User-Interfaces

Data Types: PDF and text files

Energy Consumption Analysis



  • Utilities & Energy Services, Dept of Statistics

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

Bikeshare Effectiveness and Operations


  • Dan Goldberg, GeoSAT, GeoInnovation, Geography & CSE
  • Tracy Hammond, IEEI & CSE
  • Edgar Hernandez, GeoInnovation, Geography
  • Byron Prestridge, Transportation Services
  • Ron Steedly, Transportation Services
  • Shawn Turner, TTI

Partners: Transportation Services, TTI

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