Thematic Labs Program

Thematic Labs Program is sponsored by Texas A&M Institute of Data Science (TAMIDS). Supports cutting-edge research in AI, Machine Learning, and Data Science, funding research labs for two years with seed funding, administrative and research support, and engagement opportunities.

## Generative AI Literacy Initiative Texas A&M University has joined**OpenAI’sNexGenAI**consortium, a groundbreaking $50 million collaboration launched by 15 leading institutions in artificial intelligence (AI) research and education. NexGenAI is designed to provide universities and research institutions with cutting-edge AI tools and resources to push the boundaries of knowledge and equip the next generation of AI leaders.

Through this initiative, the Texas A&M Institute of Data Science (TAMIDS), in association with the Texas A&M University College of Engineering, will spearhead the**_Generative AI Literacy Initiative_**, which aims to expand AI fluency among students, researchers, and industry professionals.

## Research in Artificial Intelligence for Science and Engineering(RAISE) Initiative _Exploring themes in Artificial Intelligence, Machine Learning, and strategic areas of Data Science. _ TAMIDS establishes new labs that develop knowledge, resources, and community around a thematic area of data science, encompassing research, education, and outreach.

There are dozens of researchers across our seven (7) _Thematic Labs_ working to strengthen Texas A&M’s research, improve data science literacy, and tackle challenges impacting society. TAMIDS provides support for outreach activities, post-doctoral researchers and graduate students working on lab research projects, and creating collaboration opportunities. **What do Thematic Labs Do?

** * Conduct collaborative pilot research projects * Develop training, tutorials, case studies, and for-credit courses * Establish research and education data resources * Organize workshops and seminar series * Recruit additional members * Involve students in Lab research and education activities * Develop proposals for funding based on the work of the Lab For more information about how the labs were created, visit Thematic Labs Program.

## Agriculture Smart Data Lab The advent of advanced instrumentation, detailed environmental data, and precision treatment capabilities in agriculture, provides new and compelling opportunities to apply Data Science to Agricultural Engineering.

The Agricultural Smart Data Lab uses machine learning techniques to model the influence of plant genetics, the environment, and treatment factors on plant growth, develops algorithms that adaptively optimize treatment over the growing season to meet goals for yield and cost, and embodies these algorithms in decision support systems that can deliver practical recommendations for crop treatment based on available knowledge.

The Data Justice Lab at Texas A&M University is a multidisciplinary research lab dedicated to Data for Social Justice. The mission of this lab is to develop knowledge, resources, and community around Data for Social Justice.

It brings together computational data scientists and social justice-oriented social scientists to address the role of data sciences in social issues in various domains such as health, education, built environments, crisis management, and finance.

The TAMIDS Digital Twin Lab aims to build up the research capacity on Digital Twins (DTs) at Texas A&M and develop innovative computing and networking technologies and efficient theory/data-driven modeling methods to speed up the creation and deployment of DTs for a wide spectrum of real world applications.

The lab also actively develops and engages in education and training programs to teach and promote DT technologies across the Texas A&M System. ## Knowledge Development Lab Data Science is a multidisciplinary field that utilizes statistics, data analysis, machine learning, algorithms, software, and computing systems to extract information, acquire knowledge, and gain insights into the underlying context from which data is generated.

TAMIDS leads and supports the development of knowledge and expertise in Data Science (and the related areas of Machine Learning and Artificial Intelligence) through multiple programmatic and research activities. The SciML Lab at Texas A&M fosters a collaborative community of researchers spanning various disciplines, dedicated to developing physics-aware machine learning algorithms.

As part of the Texas A&M Institute of Data Science, the lab accelerates education in Scientific Machine Learning through courses, seminars, workshops, and case studies. The Urban AI Lab creates digital twins and VR/AR simulations for cities and regions, aiming to enhance understanding of urban infrastructure and improve resilience.

Additionally, the lab develops and supports free open-source tools for reproducible urban problem-solving, offering dynamic analysis of real-time built environments and testing scenarios for sustainable growth and climate action. ## TAMIDS Research Community TAMIDS’s Data Science Coffee Connections program facilitates cross-departmental interactions among Texas A&M researchers interested in Data Science.

Through this program, TAMIDS provides a platform that each month randomly pairs participants, who can then arrange a 30-minute meeting to exchange ideas. It’s entirely up to you where you take your conversation.

The _Bring-Your-Own-Data (BYOD) Online Consultancy_ program helps researchers formulate approaches to their data science research projects and assists with code development to take advantage of the latest data analytics methods and high-performance computing facilities.

The structure is flexible, personalized, and free ## Texas Virtual Data Library (Texas ViDaL) TAMIDS supports the Texas ViDaL computing servers program, an on-campus, free supercomputing infrastructure managed by TAMU HPRC. ViDAL benefits data science projects that do not fit well with traditional high-performance computing environments or research that requires HIPAA compliance.

If you have projects you want to put on ViDaL, please contact Dr. Hye-Chung Kum (kum at tamu dot edu) for more information.