Knowledge Development Lab

Lab Overview

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. The Texas A&M Institute of Data Science (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.

  • Sponsored research for the design, development and delivery of professional training, including Infrastructure and programmatic support for knowledge development in center-level activities.
  • Design, development, delivery and administration of for-credit education programs and courses in collaboration with academic units.
  • Support programs enabling faculty and students to develop new for-credit courses and training activities.
  • Consultancy on development of education and training programs in Data Science and AI.

Projects and Activities

Team Members

  • Drew Casey, TAMIDS, Snehashis Chakraborty, TAMIDS & ECE, Nick Duffield, TAMIDS & ECE, Krishna Narayanan, TAMIDS & ECE, Haoyu Niu, TAMIDS & ECE Yalong Pi, TAMIDS, Christi Retzer, TAMIDS, Jian Tao, TAMIDS & PVFA

Primary Contact: Nick Duffield, TAMIDS Director, duffieldng@tamu.edu

Collaborating Organizations

  • College of Engineering, College of Arts and Science, College of Veterinary Medicine & Biomedical Sciences, Graduate & Professional School, Education Research Center, Center for Teaching Excellence, Energy Institute, Mays Business School, Health Sciences Center, Cybersecurity Center, Prairie View A&M University, Texas Southern University

Publications

  • Shaun D. Hutchins, Kim Wright, Nick Duffield (2023), Application of a Retrospective Pretest-Posttest Design to the Evaluation and Continuous Improvement of Professional Development. Annual meeting of the Southwest Educational Research Association, San Antonio, TX. February 17, 2023.
  • Patterson, C., Harlin, J., Couri, D., Fowler, D., & Duffield, N (2021), Mentoring in Artificial Intelligence and Materials Science: Applying a New Doctoral Model, The Chronicle of Mentoring and Coaching, November 2021, pp 483-483

Funding

  • [AFRL 2023] VICEROY for the NCAE-C South Central Region, DOD-Air Force Research Laboratory, PI J. Hamilton, Co-PIs N. Duffield, J. Garay, C. Lanclos, 09/01/2023-08/31/2024, $2,000,000
  • [LANL 2023] TRIAD ST&E: Professional Education Workshops in Data Science Foundations and Computational Practice, Triad National Security, LLC (for Los Alamos National Labs), PI N. Duffield, 07/24/2023-09/30/2023. $50,000
  • [NASA 2023] Data Science Equity, Access, and Priority in Research and Education (DEAP) Institute for Science Translation via Low-Resource Neural Machine Translation, NASA, PI Lijun Qian (PVAMU), Co-PI N. Duffield, X. Ye, W. Li (TSU), 04/01/2023-03/31/2026. $1,500,000 / TAMU $300,000
  • [THECB 2022] Accelerating Credentials of Purpose and Value Grant Program, Texas Higher Education Coordinating Board, PI S. Cambone, Co-PI N. Duffield, J. Geunes, L. Qian, 02/07/2022-09/30/2022, $413,070
  • [NIH 2021] IMSD at Texas A&M University: Initiative for Maximizing Student Diversity in Biomedical Sciences. DHHS-NIH-National Institute of General Medical Science, 02/01/2021-01/31/2022,  PI Karen Butler-Purry. Co-PIs Ivan Rusyn, David Threadgill, Roland Kaunas, Dorothy Shippen, Weihsueh Chiu, Candice Langford, Nick Duffield (supplement PI). $75,757
  • [CP 2020] Certificate in Data Analytics for Petroleum Industry ConocoPhillips, 2020–2024, PI N.G. Duffield, collaboration between TAMIDS, College of Arts & Sciences, College of Engineering, Mays Business School. $1,000,000
  • [US 2020] iDiscovery Workshop on Computational Data Science, 10/2020-09/2021, PI N. Duffield, $60,000.
  • [NSF 2019c] HDR Tripods: Texas A&M Research Institute for Foundations of Interdisciplinary Data Science (FIDS), National Science Foundation, Award 1934904, 10/01/2019-9/30/2022, PI Bani Mallick, Co-PIs Dilma Da Silva, Ron Devore, Nick Duffield, P.R. Kumar. Total $1,416,522
  • [NSF 2019b] CC* Team: SWEETER — SouthWest Expertise in Expanding, Training, Education and Research, National Science Foundation, Award 1925764, 09/01/2019-08/31/2023. PI D. Chakravorty; Co-PIs J. Browning, T. Cockerill, E. Hunt, D. Dugas; Co-I N. Duffield. Total $1,400,000

Events

Further Information

Professional Training

Workshops in Data Science Foundations and Computational Practice

The week-long intensive workshop equips participants with diverse skills to enable their professional practice of data science. The target audience are organizations seeking to help their workforce develop knowledge and experience applying computational methods of data science. The workshop comprises a sequence of ten interactive, three-hour modules that integrate methodological exposition with application to data through hands-on programming and computation with Python notebook. Participants use open source tools and libraries to develop proficiency and understanding of the essential methods of current data science and state-of-the-art topics.

Biomedical Data Science Online Training Program

The series of two hour online sessions is aimed primarily at graduate students in the biomedical sciences, but others interested in the field are welcome to join. Sessions will couple exposition of underlying principles with engagement of session participants through online quizzes and hands-on exercises focused on developing computational competencies using R notebooks. The program also provides self-instruction materials on the R language and statistical foundations. Instructors are faculty experts in Data Science with specialization in session topic areas. Through the program, participants are able to:

  • Engage Texas A&M faculty experts in a variety of data science domains.
  • Expand  knowledge of the R language and in the areas of computation, systems, and statistics.
  • Experience hands-on application of supervised and unsupervised statistical learning to biomedical data. 
  • Establish a foundation for understanding of FAIR data management, algorithmic fairness, data ethics, privacy preservation, and regulatory requirements, including HIPAA compliance.

Data Science Primers

Primers in topics in Data Science and AI, and the R and Python programming languages, provide students with a self-paced study through videos and hands-on experience with python and R notebooks

For-Credit Education

Masters of Science in Data Science

The Master of Science in Data Science degree is an on-campus interdisciplinary program offered by the Departments of Computer Science and Engineering, Electrical and Computer Engineering, Mathematics, and Statistics within the University’s Colleges of Engineering and Arts and Science, and administered jointly with the Texas A&M Institute of Data Science. The multidisciplinary curriculum provides students with a solid foundation in mathematics, statistics, computer science, and machine learning through core courses, after which students can choose from elective courses provided by the different participating departments.

Data Science Capstone

Data Science Capstone projects run within the academic setting of the Master of Science in Data Science and other Graduate programs.  The aim of the Data Science Capstone is to complement students’ knowledge of Data Science methods and technologies through the practice of its application to research problems in Data Science domains. It is intended to facilitate student learning by gaining experience working with real-world data and interacting with sponsoring Data Science application researchers. This provides a pathway for students to develop a track record for applied Data Science and gain insights in potential future opportunities. In turn, sponsors can benefit from the application of Data Science methods for problem solving and to develop relationships with students and faculty throughout the project.

Undergraduate Certificate in Data Analytics for the Petroleum Industry

The Data Analytics for Petroleum Industry Certificate is coordinated by the Texas A&M Institute of Data Science (TAMIDS). The certificate is interdisciplinary and is housed in the Harold Vance Department of Petroleum Engineering within the Texas A&M University College of Engineering to train undergraduate students at Texas A&M University to become more competitive in the job market of the petroleum industry. Students will receive an education in concepts, computation and case-based learning to prepare them for data analytics and machine learning careers.

Masters of Science in Energy, Energy Digitization Theme

TAMIDS has developed and provides instruction for three Data Science courses within the Energy Digitization theme of the Texas A&M Energy Institute’s Masters of Science in Energy.

This program exposes students and professionals to (a) important energy challenges and opportunities, and (b) advances in theory, methods, technologies, and applications delivered by energy leaders from academia, industry, and government, through a module-based structure and a distinguished seminar series.

College of Engineering Sprint Courses

In response to the rapid adoption of artificial intelligence (AI), machine learning (ML), and other computing- related technologies, the College of Engineering, in collaboration with the Texas A&M Institute of Data Science, piloted sprint courses during Fall 2023. These offerings aimed to familiarize students with emerging tools in engineering and beyond. Sprint courses are short: one week earning one credit that counted toward the fall semester load.

Programmatic Support

TAMIDS Course Development Grant Program

The aim of the TAMIDS Course Development Program is to support faculty to develop new courses in Data Science (including Artificial Intelligence and Machine Learning within the program scope) or to revamp existing courses to include Data Science components. The program supports the development of for-credit courses entailing at least three SCH for graduate and undergraduate Texas A&M students, training programs for Texas A&M communities, or external educational outreach that entails a similar participant effort. Possible proposed deliverables include but are not limited to, class notes, videos, labs, homework, and exam problem sets.

The program has supported 25 faculty since its inception in 2021. Awardees from 2021, 2022, 2023. Solicitation for 2024.

TAMIDS Data Science Ambassador Scholarship Program

Data Science Ambassadors are Texas A&M PhD students who serve as representatives for TAMIDS and champions for Data Science literacy in their departments. The competitive program provides ambassadors with leadership, technical and training skills which they will practice through engagement in their home departments. Ambassadors will also act as a point of contact for TAMIDS in their departments. The Data Science Ambassador Scholarship Program aims to help ambassadors become agents for change who will catalyze the adoption of Data Science, both at Texas A&M, and in their subsequent careers.

The program has supported 24 ambassadors since inception. Ambassadors from 2022, 2023.