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Update: TAMIDS Career Initiation Fellow Awards

The Texas A&M Institute of Data Science (TAMIDS) has announced eight awardees under its Career Initiation Fellow Program. The program was designed to provide early career support to faculty working in any area involving Data Science and to encourage them to propose engagements with TAMIDS activities, programs, or broader mission, to be undertaken during the year of the award. Each Fellow received a $10,000 research bursary, and TAMIDS Director Dr. Nick Duffield will work with them to promote their activities within TAMIDS programs.

The 2021 TAMIDS Career Initiation Fellows. Left to Right, Upper Row: Ashrant Aryal (Construction Science), Heath Blackmon (Biology), Dileep Kalathil (Electrical and Computer Engineering), David Lowe (Libaries). Lower Row: Manoranjan Majji (Aerospace Engineering), Arash Noshadravan (Civil and Environmental Engineering), Daniel Tabor (Chemistry), Na Zou (Engineering Technology & Industrial Distribution)

The 2021 Texas A&M Institute of Data Science Career Initiation Fellows work in varied disciplines of foundational and applied Data Science across the University, with TAMIDS engagements involving workshops, hackathons, short courses, and PK-12 outreach. Many congratulations to the 2021 Fellows! 

  • Ashrant Aryal is an Assistant Professor in the Department of Construction Science, College of Architecture, where he leads the Human-centered Intelligent Built Environments lab and works at the intersection of construction science and data science. As his TAMIDS engagement, Dr. Aryal plans to lead workshop on building sensing and data acquisition using open-source hardware with a focus on smart building applications.
  • Heath Blackmon is an Assistant Professor in the Department of Biology, College of Science, where he develops computational methods to accelerate the analysis of data within a quantitative genetic or phylogenetic framework, in order to address fundamental questions arising in evolutionary theory. Dr. Blackmon will lead an interdisciplinary hackathon on phylogenetic comparative statistical methods that explore the relations between genetics, evolution, and ecology.
  • Dileep Kalathil is an Assistant Professor in the Department of Electrical & Computer Engineering, College of Engineering, where his research focuses on reinforcement learning and its applications in large-scale real-world engineering settings such as power systems. Dr. Kalathil will pursue outreach to PK-12 students Sciences in Data Science, leveraging ideas and examples from Reinforcement Learning and AI.
  • David Lowe is an Assistant Professor and Digital Collections Management Librarian, Office of Scholarly Communications, University Libraries, where his work includes text mining and tools for document classification. He will develop a workshop on text mining and language processing with library collections. 
  • Manoranjan Majji is an Assistant Professor in the Department Aerospace Engineering, College of Engineering. where his research concerns data-driven modeling for control of autonomous system. Dr. Majji will organize a workshop on current trends and recent advances in data-driven dynamical systems in science and engineering.
  • Arash Noshadravan is an Assistant Professor in the Department of Civil & Environmental Engineering, College of Engineering, where his research concerns the intersection of uncertainty quantification and predictive computational science with several applications in civil engineering and computational mechanics. Dr. Noshadravan will develop a research workshop on uncertainty quantification and machine learning.
  • Daniel Tabor is an Assistant Professor in the Department of Chemistry, College of Science, where his research focuses on problems combining machine learning, theoretical chemistry, and materials science. He plans to host a series of computational molecular design hackathons that aim to develop quick solutions to materials problems through physics-based machine learning models.
  • Na Zou is an Assistant Professor in the Department of Engineering Technology & Industrial Distribution, College of Engineering, where her research concerns interpretability and fairness in machine learning, network modeling and inference, and brain informatics. Dr. Zou will develop a short course on fairness in AI.

TAMIDS is looking forward to contributing to the success of these projects and the interdisciplinary impact of Data Science at Texas A&M University. 

To learn more about the Texas A&M Institute of Data Science, visit