Texas A&M TRIPODS Research Institute for Foundations of Interdisciplinary Data Science (FIDS)

Mission Statement

Data Science is rapidly evolving as an essential interdisciplinary field, where advances often result from a combination of ideas from several disciplines. New types of data have emerged and present tremendous complexities and challenges that require a novel way of interdisciplinary thinking. The Texas A&M Research Institute for Foundations of Interdisciplinary Data Science (FIDS) will bring together researchers from six disciplinary areas, Statistics, Electrical Engineering, Mathematics, Computer Science, Industrial & Systems Engineering, and Operation Management to conduct research on the foundations of data science motivated by problems arising in bioinformatics, the energy arena, power systems, and transportation systems. This Institute for Foundations of Interdisciplinary Data Science will be well positioned to develop rigorous theories, novel methodologies, and efficient computational techniques to solve data challenges in many other application domains.

Announcements

Recent News

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NSF Awards $1.5 Million TRIPODS Institute to Texas A&M to Bolster Data-Driven Discovery

A cross-disciplinary team of Texas A&M University researchers lead by statistician Bani Mallick has been awarded a three-year, $1.5 million Transdisciplinary Research In Principles of Data Science (TRIPODS) grant from the National Science Foundation (NSF) to establish a new institute, the Texas A&M Research Institute for Foundations of Interdisciplinary Data Science (FIDS).

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FIDS Investigator, Simon Foucart Recognized as TAMU 2019 Presidential Impact Fellow

This is a recognition of the scholarship, personal commitment, and global impact awardees are making as they rise to meet challenges of their filed and demonstrate impact.

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FIDS Member, Yu Ding Recognized by the Institute & System Engineers Technical Innovation Award

Dr. Yu Ding received the 2019 Technical Innovation Award from the Institute & System Engineers (IISE) for his outstanding work on data science for wind energy applications.

Solar Grant

FIDS Member, P.R. Kumar and Le Xie receive $4.4 Million Department of Energy Grant to Enhance Solar Technology

The team received $4.4M for their project "Secure Monitoring and Control of Solar PV Systems through Dynamic Watermarking."

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FIDS Sponsoring OpenVINO Toolkit Workshop

FIDS partially sponsoring the workshop on on OpenVINO tool kits. OpenVINO stands for Open Visual Inference and Neural Network Optimization. It is a toolkit provided by Intel to facilitate faster inference of deep learning models.

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FIDS Executive Committee Member Receives AFS Distinguished Award for Research

Two faculty members from the Texas A&M University College of Engineering were selected to receive a 2020 Distinguished Achievement Award for research from Texas A&M and The Association of Former Students.

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Professor Le Xie Discusses How The COVID-19 Pandemic Impacts Electricity Usage

As the COVID-19 outbreak swept through Manhattan and the surrounding New York City boroughs earlier this year, electricity usage dropped as businesses shuttered and people hunkered down in their homes.

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TAMU Statisticians Identified Significant Impact of Reopening on COVID-19 Transmission in USA

The novel coronavirus disease (COVID-19) pandemic resulted in an unprecedented lock-down of the United States. After weeks of lock-down, states reopening has raised concerns about potential exacerbation of the epidemic.

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FIDS Members Receive $1.2 Million NSF Grant

This grant will allow the members to study cyber-physical system based on dynamic nanoscale imaging.

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Wiley Analytical Science Reported FIDS Member, Yu Ding's Super-Resolution Method for Enhancing Paired Electron Microscopic Images

US-based researchers have developed a super-resolution image processing method for paired electron images to improve the quality of low-resolution electron micrographs.

Collaborative Institutes

“We used to be calorie poor and now the problem is obesity. We used to be data poor, now the problem is data obesity.”

Hal Varian, Chief Economist at Google