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).
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.
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.
The team received $4.4M for their project "Secure Monitoring and Control of Solar PV Systems through Dynamic Watermarking."
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.
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.
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.
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.
This grant will allow the members to study cyber-physical system based on dynamic nanoscale imaging.
US-based researchers have developed a super-resolution image processing method for paired electron images to improve the quality of low-resolution electron micrographs.
Nineteen distinguished Texas A&M University faculty have been honored as 2020 Chancellor Enhancing Development and Generating Excellence in Scholarship (EDGES) Fellows.
Dr. Bani K. Mallick, distinguished professor of statistics and holder of the Susan M. Arseven ’75 Chair in Data Science and Computational Statistics at Texas A&M University, has been honored by the University of Connecticut Department of Statistics with its Distinguished Alumnus Award for 2020-2021.
Cancer: Basic Research to Bioinformatics. Jointly Hosted by IAMCS, Center for Statistical Bioinformatics and Texas A&M TRIPODS FIDS Institute. Friday, September 10, 2021. Check the link for the registration details.
Implicit Sparse Regularization: The Impact of Depth and early Stopping : Li, J., Nguyen, T., Hedge, C. and Wong, R.
T-LoHo: A Bayesian Regularization Model for structured sparsity and smoothness on graphs: Lee, J., Luo, T., Sang, H.
BAST: bayesian Additive Regression spanning Trees for complex constrained domain: Luo, Z., Sang, H. and Mallick, B.
Row clustering of a point process-valued matrix: Yin, L., Xu, G., Xu, G., Sang, H., Guan, Y.
ScreeNOT: Exact MSE-Optimal singular value thresholding in correlated noise
Dr. P.R. Kumar is the 2022 recipient of one of the Institute of Electrical and Electronics Engineers’ (IEEE) most prestigious honors — the IEEE Alexander Graham Bell Medal. It is the highest award by IEEE in communications and networking. Kumar was recognized for his seminal contributions to the modeling, analysis and design of wireless networks.
Texas A&M University chemist Sarbajit Banerjee has been selected by The Academy of Medicine, Engineering and Science of Texas (TAMEST) to receive one of the Lone Star State’s highest scientific honors, the Edith and Peter O’Donnell Award celebrating rising stars in the Texas research community and their cutting-edge research.
Tnsor Moments of Gaussian Mixture Models: Theory and Applications
In this paper, we propose a fully autonomous experimental design framework that uses more adaptive and flexible Bayesian surrogate models in a Bayesian Optimization procedure, namely Bayesian multivariate adaptive regression splines and Bayesian additive regression trees to replace existing Gaussian Process based methods.