Data Science & Analytics
This field, situated at the intersection of technology, statistics, and business strategy, is dedicated to extracting valuable insights from diverse datasets. Utilizing advanced algorithms and machine learning techniques, data scientists analyze information to uncover patterns, trends, and correlations, driving innovation across industries.
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64 result(s)
PARSEC: PARallel Subgraph Enumeration in CUDA
V Dodeja, M Almasri, R Nagi, J Xiong, W Hwu, 2022, IEEE International Parallel and Distributed Processing Symposium (IPDPS), 168-178, https://ieeexplore.ieee.org/iel7/9820609/9820610/09820623.pdf
Signal analysis via the stochastic geometry of spectrogram level sets
S. Ghosh, M. Lin, and D. Sun, 2022, IEEE Transactions on Signal Processing, Vol. 70, 1104-1117 , https://ieeexplore.ieee.org/document/9720125
Support Vector Machines as Bayes' Classifiers
P. Jackson, 2022, Operations Research Letters, 50, No. 5, Sept. 2022, 423-429
Tail Probabilities of Random Linear Functions of Regularly Varying Random Vectors
B. Das, V. Fasen-Hartmann and C. Klüppelberg, 2022, Extremes, 25, 721-758, https://arxiv.org/abs/1904.06824
What have we Learned About Socioeconomic Inequalities in the Spread of COVID-19? A Systematic Review
F. Benita, L. Rebollar-Ruelas, E.D. Gaytán-Alfaro, 2022, Sustainable Cities and Society, 104158, https://doi.org/10.1016/j.scs.2022.104158
A Smart Learning Ecosystem Design for Delivering Data-driven Thinking in STEM Education
F. Benita, D. Virupaksha, E. Wilhelm, B. Tunçer, 2021, Smart Learning Environments, 8, 1-20, https://doi.org/10.1186/s40561-021-00153-y
Analysis of optimization algorithms via sum-of-squares
S. S.Y. Tan , V. Y.F. Tan, A Varvitsiotis, 2021, Journal of Optimization Theory and Applications, https://link.springer.com/article/10.1007/s10957-021-01869-0?fbclid=IwAR3ODcpJfH4ByBOTmartQi_Ew_4zLEL4mTsuE8XFjrTukSRi1KHc2nA8pkE
Data-driven Thinking for Measuring the Human Experience in the Built Environment
B. Tunçer, F. Benita, 2021, International Journal of Architectural Computing, 20 (2), 316-333, https://doi.org/10.1177/14780771211025142
Determinantal Point Processes Based on Orthogonal Polynomials for Sampling Minibatches in SGD
R. Bardenet, S. Ghosh, and M. Lin, 2021, Conference on Neural Information Processing Systems (NeurIPS), https://proceedings.neurips.cc/paper/2021/file/8744cf92c88433f8cb04a02e6db69a0d-Paper.pdf
Exploiting partial correlations in distributionally robust optimization
D. Padmanabhan∗, K. Natarajan, K. Murthy, 2021, Mathematical Programming, 186, 209-255, https://link.springer.com/article/10.1007/s10107-019-01453-5#Ack1