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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121 result(s)
Growth of Common Friends in a Preferential Attachment Model
B. Das and S. Ghosh , 2021, Stochastic Models, 37(3), 427-447, https://arxiv.org/abs/1908.04510
Human Mobility Behavior in COVID-19: A Systematic Literature Review and Bibliometric Analysis
F.Benita, 2021, Sustainable Cities and Society, 70, 102916, https://doi.org/10.1016/j.scs.2021.102916
Hykernel: A Hybrid Selection of One/two-phase Kernels for Triangle Counting on GPUs
M Almasri, N Vasudeva, R Nagi, J Xiong, WM Hwu, 2021, IEEE High Performance Extreme Computing Conference (HPEC), 1-7, https://ieeexplore.ieee.org/iel7/9622740/9622741/09622856.pdf
INFORMS Annual Meeting TutORials Speaker
Karthyek Murthy, 2021
Insights on Data Quality from a Large-scale Application of Smartphone-based Travel Survey Technology in the Phoenix Metropolitan Area, Arizona, USA
S. Hong, F. Zhao, V. Livshitsa, S. Gershenfeld, J. Santos, and M. Ben-Akiva, 2021, Transportation Research Part A: Policy and Practice, vol 154, 413-429, https://www.sciencedirect.com/science/article/pii/S0965856421002482
Integrating Empirical Analysis into Analytical Framework: An Integrated Model Structure for On-Demand Transportation
Yuliu Su, Ying Xu, Costas Courcoubetis, and Shih-Fen Cheng, 2021, The Proceedings of the 2021 INFORMS International Conference on Service Science, ICSS 2021: AI and Analytics for Smart Cities and Service Systems, 300–315, https://link.springer.com/chapter/10.1007/978-3-030-90275-9_25
K-clique Counting on GPUs
M Almasri, I El Hajj, R Nagi, J Xiong, W Hwu, 2021, arXiv e-prints, 2104, 13209
Learning Large Electrical Loads via Flexible Contracts with Commitment,
P. Lai, L. Duan, and X. Lin, 2021, IEEE Transactions on Network Science and Engineering, 8(2), 1925 – 1940, https://arxiv.org/pdf/1901.09169.pdf
Limited-trust Equilibria
T Murray, J Garg, R Nagi, 2021, European Journal of Operational Research, Volume 289, Issue 1, 364-380, https://www.sciencedirect.com/science/article/pii/S0377221720306202
Machine Learning for Soil Moisture Prediction Using Hyperspectral and Multispectral Data
M. Lobato, W. R. Norris, R. Nagi, A. Soylemezoglu and D. Nottage , 2021, IEEE 24th International Conference on Information Fusion (FUSION), 1-7, https://ieeexplore.ieee.org/abstract/document/9627067/