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Co-author relationship prediction in heterogeneous bibliographic networks

Published in ASONAM, 2011

New feature framework for making predictions about network formation

Citation: Sun, Y., Barber, R., Gupta, M., Aggarwal, C. C., & Han, J. (2011, July). Co-author relationship prediction in heterogeneous bibliographic networks. In Advances in Social Networks Analysis and Mining (ASONAM), 2011 International Conference on (pp. 121-128). IEEE.



Instructor, CS 361 Probability & Statistics for Computer Scientists

University of Illinois, 2017

I taught this course as an instructor before joining the PhD program. Course description: Introduction to probability theory and statistics with applications to computer science. Topics include: visualizing datasets, summarizing data, basic descriptive statistics, conditional probability, independence, Bayes theorem, random variables, joint and conditional distributions, expectation, variance and covariance, central limit theorem. Markov inequality, Chebyshev inequality, law of large numbers, Markov chains, simulation, the PageRank algorithm, populations and sampling, sample mean, standard error, maximum likelihood estimation, Bayes estimation, hypothesis testing, confidence intervals, linear regression, principal component analysis, classification, and decision trees.