pal group|pattern analysis and learning group-澳门游戏娱乐场棋牌

research interests

my research interests include machine learning theory; probabilistic graphical models; high-dimensional signal and information processing; and physics-inspired optimization algorithms.

specifically, i’m focusing on the following problems.

1. theoretical aspects of machine learning with a special interest in the interdisciplinary field between high-dimensional statistics and statistical physics;

2. online learning and continuous learning;

3. graph neural networks and probabilistic graphical models.

biosketch

i received my ph.d. degree in theoretical physics from the institute of theoretical physics, chinese academy of science, beijing, china, in 2015. i then joined the paulson school of engineering and applied sciences at harvard university, first as a postdoctoral fellow (feb. 2015—jan. 2018) and as a research associate (feb. 2018—aug. 2019). i won the best student paper award at the ieee globalsip conference in 2014.

publications

conference:

[c.7] chuang wang, hong hu, yue m. lu, a solvable high-dimensional model of gan, advances in neural information processing systems (neurips), 2019

[c.6] chuang wang, yue m. lu, the scaling limit of high-dimensional online independent component analysis, advances in neural information processing systems (nips), 2017 (spotlight talk)

[c.5] chuang wang, yonina c. eldar and yue m. lu, subspace estimation from incomplete observations: a precise high-dimensional analysis, signal processing with adaptive sparse structured representations (spars), lisbon, portugal, 2017 (oral talk)

[c.4] chuang wang, yue m. lu, online learning for sparse pca in high dimensions: exact dynamics and phase transitions, 2016 ieee information theory workshop (itw), 186-190, 2016

[c.3] chuang wang, a. agaskar and yue m. lu, "randomized kaczmarz algorithm for inconsistent linear systems: an exact mse analysis," international conference on sampling theory and applications (sampta), washington, dc, 2015, pp. 498-502.

[c.2] a. agaskar, chuang wang, yue m. lu, randomized kaczmarz algorithms: exact mse analysis and optimal sampling probabilities, ieee global conference on signal and information processing (globalsip), 2014 (best student award paper)

[c.1] chuang wang, haijun zhou, simplifying generalized belief propagation on redundant region graphs, journal of physics: conference series, 473, 012004 (2013)

journal:

[j.8] chuang wang, yue m. lu, the scaling limit of high-dimensional online independent component analysis, journal of statistical mechanics: theory and experiment, 2019 (accepted, jcr q1)

[j.7] chuang wang, yonina c. eldar, yue m. lu, subspace estimation from incomplete observations: a high-dimensional analysis, ieee journal of selected topics in signal processing, vol. 12, no. 6, pp. 1240-1252, 2018 (jcr q1)

[j.6] chuang wang, jonathan mattingly, yue m. lu, scaling limit: exact and tractable analysis of online learning algorithms with applications to regularized regression and pca, 2017, arxiv:1712.04332 (preprint)

[j.5] g. d. ferraro, chuang wang, haijun zhou, e. aurell, on one-step replica symmetry breaking in the edwards-anderson spin glass model, journal of statistical mechanics: theory and experiment, vol.7, pp.073305 (jcr q1)

[j.4] chuang wang, shaomeng qin, haijun zhou, topologically invariant tensor renormalization group method for the edwards-anderson spin glasses model, physical review b 90, vol.17, pp. 174201, 2014 (jcr q1)

[j.3] haijun zhou, chuang wang, region graph partition function expansion and approximate free energy landscapes: theory and some numerical results, journal of statistical physics, vol. 148, pp. 513, 2012 (jcr q2)

[j.2] haijun zhou, chuang wang, zedong bi, jinqing xiao, partition function expansion on region graphs and message-passing equations, journal of statistical mechanics: theory and experiment, vol.12., pp. l12001 (jcr q1)

[j.1] haijun zhou, chuang wang, ground-state configuration space heterogeneity of random finite-connectivity spin glasses and random constraint satisfaction problems, journal of statistical mechanics: theory and experiment, vol 10, pp. p10010, 2010 (jcr q1)