| Bayesian sampling using stochastic gradient thermostats N Ding, Y Fang, R Babbush, C Chen, RD Skeel, H Neven Advances in neural information processing systems 27, 2014 | 270 | 2014 |
| Compressible generalized hybrid monte carlo Y Fang, JM Sanz-Serna, RD Skeel The Journal of chemical physics 140 (17), 2014 | 66 | 2014 |
| Identifying rare cell populations in comparative flow cytometry A Azad, J Langguth, Y Fang, A Qi, A Pothen Algorithms in Bioinformatics: 10th International Workshop, WABI 2010 …, 2010 | 25 | 2010 |
| Modelling circadian rhythms of protein KaiA, KaiB and KaiC interactions in cyanobacteria S Li, Y Fang Biological Rhythm Research 38 (1), 43-53, 2007 | 13 | 2007 |
| Quasi-reliable estimates of effective sample size Y Fang, Y Cao, RD Skeel IMA Journal of Numerical Analysis, 2020 | 10 | 2020 |
| Comparing Markov chain samplers for molecular simulation RD Skeel, Y Fang Entropy 19 (10), 561, 2017 | 10 | 2017 |
| Parzen Discriminant Analysis Y Fang, H Chang, S Shan, X Chen, W Gao International Conference on Pattern Recognition, 2008 | 8 | 2008 |
| High-speed automatic characterization of rare events in flow cytometric data Y Qi, Y Fang, DR Sinclair, S Guo, M Alberich-Jorda, J Lu, DG Tenen, ... Plos one 15 (2), e0228651, 2020 | 4 | 2020 |
| Compressible generalized hybrid Monte Carlo (vol 140, 174108, 2014) Y Fang, JM Sanz-Serna, RD Skeel Journal Of Chemical Physics 144 (2), 2016 | 2 | 2016 |
| Efficient Markov chain Monte Carlo methods Y Fang Purdue University, 2018 | 1 | 2018 |
| Erratum:“Compressible generalized hybrid Monte Carlo”[J. Chem. Phys. 140, 174108 (2014)] Y Fang, JM Sanz-Serna, RD Skeel The Journal of Chemical Physics 144 (2), 029901-029901, 2016 | 1 | 2016 |
| Computing for Science and Engineering R Skeel, Y Fang | | 2016 |
| Genome‐wide identification of vitamin D receptor (VDR) binding sites in DNA from prostate epithelial cells (PEC) by chromatin immunoprecipitation‐sequencing (ChIP‐seq) M Cui, Y Fang, Y Qi, J Fleet The FASEB Journal 24, 212.8-212.8, 2010 | | 2010 |
