Efficient Hybrid Algorithms for Computing Clusters Overlap
Javangula, P., Modarre, K., Shenoy, P., Liu, Y., & Nayebi, A. (2017). " Efficient Hybrid Algorithms for Computing Clusters Overlap 1." Procedia Computer Science. 108: 1050-1059.
CV
Full C.V. can be found here
Education
- B.S. in Mathematics and Statistics for Finance, Imperial College London, 2015
- M.S. in Statistics, Stanford University, 2017
- Ph.D in Statistics, University of Chicago, 2022
Industry experience
2024 - : Applied Scientist at Microsoft
2022 - 2024 : Applied Scientist at Amazon
Summer 2019: Ph.D. Data Science Intern at Wayfair
Summer 2016: Data Science Intern at Adobe Inc
Summer 2014: Coporate Banking Intern at Royal Bank of Scotland
Professional Service
Reviewer for Aging ISSN: 1945-4589, Bernoulli ISSN: 1350-7265, IEEE Assess ISSN: 2169-3536, Statistical Methods in Medical Research ISSN: 1350-7265, BMC Bioinformatics ISSN: 1471-2105
Awards
American Statistical Association Section on Bayesian Statistical Science Student Paper Competition 2021 Winner
Publications
Variable Selection with ABC Bayesian Forests
Liu Y, Ročková V, Wang Y (2020). "Variable Selection with ABC Bayesian Forests." Journal of Royal Statistical Society Series B, In Press .
Variable Selection via Thompson Sampling
Liu Y., Rockova, V. . Variable Selection via Thompson Sampling[J]." arXiv preprint arXiv:2007.00187, 2020. . 1(3).
Fully Interpretable Deep Learning Model of Transcriptional Control
Liu Y, Barr K, Reinitz J. (2019). " Fully Interpretable Deep Learning Model of Transcriptional Control[J]." Bioinformatics, Volume 36, Issue Supplement_1, July 2020, Pages i499–i507, Published . 1(3).
Talks
Mini-Siminar
siminar at Department of Statistics, Chicago IL, USA
Presentation on Deep Learning for Genomics
Talk at , Chicago, Illinos
Presentation at ISMB
Talk at , Chicago, Illinos