CUONG CHIEU TO, PhD

**** */* ***** **., San Diego, CA 92111, USA

adr2rb@r.postjobfree.com, 858-***-****

https://scholar.google.com/citations?user=G96AU4UAAAAJ&hl=en PERSONAL STATEMENT

Machine learning researcher with over 15 years of experience in optimization problems. Developing algorithms in clustering, biclustering, classification, regression, dimensional reduction, and feature selection problems. The research capabilities are demonstrated through a patent, several publications in good journals such as Cell, Journal of Hepatology, FASEB, Bioinformatics, BMC Bioinformatics, BMC Genomics, Cell Transplantation, JALA, Development and prestigious conferences namely GECCO and CEC. Ultimate objectives are 1) developing machine learning methods. 2) developing optimization methods. RESEARCH EXPERIENCE

University of California, San Diego, Mar. 2015 – Aug. 2022 Postdoc & Staff Research Associate

- Predicting gestational ages: use genetic algorithms to extract a subset of genes and then use machine learning methods to predict the ages of samples.

- Multi-class classification: use error-correcting output coding, genetic algorithms, and machine learning methods to predict the labels of samples.

- Deconvolution: use linear regression least squares, quantile regression, Bayesian linear regression, etc. to find proportions of mixture samples in signature samples.

- Non-negative matrix factorization: build a software for clustering and dimensional reduction.

- RNA velocity: a combination between differential equations and optimization methods to predict amount of a gene at a time point.

- RNA-seq data analysis: mapping, normalization, visualization, etc. Griffith University, Australia, Sep. 2013 – Aug. 2014 Visiting researcher

- Biclustering: develop an algorithm based on linear model and solved by optimization methods. Genopole, University of Évry-Val-d'Essonne, Feb. 2012 – Jan. 2014 Postdoctoral researcher

- Protein-protein interactions networks: use multi-objective optimization methods to solve graph clustering problem.

University of West Bohemia & Institute of Microbiology, Czech Republic, Sep. 2003 – Sep. 2006 PhD study on data mining

- Gene regulatory networks: use differential equations to model gene networks and then use optimization methods to solve.

- Classification problems: develop algorithms for classification problems and use parallel genetic algorithms to solve.

EDUCATION

- B.S. on civil engineering, University of Architecture, Viet Nam, 1991 – 1996.

- B.S. on Informatics, University of Natural Sciences, Viet Nam, 1992 – 1997.

- M.E on civil engineering, University of Technology, Viet Nam, 1997 – 2001.

- PhD on computer science, University of West Bohemia, Czech Republic, 2003 – 2006. SELECTED RESEARCH PAPERS

1. Cuong To, Jiri Vohradsky. “Measurement variation determins gene network topology reconstructed from experimental data: a case study of yeast cyclins network,” FASEB J September 2010, 24:3468-3478

(http://www.fasebj.org/content/24/9/3468.abstract). 2. Cuong To, Jiri Vohradsky. “Supervised inference of gene-regulatory networks.” BMC Bioinformatics 2008, 9:2 (http://www.biomedcentral.com/1471-2105/9/2/abstract). 3. Cuong To, Jiri Vohradsky. “A parallel genetic algorithm for single class pattern classification and its application for gene expression profiling in streptomyces coelicolor.” BMC Genomics 2007, 8:49

(http://www.biomedcentral.com/1471-2164/8/49).

4. Cuong To, T.T. Nguyen, A.W.C. Liew. “A Hough transform based biclustering algorithm for gene expression data,” Communications in Computer and Information Science 481, pp. 97-106, 2014.