Research Investigator, Li Lab, University of Michigan (since Aug 2017)Scientific Advisor, enGenome (since Dec 2016)Postdoc fellow, Laboratory for Biomedical Informatics, University of Pavia (2016 - 2017)Postdoc fellow, Akutsu Laboratory, University of Kyoto, Japan (2015 - 2016)Postdoc fellow, Laboratory for Biomedical Informatics, University of Pavia (2013 - 2015)
Simone received his PhD from the Hong Kong University of Science and Technology in 2012. Before joining the University of Michigan, he has worked for the University of Pavia (Italy) and Kyoto University (Japan). Simone designes prediction models for medicine and molecular biology with machine learning. He is particularly interested in data integration, i.e. in developing models harvesting heterogeneous data from genomics, proteomics, medical literature, and ontologies. He has experience with supervised, unsupervised, semi-supervised, ensemble, and deep learning; SVMs, random forest, Bayesian networks, matrix factorization, and genetic algorithms. The application range of his models is broad, spanning from protein affinity prediction, to simulation of clinical trajectories in diabetic patients, to single-cell RNA-seq data analysis. He mainly works with R, Matlab/Octave, and Perl.
His personal website can be accessed through this link.
Yanling Zhang, Ph.D.
Yanling received his PhD in Biochemistry from University of Iowa in 2008. From 2008-2014, he worked as a Research Fellow at the Life Sciences Institute, University of Michigan. In August of 2014, he was appointed a faculty member in the Department of Biochemistry and Molecular Biology at Soochow University in China. He is currently visiting the Li lab from Aug 2018 - Feb 2019. Yanling is interested in novel regulation mechanisms of membrane trafficking events using a combination of bioinformatics and wet lab techniques. During the Li lab fellowship, he will analyze RNA-seq data from rat models.
Hussain is an undergraduate student at the University of Michigan. He is currently studying Mathematics (Mathematical Biology subprogram) and Biochemistry and plans to graduate with a Bachelors in Science in May 2019, after which he will pursue a medical education. Hussain’s current project in the Li lab is a mathematical modeling project, where he mainly uses R and Matlab to do what he likes to call “very cool stuff.” In the summer of 2018, Hussain undertook a full-time summer internship at Wayne State Medical School’s Center for Molecular Medicine and Genetics, where he analyzed RNA and DNA microarray data to test for variations in gene expression in human patients diagnosed with Non-Alcoholic Fatty Liver Disease (NAFLD) and learned and performed wet lab experiments using a variety of molecular techniques. Lastly, Hussain loves track and field and could talk day and night about running; however, be careful about challenging him to a race, as he will never turn down a challenge…
Mashiat is an international student from Bangladesh. She received her Bachelor’s at The University of Hong Kong (HKU) and Master’s at the School of Health Professions at MD Anderson Cancer Center. She is currently working on using single cell genomics to understand male germ cell development. Previously she worked on integrative data analysis on single cell level to understand tumor heterogeneity in breast cancer. She hopes to continue leveraging single cell technologies to answer biological questions.
April graduated from Spring Arbor University with her undergraduate degree in Mathematics, with a minor in biology. She received her initial exposure to the world of computational biology in public health at the University of Minnesota’s Summer Institute in Biostatistics. She also completed a summer REU in bioinformatics at Auburn University, under the guidance of Dr. Rita Graze. She is a first year PhD student in the bioinformatics program, and is curious about understanding the interplay of lifestyles factors, genetics, and the development of neurological diseases. She is currently hoping to explore the methods and analysis of single-cell RNA-seq data, as well as explore machine learning.