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.
Lin is a transfer student from Shanghai Jiao Tong University (China) majoring in Electronic and Computer Engineering, and now she is a junior student majoring in data science.
Qianhui received her bachelor’s degree in biology and applied mathematics from the University of Pittsburgh and master’s degree in biostatistics from the University of Michigan. Her past research interest involves single cell RNA-seq data analysis to study hepatocellular carcinoma and preeclampsia. She also conducted benchmark studies on different cell-type supervised/semi-supervised classification methods for the high throughput scRNA-seq data. Currently she is a first-year PhD student doing rotations and interests in expanding her knowledge in population genetics and GWAS analysis.
John Lloyd is a computational biologist in the Departments of Human Genetics and Internal Medicine. His research interests include cancer genomics, precision medicine, and the evolution and ecology of tumors. His research focuses on developing robust statistical methods to identify molecular biomarkers for sensitivity to small molecule drugs so that patients may receive the most effective initial therapies. He received his Ph.D. from Michigan State University, with research focused on genome evolution and identification of functional intergenic transcripts and non-coding RNAs.
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…
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.