I am Research Faculty with the Michigan Center for Integrative Research in Critical Care (MCIRCC). Our team builds predictive algorithms, analyzes signals, and implements statistical models to advance Critical Care Medicine. We use electronic healthcare record data to build predictive algorithms. One example of this is Predicting Intensive Care Transfers and other Unforeseen Events (PICTURE), which uses commonly collected vital signs and labs to predict patient deterioration on the general hospital floor. Additionally, our team collects waveforms from the University Hospital, and we store this data utilizing Amazon Web Services. We use these signals to build predictive algorithms to advance precision medicine. Our flagship algorithm called Analytic for Hemodynamic Instability (AHI), predicts patient deterioration using a single-lead electrocardiogram signal. We use Bayesian methods to analyze metabolomic biomarker data from blood and exhaled breath to understand Sepsis and Acute Respiratory Distress Syndrome. I also have an interest in statistical genetics.
Jeffrey Regier received a PhD in statistics from UC Berkeley (2016) and joined the University of Michigan as an assistant professor. His research interests include graphical models, Bayesian inference, high-performance computing, deep learning, astronomy, and genomics.
The long temporal and large spatial scales of ecological systems make controlled experimentation difficult and the amassing of informative data challenging and expensive. The resulting sparsity and noise are major impediments to scientific progress in ecology, which therefore depends on efficient use of data. In this context, it has in recent years been recognized that the onetime playthings of theoretical ecologists, mathematical models of ecological processes, are no longer exclusively the stuff of thought experiments, but have great utility in the context of causal inference. Specifically, because they embody scientific questions about ecological processes in sharpest form—making precise, quantitative, testable predictions—the rigorous confrontation of process-based models with data accelerates the development of ecological understanding. This is the central premise of my research program and the common thread of the work that goes on in my laboratory.
Dr. Bai’s research interests lie in development and refinement of bioinformatics algorithms/software and databases on next-generation sequencing (NGS data), development of statistical model for solving biological problems, bioinformatics analysis of clinical data, as well as other topics including, but not limited to, uncovering disease genes and variants using informatics approaches, computational analysis of cis-regulation and comparative motif finding, large-scale genome annotation, comparative “omics”, and evolutionary genomics.
Hyun Min Kang is an Associate Professor in the Department of Biostatistics. He received his Ph.D. in Computer Science from University of California, San Diego in 2009 and joined the University of Michigan faculty in the same year. Prior to his doctoral studies, he worked as a research fellow at the Genome Research Center for Diabetes and Endocrine Disease in the Seoul National University Hospital for a year and a half, after completing his Bachelors and Masters degree in Electrical Engineering at Seoul National University. His research interest lies in big data genome science. Methodologically, his primary focus is on developing statistical methods and computational tools for large-scale genetic studies. Scientifically, his research aims to understand the etiology of complex disease traits, including type 2 diabetes, bipolar disorder, cardiovascular diseases, and glomerular diseases.
Dr. Veera Baladandayuthapani is currently a Professor in the Department of Biostatistics at University of Michigan (UM), where he is also the Associate Director of the Center for Cancer Biostatistics. He joined UM in Fall 2018 after spending 13 years in the Department of Biostatistics at University of Texas MD Anderson Cancer Center, Houston, Texas, where was a Professor and Institute Faculty Scholar and held adjunct appointments at Rice University, Texas A&M University and UT School of Public Health. His research interests are mainly in high-dimensional data modeling and Bayesian inference. This includes functional data analyses, Bayesian graphical models, Bayesian semi-/non-parametric models and Bayesian machine learning. These methods are motivated by large and complex datasets (a.k.a. Big Data) such as high-throughput genomics, epigenomics, transcriptomics and proteomics as well as high-resolution neuro- and cancer- imaging. His work has been published in top statistical/biostatistical/bioinformatics and biomedical/oncology journals. He has also co-authored a book on Bayesian analysis of gene expression data. He currently holds multiple PI-level grants from NIH and NSF to develop innovative and advanced biostatistical and bioinformatics methods for big datasets in oncology. He has also served as the Director of the Biostatistics and Bioinformatics Cores for the Specialized Programs of Research Excellence (SPOREs) in Multiple Myeloma and Lung Cancer and Biostatistics&Bioinformatics platform leader for the Myeloma and Melanoma Moonshot Programs at MD Anderson. He is a fellow of the American Statistical Association and an elected member of the International Statistical Institute. He currently serves as an Associate Editor for Journal of American Statistical Association, Biometrics and Sankhya.
My research is focused on developing efficient and effective statistical and computational methods for genetic and genomic studies. These studies often involve large-scale and high-dimensional data; examples include genome-wide association studies, epigenome-wide association studies, and various functional genomic sequencing studies such as bulk and single cell RNAseq, bisulfite sequencing, ChIPseq, ATACseq etc. Our method development is often application oriented and specifically targeted for practical applications of these large-scale genetic and genomic studies, thus is not restricted in a particular methodology area. Our previous and current methods include, but are not limited to, Bayesian methods, mixed effects models, factor analysis models, sparse regression models, deep learning algorithms, clustering algorithms, integrative methods, spatial statistics, and efficient computational algorithms. By developing novel analytic methods, I seek to extract important information from these data and to advance our understanding of the genetic basis of phenotypic variation for various human diseases and disease related quantitative traits.
My research broadly focuses on developing data analytics and decision-making methodologies specifically tailored for Internet of Things (IoT) enabled smart and connected products/systems. I envision that most (if not all) engineering systems will eventually become connected systems in the future. Therefore, my key focus is on developing next-generation data analytics, machine learning, individualized informatics and graphical and network modeling tools to truly realize the competitive advantages that are promised by smart and connected products/systems.
Dr. Lee’s research in data science concerns biological questions in systems biology and network medicine by developing algorithms and models through a combination of statistical/machine learning, information theory, and network theory applied to multi-dimensional large-scale data. His projects have covered genomics, transcriptomics, proteomics, and metabolomics from yeast to mouse to human for integrative analysis of regulatory networks on multiple molecular levels, which also incorporates large-scale public databases such as GO for functional annotation, PDB for molecular structures, and PubChem and LINCS for drugs or small compounds. He previously carried out proteomics and metabolomics along with a computational derivation of dynamic protein complexes for IL-3 activation and cell cycle in murine pro-B cells (Lee et al., Cell Reports 2017), for which he developed integrative analytical tools using diverse approaches from machine learning and network theory. His ongoing interests in methodology include machine/deep learning and topological Kolmogorov-Sinai entropy-based network theory, which are applied to (1) multi-level dynamic regulatory networks in immune response, cell cycle, and cancer metabolism and (2) mass spectrometry-based omics data analysis.
Samuel K Handelman, Ph.D., is Research Assistant Professor in the department of Internal Medicine, Gastroenterology, of Michigan Medicine at the University of Michigan, Ann Arbor. Prof. Handelman is focused on multi-omics approaches to drive precision/personalized-therapy and to predict population-level differences in the effectiveness of interventions. He tends to favor regression-style and hierarchical-clustering approaches, partially because he has a background in both statistics and in cladistics. His scientific monomania is for compensatory mechanisms and trade-offs in evolution, but he has a principled reason to focus on translational medicine: real understanding of these mechanisms goes all the way into the clinic. Anything less that clinical translation indicates that we don’t understand what drove the genetics of human populations.