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.
Prof. Huan’s research broadly revolves around uncertainty quantification, data-driven modeling, and numerical optimization. He focuses on methods to bridge together models and data: e.g., optimal experimental design, Bayesian statistical inference, uncertainty propagation in high-dimensional settings, and algorithms that are robust to model misspecification. He seeks to develop efficient numerical methods that integrate computationally-intensive models with big data, and combine uncertainty quantification with machine learning to enable robust and reliable prediction, design, and decision-making.
Professor Hines’ research focuses on the analysis of the donative behavior of Americans, and how it affects the intergenerational and interpersonal transmission of economic well-being. To what extent do parents leave property to their children and others, and how is this behavior affected by legal institutions, taxes, social norms, and other considerations? While there are no comprehensive sources of data on wills, trusts, lifetime gifts, and other forms of property transmission, there is ample available information from legal documents that with the help of natural language processing can hopefully be coded and analyzed in a systematic way.
I study how law shapes innovation in the life sciences, with a substantial focus on big data and artificial intelligence in medicine. I write about the intellectual property incentives and protections for data and AI algorithms, the privacy issues with wide-scale health- and health-related data collection, the medical malpractice implications of AI in medicine, and how FDA should regulate the use of medical AI.
The future of transportation lies at the intersection of two emerging trends, namely, the sharing economy and connected and automated vehicle technology. Our research group investigates the impact of these two major trends on the future of mobility, quantifying the benefits and identifying the challenges of integrating these technologies into our current systems.
Our research on shared-use mobility systems focuses on peer-to-peer (P2P) ridesharing and multi-modal transportation. We provide: (i) operational tools and decision support systems for shared-use mobility in legacy as well as connected and automated transportation systems. This line of research focuses on system design as well as routing, scheduling, and pricing mechanisms to serve on-demand transportation requests; (ii) insights for regulators and policy makers on mobility benefits of multi-modal transportation; (ii) planning tools that would allow for informed regulations of sharing economy.
In another line of research we investigate challenges faced by the connected automated vehicle technology before mass adoption of this technology can occur. Our research mainly focuses on (i) transition of control authority between the human driver and the autonomous entity in semi-autonomous (level 3 SAE autonomy) vehicles; (ii) incorporating network-level information supplied by connected vehicle technology into traditional trajectory planning; (iii) improving vehicle localization by taking advantage of opportunities provided by connected vehicles; and (iv) cybersecurity challenges in connected and automated systems. We seek to quantify the mobility and safety implications of this disruptive technology, and provide insights that can allow for informed regulations.
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.
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.
Tim Cernak, PhD, is Assistant Professor of Medicinal Chemistry with secondary appointments in Chemistry and the Chemical Biology Program at the University of Michigan, Ann Arbor.
The functional and biological properties of a small molecule are encoded within its structure so synthetic strategies that access diverse structures are paramount to the invention of novel functional molecules such as biological probes, materials or pharmaceuticals. The Cernak Lab studies the interface of chemical synthesis and computer science to understand the relationship of structure, properties and reactions. We aim to use algorithms, robotics and big data to invent new chemical reactions, synthetic routes to natural products, and small molecule probes to answer questions in basic biology. Researchers in the group learn high-throughput chemical and biochemical experimentation, basic coding, and modern synthetic techniques. By studying the relationship of chemical synthesis to functional properties, we pursue the opportunity to positively impact human health.
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.