Dr. Mitchell’s research focuses on the causes and consequences of family formation behavior. He examines how social context such as neighborhood resources and values influence family processes and how those processes interplay with an individual’s genetic and epigenetic makeup to influence behavior, wellbeing, and health. His research also includes the development of new methods for integrating the collection and analysis of biological and social data.
Zhenke Wu is an Assistant Professor of Biostatistics, and Research Assistant Professor in Michigan Institute of Data Science (MIDAS). He received his Ph.D. in Biostatistics from the Johns Hopkins University in 2014 and then stayed at Hopkins for his postdoctoral training before joining the University of Michigan. Dr. Wu’s research focuses on the design and application of statistical methods that inform health decisions made by individuals, or precision medicine. The original methods and software developed by Dr. Wu are now used by investigators from research institutes such as CDC and Johns Hopkins, as well as site investigators from developing countries, e.g., Kenya, South Africa, Gambia, Mali, Zambia, Thailand and Bangladesh.
Jun Li, PhD, is Professor and Chair for Research in the department of Computational Medicine and Bioinformatics and Professor of Human Genetics in the Medical School at the University of Michigan, Ann Arbor.
Ding Zhao, PhD, is Assistant Research Scientist in the department of Mechanical Engineering, College of Engineering with a secondary appointment in the Robotics Institute at The University of Michigan, Ann Arbor.
Dr. Zhao’s research interests include autonomous vehicles, intelligent/connected transportation, traffic safety, human-machine interaction, rare events analysis, dynamics and control, machine learning, and big data analysis
V.G.Vinod Vydiswaran, PhD, is Assistant Professor in the Department of Learning Health Sciences with a secondary appointment in the School of Information at the University of Michigan, Ann Arbor.
Dr. Vydiswaran’s research focuses on developing and applying text mining, natural language processing, and machine learning methodologies for extracting relevant information from health-related text corpora. This includes medically relevant information from clinical notes and biomedical literature, and studying the information quality and credibility of online health communication (via health forums and tweets). His previous work includes developing novel information retrieval models to assist clinical decision making, modeling information trustworthiness, and addressing the vocabulary gap between health professionals and laypersons.
Sriram Chandrasekaran, PhD, is Assistant Professor of Biomedical Engineering in the College of Engineering at the University of Michigan, Ann Arbor.
Dr. Chandrasekaran’s Systems Biology lab develops computer models of biological processes to understand them holistically. Sriram is interested in deciphering how thousands of proteins work together at the microscopic level to orchestrate complex processes like embryonic development or cognition, and how this complex network breaks down in diseases like cancer. Systems biology software and algorithms developed by his lab are highlighted below and are available at http://www.sriramlab.org/software/.
– INDIGO (INferring Drug Interactions using chemoGenomics and Orthology) algorithm predicts how antibiotics prescribed in combinations will inhibit bacterial growth. INDIGO leverages genomics and drug-interaction data in the model organism – E. coli, to facilitate the discovery of effective combination therapies in less-studied pathogens, such as M. tuberculosis. (Ref: Chandrasekaran et al. Molecular Systems Biology 2016)
– GEMINI (Gene Expression and Metabolism Integrated for Network Inference) is a network curation tool. It allows rapid assessment of regulatory interactions predicted by high-throughput approaches by integrating them with a metabolic network (Ref: Chandrasekaran and Price, PloS Computational Biology 2013)
– ASTRIX (Analyzing Subsets of Transcriptional Regulators Influencing eXpression) uses gene expression data to identify regulatory interactions between transcription factors and their target genes. (Ref: Chandrasekaran et al. PNAS 2011)
– PROM (Probabilistic Regulation of Metabolism) enables the quantitative integration of regulatory and metabolic networks to build genome-scale integrated metabolic–regulatory models (Ref: Chandrasekaran and Price, PNAS 2010)
Gilbert Omenn, MD, PhD, is Professor of Computational Medicine & Bioinformatics with appointments in Human Genetics, Molecular Medicine & Genetics in the Medical School and Professor of Public Health in the School of Public Health and the Harold T. Shapiro Distinguished University Professor at the University of Michigan, Ann Arbor.
Doctor Omenn’s current research interests are focused on cancer proteomics, splice isoforms as potential biomarkers and therapeutic tar- gets, and isoform-level and single-cell functional networks of transcripts and proteins. He chairs the global Human Proteome Project of the Human Proteome Organization.
The GEMS (Graph Exploration and Mining at Scale) Lab develops new, fast and principled methods for mining and making sense of large-scale data. Within data mining, we focus particularly on interconnected or graph data, which are ubiquitous. Some examples include social networks, brain graphs or connectomes, traffic networks, computer networks, phonecall and email communication networks, and more. We leverage ideas from a diverse set of fields, including matrix algebra, graph theory, information theory, machine learning, optimization, statistics, databases, and social science.
At a high level, we enable single-source and multi-source data analysis by providing scalable methods for fusing data sources, relating and comparing them, and summarizing patterns in them. Our work has applications to exploration of scientific data (e.g., connectomics or brain graph analysis), anomaly detection, re-identification, and more. Some of our current research directions include:
*Scalable Network Discovery from non-Network Data*: Although graphs are ubiquitous, they are not always directly observed. Discovering and analyzing networks from non-network data is a task with applications in fields as diverse as neuroscience, genomics, energy, economics, and more. However, traditional network discovery approaches are computationally expensive. We are currently investigating network discovery methods (especially from time series) that are both fast and accurate.
*Graph similarity and Alignment with Representation Learning*: Graph similarity and alignment (or fusion) are core tasks for various data mining tasks, such as anomaly detection, classification, clustering, transfer learning, sense-making, de-identification, and more. We are exploring representation learning methods that can generalize across networks and can be used in such multi-source network settings.
*Scalable Graph Summarization and Interactive Analytics*: Recent advances in computing resources have made processing enormous amounts of data possible, but the human ability to quickly identify patterns in such data has not scaled accordingly. Thus, computational methods for condensing and simplifying data are becoming an important part of the data-driven decision making process. We are investigating ways of summarizing data in a domain-specific way, as well as leveraging such methods to support interactive visual analytics.
*Distributed Graph Methods*: Many mining tasks for large-scale graphs involve solving iterative equations efficiently. For example, classifying entities in a network setting with limited supervision, finding similar nodes, and evaluating the importance of a node in a graph, can all be expressed as linear systems that are solved iteratively. The need for faster methods due to the increase in the data that is generated has permeated all these applications, and many more. Our focus is on speeding up such methods for large-scale graphs both in sequential and distributed environments.
*User Modeling*: The large amounts of online user information (e.g., in social networks, online market places, streaming music and video services) have made possible the analysis of user behavior over time at a very large scale. Analyzing the user behavior can lead to better understanding of the user needs, better recommendations by service providers that lead to customer retention and user satisfaction, as well as detection of outlying behaviors and events (e.g., malicious actions or significant life events). Our current focus is on understanding career changes and predicting job transitions.
Dr. Zhu’s group conducts research on various topics, ranging from foundational methodologies to challenging applications, in data science. In particular, the group has been investigating the fundamental issues and techniques for supporting various types of queries (including range queries, box queries, k-NN queries, and hybrid queries) on large datasets in a non-ordered discrete data space. A number of novel indexing and searching techniques that utilize the unique characteristics of an NDDS are developed. The group has also been studying the issues and techniques for storing and searching large scale k-mer datasets for various genome sequence analysis applications in bioinformatics. A virtual approximate store approach to supporting repetitive big data in genome sequence analyses and several new sequence analysis techniques are suggested. In addition, the group has been researching the challenges and methods for processing and optimizing a new type of so-called progressive queries that are formulated on the fly by a user in multiple steps. Such queries are widely used in many application domains including e-commerce, social media, business intelligence, and decision support. The other research topics that have been studied by the group include streaming data processing, self-management database, spatio-temporal data indexing, data privacy, Web information management, and vehicle drive-through wireless services.