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
Bryan R. Goldsmith, PhD, is Assistant Professor in the department of Chemical Engineering within the College of Engineering at the University of Michigan, Ann Arbor.
Prof. Goldsmith’s research group utilizes first-principles modeling (e.g., density-functional theory and wave function based methods), molecular simulation, and data analytics tools (e.g., compressed sensing, kernel ridge regression, and subgroup discovery) to extract insights of catalysts and materials for sustainable chemical and energy production and to help create a platform for their design. For example, the group has exploited subgroup discovery as a data-mining approach to help find interpretable local patterns, correlations, and descriptors of a target property in materials-science data. They also have been using compressed sensing techniques to find physically meaningful models that predict the properties of perovskite (ABX3) compounds.
Prof. Goldsmith’s areas of research encompass energy research, materials science, nanotechnology, physics, and catalysis.
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)
Yuekai Sun, PhD, is Assistant Professor in the department of Statistics at the University of Michigan, Ann Arbor.
Dr. Sun’s research is motivated by the challenges of analyzing massive data sets in data-driven science and engineering. I focus on statistical methodology for high-dimensional problems; i.e. problems where the number of unknown parameters is comparable to or exceeds the sample size. My recent work focuses on two problems that arise in learning from high-dimensional data (versus black-box approaches that do not yield insights into the underlying data-generation process). They are:
1. model selection and post-selection inference: discover the latent low-dimensional structure in high-dimensional data and perform inference on the learned structure;
2. distributed statistical computing: design scalable estimators and algorithms that avoid communication and minimize “passes” over the data.
A recurring theme in my work is exploiting the geometry of latent low-dimensional structure for statistical and computational gains. More broadly, I am interested in the geometric aspects of high-dimensional data analysis.
My research focuses on technology and innovation in health care with an emphasis on information technology (IT), pharmaceuticals, and empirical methods. Many of my studies explored the effect of electronic health record (EHR) systems on health care quality and productivity. While the short-run gains from health IT adoption may be modest, these technologies form the foundation for a health information infrastructure. We are just beginning to understand how to harness and apply medical information. This problem is complicated by the sheer complexity of medical care, the heterogeneity across patients, and the importance of treatment selection. My current work draws on methods from both machine learning and econometrics to address these issues. Current pharmaceutical studies examine the roles of consumer heterogeneity and learning about the value of products as well as the effect of direct-to-consumer advertising on health.