Yi Lu Murphey

By |

Dr. Yi Lu Murphey is an Associate Dean for Graduate Education and Research, a Professor of the ECE(Electrical and Computer Engineering) department and the director of the Intelligent Systems Lab at the University of Michigan, Dearborn. She received a M.S. degree in computer science from Wayne State University, Detroit, Michigan, in 1983, and a Ph.D degree with a major in Computer Engineering and a minor in Control Engineering from the University of Michigan, Ann Arbor, Michigan, in 1989. Her current research interests are in the areas of machine learning, pattern recognition, computer vision and intelligent systems with applications to automated and connected vehicles, optimal vehicle power management, data analytics, and robotic vision systems. She has authored over 130 publications in refereed journals and conference proceedings. She is an editor for the Journal of Pattern Recognition, a senior life member of AAAI and a fellow of IEEE.

Brenda Gillespie

By |

Brenda Gillespie, PhD, is Associate Director in Consulting for Statistics, Computing and Analytics Research (CSCAR) with a secondary appointment as Associate Research Professor in the department of Biostatistics in the School of Public Health at the University of Michigan, Ann Arbor. She provides statistical collaboration and support for numerous research projects at the University of Michigan. She teaches Biostatistics courses as well as CSCAR short courses in survival analysis, regression analysis, sample size calculation, generalized linear models, meta-analysis, and statistical ethics. Her major areas of expertise are clinical trials and survival analysis.

Prof. Gillespie’s research interests are in the area of censored data and clinical trials. One research interest concerns the application of categorical regression models to the case of censored survival data. This technique is useful in modeling the hazard function (instead of treating it as a nuisance parameter, as in Cox proportional hazards regression), or in the situation where time-related interactions (i.e., non-proportional hazards) are present. An investigation comparing various categorical modeling strategies is currently in progress.

Another area of interest is the analysis of cross-over trials with censored data. Brenda has developed (with M. Feingold) a set of nonparametric methods for testing and estimation in this setting. Our methods out-perform previous methods in most cases.

Satish Narayanasamy

By |

Satish Narayanasamy, Ph.D., is Associate Professor in the Electrical Engineering and Computer Science department in the College of Engineering at the University of Michigan, Ann Arbor. Satish’s interests are working at the intersection of computer architecture, software systems and program analysis. His current interests include concurrency, security, customized architectures and tools for mobile and web applications, machine learning assisted program analysis, and tools for teaching at scale.

Ding Zhao

By |

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

 

Peter Adriaens

By |

My research focus is on the development and application of machine learning tools to large scale financial and unstructured (textual) data to extract, quantify and predict risk profiles and investment grade rating of private and public companies.  Example datasets include social media and financial aggregators such as Bloomberg, Pitchbook, and Privco.

V. G. Vinod Vydiswaran

By |

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.

Z. Morley Mao

By |

Z. Morley Mao, PhD, is Professor of Electrical Engineering and Computer Science, College of Engineering, at the University of Michigan, Ann Arbor campus.

Sriram Chandrasekaran

By |

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)

 

Research Overview: We develop computational algorithms that integrate omics measurements to create detailed genome-scale models of cellular networks. Some clinical applications of our algorithms include finding metabolic vulnerabilities in pathogens (M. tuberculosis) using PROM, and designing multi combination therapeutics for reducing antibiotic resistance using INDIGO.

Research Overview: We develop computational algorithms that integrate omics measurements to create detailed genome-scale models of cellular networks. Some clinical applications of our algorithms include finding metabolic vulnerabilities in pathogens (M. tuberculosis) using PROM, and designing multi combination therapeutics for reducing antibiotic resistance using INDIGO.

Yuekai Sun

By |

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.

A visualization of an algorithm for making accurate recommendations from data that contain shared user accounts.

A visualization of an algorithm for making accurate recommendations from data that contain shared user accounts.