Eric Xing, Ph.D(PI)
Professor of Machine Learning Department & Language Technology Institute & Computer Science Department
Director of Center for Machine Learning and Health, CMU
Eric’s principal research interests lie in the development of machine learning and statistical methodology, and large-scale computational system and architecture, for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in artificial, biological, and social systems.

 

Wei Wu, PhD (Co-PI)
Associate Research Professor
Computational Biology Department, CMU
Wei’s research focuses on understanding complex human diseases by undertaking integrative approaches, which combine biology, computational and statistical learning, bioinformatics, and genomics. My role in the research being carried out in the CDAR is to apply novel machine learning methods to: i) identify genetic and gene expression biomarkers for drug abuse disorder; and ii) help diagnose and effectively treat patients with drug abuse disorder.

 

  Bryon Aragam

Machine Learning Department

School of Computer Science

Carnegie Mellon University

Bryon’s research interests involve problems at the intersection of high-dimensional statistics and machine learning, with a focus on developing scalable algorithms for personalized medicine and complex networks to better understand the genetic basis of disorders such as drug abuse.
  Haohan Wang

Language Technologies Institute

School of Computer Science

Carnegie Mellon University

Haohan’s research interests include statistical machine learning methods applied to genomic problems. He is particularly interested in advanced linear mixed models as a combination of confounding correction methods and high-dimensional statistics methods. He is seeking more reliable methods to analyze the drug abuse disorder data. Besides, he is also seeking novel solutions in the deep learning area.
  Benjamin J. Lengerich

Computer Science Department

School of Computer Science

Carnegie Mellon University

Ben is broadly motivated by statistical machine learning for healthcare and the theoretical problems that arise from the constraints of real-world data. These include building interpretable, robust systems for prediction on structured genomic, medical, and other types of data.
  Micol Marchetti-Bowick

Machine Learning Department

School of Computer Science

Carnegie Mellon University

Micol’s research focuses on developing novel machine learning algorithms to uncover i) association of genetic markers (such as SNPs) for drug abuse disorder with gene expression data; and ii) gene networks involved in drug abuse disorder using gene expression data.
  Seo-Jin Bang

Computational Biology Department

School of Computer Science

Carnegie Mellon University

Seo-Jin’s research interest is to integrate omics data using machine learning approaches including deep learning, and probabilistic graphical model. In particular she is interested in combining multiple types of high-throughput data as well as compressing human knowledge in an ensemble to improve clinical decision making.