Manuel Rivas

The Rivas lab is based in the Department of Biomedical Data Science. We are a new lab with a focus on population analytics using genomic and phenotype data. We will develop statistical models, algorithms, and computational tools for the analysis of millions of samples. Scientific themes that the lab will focus on: 1) Generating effective therapeutic and preventative hypotheses for human diseases from human genetic, imaging, wearable sensor, and environmental data; 2) developing technologies for integrated learning healthcare systems with a particular focus on underserved communities and developing regions of the world; 3) genetic epidemiology where the aim is to understand the global distribution of common and rare disease predisposition genes; and 4) high dimensional methods development and optimization.

Research Areas: 

  • Human Genetics
  • Statistical Genetics
  • Learning Healthcare Systems
  • Biostatistics

Julia Palacios

I seek to provide statistically rigorous answers to concrete, data driven questions in evolutionary genetics. My research involves probabilistic modeling of evolutionary forces and the development of computationally tractable methods that are applicable to big data problems. Past and current research relies heavily on the theory of stochastic processes, Bayesian nonparametrics and recent developments in machine learning and statistical theory.

Research Areas: 

  • Machine/ Statistical Learning
  • Computational Biology
  • Genetics
  • Evolutionary Genetics
  • Statistics

Aaron Newman

Our group combines computational and experimental techniques to study the cellular organization of complex tissues, with a focus on determining the phenotypic diversity and clinical significance of tumor cell subsets. Although our research cuts across disciplinary boundaries, we specialize in the development of robust computational strategies to address key questions in the cancer genomics field, with an emphasis on clinical translation of our findings into novel biomarkers and individualized therapies. As a member of the Department of Biomedical Data Science and the Institute for Stem Cell Biology and Regenerative Medicine, and as an affiliate of graduate programs in Biomedical Informatics, Cancer Biology, and Immunology, we are also interested in the development of impactful biomedical data science tools in areas beyond our immediate research focus, including developmental biology, regenerative medicine, and systems immunology.

Research Areas: 

  • Bioinformatics
  • Computatonal Biology
  • Cancer Genomics
  • Stem Cell Bioinformatics
  • Machine/ Statistical Learning

Ying Lu

Research Areas: 

  • Biostististics
  • Clinical Trials
  • Medical Decision Making
  • Medical Diagnosis
  • Cancer
  • Quality Control