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GLBIO 2019 | May 19- 22, 2019 | Univ. of Wisconsin at Madison | Keynote Speakers

KEYNOTE SPEAKERS



  Mark Craven, PhD
Department of Biostatistics and Medical Informatics
Department of Computer Sciences
University of Wisconsin-Madison
R. Stephanie Huang, PhD
Department of Experimental and Clinical Pharmacology
University of Minnesota
 
  Sunduz Keles, PhD
Department of Biostatistics & Medical Informatics
Department of Statistics
University of Wisconsin-Madison
Dan Knights, PhD
Department of Computer Science and Engineering
BioTechnology Institute
University of Minnesota
 
  Quaid Morris, PhD
University of Toronto
   

Mark Craven, PhD
Department of Biostatistics and Medical Informatics
Department of Computer Sciences
University of Wisconsin-Madison

Mark Craven is a professor in the Department of Biostatistics and Medical Informatics at the University of Wisconsin, and an affiliate faculty member in the Department of Computer Sciences. He is the Director of the Center for Predictive Computational Phenotyping, one of the NIH Centers of Excellence for Big Data Computing. He is also the Director of the NIH/NLM-funded Computation and Informatics in Biology and Medicine (CIBM) Training Program, and a member of the Institute for Clinical and Translational Research, the Carbone Cancer Center, and the Genome Center of Wisconsin.  The focus of his research program is on developing and applying machine-learning methods to infer models of, and reason about, networks of interactions among genes, proteins, clinical and environmental factors, and phenotypes of interest. 

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R. Stephanie Huang, PhD
Department of Experimental and Clinical Pharmacology
University of Minnesota

Dr. Huang is an Associate Professor at the Department of Experimental and Clinical Pharmacology, University of Minnesota. She is also an Associate Director for the Institute of Personalized Medicine | Pharmacogenomics U of M Alliance (PUMA-IPM) and a member of the Masonic Cancer Center at the University of Minnesota. She is a member of American Association for Cancer Research (AACR), American Society of Human Genetics (ASHG), and American Society of Clinical Pharmacology and Therapeutics (ASCPT). To date, she has published over 70 original research papers many of which are in high caliber journals, e.g., Nature, Nature Medicine, PNAS, Blood, Cancer Research, Genome Biology and American Journal of Human Genetics. Dr. Huang is a board certified clinical pharmacologist with extensive training in genetics, molecular and cell biology, clinical trials and high throughput data analysis.

The Huang laboratory’s main research focus is translational pharmacogenomics with particular interest in the pharmacogenomics of anti-cancer agents. By systematically evaluating human genome and its relationships to drug response and toxicity, their goal is to develop clinically useful models that predict risks for adverse drug reactions and non-response prior to administration of chemotherapy. With her broad training background, Dr. Huang assembles and leads a multi-disciplinary team that consists of computational biologist, geneticist, pharmacist, physician, molecular biologist and biostatistician to tackle a series of serious problems in cancer research. These include the lack of mechanistic understanding of genomic regulation of cancer phenotypes; the lack of reproducible predictive biomarkers for cancer therapeutic agents; and the lack of effective treatment for many hard to treat cancers.

More information about the Huang lab can be found online at http://huang-lab.umn.edu/

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Dan Knights, PhD
Department of Computer Science and Engineering
BioTechnology Institute
University of Minnesota

Dr. Dan Knights is a computational microbiologist. He is a professor in the Department of Computer Science and Engineering and the Biotechnology Institute at the University of Minnesota. Dan received his B.A. from Middlebury College, and his PhD from the University of Colorado, both in Computer Science and Computational Biology, followed by a post-doctoral fellowship at Harvard. His research uses data mining and machine learning to link microbial and human genomic data to human disease. Dan has co-authored articles in top multidisciplinary journals. In 2015 he was named a McKnight Land-Grant Professor by the University of Minnesota.


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