Anderson Lab

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About

If you don’t work on important problems, it’s not likely that you’ll do important work. — Richard Hamming

Geeks stay up all night disassembling the world so they can put it back together with new features. They tinker and fix things that aren't broken. Geeks abandon the world around them because they're busing soldering together a new one. They obsess and, in many cases, they suffer. — Matthew Inman

The Anderson Data Science Research Lab specializes in applying data mining, machine learning, and artificial intelligence to the fields of bioinformatics, genomics, and metabolomics. We develop algorithms and software to tackle some of the most challenging and interesting data intensive problems in the life sciences. Our research interests include data science, big data, pattern analysis in high-dimensionality data sets, evolutionary computation and optimization, machine learning, computational genomics, cloud computing, computational metabolomics, and eScience. We currently have multidisciplinary projects underway in metabolomics, human cognition, toxicology, marine biology, medical genomics, biomedical informatics, and marine genomics.

The Anderson Lab consists of several complementary research groups:

Director: Dr. Paul Anderson

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This is a little out of date and focused on a subset of the Anderson Lab, but for those looking for a glimpse into what we do:


Current Courses

DATA 101

CSCI 220L

Related Links

NEW! Omics REU

[Check us out on Facebook

Why Scientists Should Learn to Compute

Rise of the Data Scientist

College of Charleston

Department of Computer Science, College of Charleston

Data Science Program at the College of Charleston

Research Group Seminar/Meeting Schedule

Meetings will be held weekly each semester and are open to anyone with an interest in data science, machine learning, pattern recognition, data mining genomics, metabolomics, bioinformatics, etc. The meetings have two main goals:

  1. to explore topics of interest to the group and
  2. discuss specific research opportunities and the status of ongoing projects (Opportunities)
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