Paul Anderson

Paul graduated in 2004 from Wright State University with a B.S. degree in Computer Engineering. He received his masters in Computer Science in 2006 and his Ph.D. in Computer Science & Engineering in June 2010. His masters work developed a framework for analyzing chemical modification and limited proteolysis experimental data used for high confidence protein structure prediction. His dissertation research switched focus to developing signal processing and pattern recognition algorithms for the field of metabolomics through an active collaboration with the Magnetic Resonance Lab at Wright State University (Department of Biochemistry and Molecular Biology, School of Medicine). This collaboration involved the development of novel algorithms for spectroscopic quantification and biomarker pattern recognition, which led to the development of a cloud-enabled platform for data mining and spectral processing. Throughout his graduate work, Paul was also intricately involved in the implementation of the Wright State University model for engineering mathematics education. After graduation, Paul worked as a Bioinformatics Research Scientist for the Air Force Research Laboratory (AFRL) under the Consortium of Universities Research Fellows Program. While at the AFRL, he worked on a variety of research projects, including a computational model of the human immune system, a proteomics study of mustard sulfur exposure, feature selection techniques for quantitative structure-activity relationship (QSAR), and metabolomics studies of human fatigue and performance. At present, Paul is the Director of the Data Science Program and an Assistant Professor in the Computer Science Department at the College of Charleston. Paul directs the Anderson Lab at the College of Charleston that is comprised of several related interdisciplinary research groups, including the Charleston Computational Genomics Group, the Data Science Research Group, the Computational Metabolomics Group, and the Bioinformatics Research Group. These groups develop algorithms and software to tackle some of the most challenging and interesting data intensive problems in the life sciences. Our research interests include pattern analysis in high-dimensionality data sets, evolutionary computation and optimization, machine learning, data science, computational genomics, cloud computing, computational metabolomics, and eScience.

= Other ways to contact me = If you are looking for some quick advising help, you can contact me via gChat using pauleanderson@gmail.com.

Professional Facebook Profile

Office hours this semester:
 * M: 1 - 2 PM
 * W: 4 - 5 PM
 * F: 2 - 3 PM

Need an advising appointment?
My advising hours are:
 * Tuesday: 2 - 3 PM
 * Thursday: 2 - 3 PM

= Calendar =

= Courses =

Fall 2013
DATA 101: Introduction to Data Science

HONS 380: Bioinformatics

Spring 2013
CSCI 221: Programming 2

CSCI 334: Data Mining

BIOL 502L: Vertebrate Genome Biology Lab

Fall 2012
DISC 101: Introduction to Discovery Informatics

CSCI 250: Introduction to Computer Organization and Assembly Language Programming

Spring 2012
DISC 210: Dataset Organization/Management

CSCI 220: Computer Programming I

BIOL 502L: Vertebrate Genome Biology Lab

Fall 2011
DISC 101: Introduction to Discovery Informatics

CSCI 220: Computer Programming I

= Lectures = Hidden Markov Models and Phylogenetic Tree Construction using Markov Chain Monte Carlo

Galaxy RNA-seq Tutorial

= Teaching Philosophy = Teaching excellence is invariably driven by an instructor’s passion for the education of their students. My experience as an educator has led me to recognize the importance of building confidence and motivation while challenging my students with rigorous and exciting material. Conversely, educators who dryly dish out doctrine to note-taking students discourage student learning and do themselves and the students a disservice. My role as an instructor is to inspire students to set their expectations high and then help them reach their education goals by providing direct, practical, and enthusiastic support. I take pride in my students’ success and strive to help each one of my students attain his or her potential. A key to fostering this type of student development is to engage in an ongoing dialogue with the students, and I have found that nothing increases student motivation as much as a truly motivated instructor.

Inspiring students to challenge themselves and pursue their educational goals is one crucial element of effective teaching, but it is also important to teach lifelong learning skills for both inside and outside the classroom, such as the ability to think critically when approaching an unfamiliar problem. These principle skills will be incorporated not only into my classroom but also into my interactions with undergraduates interested in pursuing research. As an advisor, part of my role is to provide my undergraduate researchers with the skills necessary to conquer difficult problems, such as digesting a highly technical research paper and communicating a complicated research topic.

Student Mentoring
Successful Student Research and Mentoring