This page is always the hardest to keep up to date. If you are an interested student or collaborator, please contact me for more up to date information. Cheers :)

Anderson CV


Institute of Graph Analytics, Rankability, and Data Science (IGARDS)

IGARDS has a three-part vision for the foundations of data science: a problem-driven focus, a strong interaction between theory and practice, and deliberate, balanced training of the data science team. Our institute’s goals are: (1) to advance research on the rankability problem and its related graph analytics problems, (2) to train faculty, sharing our vision for the conduct of foundational data science work, and (3) to develop a list of skills for data scientists at each educational level (B.S., M.S., Ph.D.) and provide training tutorials for infusing foundational skills into existing and new curricula.

The first foundational research problem of interest is rankability, which refers to a dataset’s ability to produce a meaningful ranking of its items. A ranking is a list of items from most to least important. Ranking is a fundamental and underappreciated data science task that permeates almost every aspect of translating computational and algorithmic results into a form that a human can use. Its applications are numerous and include web search, cybersecurity, machine learning, and statistical learning theory. Minimal attention has been paid to the question of whether a data set is suitable for ranking. Most consumers apply a ranking method without asking questions such as: Can this ranking be trusted? Are parts of the ranking too similar to be disambiguated and possibly meaningless? How can rankability be quantified? Can rankable subgraphs be identified? At what point is a dynamic, time-evolving graph rankable?

Abstract from Omics REU (2015-2017)

Are you interested in data-driven next-generation genomics, bioinformatics, or data science? Are you a biologist, computer scientist, mathematician, bioinformatician, data scientist, or biochemist interested in the discovery only possible through interdisciplinary collaboration? Are you interested in joining and leading interdisciplinary teams as we work together to study genome biology, molecular evolution, and the computational approaches and algorithms necessary for big data genomic analysis. This is the Omics Experience - a research site dedicated to Next-Generation Bioinformatics for Genomics-enabled Research in the Life Sciences funded by the Division of Biological Infrastructure at the National Science Foundation.

Our vision for this NSF Research Experience for Undergraduates (REU) site is to cultivate the talents of an intellectually and culturally diverse group of 10 undergraduate students each year drawn from fields related to the life science, bioinformatics, and computer science by engaging them in ongoing biological research projects that employ next-generation DNA sequencing technologies and high performance computing. These projects will span areas of bioinformatics, data science, cyberinfrastructure, data mining, e-Science, genome biology, organismal biology and molecular evolution. Students will be expected to lead and operate in small interdisciplinary teams. For more information see

Below you will find a video created to showcase one of the ongoing projects that you can be a part of if accepted to this REU.