I will be teaching a special topics in data science course this spring. The information can be found below or at http://specialtopics.deblasiolab.org/s22/. The course numbers are CS 4364 for undergrads and CS 5364 for graduates (the CRNs can be found on the CS department course schedule).
I will be teaching a special topics in data science course this spring. The information can be found below or at http://specialtopics.deblasiolab.org/s21/. The course numbers are CS 4364 for undergrads and CS 5364 for graduates (the CRNs can be found on the CS department course schedule).
Special Topics in Data Science:
Continue reading “Spring 2021 Course”
Algorithms for Computational Biology
Our group studies how to improve science by automating and optimizing the tools used by domain scientists. We do this primarily by making input specific parameter value choices which help to reduce false information introduced by using less than ideal (or default) parameter choice. Using a framework called Parameter Advising we are able to, without an increase in wall clock time in most cases, find parameter vectors that are much better then the defaults. This framework has been applied to both protein multiple sequence alignment and reference-based transcript assembly, but is very general and can be applied to domains both within and outside of computational biology.
Beyond the algorithm configuration problem, Dr. DeBlasio also has interests in hashing and sketching, primarily focused on minimizer schemes (also called winnowing schemes). Minimizer schemes are a method to represent long strings by some representative k-mer (k length substring) in order to improve the resource consumption of sequence analysis applications (such as genomic read mapping, or document similarity).
This site is currently under development, for details about Dr. DeBlasio’s previous work see his personal website: dandeblasio.com