Undergraduate student Luis Cedillo will present work related to our project “Innovative Analysis of Spectra-Temporal Signatures using Machine Learning for Ground-Based Remote Sensing of Unresolved Resident Space Objects”, collaborative work with Kevin Acosta and Dr. Miguel Velez-Reyes (Electrical and Computer Engineering) at the University Consortium Research Opportunity meeting in Boulder, CO. This stands as our one year anniversary of the project funded by the US Space Force through the University Consortium Research Opportunity.
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Demetrius to present poster at RECOMB 2022
Demetrius will present a poster titled “Efficient Minimizer Schemes using Deep Networks” at the 26th Annual International Conference on Research in Computational Molecular Biology (RECOMB) in San Diego, CA in May.
Spring 2022 Course
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).
Special Topics in Data Science:
Algorithms in Computational Biology
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Spring 2021 Course
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:
Algorithms for Computational Biology
Continue reading “Spring 2021 Course”
Research Focus
Dr. DeBlasio has recently moved to Carnegie Mellon University‘s Computational Biology Department as an Assistant Teaching Professor. As a result, his lab is currently in transition and not highly active. For details about Dr. DeBlasio’s previous work see his personal website: dandeblasio.com.
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).