Jayvee Abella is currently working for Mercury Data Science. He received his PhD in Computer Science at Rice University under Prof. Lydia Kavraki and Prof. Cecilia Clementi. His thesis focused mainly on the computational modeling, simulation, and analysis of proteins and their structure and dynamics. Jayvee’s passion lies in providing informatics and data science solutions to biomedical problems.
PhD in Computer Science, 2020
MS in Computer Science, 2016
BS in Biomedical Engineering, 2014
The University of Texas at Austin
I have developed algorithms, inspired from the field of robot motion planning, to successfully model low-energy conformations of protein and protein-ligand systems in a scalable manner.
Sampling rare events with Molecular Dynamics can be accelerated with adaptive sampling methods, which use machine learning to automatically determine a bias that can speed up the exploration of the simulation. I have experience with the analysis of these methods, and I have applied them to study peptide binding with MHCs.
I have developed a random forests model with the ability to perform large-scale structure-Based prediction of stable peptide binding to Class I HLAs.