Teaser Image

Alejandro Velasco

PhD Candidate - Computer Science. Arts & Sciences, William & Mary.

About me

Greetings! my name is Alejandro Velasco. I am a PhD Candidate of Computer Science at William and Mary under the mentorship of Dr. Denys Poshyvanyk. I currently work as a member of SEMERU research laboratory. I am a Software Engineer with years of experience in research and industry.

My research interest are Software Engineering, Machine Learning, Deep Learning Interpretability and Deep Represention.

Teaching Experience

def lecturer(software_engineering_II) 
prints Universidad Nacional de Colombia
prints January 2018 - August 2019

def lecturer(data_structures) 
prints Universidad Nacional de Colombia
prints August 2018 - December 2018

def lecturer(object_oriented_programming) 
prints Universidad Nacional de Colombia
prints July 2016 - December 2017
def teaching_assistant(applied_cybersecurity) 
prints William & Mary
prints Spring 2024

def teaching_assistant(software_engineering) 
prints William & Mary
prints Fall 2022, Fall 2023

def teaching_assistant(test_automation) 
prints Universidad de los Andes (Colombia)
prints March 2022 - June 2022

def teaching_assistant(software_evolution_and_maintenance) 
prints Universidad Nacional de Colombia
prints June 2015 - August 2015

def teaching_assistant(object_oriented_programming) 
prints Universidad Nacional de Colombia
prints February 2015 - November 2015

def teaching_assistant(data_structures) 
prints Universidad Nacional de Colombia
prints February 2014 - November 2014


D. N. Palacio, A. Velasco, N. Cooper, A. Rodriguez, K. Moran, and D. Poshyvanyk, “Toward a Theory of Causation for Interpreting Neural Code Models.” arXiv, Mar. 18, 2024. doi: 10.48550/arXiv.2302.03788.

A. Velasco, D. N. Palacio, D. Rodriguez-Cardenas, and D. Poshyvanyk, “Which Syntactic Capabilities Are Statistically Learned by Masked Language Models for Code?” Feb. 21, 2024. doi: 10.1145/3639476.3639768.

D. N. Palacio, A. Velasco, D. Rodriguez-Cardenas, K. Moran, and D. Poshyvanyk, “Evaluating and Explaining Large Language Models for Code Using Syntactic Structures.” arXiv, Aug. 07, 2023. doi: 10.48550/arXiv.2308.03873.

J. Hernández-Serrato, A. Velasco, Y. Nifio and M. Linares-Vásquez, “Applying Machine Learning with Chaos Engineering,” 2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), 2020, pp. 151-152, doi: 10.1109/ISSREW51248.2020.00057.

Velasco, A. and Aponte, J. 2020. Automated Fine Grained Traceability Links Recovery between High Level Requirements and Source Code Implementations. ParadigmPlus. 1, 2 (Aug. 2020), 18-41. DOI:https://doi.org/10.55969/paradigmplus.v1n2a2.

Velasco, A., Aponte Melo, J.H. (2019). Recovering Fine Grained Traceability Links Between Software Mandatory Constraints and Source Code. In: Florez, H., Leon, M., Diaz-Nafria, J., Belli, S. (eds) Applied Informatics. ICAI 2019. Communications in Computer and Information Science, vol 1051. Springer, Cham. https://doi.org/10.1007/978-3-030-32475-9_37

D. Delgado, A. Velasco, J. Aponte and A. Marcus, “Evolving a Project-Based Software Engineering Course: A Case Study,” 2017 IEEE 30th Conference on Software Engineering Education and Training (CSEE&T), 2017, pp. 77-86, doi: 10.1109/CSEET.2017.22.