Henry Salgado, PhD

Hello! My name is Henry. I’m a computer scientist who approaches research using a variety mixed methods (Qual + Quant). I earned my PhD in Computer Science from the University of Texas at El Paso (UTEP); I was advised by Dr. Meagan Kendall and Dr. Martine Ceberio. For my dissertation, I studied explainable AI (XAI) through causal inference and abstract argumentation methods.

research interests

Deep Learning: Causal Machine Learning, Explainable Artificial Intelligence
Science of Science: Engineering Education, Faculty Career Pathways & Satisfaction

I welcome collaboration opportunities — if you share similar interests or have project ideas, please reach out.

news

Sep 01, 2026	Joining Cornell University as a postdoctoral researcher, working with Dr. Alexandra Coso Strong (and other cool scholars). Excited to start this next chapter in September 2026.
Aug 04, 2026	Successfully defended my PhD dissertation, “Towards a Multi-Framework Approach to XAI”!
Jul 02, 2026	Paper presented at XAI 2026 in Fortaleza, Brazil!
May 01, 2026	Journal article published in the Journal of Engineering Education: Amplifying the Voices of Engineering Instructional Faculty: Examining the Factors Influencing Agency toward Impact at Hispanic-Serving Institutions. Read here.
Apr 15, 2026	Presented our paper Causal Discovery for Explainable AI: A Dual-Encoding Approach. Read here. at the third International Workshop of Causality of Agents and Large Modles (CALM-26) in Istanbul, Turkey!
Apr 01, 2026	Paper accepted at the 4th World Conference on eXplainable AI (xAI 2026): A Causal Argumentation Method for Explainability of Machine Learning Models.
Mar 15, 2026	Paper accepted at ASEE 2026 (Charlotte, NC): LLMs in Qualitative Research: Opportunities, Limitations, and Practical Considerations. Preprint available on arXiv.
Nov 01, 2025	Preprint posted to HAL Open Science: Contribution to Error Analysis of Deep Neural Networks: Case of the Activation Functions. Read here.
Oct 01, 2025	Paper presented at the International Workshop on Causality, Agents and Large Models (CALM-25) in Luxembourg: Does the Model Say What the Data Says? A Simple Causal-Inspired Heuristic for Model–Data Alignment.
Aug 27, 2025	Passed my dissertation proposal defense. Now officially ABD (All But Dissertation)!
Aug 04, 2025	Passed my qualifying exam. One step closer!
Jun 01, 2025	Paper presented at ASEE 2025 (Montreal, QC): Exploring Department vs. Institution Workplace Satisfaction Alignment Among STEM Professional Track Faculty at HSIs Using Machine Learning. Read here.

selected publications

xAI-26
A Causal Argumentation Method for Explainability of Machine Learning Models

Henry Salgado, Meagan R. Kendall, and Martine Ceberio

In 4th World Conference on eXplainable Artificial Intelligence (xAI 2026), Jul 2026

Abs Bib HTML

Explainable AI (XAI) methods identify which features are relevant to a model’s predictions but often fail to clarify why certain decisions are made. In this work, we present a novel method that integrates causality with argument-based reasoning to explain why models may be making predictions. Our approach first identifies causal relationships among variables using causal discovery methods and then translates these into a Bipolar Argumentation Framework (BAF) to represent supportive and opposing interactions among features. By using semi-stable semantics, we find extensions of features that explain why certain outcomes may have been chosen.
@inproceedings{salgado2026xai, title = {A Causal Argumentation Method for Explainability of Machine Learning Models}, author = {Salgado, Henry and Kendall, Meagan R. and Ceberio, Martine}, booktitle = {4th World Conference on eXplainable Artificial Intelligence (xAI 2026)}, year = {2026}, month = jul, }
JEE
Amplifying the Voices of Engineering Instructional Faculty: Examining the Factors Influencing Agency toward Impact at Hispanic-Serving Institutions

V. Bracho Perez, Henry Salgado, Y. A. Urquidi Cerros, and 2 more authors

Journal of Engineering Education, 2026

Abs DOI Bib

This study examines the strategic actions of engineering instructional faculty to support their students’ experiences at Hispanic-serving institutions (HSIs). Specifically, it investigates the factors influencing faculty agency using the Contextualized Theory of Professional Agency for Impact in Engineering Education. By centering instructional faculty voices, the study identifies institutional, relational, and structural conditions that shape faculty members’ capacity to enact meaningful change in support of student success.
@article{brachoperez2026amplifying, title = {Amplifying the Voices of Engineering Instructional Faculty: Examining the Factors Influencing Agency toward Impact at Hispanic-Serving Institutions}, author = {Bracho Perez, V. and Salgado, Henry and Urquidi Cerros, Y. A. and Coso Strong, A. and Kendall, Meagan R.}, journal = {Journal of Engineering Education}, volume = {115}, number = {2}, pages = {e70063}, year = {2026}, doi = {https://doi.org/10.1002/jee.70063}, }