Salvador Robles Herrera

About

Hello everyone, I am Salvador, a motivated Master's student in Computer Science with a strong work-ethic. I was born in Ciudad Juarez, in Mexico, and I am currently an international student at UT Austin. I am expecting to graduate in May of 2026. So far I have interned two times as a Software Engineering Intern at Google and one time at Uber. Also, I am enthusiastic student researcher, having published 6 peer-reviewed research papers in the area of Machine Learning. I hold two Bachelor degrees in Computer Science and Mathematics from The University of Texas at El Paso (UTEP - Go miners!).

Hobbies: Weightlifting, competitive Soccer, Chess, Reading Dostoevsky (top favorite is The Brothers Karamazov), and Sustainable Gardening.

Research Interests: Machine Learning, Deep Learning, Responsible AI, AI Fairness

Publications

This is my Google scholar link, where you can find more about my research papers:

Predicting Fairness of ML Software Configurations. Salvador Robles Herrera, Verya Monjezi, Vladik Kreinovich, Ashutosh Trivedi, and Saeid Tizpaz-Niari, Proceedings of the 20th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE'24)

When is Deep Learning Better and When Is Shallow Learning Better: Qualitative Analysis. Salvador Robles Herrera, Martine Ceberio, Vladik Kreinovich, International Journal of Parallel, Emergent and Distributed Systems, 2022

How to Get the Most Accurate Measurement-Based Estimates. Salvador Robles Herrera, Martine Ceberio, Vladik Kreinovich, In: M. Ceberio and V. Kreinovich (eds.), Uncertainty, Constraints, and Decision Making, Springer, Cham, Switzerland, 2023, pp. 165–175

Computing the Range of a Function-of-Few-Linear-Combinations Under Linear Constraints: A Feasible Algorithm. Salvador Robles Herrera, Martine Ceberio, Vladik Kreinovich, Proceedings of the 15th International Workshop on Constraint Programming and Decision Making CoProD’2022, Halifax, Nova Scotia, Canada, May 30, 2022

Why Model Order Reduction. Salvador Robles Herrera, Martine Ceberio, Vladik Kreinovich, In: M. Ceberio and V. Kreinovich (eds.), Decision Making under Uncertainty and Constraints: A Why-Book, Springer, Cham, Switzerland, 2023, pp. 233–237

Foundations of Neural Networks explain the Empirical Success of the ‘Surrogate' Approach to Ordinal Regression – and Recommend What Next. Salvador Robles Herrera, Martine Ceberio, Vladik Kreinovich, Submitted to an edited book.

Contact

Email: [email protected]