Alex Vidal

Alex Vidal

Applied Mathematician

Curriculum Vitae

Alex Vidal

Applied Mathematician


  alexanderrobertvidal@gmail.com  |  Google Scholar

Education

Ph.D. — Applied Mathematics and Statistics

Colorado School of Mines
December 2024

M.Sc. — Applied Mathematics and Statistics

Colorado School of Mines
May 2020

  • Magna Cum Laude, GPA: 3.8/4.0

B.Sc. — Mechanical Engineering, Minor: Economics

University of Colorado Boulder

Teaching & Instructional Experience

Teaching Assistant

Colorado School of Mines — Golden, CO
August 2018 – May 2022

  • Delivered instructional support, hosted tutoring sessions, and graded evaluations for the following advanced undergraduate and graduate-level courses:
    • MATH534/535: Mathematical Statistics (Probability)
    • MATH530/531: Statistical Methods
    • MATH537: Multivariate Analysis
    • MATH536: Advanced Statistical Modeling
    • MATH225: Differential Equations

Teaching Assistant

University of Colorado Boulder — Boulder, CO
August 2010 – December 2010

  • Served as the lab teaching assistant, providing in-person technical instruction and assistance to students working on assignments for the following course:
    • MCEN4037: Experimental Design and Data Analysis

Expertise & Skills

  • Mathematics & Statistics: Probability, Multivariate Analysis, Differential Equations, Calculus, Linear Algebra
  • Mathematical Modeling: Numerical Optimization, Scientific Computing
  • Machine Learning: Deep Learning, Generative Modeling (Diffusion/Normalizing Flows), Ranking
  • Programming Languages: Python, R, MATLAB, SQL, Bash/Shell Script, LaTeX
  • Libraries & Tools: PyTorch, TensorFlow, XGBoost, NumPy, Pandas, Polars, Scikit-Learn, Git

Industry and Technical Experience

Owner and Machine Learning Scientist

Rigorous Machine Learning Solutions, LLC — Remote/Durango, CO
October 2020 – Present

  • Advised diverse industry clients on end-to-end predictive data modeling architectures and taught teams how to integrate statistical frameworks into existing infrastructures.
  • Designed custom machine learning models to solve specialized client issues, transforming raw mathematical principles into robust algorithmic deployments.

Senior Machine Learning Researcher

Launch Potato — Remote/Durango, CO
February 2025 – June 2026

  • Served as the primary machine learning technical resource across multiple business verticals, focusing on mentoring data scientists and translating complex mathematical models for engineering teams.
  • Led the end-to-end design and deployment of large-scale mathematical recommendation architectures.
  • Utilized rigorous A/B testing frameworks and statistical inference to evaluate model efficacy, communicate findings, and guide data-driven strategy.

Senior Data Scientist

On The Barrelhead / NerdWallet — Remote/Durango, CO
October 2021 – August 2024

  • Supervised the data science division for credit cards and lending, providing direct professional mentorship, training, and academic direction to junior researchers.
  • Directed the complex technical integration of machine learning operations following corporate acquisition, clarifying mathematical optimization strategies to cross-functional stakeholders.
  • Formulated personalized predictive models and deployed portfolio optimization techniques to balance risk matrices against core system utilities.

National Science Foundation (NSF) Intern

United States Geological Survey — Remote/Lakewood, CO
Summer 2021

  • Partnered with the USGS hyperspectral research branch to model and parse hyperspectral datasets, establishing predictive analyses for planetary geology.

Graduate Research Assistant

Center for Advanced Subsurface Earth Resource Models — Remote/Golden, CO
August 2019 – August 2021

  • Developed image recognition and computer-vision workflows to isolate anomalies and cross-verify distinct mineralogical datasets.
  • Built convolutional neural networks and stochastic autoencoders to project distinct physical parameters into a shared mathematical latent space.

Data Science Intern

Lumen Technologies (formerly CenturyLink) — Remote/Broomfield, CO
Summer 2019

  • Applied natural language processing architectures and deep learning classifiers to automate document indexing and classification.

Peer-Reviewed Publications

  • Vidal, A., Wu Fung, S., Osher, S., Tenorio, L., and Nurbekyan, L. “Kernel Expansions for High-Dimensional Mean-Field Control with Non-local Interactions.” 2025 American Control Conference (ACC), 2025. DOI: 10.23919/ACC63710.2025.11107993
  • Vidal, A., Wu Fung, S., Tenorio, L., Osher, S., and Nurbekyan, L. “Taming Hyperparameter Tuning in Continuous Normalizing Flows Using the JKO Scheme.” Scientific Reports, 13(1), 2023.
  • Rotem, A., Vidal, A., Pfaff, K., Tenorio, L., Chung, M., Tharalson, E., and Monecke, T. “Interpretation of Hyperspectral Shortwave Infrared Core Scanning Data Using SEM-Based Automated Mineralogy: A Machine Learning Approach.” Geosciences, 13(7), 192, 2023.

Dissertation

  • Vidal, A. Deep Learning Methods for Large-Scale Physics. Ph.D. thesis, Colorado School of Mines, 2024.

Academic Service

  • Reviewer, IEEE American Control Conference (ACC 2026)

Open-Source Software

Select Conference Contributions and Talks