Howard specializes in optimization algorithms for big data problems, particularly the creation, training, and evaluation of models that are data-driven and require hard constraints be satisfied (e.g. large systems of equations for physical systems).
Recent Explainable AI work, published in Nature's Scientific Reports
Howard creates graphics for communicating research and educating aspiring mathematicians.
A friendly introduction to real analysis
Research at the intersection of big data, optimization, and explainability
Notes for qualifying exams in the math PhD program at UCLA
Howard enjoys activities that require focus, especially running, powerlifting, and slacklining.