Nathan is an Assistant Professor of Computer Science at Colorado State University. His research focuses on the design of state-of-the-art algorithms to facilitate next-generation AI systems in domains like education, energy, and environmental science.
In his work, he pursues four major goals: 1) designing and building models that perceive the world with consistency, a requirement for generalized artificial intelligence to operate predictably; 2) evaluating how to best measure generalization potential across different modalities; 3) studying how biological and psychological knowledge can improve model design; and 4) designing and training machine learning models with real-world applicability. In his research, he has utilized data modalities such as vision, acoustic, and text, and he has a background in various other research domains, such as classroom education and sentiment analysis.