Machine learning is about developing efficient algorithms that can learn to make accurate decisions by finding patterns in past examples. There are a large number of techniques, but a few underlying principles that make machine learning work well. The applications are wide-ranging and numerous: industry, science, education, medicine, law, and arts all have many opportunities to use learning, and we work on many of them at Baylor.
Dr. Hamerly's research is focused on machine learning, covering efficient and robust clustering, detecting photographic symptoms of disease, quantification of invasive freshwater species, text mining and critical analysis, collaborative learning, and competitive programming.
Dr. Rivas’ research is in the deep learning areas of machine learning, including efficient and fast learning algorithms, multispectral data analysis for aerosol detection, computer vision for general object recognition and facial recognition, and natural language processing models for robust latent sentence representations. He’s also working with IEEE developing the AI ethics standards in the P7000 series.
Dr. Fendt's research is focused on cross-disciplinary collaboration to build educational and learning apps and games, machine learning, and AI applied to narrative and story generation.