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. Javier Orduz (Javier_OrduzDucuara@baylor.edu)
Dr. Orduz is a Postdoctoral Research Scientist at Baylor. His research is in Quantum Machine Learning and Quantum Computing. He also works on applications in areas such as cybersecurity, computer vision, and Physics, specifically High Energy Physics. He is QMexico’s coordinator, where promotes quantum technology and related topics.
Dr. Gissella Bejarano (firstname.lastname@example.org)
Dr. Bejarano received her Ph.D. from the State University of New York at Binghamton. She is a lecturer at Universidad Peruana Cayetano Heredia, Perú. She joined Baylor University – Computer Science Department as Postdoctoral Researcher in August 2021. She is currently researching sign language processing, including sign language recognition, temporal segmentation, and automatic translation of American and Peruvian Sign Language. Another research interest is signal processing and forecasting prediction of problems related to smart cities information such as energy and water consumption.