PIs: Andrew H. Fagg and Amy McGovern Institutions: University of Oklahoma and University of New Mexico
This REU site focuses on integrating machine learning into real-world applications though interdisciplinary collaborations. Machine learning techniques enable computing devices to automatically discover how to extract salient information from complex data sets and how to optimally perform tasks. Applications include robot control, severe weather prediction, computer security, brain-machine interfaces, computational neuroscience, bioinformatics, law, and interactive art. Students will receive training in a variety of areas, including statistical machine learning, embedded system design, empirical methods, sensor processing, control, embedded interfaces, technical writing, oral presentation, ethics, and graduate school preparation. Each student will be paired with a faculty mentor who is an expert in machine learning and with a supporting mentor who is an expert in the real-world application area. Due to the advanced nature of this topic, students will be involved during both the summer and academic year (March - October) for up to two years. During the summer, students will spend a 12- week period working full time at either the University of Oklahoma or the University of New Mexico. During the academic year, students will participate from their home institution via video- and tele- conference. This latter time (about 5 hours per week) will be used to prepare for the coming summer and to complete experiments and writing projects.