Allen Chang

Allen is an undergraduate student (BS computer science, BS applied math) at the University of Southern California. His research enhances machine learning (ML) guidance by integrating human behaviors with directives and improving the robustness and fairness of ML (a longer bio is posted on cylumn.com). This summer, he feels fortunate to have the opportunity to work with Jean Oh on behavioral feedback in conditional generative art: this work investigates people’s behavioral and affective responses while in the creative loop and how these behaviors can be used as feedback to align conditional image synthesis models. A long-term outcome of this project is to enhance human-machine collaborative intelligence rather than pure mimicry of individual human skills. Allen feels encouraged and excited to join such a talented cohort; he’d like to thank the RISS staff and sponsors for this great opportunity!