Oliva’s interdisciplinary research and publications span human perception/cognition, computer vision (visual AI), and cognitive neuroscience, focusing on their intersection. Her work in computational perception and cognition builds on the synergy between human and machine recognition, and how it applies to solving high-level recognition problems like understanding scenes and events, perceiving space, modeling attention, eye movements and memory, as well as predicting subjective properties of images (like memorability). Her research integrates knowledge and tools from computer vision, machine learning, deep neural networks as well as human perception, cognition and neuro-imaging (fMRI, MEG).

She is the recipient of several honors, including a National Science Foundation (NSF) CAREER Award in computational neuroscience, a Guggenheim fellowship in computer science, and a 2016 Vannevar Bush Faculty Fellowship in cognitive neuroscience. Oliva is an elected fellow of the Association for Psychological Science. She has served as an expert to the NSF Directorate of Computer and Information Science and Engineering on the topic of human and artificial intelligence and is a current member of the scientific advisory board for the Allen Institute for Artificial Intelligence. Oliva has served as editor and on the board on cognitive science journals, is listed on five patents, and has co-authored over 200 book chapters and reviews, journal publications and conference proceedings. Several of her trainees have created start-ups in the fields of AI and ML.