One of the major focuses of our research group is to develop novel machine learning approaches to facilitate new applications in diverse fields. A major recent methodological focus of the research group is on "little big data," which occurs where we have lots of data samples but they only come from a small number of groups; a real-world example of this is trying to understand the relationship between neural activity and behavior, where we get many repeats per individual but only from a few participants (in clinical studies) or animals. We are building machine learning methods to adapt to this situation by utilizing the "big data" but respecting the fact that there are inherent limitations in the number of individuals. These techniques are drastically improving generalization in many real-world data problems.
In addition, a lot of the developed techniques have many uses. For example, some of our adversarial learning techniques, which we use in the above statistical modeling, is also useful for artificial intelligence tasks such as "Story Visualization" (see Li et al., CVPR 2019).
