We explore visual analytics for preferences, active/online learning of preference models, visualizing forests of partial lexicographic preference trees through clustering, automated feature extraction from object images, and transfer learning to support cross-domain recommendations.
Using deep neural network models, we try to automate the process of determining the percentage of the marsh coverages for various species of vegetation.
We design and implement a parallel framework for machine learning models that are deployed to cloud-based serverless platforms. This framework aims to accelerate the training of large scale models through the utilization of dynamically allocated resources.
We design and implement a decision analysis system using interactive learning to learn interpretable predictive decision models (e.g., lexicographic preference trees and conditional preference networks) to provide insight into agents' decision making process. Related: FLAIRS 2019.
We designed and developed a smart multi-modal transportation planner that allows user-specific metrics (e.g., crime rates and crash data), to specify constraints as a theory in the linear temporal logic, and to express preferences as a preferential cost function. In the demo, an optimal trip is computed for Alice who doesn't have a car but has a bike, and she wants to bike at least 1 and at most 2 hours. Moreover, she prefers biking and public transits over uber. Related: FLAIRS 2019.