Research

Data-Centric AI

Data-centric AI calls for intelligently obtaining the best possible data for training a model. Data-centric practices can significantly reduce the financial, labor, and time costs of developing AI systems in the wild. We explore these problems for modern AI systems.

Data-driven Decision Making

Data-driven decision-making requires domain experts to manage data, tune models, and personalize decisions, all of which is laborious and can lead to delays in downstream operations. This research introduces new machine learning-based techniques for automated decision-making that can reduce this human labor and improve the quality of operational services.

AI in Healthcare

Machine learning and data-driven optimization can automate healthcare services in order to improve effectiveness, quality of care, and access to treatment. This research applies predictive and prescriptive modeling in personalized medicine (e.g., cancer therapy and otolaryngology) and clinical services (e.g., milk bank donation management).

Operations Management for AI

The AI lifecycle of data, training, deployment, and governance are operational problems. We study these problems by leveraging techniques from operations management.