Title: 

Bridging Machine Learning and Optimization for Human-Centered AI

Abstract: 

From healthcare delivery to resilient power grid management, predictive and prescriptive analytics tools have the potential to improve decision-making for some of today’s most pressing problems, yet their impact is often limited by the technical barriers required to access these tools and to interpret and trust their results. This talk will explore how the synthesis of machine learning and optimization can lower these barriers to advance human-centered artificial intelligence (AI). The first part of the talk will demonstrate how generative AI can broaden access to optimization tools through an interactive decision-support framework, developed in collaboration with Microsoft Outlook, that leverages large language models to translate natural-language user requests into underlying constraint programming models. The second part of the talk will focus on trust, showing how optimization can identify regions where machine learning models make fixed predictions that preclude individuals from changing their outcomes, such as a loan applicant who can never be approved regardless of their actions. We will conclude by outlining broader opportunities for integrating AI and optimization, moving toward a future in which advanced analytics tools are as accessible and trustworthy for managers at a local food bank as they are for applied scientists at Amazon.

Bio: 

Connor Lawless is a Postdoctoral Fellow at the Stanford Institute for Human-Centered Artificial Intelligence advised by Ellen Vitercik and Madeleine Udell. His research blends tools from optimization, machine learning, and human-computer interaction to make advanced analytics tools more accessible and trustworthy. He received his PhD in Operations Research from Cornell University where he was advised by Oktay Gunluk, and previously spent time at Microsoft Research, IBM Research, and the Royal Bank of Canada.