Overview

My research interest lie at the intersection of discrete optimization, machine learning and network design with application in sustainability and social good. Here are some themes that we are currently pursuing:

– Learning-driven algorithm design: how can we design combinatorial algorithms that have learnable components capable of automatically learning effective strategies on distributions of problems. Examples are learning for branch-and-bound for MIP, learning greedy strategies for graph optimization, learning search strategies in planning, among others

– Decision-focused learning: how can we integrate downstream combinatorial decision making tasks in the training ML models, such that forecasted parameters used in downstream optimization lead to solutions with good ground-truth quality. Examples are clustering as a layer, MIP as a layer, submodular optimization as a layer in gradient-descent-based ML models

– Computational Sustainability: use of AI (ML and optimization) to inform some of the most pressing sustainability challenges. Examples include biodiversity conservation and poaching, disaster resilience & critical infrastructure planning (roads, water, etc.), climate mitigation, opioid/stimulant use, fires, land cover mapping, urban planning

A postdoc will be part of the USC Center for AI in Society (https://www.cais.usc.edu/) and become part of a vibrant and interdisciplinary community of researchers, passionate about using AI for some of the most pressing societal and environmental problems, that value data-driven decision making and engagement of shareholders and domain experts. USC also has a large community of AI researchers (https://ai.usc.edu/key-areas-new/).

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