Kangrui Ruan (Darren)

I am currently a final-year Ph.D. student at Columbia University advised by Prof. Xuan Di (Ditect Lab). Before joining CU, I obtained my Master's degree from Carnegie Mellon University (CMU) in 2020.

My research focuses on Causal Inference, Machine Learning, Deep Learning and their applications to learning human behaviors. I also worked on other topics like Large Language Models (LLMs), probabilistic graphical models (PGMs), and Graph Neural Networks (GNNs). Representative papers are highlighted.

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S2E: Towards an End-to-End Entity Resolution Solution from Acoustic Signal

Traditional cascading Entity Resolution (ER) pipeline suffers from propagated errors from upstream tasks. We address this issue by formulating a new end-to-end (E2E) ER problem, Signal-to-Entity (S2E), resolving query entity mentions to actionable entities in textual catalogs directly from audio queries instead of audio transcriptions in raw or parsed format.

Causal Imitation Learning via Inverse Reinforcement Learning

This paper has 2 key contributions. First, the paper analyzes structural conditions on the causal model under which learning the expert policy is possible in the presence of unobserved confounding. Second, the authors further exploit knowledge of the graphical structure to extend IRL algorithms such as GAIL or MWAL to the confounded settings.

Learning Human Driving Behaviors with Sequential Causal Imitation Learning

We develop a sequential causal template that generalizes the default MDP settings to one with Unobserved Confounders (MDPUC-HD).

Autonomous navigation at unsignalized intersections: A coupled reinforcement learning and model predictive control approach

This paper develops an integrated safety-enhanced reinforcement learning (RL) and model predictive control (MPC) framework for autonomous vehicles (AVs) to navigate unsignalized intersections.

CVLight: Decentralized Learning for Adaptive Traffic Signal Control with Connected Vehicles

This paper develops a decentralized reinforcement learning (RL) scheme for multi-intersection adaptive traffic signal control (TSC), called “CVLight”, that leverages data collected from connected vehicles (CVs).