Research Interests
Graph Neural Networks
We explore the frontier of graph-based learning, where the structure of data is as vital as the data itself. Our research in Graph Neural Networks (GNNs) models complex relational systems—ranging from social and urban networks to biological processes—by learning from both topology and feature interactions.
We focus on advancing the theoretical foundations of GNNs, including expressivity, heterophily-aware architectures, and scalability, while also driving high-impact applications in domains where connectivity is not optional but fundamental. Our goal is to move GNNs beyond academic prototypes, enabling them to power real-world decision-making systems with transparency and robustness.
Human–Computer Interaction & Data Visualization
At the intersection of human cognition and computational systems, our research in Human–Computer Interaction (HCI) and Data Visualization is grounded in a singular belief: data must be seen to be understood, and felt to be believed.
We design intuitive, expressive, and aesthetically powerful interfaces that transform abstract data into compelling narratives and actionable insights. From interactive dashboards and visual analytics systems to exploratory platforms for large-scale data, our mission is to amplify human intelligence and bridge the gap between algorithmic output and human understanding.
Empirical Causal Inference
In a world awash with correlations, we ultimately seek cause. Our research in empirical causal inference integrates traditional statistical theory with modern machine learning to identify, estimate, and validate causal effects from complex observational data.
We focus on challenging, high-dimensional settings—such as policy evaluation, market dynamics under uncertainty, and social interventions—where rigor meets complexity. From synthetic control methods to instrumental variables and beyond, we advance methodologies that enable data-driven reasoning about “what would have been” and guide decisions toward “what should be.”