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I am interested in building safe and robust intelligent systems with a human-centered design. To accomplish this, my research develops novel machine learning techniques, which are often rooted in probabilistic modeling and computational statistics. Questions I am particularly interested in are: how can we incorporate a human's prior knowledge?, how can we detect when the system is failing?, and how to combine human and machine decision making? Some applications of importance to me are: healthcare, moderation of online content, and sign language processing.

JHU Affiliations: Institute for Assured Autonomy, Mathematical Institute for Data Science, Data Science and AI Institute

Selected Publications

Selected Talks

Research Group

Software

• Lightning UQ Box: Implements various uncertainty quantification techniques for neural networks. [JMLR article]
• Learning to Defer: A lightweight, unified implementation of various loss functions for the learning to defer framework for human-AI collaboration. [Jupyter Notebook Demo]

Courses Taught

• Deep Learning (Graduate, 2025)
• Human-in-the-Loop Machine Learning (Graduate, 2023 - present)
• Machine Learning I (Graduate, 2023)
• Leren: Introduction to Machine Learning (Undergraduate, 2020 - 2022)
• Module on Bayesian Deep Learning, Deep Learning II (Graduate, 2022 - 2023)

Venues

• Workshop on Bayesian Deep Learning
• Seminar on Anomaly Detection for Scientific Discovery
• Workshop on Tractable Probabilistic Modeling
• Entropy: Probabilistic Methods for Deep Learning