Research Interests
Including but not limited to:
- Robust Machine Learning under Imperfect and Complex Conditions
Developing resilient algorithms that perform reliably in the presence of noisy data, weak supervision, or dynamic environments. - Safety and Reliability of Large Language Models (LLMs)
Investigating methods to assess and enhance the trustworthiness of LLMs, especially when deployed in real-world, high-stakes scenarios. - Uncertainty Estimation and Calibration in Imperfect Models
Improving model interpretability and decision-making by quantifying uncertainty and ensuring well-calibrated predictions. - Applications in Healthcare and Medical Imaging with Imperfect Data
Applying robust machine learning techniques to clinical and imaging data, which often contain missing values, noise, or limited annotations.
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