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@@ -176,6 +176,23 @@ Advanced Machine Learning Method Development
Outlier Detection Using Transformer Models: Outlier detection, or finding the "weirdos" in a dataset, is a critical step in many data analysis pipelines. With the increasing complexity of modern data, the methods used to detect outliers are also becoming more sophisticated. Our research focuses on leveraging the power of Transformer architecture--which is widely used in natural language processing and other domains—-to develop efficient and effective methods for detecting outliers in large and complex datasets. By integrating Transformer models into outlier detection tasks, we aim to enhance the accuracy and robustness of identifying anomalies across various domains.
Learning with Multiple Modalities: An emerging keyword in contemporary machine learning research is "multi-modal." As the research community has extensively explored how to develop models for single-modality data, attention has now shifted toward integrating multiple modalities to maximize predictive power. We focus on creating new techniques and models that can effectively blend data from different modalities—such as images and tabular data, videos and text, or time-series and image data, etc. Our goal is to design models that can seamlessly integrate these diverse data types and can extract the most relevant information to offer more precise predictions.
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Medical & Clinical AI: Transforming Healthcare with AI
Risk Model and Analysis for Pancreatic Cancer (with Herings): In South Korea, the National Health Insurance program mandates biennial health checkups for all citizens. Using health checkup data combined with follow-up records from the National Cancer Center on pancreatic cancer diagnoses, we have built predictive models that assess an individual's risk of developing pancreatic cancer. Our risk analysis offers the potential to identify high-risk individuals early, which in turn leads to better prevention and treatment strategies.
Multi-Modal Healthcare Method Development (with Prof. Yuyin Zhou, UCSC): One of the next milestones in machine learning is the ability to blend data from multiple modalities to maximize available information and refine predictive models. Starting with the RadFusion dataset from the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), this project explores how to integrate various data modalities generated in healthcare settings, such as EMRs, imaging data, drug structures, free-text notes, and medical ontologies, into robust decision-making models. By doing so, we aim to improve the accuracy of clinical predictions and support optimal healthcare decisions.
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