4 papers
LLMs can construct powerful representations and streamline sample-efficient supervised learning
Ilker Demirel, Lawrence Shi, Zeshan Hussain +1
As real-world datasets become more complex and heterogeneous, supervised learning is often bottlenecked by input representation design. Modeling multimodal data, such as time-serie…
Evaluating Physician-AI Interaction for Cancer Management: Paving the Path towards Precision Oncology
Zeshan Hussain, Barbara D. Lam, Fernando A. Acosta-Perez +4
As machine learning (ML)-based decision support tools proliferate in clinical practice, understanding how clinicians integrate personalized ML predictions alongside randomized cont…
Uncovering Bias Mechanisms in Observational Studies
Ilker Demirel, Zeshan Hussain, Piersilvio De Bartolomeis +1
Observational studies are a key resource for causal inference but are often affected by systematic biases. Prior work has focused mainly on detecting these biases, via sensitivity…
Med-Real2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning
Keying Kuang, Frances Dean, Jack B. Jedlicki +4
A digital twin is a virtual replica of a real-world physical phenomena that uses mathematical modeling to characterize and simulate its defining features. By constructing digital t…