1 citations · 1 across the 6 of their papers we have counts for
6 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…
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…
Prediction-powered Generalization of Causal Inferences
Ilker Demirel, Ahmed Alaa, Anthony Philippakis +1
Causal inferences from a randomized controlled trial (RCT) may not pertain to a target population where some effect modifiers have a different distribution. Prior work studies gene…
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…
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…
Benchmarking Observational Studies with Experimental Data under Right-Censoring
Ilker Demirel, Edward De Brouwer, Zeshan Hussain +3
Drawing causal inferences from observational studies (OS) requires unverifiable validity assumptions; however, one can falsify those assumptions by benchmarking the OS with experim…