1 citations · 1 across the 6 of their papers we have counts for
7 papers
Probabilistic Counterfactual Inference for Discrete Outcomes in Gaussian-Process Causal Models
Juliette Sinnott, Amir-Hossein Karimi, Mohammad Kohandel
Counterfactual inference in Gaussian-process structural causal models (GP-SCMs) has been developed primarily for continuous endogenous variables, limiting applicability to causal g…
Private and interpretable clinical prediction with quantum-inspired tensor train models
José Ramón Pareja Monturiol, Juliette Sinnott, Roger G. Melko +1
Publicly available clinical machine learning models pose an underappreciated privacy risk: their parameters or outputs can be exploited to recover information from patients whose d…
Integrating Mechanistic Modeling and Machine Learning to Study CD4+/CD8+ CAR-T Cell Dynamics with Tumor Antigen Regulation
Saranya Varakunan, Melissa Stadt, Mohammad Kohandel
Chimeric antigen receptor (CAR) T cell therapy has shown remarkable success in hematological malignancies, yet patient responses remain highly variable and the roles of CD4+ and CD…
Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis
Lena Podina, Diba Darooneh, Joshveer Grewal +1
In engineering and applied mathematics, developing accurate mathematical models to predict and understand real-world phenomena is of utmost importance. Symbolic regression is a use…
Conformalized Physics-Informed Neural Networks
Lena Podina, Mahdi Torabi Rad, Mohammad Kohandel
Physics-informed neural networks (PINNs) are an influential method of solving differential equations and estimating their parameters given data. However, since they make use of neu…
Learning Chemotherapy Drug Action via Universal Physics-Informed Neural Networks
Lena Podina, Ali Ghodsi, Mohammad Kohandel
Quantitative systems pharmacology (QSP) is widely used to assess drug effects and toxicity before the drug goes to clinical trial. However, significant manual distillation of the l…
Denoising Diffusion Restoration Tackles Forward and Inverse Problems for the Laplace Operator
Amartya Mukherjee, Melissa M. Stadt, Lena Podina +2
Diffusion models have emerged as a promising class of generative models that map noisy inputs to realistic images. More recently, they have been employed to generate solutions to p…