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publications
Investigating the Predictive Reproducibility of Federated Graph Neural Networks Using Medical Datasets
Published in Predictive Intelligence in Medicine: 5th International Workshop, PRIME 2022, Held in Conjunction with MICCAI, 2022
In this work, we investigate the predictive reproducibility of federated graph neural networks (GNNs) using medical datasets. We evaluate the performance of various GNN models in a federated learning setting and analyze their performance.
Interpretable Deep Learning for Myocardial Infarction Detection from ECG Signals
Published in 31st Signal Processing and Communications Applications Conference (SIU), 2023
In this study, we show that deep learning models can be trained to detect myocardial infarction (MI) from 12-lead ECG signals. We also show that the model can be made interpretable by using gradient class activation maps (Grad-CAMs) to highlight the segments of the ECG that contribute most to the decision.
Interpretable ECG analysis for myocardial infarction detection through counterfactuals
Published in Biomedical Signal Processing and Control, 2025
In this study, we propose a novel method for utilizing counterfactual explanations in ECG analysis, specifically for the detection of myocardial infarction. Our approach leverages the PTB-XL dataset and incorporates systematic feature extraction and refinement techniques to enhance interpretability for clinicians. The Visualizing Counterfactual Clues on Electrocardiograms (VCCE) method aims to bridge the gap between advanced data analysis and clinical decision-making.
Bayesian Basis Function Approximation for Scalable Gaussian Process Priors in Deep Generative Models
Published in Proceedings of the 42nd International Conference on Machine Learning (ICML), 2025
We propose a new way of utlizing Gaussian process priors in deep generative models, specifically variational autoencoders, with global parameterization that avoids explicit kernels, runs in linear time, eliminates the amortization gap, overcomes the limitations of categorical inducing point optimization, enhances interpretability in the latent space by quantifying the contributions of different covariates/effects using Sobol indices, allows for standard mini‑batch training, and treats kernel hyperparameters probabilistically.
Modeling Temporal scRNA-seq Data with Latent Gaussian Process and Optimal Transport
Published in Proceedings of the 43rd International Conference on Machine Learning (ICML), 2026
Single-cell RNA sequencing provides insights into gene expression at single-cell resolution, yet inferring temporal processes from these static snapshot measurements remains a fundamental challenge. Current approaches utilizing neural differential equations and flows are sensitive to overfitting and lack careful considerations of biological variability. In this work, we propose a generative framework that models population trends using a latent heteroscedastic Gaussian process (GP) approximated by Hilbert space methods. To address the absence of genuine cell trajectories, we leverage an optimal transport (OT) objective that aligns generated and observed population distributions. Our method explicitly captures biological heterogeneity by incorporating cell-specific latent time and cell type conditioning to disentangle temporal asynchrony and trajectories to different cell types. We demonstrate state-of-the-art performance on complex interpolation and extrapolation benchmarks and introduce a novel gradient-based strategy for inferring perturbation trajectories.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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