Multi-Stage Gaussian Noise Reduction with Recurrent Neural Networks. - Aditya Ranganath; Omar DeGuchy; Mukesh Singhal; Roummel F. Marcia (ACSCC, 2021)

Second-Order Trust-Region Optimization for Data-Limited Inference. - Aditya Ranganath; Omar DeGuchy; Mukesh Singhal; Roummel F. Marcia (EUSIPCO, 2021)

Adversarial Label-Poisoning Attacks and Defense for General Multi-Class Models Based on Synthetic Reduced Nearest Neighbor. - Pooya Tavallali; Vahid Behzadan; Azar Alizadeh; Aditya Ranganath; Mukesh Singhal (ICIP, 2022)

Stochastic Induction of Decision Trees with Application to Learning Haar Trees. - Azar Alizadeh; Mukesh Singhal; Vahid Behzadan; Pooya Tavallali; Aditya Ranganath (ICMLA, 2022)

A Novel Approach for Synthetic Reduced Nearest-Neighbor Leveraging Neural Networks. - Azar Alizadeh; Pooya Tavallali; Vahid Behzadan; Aditya Ranganath; Mukesh Singhal (ICMLA, 2022)

Interpretability of ReLU for Inversion. - Boaz Ilan; Aditya Ranganath; Jacqueline Alvarez; Shilpa Khatri; Roummel F. Marcia (ICMLA, 2022)

Recurrent Nerual Imaging: An Evolutionary Approach for Mixed Possion-Gaussian Image Denoising. - Aditya Ranganath; Omar DeGuchy; Fabian Santiago; Mukesh Singhal; Roummel F. Marcia (ICMLA, 2022)

Novel Adversarial Defense Techniques for White-Box Attacks. - Jason Van Tuinen; Aditya Ranganath; Goran Konjevod; Mukesh Singhal; Roummel F. Marcia (ICMLA, 2022)

Optimization for machine learning: Memory-efficient and tractible solutions to large-scale non-convex systems - Aditya Ranganath (2023)

Quasi-Adam: Accelerating Adam Using Quasi-Newton Approximations. - Aditya Ranganath; Irabiel Romero Ruiz; Mukesh Singhal; Roummel F. Marcia (ICMLA, 2024)

Signal Reconstructions for ECG-Transmembrane Voltage Potentials Using Transformers. - Jocelyn Ornelas Munoz; Gbocho M. Terasaki; Aditya Ranganath; Omar DeGuchy; Roummel F. Marcia (ISBI, 2025)

Symmetric Rank-One Quasi-Newton Methods for Deep Learning Using Cubic Regularization. - Aditya Ranganath; Mukesh Singhal; Roummel F. Marcia (CoRR, 2025)