Supervised pretraining through contrastive categorical positive samplings to improve COVID-19 mortality prediction. - Tingyi Wanyan; Mingquan Lin; Eyal Klang; Kartikeya M. Menon; Faris F. Gulamali; Ariful Azad; Yiye Zhang; Ying Ding 001; Zhangyang Wang; Fei Wang 001; Benjamin S. Glicksberg; Yifan Peng 002 (BCB, 2022)

Autoencoders for sample size estimation for fully connected neural network classifiers. - Faris F. Gulamali; Ashwin Sawant; Patricia H. Kovatch; Benjamin S. Glicksberg; Alexander Charney; Girish N. Nadkarni; Eric K. Oermann (npj Digit. Medicine, 2022)

Online Unsupervised Representation Learning of Waveforms in the Intensive Care Unit via a novel cooperative framework: Spatially Resolved Temporal Networks (SpaRTEn). - Faris F. Gulamali; Ashwin Sawant; Ira S. Hofer; Matthew A. Levin; Alexander Charney; Karandeep Singh; Benjamin S. Glicksberg; Girish N. Nadkarni (MLHC, 2023)

An AI-Guided Data Centric Strategy to Detect and Mitigate Biases in Healthcare Datasets. - Faris F. Gulamali; Ashwin S. Sawant; Lora Liharska; Carol R. Horowitz; Lili Chan; Patricia H. Kovatch; Ira S. Hofer; Karandeep Singh; Lynne D. Richardson; Emmanuel Mensah; Alexander W. Charney; David L. Reich; et al. (CoRR, 2023)

Derivation, external and clinical validation of a deep learning approach for detecting intracranial hypertension. - Faris F. Gulamali; Pushkala Jayaraman; Ashwin S. Sawant; Jacob Desman; Benjamin Fox; Annette Chang; Brian Y. Soong; Naveen Arivazagan; Alexandra S. Reynolds; Son Q. Duong; Akhil Vaid; Patricia H. Kovatch; et al. (npj Digit. Medicine, 2024)