no code implementations • 26 Sep 2023 • Muhammad Aurangzeb Ahmad, Ilker Yaramis, Taposh Dutta Roy
Large language models have proliferated across multiple domains in as short period of time.
no code implementations • 7 Mar 2023 • Muhammad Aurangzeb Ahmad, Vijay Chickarmane, Farinaz Sabz Ali Pour, Nima Shariari, Taposh Dutta Roy
Recently there has been a surge of interest in developing Digital Twins of process flows in healthcare to better understand bottlenecks and areas of improvement.
no code implementations • 15 Apr 2021 • Aloysius Lim, Ashish Singh, Jody Chiam, Carly Eckert, Vikas Kumar, Muhammad Aurangzeb Ahmad, Ankur Teredesai
Prediction of diabetes and its various complications has been studied in a number of settings, but a comprehensive overview of problem setting for diabetes prediction and care management has not been addressed in the literature.
no code implementations • 7 Feb 2021 • Ming Yuan, Vikas Kumar, Muhammad Aurangzeb Ahmad, Ankur Teredesai
Fairness in AI and machine learning systems has become a fundamental problem in the accountability of AI systems.
no code implementations • 6 Feb 2021 • Karthik K. Padthe, Vikas Kumar, Carly M. Eckert, Nicholas M. Mark, Anam Zahid, Muhammad Aurangzeb Ahmad, Ankur Teredesai
Over the past several years, across the globe, there has been an increase in people seeking care in emergency departments (EDs).
no code implementations • 21 Sep 2020 • Boris Kovalerchuk, Muhammad Aurangzeb Ahmad, Ankur Teredesai
Next, we present methods of visual discovery of ML models, with the focus on interpretable models, based on the recently introduced concept of General Line Coordinates (GLC).
no code implementations • 29 Jul 2019 • Muhammad Aurangzeb Ahmad, Carly Eckert, Ankur Teredesai
In this paper, we explore different settings where AI models with imputation can be problematic and describe ways to address such scenarios.