Research

AI algorithms to detect/predict neurological disorders

In this project, we want to improve and automate the detection and prediction of neurological disorders by encompassing different modalities. These modalities are physiological signals (ECG, EMG, EEG, etc.), imaging data (CT, MRI, NIRS, PET, etc.), and omics data. Integration of these modalities gives a full and comprehensive picture of brain disorders and greatly augments reliable data extraction.

AI in cancer imaging

The main focus of this project is to develop novel integrated deep learning models that will help in early diagnosis of cancer through carrying out localization, segmentation, and classification tasks on MRI/CT scans. Additionally, we develop risk assessment models that predict cancer comorbidity like cardiovascular disease associated with some cancer treatment options. Through determining new imaging biomarkers and relating them with specific treatments, these treatment options can be avoided to reduce the risk of the associated conditions.

AI tools to accelerate drug discovery 

The high cost and long time required for biopharmaceutical companies to develop a treatment are considered challenges over the years. In this project, we develop new AI-based methodologies to identify promising drug candidates and raise the hit rate or the percentage of candidates that can make it through clinical trials. The target of this project is to have a platform that quickly scans the chemical compounds and designs a drug that can safely and effectively work on a known target like protein that is associated with a disease. The goal is to find a molecule that can chemically bind to the target protein and modulate it so that it no longer contributes to the disease. 

Neuromorphic computing

Neuromorphic technology emulates the human brain, leading to much lower power consumption and very fast computation time in comparison with conventional computers. This is due to the massively parallel architecture and the nature of the event-driven operation. We address the limitations of the current neuromorphic technology and provide solutions that enable effective implementation of our AI algorithms so that practical and economical medical devices are designed.