The objective of this project is to develop QIK, a fast graph-based query processing system for searching images via metadata using the concept of knowledge graphs. QIK will also recommend interesting questions to users to enable easier search.
The goals of this project are (1) to make new end to end image processing pipeline that performs the extremely low light image denoising and enhancement task and (2) to develop a recognition friendly super resolution method for low resolution image recognition.
The goal of this project is to create a novel “Semantic Deep Mining” method to analyze the electronic health records (EHRs) of real patients.
The goals of this project are to (1) evaluate contemporary techniques for deep learning model explanations and (2) utilize DL Explanation approach for improving model performance.
The goal of this project is to design ontology-based interpretable deep models for consumer complaint explanation and analysis.
The goal of this project is to create a novel “Multilingual Knowledge Alignment” method in the medical domain with no/less parallel corpus, to enhance/improve Medical Knowledge in Chinese.
The objective of this project is to create data-driven deep learning models that (1) optimizes the institutional investor’s investment strategy in the stock market (2) provides decision support for stock pick 93) are “accountable” and (4) are flexible enough to properly accommodate heterogeneous data.
The goal of this projects is to prototype to segment 3D objects into equivalence classes using known Deep Learning techniques; understand performance of known techniques.
The goals of this projects are to (1) develop ML/DL tools to understand high-resolution temporal/spatial neurological data and (2) use ML/DL to create/refine brain models (mouse, human) to emulate brain dynamics.
We congratulate the PIs whose projects have been chosen for funding by the Industrial Advisory Board.