Drexel University

College of Computing and Informatics


IT / Computer Security
We are designing and developing a ML framework focused on integrating and exploring distinct ML algorithms, applying multiple data representation and deep learning techniques to provide explainable responses (e.g., correlations, clustering, etc.). 
We are in a transformative period where we are relying more and more on Machine Learning algorithms which are affecting our day to day lives. Machine learning is everywhere, whether its medical diagnosis, directing our social interactions, or pushing the frontiers of science. We increasingly rely on machine learning algorithms to make daily and life-changing decisions for us.

These algorithms can be complex in nature, which creates an effect called a “black box” situation, where even the programmers and designers cannot explain the results of an algorithms. The researchers at WHY will create an Application Programming Interface (API) framework that will allow engineers to provide explanations to algorithms which have this “black box” effect. They will be able to demystify the complexity and analyze the contents of these algorithms. This will allow them to understand how a result was achieved and identify the potential risks involved with using an algorithm.

Team Members