Biomedical Informatics, Emory University

Mentors: Student: Vinay Pandramish

<p>We come up with models that give the effective and efficient visualizations as recommendations to the given input. Current visualization tools require the user to manually select attributes and analyze the data. For someone who has limited time and domain,this gets challenging if there are millions of attributes to derive insights. To overcome this problem, we automate the process with deep models</p>

Mentors: Student: Vikas Gola

<p>The idea of this project is to make a software in which a user can make deep learning models in an easy way using a graphical user interface with backend supported by tensorflow. Through the graphical user interface, a user will able to add, delete, edit deep learning layers in a model. The main purpose of the project is to make the implementation of deep learning models quick and easy.</p> <p>The software will be built using electron-js which is a framework for building cross-platform desktop apps with HTML, CSS, and JavaScript. It will have a drag-and-drop feature to build deep learning models in the form of a graph which will then converted to a python code by the software. The generated code will then be executed in the child process which trains the deep learning model and sends the metrics data( loss, accuracy ) to the parent process which then plots the statistics.</p>

Mentors: Student: Tushar Aggarwal

<p>Bindaas acts as a unified interface to various data sources like Apache Drill, MySQL and MongoDB. The aim of this project is to add new modules that support standard industry authentication and authorization grant flows. This will be achieved by integrating an identity management system that will grant access tokens (in the form of JSON Web Tokens) to be used for the endpoints supported by Bindaas.</p>

Mentors: Student: Insiyah Hajoori

<p>The following modules are to be integrated with caMicroscope:</p> <ul> <li>Creating a workflow to allow model developers to allow their model to be run on a selected image or region of interest by the client, to identify cellular features or cancer.</li> <li>Add the latest deep learning models researched in the area of digital pathology to caMicroscope to help the pathologists.</li> <li>Improve the segmentation app.</li> <li>A tutorial to enable users to make models compatible with caMicroscope.</li> </ul> <p>The motto of this project is to make the recent research in deep learning more accessible</p>

Mentors: Student: Irene

<p>I plan to complete the project with method of classification and indexing technology on Spark as well as Hazelcast if necessary. Hope for more guidence</p>