Stony Brook University Biomedical Informatics

Mentors: Student: Chenyang Hong

<p>The project goal is to develop a deep learning model to discover the regulatory motifs that are related with cancer drug responses. After building the model, I will evaluate the model and then try to use it to get more biological insight which is useful for personalized genomic medicine design.</p>

Mentors: Student: Yue Cao

<p>I want to contribute to Stony Brook University Biomedical Informatics by developing a webapp for OpenHealth. This involves implementing an online platform that can handle and visualize huge datasets (as public health-related data) in JSON format. With the development of web and the growing popularity of JavaScript in recent years, web applications become necessary for many projects in science. It provides the online aids for probability, statistics and health science research and supports efficient computing.</p>

Mentors: Student: Lakshay Nagpal

<p>Processing large amounts of spatial data involves a lot of overheads, higher latency, lower bandwidth, high processing time and a well defined framework. All the issues addressed needs to be solved using different tools available in big data analytics. In this project, we will be focusing on solving these issues and combining the benefits of RDMA with SparkGIS framework to improve the overall performance in distributed computing. Our main goal is to understand RDMA and it’s deployment, configuring RDMA over Conventional Ethernet(RoCE), deploying RDMA-Spark on RoCE and finally deploying SparkGIS on RDMA-Spark. After all these deployments are done, we will shift our focus to optimizing SparkGIS for more accurate and faster results.</p>

Mentors: Student: So Yeon Min

<p>Development of a deep learning framework to discover personalized genomic medicine for lung and breast cancer</p> <p>I’d like to solve the above problem with supervised, unsupervised, deep, and wide models. Models I’d like to pay special attention to are recurrent neural networks and hidden markov models. I will read relevant papers for reference, and will implement based on them. I'd also like to apply some linear algebra techniques for better feature extraction. Some of the papers that I’d like to refer to are like the following:</p> <p><a href="https://www.ncbi.nlm.nih.gov/pubmed/17069508" target="_blank">https://www.ncbi.nlm.nih.gov/pubmed/17069508</a> <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4965871/" target="_blank">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4965871/</a></p>

Mentors: Student: Andrejs Jurčenoks

<p>Use machine learning frameworks - ConvNetJs, Tensor Flow, or now also Google Cloud Machine learning engine - to recognize or forecast clinic outcomes, after training on open health clinical data available - from Statewide Planning and Research Cooperative System, National Cancer Institute’s Genomic Data Commons or listed in <a href="https://github.com/beamandrew/medical-data" target="_blank">https://github.com/beamandrew/medical-data</a></p>