Although machine learning (ML) has greatly advanced how healthcare data is analyzed today, neither the state-of-the-art ML models nor the ML strategies are developed to address these healthcare-specific concerns.  And, so far there is no well-known ML platform dedicated to addressing healthcare-specific problems, despite healthcare being one of the biggest data-generating industries. However, throughout the history of computational analysis, there is evidence that the availability of easy-to-use libraries, datasets, toolkits, etc. can cause a cascade of research resulting in the rapid evolution of specific analytical inter-disciplinary fields. The best examples are that of Computer Vision and Natural Language Processing (NLP). The availability and cumulative progress of ImageNet, a dataset with over 30000 labeled images, is one of the main breakthroughs in the recent growth of computer vision and AI. The availability of various corpus and toolkits that allow the easy creation of additional corpus, has allowed the development of context-aware and self-attention NLP models. We expect an ML platform dedicated to healthcare data analysis to have a similar impact on the emergent field of healthcare informatics. 

Lab Research

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Lab Values

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Lab Members

Ph.D. Students 

• Peace Ishola – Research area in Computational Brain Analysis, Title of thesis: To Be Announced, Expected Graduation: Fall 2025 

• Geetha Karuppasamy – Research area in Computational Features for Medical Images, Spring 2022 

• Abid Rasheed - Research area in Posture Analysis for Predicting Relapse of Substance Abuse, Spring 2020, Fall 2020 Spring 2021 

• Gideon Adele - Research area in Healthcare Data Synthesis, Spring 2021, Fall 2021 

Master’s Thesis 

• Pavan Reddy Gottimukkula – Research area in Explainable ML, Thesis Title: “Using the concept of nearest neighbors to develop an  explainable model”,   Expected Graduation: Spring 2023 

Master's Creative Components 

• Nikhila Guttha, “Towards the Development of a Substance Abuse Index (SEI) through Informatics”, Graduated Fall 2021 (1 journal paper) 

• Srikar Reddy Chenreddy, “Using Bayesian Algorithm to Identify Important Features for Heart Attack” Spring 2021 

• Karthik Vallabhaneni, “Review of Weak Supervision”, Spring 2021 

• Nandhini Veluswamy (co-advisee), “Comparative Study on Early Sepsis Detection”, Graduated Fall 2020 

Undergraduate 

• Morton Thomas, Research and Honors Student, Summer 2022-present

• Sage Woodard, Independent Study, Fall 2022

• Jacob Charvat, Summer 2022,  (1 poster) 

• Sumeyye Sena Kiyma, Summer 2022, (1 poster) 

• Ethan Alexander Tyler Strickler,  Summer 2021, (1 poster, 1 paper pending in Nature Scientific Report), Now a PhD student in Data Science at Oklahoma University 

• Charles Lett, Summer 2021