Rameshbabu Manyam
Research Assistant Professor
Research Assistant Professor
Faculty, Biostatistics and Bioinformatics
Dr. Manyam's research focuses on data science, database management, big data analytics, and risk prediction frameworks using machine learning algorithms and high-performance computing environments. Specific fields of interest include longitudinal EMR data analytics using statistical and machine learning and AI algorithms (e.g., survival regression. decision trees, eXtreme Gradient Boosting: XGBoost, random survival forests, support vector machines, and neural networks).
Dr. Manyam has over 25 years of professional experience working in several data-driven research projects and building custom databases, software applications, and data visualization portals. He has hands-on working knowledge with Emory healthcare data sources (such as Cerner’s Electronic Medical Records (EMR), EPIC’s Electronic Health Records (EHR) database systems, the society of thoracic surgeons’ institutional adult cardiac surgical database and clinical data warehouse).
Dr. Manyam's current collaborative research efforts include, but not limited to: 1) developing scalable, portable, and reproducible machine learning powered frameworks - via feature engineering, hybrid feature selection, survival analysis and ensemble learning to accurately predict the risk factors for (a) Failure-to-Rescue after coronary artery bypass grafting (CABG), (b) hospital readmission after CABG, and (c) mortality after surgery for femoral shaft fractures; 2) developing personalized HIV testing frequency recommendations based on AI-powered recommendation engine, 3) analyzing EHR data to evaluate differences in the utilization of outpatient rehabilitation services among young adults with stroke (YAS) between the ages 21-60 years old, and 4) analyzing EHR data to develop and pilot a telehealth ‘Health-related social needs (HRSN)’ screening and referral uptake intervention in a population with uncontrolled diabetes.
Recent honors and awards
- 2024: 1st place Mike Kutner Faculty Poster Award Winner and Travel Award Recipient at the Southern Regional Council on Statistics (SRCOS) Summer Research Conference
- 2023: Dean's Pilot Innovation Award, Emory Rollins School of Public Health
- 2022: Biostatistics and Bioinformatics Faculty Mentor Award, Emory University
- 2021: Biostatistics and Bioinformatics Distinguished Teaching Award, Emory University
- 2020: Outstanding Graduate Student Award, Dept.of Computer Science, Georgia State Univeristy
- 2019: Three Minute Thesis (3MT) event second place winner, Georgia State University
Contact Information
1518 Clifton Road NE
Atlanta , GA 30322
1518-002-3AA
Phone: 404-727-4869
Fax: 404-727-1370
Email: rmanyam@emory.edu
Areas of Interest
- Cardiovascular Diseases
- Data Mining
- Data Science
- High Performance Computing
- Longitudinal Analysis
- Machine Learning
- Missing and Mismeasured Data
- Survival Analysis
- Systems Integration
Education
- PhD 2019, Georgia State University, Atlanta, GA, USA
- MS 2002, Georgia State University, Atlanta, GA, USA
- MTech 1993, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
- BTech 1991, National Institute of Technology, Warangal, India
Courses Taught
- DATA 521: Database Development for PH
- BIOS 585: Python Programming
Affiliations & Activities
- Fall 2023: Mentor for PhD Consortium at Georgia Statistics Day, Georgia Institute of Technology
- Spring 2023: Python workshop in collaboration with the student organization, Rollins mHealth Collaborative (RmC)
- Fall 2022 : SQL workshop in collaboration with RmC
- Summer 2022: Python workshop in collaboration with RmC
Publications
- Manyam R, Liu Z, Shen H, Zhang Y, Hu X, Keeling WB., 2024, Ensemble Leraning Model with Effective Feature Selection for Accurately Predicting Risk Factors for Failure to Rescue After Coronary Artery Bypass Grafting, Abstract accepted at: The Southern Regional Council on Statistics Summer Research Conference, Clemson, SC, USA,
- Manyam R, Shen H. Liu Z, Zhang Y, Hu X, Keeling WB., 2024, Machine Learning Algorithms Accurately Predict Risk Factors for Failure to Rescue After Coronary Artery Bypass Grafting, Abstract accepted at: 60th Annual Meeting of the Society of Thoracic Surgeons, San Antonio, TX, USA ,
- Manyam R, Zhang Y, Binongo J, Rosenblum JM, Keeling WB., 2022, Unraveling the impact of time-dependent perioperative variables on 30-day readmission after coronary artery bypass surgery., Journal of Cardiovascular and Thoracic Surgery. 2022 Sep; 164(3) 943-955.e7, doi: 10.1016/j.jtcvs.202009.076. , Epub 2020 Sep 29. PMID: 33127082.
- Manyam R, Zhang Y, Binongo J, Rosenblum JM, Keeling WB, 2019, A New Scalable, Portable Predictive Analytics Framework for Predicting Time-to-event Outcomes in Healthcare., Paper presented at: 2nd Symposium on Machine Learning in Science and Engineering Symposium, June 9-12, 2019, Atlanta, GA, USA. ,
- Manyam R, Zhang, Y, Binongo J, Rosenblum JM, Keeling, W B, 2019, A Simple, Scalable And Portable Machine Learning Model With Effective Feature Selection For Accurately Predicting 30-day Readmission After Discharge Following CABG., Paper accepted at: 100th Annual Meeting of the Association of American Thoracic Surgeons, May 22-23, 2020; Virtual; New York, NY, USA.,
- Manyam R, Zhang Y, Binongo J, Rosenblum JM, Keeling, WB., 2019, Unraveling the impact of time-dependent perioperative variables on 30-day readmission following CABG., Paper presented at: 45th Annual Meeting of Western Thoracic and Surgical Association, June 26-29, 2019; Olympic Valley, CA, USA,
- Manyam R, Zhang Y, Keeling WB, Binongo J, Kayatta, M, Carter, S., 2018, Deep Learning Approach for Predicting 30 Day Readmissions after Coronary Artery Bypass Graft Surgery, Paper presented at: Workshop on Machine Learning for Health, Thirty-second Conference on Neural Information Processing Systems (NeurIPS, 2018), December 8, 2018, Montreal, Canada.,