Center for Data Science™

Dr. Simpson lectures extensively around the world and has published more than 500 articles on nursing informatics. He also sits on 12 editorial review boards, and is a fellow of the American Academy of Nursing, New York Academy of Medicine and the National Academies of Practice. He attained a doctorate in nursing practice, executive leadership/informatics, from American Sentinel University.

Dr. Hertzberg is an internationally-recognized expert on "big data" and its impact on health care. She is widely known for her work measuring the social contacts in emergency departments and disease transmission on airplanes.

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Dr. Hu conducts research at the intersection of computational and health sciences where he develops signal processing, mathematical modeling, machine learning, and deep learning algorithms to transform physiological and clinical data into actionable information for improving patient care. Through his research, Hu has created knowledge advancing the relationship among clinicians, scientists, engineers, innovators, policy makers, and healthcare administrators. Dr. Hu also serve as Associated Faculty of Biomedical Informatics, School of Medicine and Associated Faculty of Computer Science, College of Arts and Sciences and serves as advisory board for the AI Humanity Initiative.

Dr. Xiao is a biomedical engineer by training, with a research focus on applying engineering principles, machine learning, and data science to healthcare research. Ran received his M.S. and Ph.D. degrees in Electrical and Computer Engineering from the University of Oklahoma in 2015. He then received his postdoctoral training at the University of Southern California and the University of California San Francisco. Before joining Emory, he was an Assistant Professor at Duke University School of Nursing from 2020-2022. He is also a recent AI recruit in AI Humanity Initiative.

Dr. Houser earned my PhD in immunology and my BS in Biological Sciences with an honors concentration in Biochemistry and Cellular and Molecular Biology with additional coursework in microbiology. My dissertation research focused on inflammation as a mediator of the gut-brain connection in Parkinson's disease. As a postdoctoral fellow, I was involved in a wide range of projects on subjects including amyotrophic lateral sclerosis, Alzheimer's disease, heat-related illness and kidney injury, sleep quality in individuals with HIV, COVID-19 risk among agricultural workers, and social determinants of health.

Monique Bouvier is an Assistant Professor for Emory University, School of Nursing and a research nurse scientist for Emory Healthcare. She is working on nursing care delivery model redesign and improvements in nursing documentation. She has mentored and guided numerous frontline nurses and nurse leaders on research, EBP, and quality improvement.  She has authored several articles for peer-reviewed journals and has presented at conferences nationally and internationally.  She obtained her PhD from the University of San Diego with her research focus on influenza-like illness and symptomatology.

Dr. Zhang earned her BSN from the Xiang-Ya Schools of Medicine  and Nursing, Central South University, China. Together with her PhD in  Nursing, she obtained a master’s degree in Statistics (Department of  Statistics and Data Science), a minor portfolio certificate in Aging and  Health (Department of Human Development and Family Sciences), and training in  health informatics and telehealth at the University of Texas at Austin. She  then completed a 2-year postdoctoral training in the Center for Data Science,  Emory University NWH School of Nursing. During these two years, she received  further training in geriatrics, data science, clinical informatics, and  omics. She also advanced her multimorbidity research training with the Center  for the Study of Symptom Science, Metabolomics, & Multiple Chronic  Conditions.

Dr. Ho has a Ph.D. in Electrical and Computer Engineering from  the University of Texas at Austin, and an M.A. and B.S. in Electrical  Engineering and Computer Science from Massachusetts Institute of Technology.  Her research involves the development of novel data mining and machine  learning algorithms to address problems in healthcare. She has papers in  premier journals and conferences across both computer science and clinical  venues including KDD, AISTATS, TKDD, AMIA, JMLR, and JBI. Her work received  the best paper award at the 2017 AMIA Joint Summits on Translational Science.  She also co-founded a successful healthcare analytics company (Accordion  Health) and previously worked at Lawrence Livermore National Laboratory.

Dr. Li’s research is aimed at uncovering knowledge related to  nursing workforce, as well as the costs and quality of care at both the  national and international levels. Dr. Li has an interdisciplinary education  and training in medicine, nursing, economy, business, and sociology. She also  has extensive background and experience in data management and analysis,  especially using large datasets. Her dissertation used an economic theory to  examine the primary care nurses’ contributions to the quality of primary care  and their economic returns. Currently funded studies focus on developing a  business case for employment of hospital-based advanced practice registered  nurses and examining how nursing licensure compact may improve the response  to natural disaster (both funded by NCSBN).

Dr. Kim is committed to reducing the racial/ethnic disparities  in women's and fetal health outcomes by identifying at-risk populations based  on their psychosocial and biological chronic stress profiles as well as  cultivating their resilience. She earned her doctoral degree at the  University of Pennsylvania School of Nursing with her dissertation addressing  the role of chronic stress in the different maternal age patterns of preterm  birth (PTB) among four major racial/ethnic groups of pregnant women in the  U.S. She has participated in multiple pilot and national-level research  projects that developed and tested culturally tailored, technology-based  intervention programs among Asian American women in unique health needs  (e.g., midlife women and breast cancer survivors).

Dr. Eldridge received his B.S. from Loyola University Chicago,  his MPH from the Rollins School of Public Health, and his PhD from Emory  University. He is an expert in the fields of cancer epidemiology, causal  inference methods, applied statistics, and data analysis. He uses his  interest in advanced methods to study the biological mechanisms of cancer  symptoms and survival. Currently, he is involved in identifying the  differential metabolic pathways of head and neck cancer survival through  high-resolution metabolomics and identifying complex longitudinal patterns in  cancer patient reported outcomes that predict poor survival.

Dr. Chee holds doctoral and master’s degrees in mechanical engineering with digital signal processing as a minor from the University of California, Berkeley. During the PhD program at UC Berkeley, he developed algorithms to make an intelligent vehicle perform lane change maneuvers and implemented the algorithms to actual vehicles though real-time software.

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Dr. Cimiotti is an internationally recognized health services researcher with expertise in nurse workforce issues and the quality of patient care. Her research has been cited for its expertise on nurse outcomes such as job dissatisfaction, burnout, and patient outcomes, specifically healthcare-associated infections and the sequela of infection such as extended lengths of stay, hospital readmissions, and mortality. Her work on healthcare-associated infections has been nationally and internationally recognized through NBC News, the Daily Mail, Modern Healthcare, and National Public Radio (NPR). Dr. Cimiotti is a Fellow in the American Academy of Nursing, a Distinguished Fellow in the National Academies of Practice, and a member of Sigma Theta Tau International. She currently serves as a Health Services Research & Development reviewer for the U.S. Department of Veterans Affairs.

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Dr. Hung-I currently a post-doctoral fellow participating in the development and use of innovative machine learning algorithms and tools to describe and forecast nursing quality indicators is my responsibility. I contribute to the development of papers for peer-reviewed journals and grant proposals for funding agencies.

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Dr. Hasan earned his PhD in Electrical and Computer Engineering from Florida International University. As a postdoctoral fellow, he is responsible for the integration of data from electronic health records with physiological data to develop predictive models for post stroke cognitive impairment and dementia. His work also includes analysis of neurophysiological signals (EEG, blood flow velocity, blood pressure, and intracranial pressure) using various signal processing, machine learning, and data analysis techniques.

Dr. Tumurbaatar working on the Nursing Quality Indicator projects to detect pressure injury using EHR data, temporal network analysis, and machine learning models. Dr. Tumurbaatar earned her PhD in Mathematics and MS in Computer Science from Washington State University. Her research focuses are algebraic graph algorithms, machine learning, deep learning, computational topology, and their applications in Biomedical and Health Science.

Dr. Sweet earned his PhD and MS in Quantitative and Systems  Biology from the University of California Merced where he focused on  microbial ecology and leveraging data analytics and Software engineering to  learn more about bacteriophages and their impacts on biological ecosystems.  Dr. Sweet also has 10 years of industry experience as a software engineer for  both enterprise IT systems and scaling startup company software systems. He  focuses on data collection, database maintenance, and/or data integration,  data analysis/visualization and 'Omic Sciences.

Dr. Mustapha is a post-doctoral fellow and earned his doctorate  (PhD) in Computer Science from the School of Mathematics, Statistics and  Computer Science, University of KwaZulu-Natal, Durban, South Africa. His  research interest focuses on learning algorithms (Machine Learning and Deep  Learning) models to solve Artificial Intelligence and Data Science problems.

Dr. Modhe received his PhD in Computer Science from the School of Interactive Computing within the College of Computing at Georgia Tech, where I was advised by Prof. Dhruv Batra. He received his bachelor’s degree in Computer Science from IIT Kanpur, where he worked with Prof. Piyush Rai. He is working on reinforcement learning algorithms for biomedical data analysis and sequential decision making in medical treatment.

Dr. Mohagheghian is a Biomedical Engineer by training and got her PhD in

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Cheng Ding is a PhD student in Biomedical Engineering joint program from Georgia Tech and Emory University advised by Dr. Hu. His research focuses on using AI/ML techniques on physiological signal and EHR data to detect heart diseases. He is advising several undergrad and master students on Deep Learning.

Colette is a Graduate Research Assistant focusing on artificial intelligence and machine learning in clinical nurse practice. Other projects include annotation of clinical notes, waveforms, and PPG signals, as well as facilitation of research presentations.

Ruochen is a master student in Computer Science and her project compare and develop methods for segmenting out bad quality signals from PPG signals, which will test on multiple datasets.

Jennifer is an undergraduate student majoring in Quantitative Science, and her project in our center is the seizure onset project, where she manually categorize the seven types of seizure, and later extract the necessary data like duration or linelength from the graph or dataset.

Tae Yeon is an undergraduate student, BS in Mathematics & Computer Science an she is working on seizure categorization and prediction from EEG data using machine learning under Dr. Hu's supervision.

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Ms. Govindarajan leads efforts to enhance healthcare research and incorporate data literacy into healthcare education of all levels. Merging her experience in diverse informatics areas such as bioinformatics, public health informatics and healthcare informatics, she has led the development of several unique, proprietary data science resources. She is also involved in curriculum development and teaching to incorporate data literacy in nursing education. She is currently leading the national implementation of Project NeLL, an EHR big database and suite of applications for teaching data science.

Ms. Bold is a Full-Stack developer and data engineer assisting on various research projects with signal processing, image processing, natural language processing and machine learning model integrations. She has a role as Cloud/Solutions architect, Full Stack Developer on NIH Bridge2AI initiative. She is also an EPIC Data Model trained BI developer.

Ms. Bateh performing a network analysis on cognitive domains based on cognitive assessment data. Her team are also conducting a scoping review to investigate digital cognitive assessments used for post-stroke adult patients. In clinical trials, she create the protocol and patient consent documents and oversee the IRB submission for our research projects. The main clinical trial she will be a part of is NOVEL, which focuses on utilizing medical devices to non-invasively monitor intracranial pressure in the Neuro ICU.

Mr. Williams will be performing duties to support an atrial  fibrillation multidisciplinary study monitoring patients using wearable  devices. He will be contributing to data analysis and reporting with  opportunities to co-author publications and develop grant proposals. His  previous work included coordinating cellular therapy clinical trials at Winship Cancer Institute with past experiences of non-interventional studies  during his time at a NCI comprehensive cancer center in Tampa, FL. He is  currently pursuing his Master’s degree in Public Health at Emory University  Rollins School of Public Health in pursuit to becoming a public health professional.