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, PhD, is Professor in the Nell Hodgson Woodruff School of Nursing at Emory University. She also has courtesy appointments as a professor in the Departments of Biostatistics & Bioinformatics, Computer Science, and Mathematics at Emory. Dr. Hertzberg received her BS cum laude from Miami University in 1976 and her PhD in biostatistics from the University of Washington in 1980. She then served on the Division of Biostatistics and Epidemiology faculty at the University of Cincinnati until moving to Emory University in 1995 to become chair of the Department of Biostatistics in the Rollins School of Public Health. In 2015, she moved to the Nell Hodgson Woodruff School of Nursing where she founded and continues to direct the Center for Data Science.

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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.

Tracie Graham is a dynamic, engaging, and authentic leader. She has over 20 years of experience in public health research project coordination and management in an academic setting. The consistent thread in her career is her ability to successfully implement research protocols while also mentoring and training the next generation of public health professionals. Her mindset is driven by her commitment to the ethical conduct of research and improving the health of marginalized communities. Tracie will support the CDS through project leadership and by managing day-to-day operations of the Center. Tracie received her BA in Psychology and Sociology at Emory University and her MPH in Health Behavior at the University of Alabama at Birmingham.

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 her PhD in Emory’s Immunology and Molecular Pathogenesis program and has extensive experience with laboratory and computational research studying the interplay between gut microbes and the host immune system and the role of inflammatory processes in chronic disease. She works collaboratively with numerous research teams, contributing topical as well as methods expertise and technical skills in bioinformatics and analysis of omics data sets. Consistent themes in Dr. Houser’s research are the integration of multiple data sources (often including microbiome, metabolomics, and/or inflammation-related data) and assessment of the interactions of complex systems to identify risk factors and contributors to adverse health outcomes.

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. Wenhui (Vivian) 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.

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. 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. Fedorov is committed to developing trustworthy medical artificial intelligence systems across diverse healthcare data. His research focuses on developing novel semi-supervised and self-supervised multimodal machine learning algorithms designed to adapt seamlessly to new data, promoting broader clinical applications. Throughout his academic pursuits, Dr. Fedorov has amassed invaluable experience by presenting his work at venues such as ICLR, NeurIPS, MICCAI, and IEEE, as well as interning at leading technology companies such as Meta, Descript, Microsoft Research, and the academic lab Mila - Quebec AI Institute, led by 2018 A.M. Turing Award recipient, Dr. Yoshua Bengio. Dr. Fedorov earned his Ph.D. in Electrical and Computer Engineering at the Georgia Institute of Technology. He obtained an MS in Electrical and Computer Engineering from the University of New Mexico. Additionally, he holds a BS in Computer Science and an MS in Applied Mathematics, both from the Moscow Institute of Electronic Technology.

Ike Choi is a learning systems scientist investigating the role of authentic experiences and reflective thinking in developing human expertise to solve uncertain, ill-defined real-world problems. He worked with leading scholars in health sciences (medicine, pharmacy, vet medicine, and nursing), basic sciences, engineering, and teacher education. He developed and disseminated generalizable instructional theories and models that enhance authenticity, reflection, and learning. His recent interdisciplinary research focuses on applying learning science, data science, and advanced technologies—virtual reality, simulation, machine learning, and biosensing technologies—to improving health professions education.

Dr. Close is a board-certified pediatric nurse practitioner with a BS, MS and PhD from Columbia University School of Nursing in New York. She is also inducted as a Fellow in the New York Academy of Medicine. Her area of clinical and research interest is pediatric primary care, developmental pediatrics, management of chronic conditions of genetic origin, and variations of sex chromosome aneuploidy. Her current work at the School of Nursing includes teaching in genetics and pediatric primary care.

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Dr. Sharon Sonenblum is dedicated to improving patient outcomes and enhancing the lives of individuals with disabilities through the application of data-driven insights to clinical practice and engineering innovation. She joins the Center for Data Science after spending 20 years at Georgia Institute of Technology, bringing her unique ability to collaborate between researchers and clinicians to the School of Nursing. Her research program spans pressure injury prevention, detection, and etiology, and wheelchair seating and mobility. She conducts influential studies on the impact of real-world behavior on pressure injury development and technology's role in behavior modification. She also employs advanced approaches to investigate how tissues respond to loading considering differences across diverse skin tones. Dr. Sonenblum is an alumnus of the National Pressure Injury Advisory Panel's board of directors and a member of the Prophylactic Dressing Standards Initiative. She holds a Ph.D. in Bioengineering from the Georgia Institute of Technology, an ScM in Bioengineering and an ScB in Mechanical Engineering from Brown University.

Foster Osei Baah, PhD, RN is a cardiovascular nurse scientist with a program of research focused on the social determinants of cardiovascular health behavior, self-care, cardiometabolic disease and cardiovascular health disparities in marginalized groups. The fundamental goal of this research program is to inform, design, and test interventions that enhance health behavior, improve health outcomes, and promote equity across diverse population groups. Prior to joining the Nell Hodgson Woodruff School of Nursing as a Tenure Track Assistant Professor, he completed a PhD in Nursing Science at the University of Pennsylvania and postdoctoral fellowship at the Social Determinants of Obesity and Cardiovascular Risk Laboratory at the National Heart, Lung, and Blood Institute (NHLBI) with a focus on intervention design to address obesity and cardiovascular health disparities in resource limited neighborhoods.

Dr. Ryan Suk (she/they) is a Health Economist by training, and her research involves leveraging real-world data and decision modeling for economic evaluation to gain insights into the equity and efficiency of healthcare interventions. Dr. Suk’s research focuses on HPV-associated cancer prevention and control, with a particular emphasis on multi-level social determinants of health, LGBTQIA+ health, and underrepresented populations in cancer research. They are interested in utilizing AI-driven decision support tools for cancer prevention and control, as well as employing distributional cost-effectiveness analysis to address health inequities. Dr. Suk’s research has been published in JAMA Pediatrics, JAMA Network Open, JAMA Health Forum, JNCI, Annals of Internal Medicine, Annals of LGBTQ Public and Population Health, Journal of General Internal Medicine, and more. She received a Bachelor's in Business Administration from Korea University (Seoul, Korea), an MS in Economics from the University of Utah (Salt Lake City, Utah), and a PhD in Health Economics from The University of Texas Health Science Center (UTHealth) (Houston, Texas). Before joining Emory and Center for Data Science, Dr. Suk served as an Assistant Professor at the University of Florida College of Pharmacy and at UTHealth School of Public Health. Dr. Suk is a Seoul (Korea) native.

Dr. Huzurbazar joined the Nell Hodgson Woodruff School of Nursing in early 2024 with a secondary position as Director of the provisional AI track in the Nursing PhD program. Most recently, she was Professor of Data Science and Associate Director in the School of Mathematical and Data Sciences at West Virginia University (WVU), where she founded the Data Science program in 2020. She was Chair of Biostatistics at WVU (2017-2019), Professor of Statistics at the University of Wyoming (1995-2017), and Deputy Director (2012-14) of the NSF’s Statistical and Applied Mathematical Sciences Institute in North Carolina. Earlier in her career, she was a Visiting Scientist at the Institute of Arctic and Alpine Research at the University of Colorado, Boulder and she also initiated and obtained NIJ funding to start the STOP Violence program at the Univ of Wyoming. She is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute. Her methodological and interdisciplinary research is now at the intersection of statistics and data science, with a view towards uncertainty quantification. She earned her PhD in Statistics from Colorado State University, her MA in Economics from Vanderbilt University and her BA from Grinnell College. She is one of the two statistical editors for the journal Anesthesiology

Dr. Carl Yang received his Ph.D. in Computer Science at University of Illinois, Urbana-Champaign in 2020, and B.Eng. in Computer Science and Engineering at Zhejiang University in 2014. His research interests span graph data mining, applied machine learning, knowledge graphs and federated learning, with applications in recommender systems, social networks, neuroscience and healthcare. Carl's research results have been published in 120+ peer-reviewed papers in top venues across data mining and health informatics. He is also a recipient of the Dissertation Completion Fellowship of UIUC in 2020, the Best Paper Award of ICDM in 2020, the Best Paper Award of KDD Health Day in 2022, the Best Paper Award of ML4H in 2022, the Amazon Research Award in 2022, the Microsoft Accelerating Foundation Models Research Award in 2023, and multiple Emory internal research awards. Carl's research receives funding support from both NSF and NIH in USA.

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 earned her PhD from Shahid Beheshti University of Medical Sciences in Tehran, Iran. During her doctoral studies, her research focused on brain mapping, EEG source reconstruction methods, as well as connectivity and network analysis approaches, specifically on EEG data recorded from patients with tinnitus. Following the completion of her PhD, Dr. Mohagheghian joined Professor Chon's lab (Biosignal Processing and Wearable Device Lab) at UConn as a Postdoctoral Research Associate. In Dr. Hu's lab at Emory, Dr. Mohagheghian's current research revolves around developing algorithms for the creation of a continuous early warning system to rapidly detect and predict delayed cerebral ischemia (DCI) in patients with aneurysmal subarachnoid hemorrhage (aSAH).

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Dr. Yan earned his Doctorate and Master degrees in Systems and Information Engineering from the University of Virginia in 2023 and 2019, respectively, following the completion of his Bachelor's degree in Telecommunication Engineering from Beijing University of Posts and Telecommunications in 2018. His research focuses on employing machine learning and deep learning techniques to model human behaviors and predict outcomes related to health and well-being.

Dr. Kwon earned her PhD in Learning, Design, and Technology from the University of Georgia Mary Frances Early College of Education. Dr. Kwon played one of the core members of Research and Innovation in Learning (RAIL, an interdisciplinary research lab) at UGA and participated in the Translational Workforce Development (TWD) project of the Georgia Clinical & Translational Science Alliance (Georgia CTSA) for six years. Her research focuses on investigating effective ways of enhancing problem-solving and decision-making abilities, with a particular emphasis on enhancing clinical reasoning and emotional regulation among health professionals.

Dr. Dong received her PhD in Information Science and Engineering from Southeast University, China. Her PhD studies focused on cardiorespiratory coupling-based sleep monitoring.  As a postdoctoral researcher, she is working on the detection of autonomic nervous system dysfunction by analyzing ECG and blood pressure signals, development and application of advanced signal processing and machine learning approaches to analyze physiological signals and other data modalities from various environments.

Alex Bateh is an undergraduate intern majoring in Neuroscience at the Georgia Institute of Technology. He is currently working in tangent with KUMC to analyze TCD waveforms to identify biomarkers for age and physiological states.

Yijun is an undergraduate student majoring in Computer Science and Quantitative Science. The project she is currently working on is developing machine learning models for predicting of atrial fibrillation following the atrial fibrillation ablation procedure under the supervision of Dr. Hertzberg.

Kaprice Welsh has worked in women’s health care for over 30 years. She holds a Bachelor of Science degree in Nursing from Syracuse University, and her Master's of Science degree in Nursing, a Certificate of Midwifery and a Master of Public Health from Columbia University. Her research interest will focus on using HIT as a strategy to address the key drivers of maternal morbidity and mortality in black birthing women.

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.

Research and practice interests’ center around neuro critical care nursing as it pertains to improving stroke outcomes. (BSN, Emory University 2018) Advisor, Dr. Vicki Hertzberg.

Dr. Patel has a B.Sc. degree in Computer Science, an M.Sc. degree in Artificial Intelligence, and a Ph.D. in Biological Engineering. H brings over 25 years of experience managing and supporting IT infrastructure, bioinformatics, software development, web development, high-speed networking, on-prem and cloud computing and cybersecurity. At the SON, Dr. Petal is focused on developing the SON's research IT technologies, specifically in the areas of high-performance computing, big data, simulation, artificial intelligence, virtual reality, and immersive teaching and learning.

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.

Yi Ge is a new graduate from the Quantitative Sciences department at Emory. He is responsible for the integration of EHR data between Cerner and Epic as well as practicing machine learning techniques to forecast medical outcomes. His latest work centers around predicting Atrial Fibrillation among Embolic Stroke of Undetermined Sources patients and testing ML models against social biases through US Census data.

Evan Sinclair Smith graduated with a bachelor's degree in Computer Information Systems from Georgia State University. He is now pursuing a master's in Computer Science with a focus on Artificial Intelligence at Emory University. Evan is actively involved with Project Nell. He is currently enhancing Project Nell's data and working on an AI-driven process to deidentify nursing notes.

Darren earns his master's degree in computer science from Binghamton University, and his bachelor's degree in software engineering from Sichuan University. His research focuses on natural language processing on clinical data and software development.