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. 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. 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. 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. Kang received her PhD in Nursing from the University of Pennsylvania. Her area of expertise is heart failure. She is currently conducting a randomized controlled trial study examining the feasibility and acceptability of home-based symptom monitoring for heart failure patients. The National Heart, Lung and Blood Institute is funding the study.

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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. 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. Jiaying Lu received his Ph.D. in computer science & informatics from Emory University in 2024, an M.Eng. in Electronics & Communication Engineering and a B.Sc. in Information Engineering from Beijing University of Posts and Telecommunications. His research involves developing trustworthy artificial intelligence solutions for healthcare applications in alliance with nurses, physicians, and biostatisticians. He is an expert in the fields of natural language processing, data mining, and machine learning, with extensive experience in analyzing EHR, omics data, physiological data, and more. Dr. Lu is a recipient of the Young Scientist Excellence Award from MCBIOS in 2024.

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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. Hyunjung Gloria Kwak focuses on developing equitable AI for healthcare by integrating machine learning techniques to address social determinants of health and promote fairness in healthcare research. She has collaborated with prominent research efforts worldwide, including the MIT LCP and the CHoRUS project. Dr. Kwak has also worked with hospitals across Hong Kong, Korea, and other regions during her PhD and postdoctoral training at Harvard Medical School and Massachusetts General Hospital. Before transitioning to academia, she held professional positions at two leading tech companies, contributing to AI innovations. Her global journey in computer science spans HKUST, University College London, and Korea University. She also serves as a section editor for PLOS Digital Health.

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Dr. Chung (she/her) is a nurse scientist with expertise in technology and aging research. Focusing on technology-enabled risk detection, prevention, and interactive interventions, she aims to promote independent living in community-dwelling older adults including those with health disparities. Additionally, she continues to work on advancing technology implementation science by exploring the acceptability of digital health technology among older adults and caregivers in a sociocultural context. She utilizes design thinking to create user-centered technology solutions that meet the needs of older adults from socioeconomically diverse backgrounds. In recognition of the impact of her work, she has been selected as a fellow of the Betty Irene Moore Fellowship Program for Nurse Leaders and Innovators and as a fellow of the Gerontological Society of America (GSA). She was a fellow of the Latino Aging Research Resource Center, one of the Resource Centers for Minority Aging Research (RCMAR) funded by the National Institute on Aging (NIA). She is currently working on an NIH/NIA-funded R01 project to develop digital biomarkers of dementia based on sensor-based mobility and activity data, as well as an R21 project focused on developing a machine learning algorithm to automatically assess older adults’ loneliness risks in the home setting.

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As an aging-focused health services researcher, Dr. Xu has extensive experience studying long-term care, health policy, and cancer survivorship, using large Medicare claims, resident assessments, and various survey data. His NIH-funded research projects seek to understand the interaction of federal and state policies with nursing home operations and the ways in which this interaction may improve or unintendedly compromise quality of care or health equity. He has also applied various supervised and unsupervised machine learning models to aging research. He previously participated in multiple NIH-funded projects in cancer survivorship. Dr. Xu was selected into several prestigious aging training programs such as the RL5 Pepper Scholar and RCCN Scholars Program in Multidisciplinary Research. Dr. Xu has published 60+ peer-reviewed articles in leading medical and policy journals including The Lancet, JAMA Internal Medicine, and Health Affairs; he coauthored two book chapters and 90+ scientific abstracts. His work has been cited 2,500+ times, with an H-index of 25. Dr. Xu holds national leadership positions and serves as a scientific reviewer for NIH and 25+ scientific journals. Before coming to Emory, Dr. Xu held faculty positions at the University of Rochester Medical Center and the University of Texas Medical Branch.

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.

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. 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. Kataria earned an MS and PhD in Electrical Engineering from Johns Hopkins University, and a B. Tech. and M.Tech. in Electrical Engineering with a minor in Artificial Intelligence from IIT Kanpur. During his PhD studies, Dr. Kataria worked on data-driven, speech signal processing applications like speaker recognition and speech enhancement. He also has experience working on Automatic Speech Recognition (ASR) and speech emotion recognition. Currently, he is working with Dr. Xiao Hu to apply his time-series analysis knowledge to developing large language models for cardiovascular disease prediction. In the future, Dr. Kataria is interested in working on novel applications which analyze human bio-signals like PPG and pathological speech together.

Dr. Bennett earned her PhD in Nursing Research from the University of Cincinnati. She is a Robert Wood Johnson Foundation Future of Nursing Scholar. She has mentored students in areas of patient-centered care, health literacy, and interprofessional care planning. Her research interests relate to improving outcomes for aging populations, primarily focusing on Black women’s health and intersectionality. Particularly, she will leverage data science and HIT to research intersectional drivers of high rates of dementia in black women and to develop relevant psychoeducational interventions. She is the WISDOM postdoc, which is a T32 trainee program led by Drs. Rasheeta Chandler and Vicki Hertzberg.

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Dr. Finch earned a PhD in Computer Science and Informatics from Emory University and a BS in Computer Science from Michigan State University. During her PhD studies, her research targeted the evaluation and development of conversational AI and automated dialogue systems. Her work focused on developing fine-grained behavior analyses of chatbot systems and improving social awareness of chatbot systems through improved commonsense understanding. In addition, she was co-team-lead for the 1st place team of the Amazon Alexa Prize in 2020, developing a social chatbot called Emora that achieved an average rating of 3.8 out of 5.0 from thousands of real human users. Following the completion of her PhD, she has joined Dr. Ike Choi's lab and is working on integrating natural language processing and conversational AI in education and healthcare applications.

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Dr. Davood Fattahi earned his Ph.D. in Biomedical Engineering (Bioelectric) from Shiraz University in 2022. His research focuses on biological signal processing and the application of artificial intelligence (AI) in medicine. Over his career, he has contributed to advancements in analyzing biological data, including Electrocardiography (ECG), Phonocardiography (PCG), Electroencephalography (EEG), lung sounds, and other physiological signals, to improve the diagnosis and treatment of cardiopulmonary, epileptic, and seizure-related conditions. Currently, Dr. Fattahi is a post-doctoral researcher under the mentorship of Dr. Xiao Hu. His ongoing research projects involve the analysis of intracranial pressure (ICP), photoplethysmography (PPG), ECG, and EEG, with a focus on leveraging these signals for clinical applications in critical care and neurological disorders.

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Dr. Yi Wu earned his Ph.D. in Computer Science from The University of Tennessee, Knoxville (UTK). His research interests include Embedded/Wearable Computational Sensing Software, Cybersecurity & Privacy, and Smart Health. His research work has been published at top-tier mobile-computing/security/HCI/ venues (e.g., ACM MobiCom, IEEE, ACM CCS, ACM UbiComp, and ACSAC). He is the recipient of multiple awards, including Outstanding Graduate Research Assistant of UTK and ACM SigMobile Research Highlights 2022. He also filed three U.S. patents. He is working with Dr. Xiao Hu on large-scale evaluation of Foundation Models for physiological signal.

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Dr. Maryam Kheirandish is a member of Dr. Ryan Suk’s research team. Maryam’s research is at the intersection of statistical modeling, machine and deep learning, and stochastic dynamic programming, with a focus on risk-based decision-making in healthcare operations. Her doctoral dissertation explored the complexities of predicting tuberculosis treatment outcomes through advanced data analytics, addressing the challenges of modeling irregular multivariate longitudinal health data, quantifying prediction uncertainty in neural networks, and developing non-stationary reinforcement learning algorithms. She aspires to contribute significantly to the fields of healthcare quality improvement and patient outcome prediction and optimization through her innovative research aiming for impactful advancements in global health initiatives.

Dr. Zhao obtained her PhD in Mechanical Engineering from the University of California Merced in 2023. During her PhD studies, she focused on improving control performance through data-driven control, optimal control, and machine learning algorithms for various mechanical systems. Her research interests include, but are not limited to: dynamics and control, robotics, state and parameter estimation, reinforcement learning, and system modeling and simulation. Currently, she is working with Dr. Xiao Hu on noninvasive intracranial pressure estimation, leveraging both model-based and machine learning techniques.

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.

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.

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.

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. Bold is a highly skilled  Full-Stack Developer, Solutions Architect, Cloud and Data Engineer at Emory University's School of Nursing. With a focus on AI-driven software systems, she has significantly contributed to the Center for Data Science, particularly in developing critical care and in/outpatient applications. Ms. Bold excels in translating complex technical requirements into practical, scalable solutions. She plays a pivotal role in evaluating cloud services, defining custom roles, and enabling secure machine learning model development within data environments. Her work on the on NIH Bridge2AI initiative as a Cloud/Solutions Architect and Full-Stack Developer showcases her expertise in integrating signal processing, image processing, natural language processing, and machine learning models. As an EPIC Data Model trained BI developer, she brings a comprehensive approach to data solutions.

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.

Kathleen Jordan received her BS in Biomedical Engineering from GeorgiaTech. She has been the human subjects coordinator on multiple research studiesinvestigating pressure injury prevention in persons with SCI/D over the past 3years and has recruited dozens of wheelchair users for long-term behaviormonitoring clinical trials. She has experience with testing pressure sensormats and related hardware to optimize data output from various wheelchairin-seat activity trackers. Kathleen also has experience in clinicaltranslational research, working on device usability and protocol development tostudy the usefulness of non-clinical commercial devices as early detectiontools for monitoring skin health in hospitals.

Jongwon Kim is an advanced Artificial Intelligence (AI) specialist with a focus on Vision AI and has extensive experience in the development of AI models and their practical applications in the field of industrial artificial intelligence. He is currently involved in various interdisciplinary projects to understand human behaviors and enhance human performance for better health outcomes using AI applications.

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.