Lab Members

Dr. Khera is a Cardiologist and Data Scientist and the Director of the Cardiovascular Data Science (CarDS) Lab, which is a multidisciplinary group focusing on data-driven discovery in cardiovascular disease. He is the Clinical Director of the Center for Health Informatics and Analytics at the Yale Center for Outcomes Research and Evaluation. He is also an Associate Editor for Artificial Intelligence and Digital Health at JAMA. The CarDS Lab, which Dr. Khera leads, is developing and implementing strategies to improve outcomes for patients with or at risk for cardiovascular disease through data-driven innovations in delivering evidence-based, patient-centered care. Dr. Khera’s work focuses on novel applications in medical informatics, machine learning, and artificial intelligence to evaluate patient care and develop precision care solutions. His work spans broad digital data sources, including electronic health records, electrocardiography, cardiovascular imaging, and wearable devices, with applications to modernize US and global healthcare. The work in his Lab is supported by grants from the National Institutes of Health and the Doris Duke Charitable Foundation. Dr. Khera graduated from the All-India Institute of Medical Sciences as a National Young Investigator Scholarship awardee. During his internal medicine residency training at the University of Iowa and his cardiology fellowship training at UT Southwestern Medical Center, Dr. Khera received the American College of Cardiology’s Young Investigator Award and the Francois Abboud Young Investigator Award, in addition to being inducted into the Alpha Omega Alpha Medical Honor Society. For his work at Yale, Dr. Khera received the 2023 ASCI Young Physician-Scientist Award, the 2023 Blavatnik Award, and the 2021 Jeremiah Stamler Award. More details on his work can be found at www.cards-lab.org.

Philip graduated from the University of Maryland, Baltimore County as a Meyerhoff Scholar, earning degrees with distinction both in Biochemistry/Molecular Biology and Statistics. Following his studies, he served as a research assistant at Vanderbilt University's Department of Biomedical Informatics where he investigated the off-target effects of statins using BioVU. Prior to starting medical school, Philip competed as a professional swimmer for the country of Nigeria, where he was a national record holder and team captain. He competed in a host of international events such as the All African Games and World Championships. Philip is currently working in the Cardiovascular Data Science (CarDS) Lab under the guidance of Dr. Rohan Khera where he is applying machine learning techniques to assess and improve quality of care and patient outcomes.

Arya Aminorroaya is a PGY-1 resident in the Yale Internal Medicine Traditional Residency Program and a research affiliate at the Cardiovascular Data Science (CarDS) Lab. He earned his MD-MPH from Tehran University of Medical Sciences in Iran, where he conducted clinical and epidemiological research at the Tehran Heart Center and the Non-Communicable Diseases Research Center, and collaborated with the Institute for Health Metrics and Evaluation at the University of Washington. His research explored cardiovascular disease prevention, risk stratification, and large-scale population health studies. Building on this foundation, his postdoctoral fellowship at the CarDS Lab focused on leveraging artificial intelligence (AI) and medical informatics to develop and validate AI tools for advancing cardiovascular care, with an emphasis on real-world implementation and improving outcomes in low-resource settings. His research has been published in leading journals, including The Lancet, JACC, JAMA Cardiology, and Nature Cardiovascular Research. Arya is also actively engaged in scientific publishing, serving as a Junior Deputy Editor at the European Journal of Preventive Cardiology and as a peer reviewer for several top-tier journals. His career goal is to bridge clinical care and research to enhance healthcare quality and patient outcomes.

Dr. Lovedeep Singh Dhingra is a Postdoctoral Associate at the Cardiovascular Data Science (CarDS) Lab and holds a Master of Health Science (MHS) degree in the Clinical Informatics & Data Science (CIDS) track from the Yale School of Medicine. Dr. Dhingra trained in Medicine at the All India Institute of Medical Sciences (AIIMS), New Delhi. During his MBBS, he served as a clinical consultant in TavLab, where he applied computer vision to detect pediatric shock. He also held leadership roles as the Academic Secretary (2017–2018) of the Asian Medical Students Association (AMSA, India) and the Chief Curator of TEDxAIIMS (2016) – the first event of its kind at a medical school in India. After completing his training, he practiced as a Medical Officer in a multi-specialty hospital in North India before joining the Yale/YNHH Center for Outcomes Research and Evaluation (CORE) as a Research Affiliate. Dr. Dhingra’s research focuses on the application of machine learning, computer vision, and natural language processing for advanced cardiovascular diagnostics, electronic health record (EHR)-based investigations, and broader public health applications. His work has been published in leading academic journals including the European Heart Journal (EHJ), Journal of the American College of Cardiology (JACC), Circulation, JAMA Cardiology, and Nature Cardiovascular Research. His current work involves using artificial intelligence for detecting structural heart disorders from electrocardiogram (ECG) images and applying these tools to multinational, real-world settings that aim to enhance cardiovascular care. He is also developing a multi-site digital registry for type 2 diabetes (DIRECT-DM) in the Yale New Haven Health System and contributes to the LEGEND-T2DM initiative, a multinational effort to assess real-world cardiovascular effectiveness and safety of diabetes therapies. In his free time, Lovedeep enjoys reading, playing board games, and stargazing.

Evangelos (Evan) K. Oikonomou, M.D., D.Phil. is a cardiologist, physician-scientist, and Assistant Professor in the Section of Cardiovascular Medicine (Internal Medicine) at the Yale School of Medicine. He specializes in the application of computer vision and statistical machine learning to advance precision phenotyping in cardiovascular disease. His research is grounded in developing scalable, cost-effective digital tools that can be integrated into existing care pathways to improve diagnosis, risk stratification, and therapeutic decision-making. He graduated as valedictorian from the University of Athens Medical School and earned his doctorate (D.Phil.) in Medical Sciences from the University of Oxford. He subsequently joined the Physician-Scientist Training Program at Yale, where he completed his internal medicine residency and clinical fellowship in cardiology. His post-doctoral research was supported through an NIH F32 Ruth L. Kirschstein National Research Service Award from the National Heart, Lung, and Blood Institute. He is also the recipient of Young Investigator Awards from the American Heart Association (2021, 2023), American College of Cardiology (2024), European Society of Cardiology (2018, 2019) and the Society for Cardiovascular Computed Tomography (SCCT, 2017). His interdisciplinary research spans multiple domains of AI in cardiovascular and cardiometabolic medicine. He has: Developed and translated perivascular adipose tissue imaging biomarkers into clinically actionable tools for vascular inflammation and cardiovascular risk assessment using routine cardiac CT; Designed and validated deep learning algorithms tailored to point-of-care echocardiography for the diagnosis of both common and under-recognized cardiomyopathies; and Led data-driven evaluations of treatment effect heterogeneity across clinical trials, contributing to the design of adaptive, precision-enriched trial frameworks. His work has been published in The Lancet, JAMA, The Lancet Digital Health, European Heart Journal, JACC, Circulation, JAMA Cardiology, and Diabetes Care, among others. Looking ahead, Dr. Oikonomou is focused on leveraging multimodal AI to redefine diagnostic and prognostic frameworks in cardiovascular disease, from subclinical detection to dynamic risk prediction, with an emphasis on real-world implementation and equity in access to advanced diagnostics. A detailed list of Dr. Oikonomou's bibliography can be accessed at https://www.ncbi.nlm.nih.gov/myncbi/evangelos.oikonomou.1/bibliography/public/ or https://scholar.google.com/citations?user=GgJv1SMAAAAJ&hl=en

Phyllis is a cardiology fellow in the ABIM Physician Scientist Training Program. She is completing her post-doctoral research fellowship under Dr. Rohan Khera through the T32 Implementation Science Fellowship. She graduated from Yale College with a B.S. in Physics and then completed her M.D./Ph.D. at Columbia University, where she developed machine learning methods to improve the phenotyping of stroke in the electronic health record and improve the power of genome-wide association studies in the UK Biobank. Most recently, she completed her internal medicine residency at Yale New Haven Health. Her interests include the development of methods to improve precision medicine in cardiology, harmonization of multi-modal and multi-site data, and characterizing the gray areas between clinical trial and real-world patient populations.