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 Postdoctoral Associate in the Cardiovascular Data Science Lab. He is an MD-MPH graduate from Tehran University of Medical Sciences in Iran, where he was an Avicenna awardee for Excellence in Research and Education. During medical school, he joined the research department of Tehran Heart Center and Non-Communicable Diseases Research Center at Tehran University of Medical Sciences, and the Institute for Health Metrics and Evaluation at the University of Washington to pursue his interest in medical research. He was involved in implementing clinical and epidemiological studies in the field of cardiovascular medicine, through which he established a solid foundation in research methodology and statistical analysis. His research has been published in The Lancet, European Heart Journal, American Heart Journal, and European Journal of Preventive Cardiology. Also, he is a reviewer at European Heart Journal, European Journal of Preventive Cardiology, and BMC Cardiovascular Disorders. He was selected as the Reviewer of the Year by the European Journal of Preventive Cardiology in 2022. At Cardiovascular Data Science (CarDS) Lab, Arya's research focuses on improving the quality of care for patients with cardiovascular disease using data science and machine learning strategies.

Dr. Lovedeep Singh Dhingra is a postdoctoral associate at the Cardiovascular Data Science (CarDS) Lab (led by Dr. Rohan Khera) in the section of Cardiovascular Medicine. Lovedeep trained in Medicine at the All India Institute of Medical Sciences (AIIMS), New Delhi. During medical school, he worked as the clinical consultant for TavLab, a multidisciplinary group working on computer vision projects for shock detection in the Pediatric ICU. He also served as the Academic Secretary (2017-2018) for the Asian Medical Students Association (AMSA, India) and the Chief Curator of TEDxAIIMS (2016), the first event of its kind in a medical school in India. Since graduating from his MBBS course, Lovedeep has worked as the Medical Officer for a multi-specialty hospital in North India, and then remotely as a Research-Affiliate at the Yale/YNHH Center for Outcomes Research and Evaluation (CORE). During the early phase of the COVID-19 pandemic, he contributed to research projects evaluating the vulnerable physician workforce in the US and the practice of telemedicine in India. His research has been published in reputed journals including Hypertension, Scientific Reports, and the Journal of General Internal Medicine. His research interests include medical machine learning and patient-centered cardiovascular disease outcomes. In his free time, Lovedeep can be found with his eyes down reading a book on his iPad, or with his eyes up hunting for constellations in the night sky.

Evangelos (Evan) Oikonomou is a clinical fellow in cardiovascular medicine, a post-doctoral research fellow in the Cardiovascular Data Science (CarDS) lab, and a member of the ABIM Physician-Scientist Research Pathway at Yale. His work focuses on the intersection of applied computer vision and statistical machine learning, with a specific focus on developing tools for the improved phenotyping of cardiovascular disease using scalable approaches that can be deployed at minimal cost using existing care pathways. He graduated as valedictorian of his class from the University of Athens Medical School in Greece, before pursuing a Ph.D. (D.Phil.) degree at the University of Oxford, where he was recognized with the Radcliffe Department of Medicine Graduate Prize for his scientific work. In 2019 he joined the Physician-Scientist Training Program at the Yale School of Medicine, and he has since completed his internal medicine residency and his core clinical fellowship in cardiology. He is a recipient of an F32 NRSA fellowship award from NHLBI (National Institutes of Health), and his work has been recognized through numerous Young Investigator Awards sponsored by the American Heart Association, American College of Cardiology, Northwestern Cardiovascular Young Investigator Forum, the European Society of Cardiology and European Association of Preventive Cardiology. He has led a broad portfolio in applied artificial intelligence in cardiovascular and cardiometabolic medicine. First, he has defined and translated a key interplay between the perivascular adipose tissue and vascular inflammation in humans into a clinically actionable algorithmic tool that can refine cardiovascular risk on routine cardiac CT scans. Second, he has developed and validated deep learning algorithms for the efficient diagnosis of common and rare cardiomyopathies specifically adapted for point-of-care echocardiography. Finally, he has led an extensive body of work on defining treatment effect heterogeneity across clinical trials, with direct implications for evidence translation and the design of new adaptive trials with data-driven predictive enrichment. His work has been published in several peer-reviewed journals, including the Lancet, Lancet Digital Health, European Heart Journal, JACC, Circulation, JAMA Cardiology, Diabetes Care.

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.