Mohanad Alkhodari is a biomedical research associate (RA) at the Healthcare Engineering Innovation Center (HEIC) under the Department of Biomedical Engineering at Khalifa University (KU), United Arab Emirates (UAE). Alkhodari received his Master of Science (M.S.) degree (top 5-10{0961db0d5220b4955a3f07ef234db6dc96890fe8a6be6f8a7c66584c2fe2ed1b}) in Biomedical Engineering from American University of Sharjah (AUS), UAE and his Bachelor of Science (B.S.) degree (Hons.) in Electrical Engineering from Abu Dhabi University (ADU), UAE in 2019 and 2017, respectively. Alongside his main full-time research position at KU, Alkhodari held part-time research appointments for 2 years at the Department of Computer Science and Engineering of AUS and at the Department of Electrical and Computer Engineering of ADU. While pursuing his graduate M.S. degree at AUS, Alkhodari worked as a graduate teaching and research assistant (GTA/GRA) at the College of Engineering, where he was awarded a full (100{0961db0d5220b4955a3f07ef234db6dc96890fe8a6be6f8a7c66584c2fe2ed1b}) scholarship from the Bioengineering and Biosciences Research Institute (BBRI) at the university for his academic excellence in the undergraduate studies. Furthermore, he was awarded a partial scholarship (30{0961db0d5220b4955a3f07ef234db6dc96890fe8a6be6f8a7c66584c2fe2ed1b}) to purse the undergraduate B.S. degree at ADU. Alkhodari had a research internship at the Institute Center for Microsystems (iMicro) at KU during his undergraduate studies and a practical training in biomedical engineering at Specialized Rehabilitation Hospital and Health Shield Medical Center of Capital Health, Abu Dhabi, UAE while pursuing his M.S. degree. Alkhodari has been conducting research in multidisciplinary biomedical engineering areas including biological signal analysis, medical imaging development, and artificial intelligence. His current research focuses mainly on developing machine and deep learning clinical solutions for various medical applications that highlight cardiovascular dysfunctionality, maternal-fetal cardiac interactions, coronary artery disease (CAD), heart failure, and cardiac arrhythmias. He developed machine learning models that are capable of quantifying left ventricular ejection fraction in electrocardiography (ECG) signals and heart rate variability (HRV) data for the purpose of heart failure prognosis and progression diagnosis in CAD patients. In addition to his current research, he had several contributions on other research areas including lungs computed tomography (CT) motion-management, medical microwave tomography enhancements, diabetes diagnostics, and lung/heart sounds analysis. By far, he authored and co-authored three book chapters and more than 35 scientific papers in international journals and conferences, where he was the first/leading author in 27 and the corresponding author or speaker in 18. He is currently processing the submission of a patent for a stand-alone app for novel heart failure assessment using deep learning. Besides his regular supervision and assistance to fresh and junior biomedical engineering researchers at KU, Alkhodari supervised 2 PhD. students in dissertation-related research, 6 M.S. groups in course project activities, and 2 B.S. group on their Senior Design Project. He worked alongside Ph.D. students and postdoctoral researchers at international universities in Greece and Japan in collaborative research projects. In addition, he spared some time to serve as an active reviewer for several papers in Frontiers in Artificial Intelligence/Big Data, IEEE Access, BMC BioMedical Engineering OnLine, and Springer Medical & Biological Engineering & Computing journals. During his M.S. studies, Alkhodari was a representative of the electrical and biomedical engineering graduate programs for the IEEE Engineering in Medicine and Biology Society (EMBS) students’ chapter at AUS. Alkhodari is currently representing KU at the international-level PhysioNet/Computing in Cardiology Challenge 2022 as a continuation of the 2021 and 2020 challenges (31st/38 and 26th/41). He was awarded the 1st place in the regional-level 2017 Undergraduate Research Competition (URC): Information Technology and Computer Engineering Category for Gulf Cooperation Council (GCC) countries held at ADU with his B.S. graduation SDP of a smartphone application for early detection of diabetes, the 1st place at the national-level 2019 UAE Ministry of Health and Prevention (MOHAP) innovations in health hackathon with a group design of a smart wearable cap for epileptic patients, and the 3rd place at the national-level 2017 Think Science Fair: Biomedical Systems Category with the B.S. graduation SDP. Alkhodari spends his free time by doing gym workouts. He enjoys graphic designing, drawing, as well as taking care of his three little bunnies
