PhD candidate, Graduate Research Assistant at Texas A&M University, United States
Rana Raza Mehdi is a dynamic fourth-year Ph.D. candidate in Biomedical Engineering at Texas A&M University, specializing in computational cardiovascular bioengineering. His interdisciplinary research fuses deep learning, medical imaging, and computational biomechanics to design non-invasive diagnostic tools for cardiovascular disease. With a strong foundation in mechanical engineering and advanced training in biomedical systems, Rana’s work is highly translational, targeting clinical applications in early disease diagnosis and cardiac tissue remodeling. He has published extensively in peer-reviewed journals and presented his findings at international conferences, earning recognition for scientific innovation and technical rigor. His contributions span human-guided machine learning, in-silico heart modeling, and biomechanical characterizations of myocardial infarction. He has also collaborated with experts across engineering, cardiology, and computational science domains. Recognized by prestigious awards and fellowships, his trajectory reflects both academic excellence and research leadership. Rana is poised to make significant contributions to the future of cardiovascular health and medical AI.
Professional Profile
Education🎓
Rana Raza Mehdi holds a diverse and globally enriched academic background, beginning with a Bachelor of Science in Mechanical Engineering from the University of Engineering and Technology, Lahore, Pakistan, where he focused on prosthesis design and biomechanics. He then pursued a Master of Science in Mechanical Engineering at Sejong University in Seoul, South Korea, where he conducted thesis research on acoustoelasticity-based measurements and the influence of temperature on third-order elastic constants. Currently, he is a Ph.D. candidate in Biomedical Engineering at Texas A&M University, College Station, USA. His doctoral research explores the integration of deep learning and medical imaging for predicting cardiac biomechanical remodeling. His interdisciplinary thesis bridges engineering and medical science to address diagnostic challenges in cardiovascular diseases. Through each academic stage, Rana has cultivated a blend of mechanical, computational, and biomedical skills that serve as the foundation for his cutting-edge work in computational cardiology and machine learning-driven healthcare solutions.
Professional Experience📝
Rana Raza Mehdi has acquired substantial research and teaching experience across three countries. At Texas A&M University, he has been serving as a Graduate Research Assistant since January 2022 in the Computational Cardiovascular Bioengineering Laboratory under Dr. Reza Avazmohammadi, working on machine learning-enabled diagnostics in cardiac imaging. He has also contributed as a Graduate Teaching Assistant in biomaterials and soft tissue mechanics courses, fostering a solid understanding of both experimental and theoretical aspects of biomedical engineering. Prior to this, he held research positions at Sejong University, South Korea, where he focused on the acoustoelastic behavior of materials and served as a Master’s Researcher under Dr. Gang Won Jang. His global research experience spans experimental mechanics, finite element analysis, cardiac biomechanics, and deep learning, offering a broad and adaptable skill set. His collaborative projects and mentorship roles in interdisciplinary teams further highlight his growing leadership in biomedical research.
Research Interest🔎
Rana Raza Mehdi’s research interests lie at the intersection of medical imaging, computational biomechanics, and machine learning, with a central focus on cardiovascular health. He aims to develop non-invasive, data-driven diagnostic tools that predict cardiac biomechanical remodeling and identify myocardial dysfunction. His work involves the integration of in-vivo imaging, ex-vivo tissue data, and in-silico models to study pathologies such as myocardial infarction and pulmonary hypertension. He is particularly interested in applying deep learning algorithms to estimate cardiac tissue stiffness, scar localization, and hemodynamic changes, facilitating early diagnosis and personalized treatment planning. Rana also explores human-guided feature selection and hybrid models that combine physiological knowledge with AI frameworks. His broader interest extends to cardiac strain imaging, sarcomere dynamics, and the use of high-fidelity simulations to improve cardiac care. Ultimately, his research aims to bridge the gap between engineering and clinical medicine, enhancing cardiovascular diagnostics and treatment efficacy.
Award and Honor🏆
Rana Raza Mehdi has earned several prestigious awards and honors that underscore his academic excellence and research impact. He was awarded the highly competitive American Heart Association (AHA) Predoctoral Fellowship (2025–2026), supporting his work in cardiovascular biomechanics. He also received the Heep Graduate Fellowship from the Hagler Institute for Advanced Study (2024–2025), recognizing his interdisciplinary innovation and collaborative potential. His research excellence has been acknowledged through multiple abstract and presentation awards, including the Best Abstract Award at the 8th Annual Cardiovascular Bioengineering Symposium and finalist honors at the Summer Biomechanics, Bioengineering, and Biotransport Conference (SB3C). These accolades reflect his technical sophistication and ability to communicate complex biomedical findings effectively. Beyond formal awards, his invitations to speak at institutions like Brown University and his leadership in collaborative research projects further confirm his emerging prominence in computational cardiology and biomedical AI.
Research Skill🔬
Rana Raza Mehdi possesses a robust and multidisciplinary research skill set tailored to the biomedical and computational sciences. He is proficient in developing and validating deep learning models for medical imaging analysis, particularly for predicting cardiac remodeling and myocardial tissue properties. His skills include convolutional and recurrent neural networks (CNNs, RNNs), physics-informed learning, feature selection, and model interpretation. He is adept in using software like MATLAB, Python, TensorFlow, and COMSOL for modeling, simulation, and data processing. Additionally, he has hands-on experience in in-silico modeling, cardiac strain imaging, finite element analysis, and integration of multimodal data (e.g., ex-vivo, in-vivo, and simulated datasets). His expertise extends to computational fluid dynamics, acoustoelastic testing, and myocardial fiber architecture estimation. Through international collaborations and high-impact research, he has demonstrated technical excellence, analytical rigor, and innovation. Rana’s ability to blend physiological knowledge with machine learning makes him uniquely equipped to solve real-world problems in cardiovascular diagnostics.
Conclusion💡
Rana Raza Mehdi is an exceptionally strong candidate for the Best Researcher Award, especially in the PhD or early-career researcher category. His work blends deep technical skills, impactful health applications, and international research experience. With his trajectory, he stands out as a future leader in computational cardiovascular bioengineering.
Publications Top Noted✍
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Title: Determination of third-order elastic constants using change of cross-sectional resonance frequencies by acoustoelastic effect
Authors: B. Ji, R.R. Mehdi, G.W. Jang, S.H. Cho
Year: 2021
Citations: 15
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Title: Comparison of three machine learning methods to estimate myocardial stiffness
Authors: R.R. Mehdi, E.A. Mendiola, A. Sears, J. Ohayon, G. Choudhary, R. Pettigrew, et al.
Year: 2023
Citations: 14
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Title: In-silico heart model phantom to validate cardiac strain imaging
Authors: T. Mukherjee, M. Usman, R.R. Mehdi, E. Mendiola, J. Ohayon, D. Lindquist, et al.
Year: 2024
Citations: 11
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Title: On the possibility of estimating myocardial fiber architecture from cardiac strains
Authors: M. Usman, E.A. Mendiola, T. Mukherjee, R.R. Mehdi, J. Ohayon, P.G. Alluri, et al.
Year: 2023
Citations: 9
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Title: Machine learning-based classification of cardiac relaxation impairment using sarcomere length and intracellular calcium transients
Authors: R.R. Mehdi, M. Kumar, E.A. Mendiola, S. Sadayappan, R. Avazmohammadi
Year: 2023
Citations: 6
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Title: Multi-Modality Deep Infarct: Non-invasive identification of infarcted myocardium using composite in-silico-human data learning
Authors: R.R. Mehdi, N. Kadivar, T. Mukherjee, E.A. Mendiola, D.J. Shah, et al.
Year: 2024
Citations: 3
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Title: Abstract P2008: Contractile Adaptation Of The Right Ventricular Myocardium In Pulmonary Hypertension
Authors: R.R.R. Mehdi, S. Neelakantan, E. Wang, P. Zhang, G. Choudhary, et al.
Year: 2023
Citations: 3
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Title: Multi-material Cardiac Sleeves with Variable Stiffness Enhance Regional Strain Markers
Authors: V. Naeini, E.A. Mendiola, R.R. Mehdi, P. Vanderslice, V. Serpooshan, et al.
Year: 2024
Citations: 1
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Title: Right ventricular stiffening and anisotropy alterations in pulmonary hypertension: Mechanisms and relations to function
Authors: S. Neelakantan, A. Vang, R.R. Mehdi, H. Phelan, P. Nicely, T. Imran, P. Zhang, et al.
Year: 2024
Citations: 1
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Title: Effects of scar architecture on cardiac strains in myocardial infarction
Authors: V. Naeini, S.B. Peighambari, R.R. Mehdi, E.A. Mendiola, T. Mukherjee, et al.
Year: 2025
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Title: Right Ventricular Stiffening and Anisotropy Alterations in Pulmonary Hypertension: Mechanisms and Relations to Right Heart Failure
Authors: S. Neelakantan, A. Vang, R.R. Mehdi, H. Phelan, P. Nicely, T. Imran, P. Zhang, et al.
Year: 2025
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Title: Non‐Invasive Diagnosis of Chronic Myocardial Infarction via Composite In‐Silico‐Human Data Learning
Authors: R.R. Mehdi, N. Kadivar, T. Mukherjee, E.A. Mendiola, A. Bersali, D.J. Shah, et al.
Year: 2025
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Title: Role of left ventricular anisotropy in the outcome of myocardial infarction: Insights from a rodent model
Authors: S. Neelakantan, E. Mendiola, R.R. Mehdi, Q. Xiang, X. Zhang, K. Myers, et al.
Year: 2024
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Title: Abstract Tu048: Viscoelastic remodeling of the left ventricular myocardium in myocardial infarction
Authors: S. Neelakantan, R.R. Mehdi, Q. Xiang, X. Zhang, P. Vanderslice, et al.
Year: 2024
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Title: On in-silico estimation of left ventricular end-diastolic pressure from cardiac strains
Authors: E.A. Mendiola, R.R. Mehdi, D.J. Shah, R. Avazmohammadi
Year: 2024
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Title: Does EDPVR Represent Myocardial Tissue Stiffness? Toward a Better Definition
Authors: R.R. Mehdi, E.A. Mendiola, V. Naeini, G. Choudhary, R. Avazmohammadi
Year: 2024
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Title: Acoustoelasticity-Based Measurement of Third-Order Elastic Constants Considering Temperature Effect
Authors: R.R. Mehdi, B. Ji, G.W. Jang, S.H. Cho
Year: 2021
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Title: Estimating Pulmonary Arterial Pressure Differences Using Integrated Machine Learning-Computational Fluid Dynamics
Authors: S.B. Peighambari, T. Mukherjee, R.R. Mehdi, E.A. Mendiola, et al.
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Title: Early works on estimating left ventricle pressure from ventricular strains
Authors: E.A. Mendiola, R.R. Mehdi, R. Avazmohammadi