Mohammad Mahdi Ershadi | Biomedical Applications | Innovative Research Award

Published on by Computer Vision Awards

Innovative Research Award

    Mohammad Mahdi Ershadi
Affiliation Amirkabir University of Technology
Country Iran
Scopus ID 57212585059
Documents 22
Citations 196
h-index 10
Subject Area Biomedical Applications
Event Global Tech Excellence Awards
ORCID 0000-0002-7409-6469

Mohammad Mahdi Ershadi, affiliated with Amirkabir University of Technology, has established an emerging research profile in biomedical applications through interdisciplinary studies involving medical imaging, machine learning, and healthcare data analytics. His publications demonstrate contributions toward artificial intelligence methods for clinical decision support and image analysis while maintaining active scholarly engagement within internationally indexed research platforms.[1]

Abstract

Mohammad Mahdi Ershadi has developed a focused research portfolio in biomedical applications by integrating artificial intelligence, medical image analysis, and healthcare data interpretation. His publications investigate advanced segmentation methods, ensemble learning strategies, and data quality assessment for clinical decision support. Indexed scholarly outputs, measurable citation performance, and interdisciplinary collaboration collectively demonstrate a growing academic influence. These contributions support innovation in healthcare technologies while reflecting scientific rigor, reproducibility, and practical relevance. The documented research achievements indicate meaningful progress toward improving computational biomedical systems and advancing evidence-based medical analytics through modern intelligent computing methodologies.[1][2][3]

Keywords

Biomedical Applications, Artificial Intelligence, Medical Imaging, Chest X-ray Analysis, Machine Learning, Deep Learning, Ensemble Learning, Image Segmentation, Healthcare Analytics, Clinical Decision Support.

Introduction

The research activities of Mohammad Mahdi Ershadi emphasize computational intelligence for biomedical applications, particularly medical imaging and healthcare analytics. His interdisciplinary investigations combine artificial intelligence with clinical datasets to improve diagnostic reliability, segmentation accuracy, and decision-support methodologies, contributing practical scientific value within contemporary biomedical engineering research.[1]

Research Profile

According to indexed academic records, the researcher has authored twenty-two scholarly documents, received one hundred ninety-six citations, and achieved an h-index of ten. These indicators demonstrate sustained publication activity and increasing scholarly recognition within biomedical applications, machine learning, and computational healthcare research communities internationally.[1]

Research Contributions

Major research contributions include intelligent chest X-ray segmentation, ensemble learning for respiratory disease diagnosis, and investigations into data quality metadata supporting evidence-based decision making. These studies integrate advanced machine learning algorithms with healthcare applications, encouraging accurate medical interpretation and computational innovation across biomedical environments.[1][2]

Publications

The publication portfolio reflects consistent contributions to internationally recognized journals and scholarly platforms focusing on artificial intelligence, medical image processing, and biomedical engineering. Research outputs demonstrate methodological development, experimental validation, and practical healthcare relevance, supporting continuous academic advancement through peer-reviewed scientific dissemination.[1][3]

Research Impact

Citation metrics, interdisciplinary collaborations, and practical biomedical applications collectively indicate growing research impact. The published studies support advancements in healthcare technologies through robust computational methods, while influencing ongoing investigations involving medical image interpretation, clinical analytics, and intelligent diagnostic systems across international scientific communities.[1][2]

Award Suitability

Considering documented publication performance, measurable citation indicators, interdisciplinary biomedical research, and internationally indexed scholarly contributions, Mohammad Mahdi Ershadi demonstrates qualifications consistent with recognition under the Innovative Research Award. His scientific achievements illustrate meaningful advancement of intelligent healthcare technologies through rigorous academic investigation and innovation.[1][3]

Conclusion

Mohammad Mahdi Ershadi has established an emerging academic profile characterized by interdisciplinary biomedical research, measurable scholarly impact, and contributions to artificial intelligence for healthcare. Continued publication activity and collaborative scientific engagement are expected to strengthen future influence while supporting innovations addressing contemporary medical and computational challenges.[1][2][3]

References

  1. Ershadi, M. M., et al. (2026). Entropy-guided semi-supervised framework for robust chest X-ray segmentation using dynamic competition and patch-wise contrastive learning. Biomedical Signal Processing and Control.
    https://www.sciencedirect.com/science/article/abs/pii/S1746809426004878?via%3Dihub
  2. Ershadi, M. M., et al. (2025). Decoding DQM for Experimental Insights on Data Quality Metadata’s Impact on Decision-Making Process Efficacy.
    https://www.scopus.com/pages/publications/105023471399
  3. Ershadi, M. M., et al. (2025). Application of Ensemble Learning for Respiratory Ailment Diagnosis: Case Studies on Biomedical and Chest X-ray Image Datasets. Qeios.
    https://www.qeios.com/read/1NMNYE.3
  4. Elsevier. (n.d.). Scopus Author Details: Mohammad Mahdi Ershadi, Author ID 57212585059. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57212585059

Beyazit Bestami Yuksel | Biomedical and Healthcare Applications | Best Researcher Award

Published on by Computer Vision Awards

Best Researcher Award

Beyazit Bestami YUKSEL
Istanbul Technical University
Beyazit Bestami YUKSEL
Affiliation Istanbul Technical University
Country Turkey
Scopus ID 57221928158
Documents 4
Citations 5
h-index 2
Subject Area Biomedical and Healthcare Applications
Event Global Tech Excellence Awards
ORCID 0000-0001-5060-6236

This academic recognition article presents a structured overview of the scholarly profile of Beyazit Bestami YUKSEL and considers publication activity, citation indicators, disciplinary specialization, and measurable research visibility within biomedical and healthcare application studies. Evaluation under the Best Researcher Award framework emphasizes documented scholarly dissemination, research continuity, and engagement with contemporary scientific inquiry and interdisciplinary innovation.[1]

Abstract

The Best Researcher Award evaluation framework recognizes measurable academic contribution through transparent indicators that include publication output, citation performance, research continuity, and thematic relevance within a defined academic field. Beyazit Bestami YUKSEL’s scholarly profile reflects documented participation in biomedical and healthcare applications supported by indexed publications and observable citation activity. Evaluation emphasizes evidence of research dissemination, engagement with scientific inquiry, and contribution to emerging interdisciplinary developments. Academic recognition within this framework considers both quantitative research indicators and broader scholarly visibility, providing a structured perspective on academic performance and demonstrated involvement in contemporary biomedical and healthcare-oriented research activities..[1]

Keywords

  • Biomedical Research
  • Healthcare Applications
  • Academic Recognition
  • Citation Analysis
  • Research Evaluation

Introduction

Recognition frameworks in contemporary research environments commonly integrate quantitative and qualitative indicators to assess scholarly development. Metrics such as indexed publications, citation accumulation, and disciplinary focus contribute to broader assessments of academic visibility and sustained research engagement.[1]

Research Profile

The available scholarly indicators associated with Beyazit Bestami YUKSEL demonstrate participation in research activity connected to biomedical and healthcare applications. Indexed publication records and citation measures provide a structured basis for evaluating research continuity and academic dissemination.[1]

Research Contributions

Research contributions are considered through documented scholarly outputs and their relevance to evolving healthcare technologies and biomedical implementation contexts. Such contributions support knowledge exchange and provide evidence of active participation within the scientific ecosystem.[2]

Publications

Indexed publication activity serves as a measurable indicator of research dissemination. Publication records contribute to scholarly visibility and facilitate evaluation across citation databases and institutional assessment frameworks.[1]

Research Impact

Citation accumulation and h-index values provide contextual indicators of scholarly engagement and knowledge circulation. These measures represent one component of broader academic evaluation and are interpreted together with publication quality and disciplinary relevance.[1]

Award Suitability

Within the Best Researcher Award framework of the Global Tech Excellence Awards, suitability assessment considers publication evidence, scholarly engagement, and measurable research indicators. Recognition reflects documented academic participation rather than promotional endorsement.[3]

Conclusion

The scholarly profile summarized in this article provides an academic overview of Beyazit Bestami YUKSEL based on available research indicators and recognized evaluation dimensions. The profile illustrates documented engagement in biomedical and healthcare applications and supports structured consideration within academic recognition contexts.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Beyazit Bestami YUKSEL, Author ID 57221928158. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57221928158
  2. Global Tech Excellence Awards.

    Global Tech Excellence Awards


  3. Credit Card Fraud Detection with NCA Dimensionality Reduction.
    https://www.researchgate.net/publication/348973412_Credit_Card_Fraud_Detection_with_NCA_Dimensionality_Reduction

  4. Advancing Biomedical Signal Security: Real-Time ECG Monitoring with Chaotic Encryption.
    https://www.researchgate.net/publication/385529440_Advancing_Biomedical_Signal_Security_Real-Time_ECG_Monitoring_with_Chaotic_Encryption

Arsene Jaures Ouemba Tasse | Biomedical and Healthcare Applications | Best Researcher Award

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Dr . Arsene Jaures Ouemba Tasse | Biomedical and Healthcare Applications | Best Researcher Award

Postdoc at University of the Witwatersrand, Johannesburg, South Africa

Dr. Arsène Jaures Ouemba Tasse is a dynamic Postdoctoral Research Fellow at the University of the Witwatersrand, South Africa, with a specialized focus in applied dynamical systems and mathematical modeling of infectious diseases. He holds a Ph.D. in Mathematics from the University of Dschang, Cameroon, and has published extensively in high-impact journals on topics including Ebola, COVID-19, Typhoid, and Monkeypox. His research contributions have global significance, particularly in understanding disease transmission dynamics and control strategies. Dr. Ouemba Tasse has participated in numerous international conferences, received prestigious research grants, and supervised both undergraduate and postgraduate students. He is also an active reviewer for several scientific journals and has contributed to collaborative projects funded by renowned institutions like the Bill & Melinda Gates Foundation. His growing leadership in academic events, combined with his commitment to public health through mathematics, positions him as a highly influential figure in the field of mathematical epidemiology.

Professional Profile 

Education🎓 

Dr. Arsène Jaures Ouemba Tasse has a strong academic foundation in mathematics, beginning with a Bachelor’s degree from the University of Yaoundé I in 2009. He pursued his Honours and Master’s degrees in Mathematics at the University of Dschang, Cameroon, where he graduated with distinction. His academic journey culminated in earning a Ph.D. in Applied Dynamical Systems and Mathematical Modeling from the same institution in 2021. His doctoral studies equipped him with advanced knowledge in differential equations, epidemiological modeling, partial differential equations, and numerical analysis. Additionally, he obtained a Secondary and High School Teacher’s Diploma in Mathematics from the Higher Teacher’s Training College of Yaoundé in 2008, highlighting his early commitment to education. To enhance his international research engagement, he completed an English language course at the University of the Witwatersrand in 2023. His academic pathway reflects both depth and breadth in mathematical sciences, with a strong emphasis on applied research for real-world impact.

Professional Experience📝

Dr. Ouemba Tasse brings over 15 years of professional experience in teaching, research, and academic mentorship. Since November 2022, he has served as a Postdoctoral Research Fellow at the University of the Witwatersrand, Johannesburg, where he engages in cutting-edge research, student supervision, and academic event organization. Before this, he spent over a decade teaching mathematics in various high schools in Cameroon, including the General High School Tsela and the General Bilingual High School Bameka. His university-level experience includes lecturing and tutoring in courses such as algebra, calculus, mathematical modeling, and discrete population dynamics. He has participated in academic panels, supervised postgraduate research groups, and served as an external examiner. His professional journey reflects a seamless transition from secondary education to advanced research, demonstrating versatility, leadership, and commitment to educational excellence in both national and international academic environments.

Research Interest🔎

Dr. Ouemba Tasse’s research interests are centered around mathematical epidemiology, applied dynamical systems, and optimal control theory, particularly in the context of infectious disease modeling. He focuses on developing and analyzing mathematical models that simulate the transmission dynamics of diseases such as Ebola, COVID-19, Typhoid, Malaria, Monkeypox, and HIV. His models incorporate various control strategies including awareness programs, vaccination, isolation, and traditional versus modern treatment methods. He also works on the mathematical formulation and numerical solutions using nonstandard finite difference schemes that ensure stability and accuracy in epidemic simulations. His recent projects explore the co-dynamics of multiple infections and the role of environmental and behavioral factors in disease propagation. Additionally, he is interested in the intersection of public health and mathematics, including modeling cancer progression and mother-to-child HIV transmission. His interdisciplinary approach bridges mathematical theory and health policy, offering vital insights for effective disease control and healthcare intervention strategies.

Award and Honor🏆

Dr. Ouemba Tasse has received numerous accolades and support from prestigious institutions for his impactful research in mathematical modeling. He was awarded a Postdoctoral Fellowship by the University of the Witwatersrand, along with funding from the Bill & Melinda Gates Foundation for a cervical cancer modeling project. He received full sponsorships from the Simons Foundation and the Pacific Institute of Mathematical Sciences to attend international conferences and research schools in Canada and the USA. He won first prize in Analysis at a postgraduate workshop in Cameroon and was a recipient of the Humboldt Foundation’s funding during the Dschang Humboldt Kolleg. His academic excellence has been further recognized through grant awards from the Society for Mathematical Biology and participation in global conferences like BIOMATH and CIMPA. These honors not only acknowledge his research excellence but also reflect his growing reputation as a leading contributor to mathematical modeling in epidemiology and public health.

Research Skill🔬

Dr. Ouemba Tasse possesses advanced research skills in both theoretical and computational aspects of applied mathematics. He is proficient in developing deterministic and stochastic models of infectious diseases, applying optimal control techniques, and performing stability analysis of equilibrium states. His expertise in nonstandard finite difference schemes enhances the accuracy and robustness of numerical simulations. He is well-versed in software tools such as MATLAB, MATHEMATICA, R, and LaTeX, which he uses extensively for simulations, data analysis, and scientific writing. His research includes data fitting and parameter estimation, and he has applied these techniques in real-world epidemiological studies. He also has strong collaborative skills, having led and co-supervised numerous multidisciplinary projects and study groups. Additionally, he contributes as a peer reviewer for reputed journals and book chapters, showcasing his analytical precision and subject-matter authority. These combined skills make him an adept and resourceful researcher capable of addressing complex public health challenges through mathematics.

Conclusion💡

Dr. Arsène Jaures Ouemba Tasse is a highly promising and competitive candidate for the Best Researcher Award, especially in fields involving epidemiological modeling, applied mathematics, and computational public health. His international exposure, robust publication record, academic mentoring, and societal relevance of his research make him exceptionally well-qualified for this honor.

While still in an early career stage, his trajectory shows exemplary leadership potential and deep scholarly contributions. With minor improvements such as increasing PI roles and broader interdisciplinary outreach, he would not only be eligible but also a standout award recipient.

Publications Top Noted✍

  1. Title: Mathematical modeling of contact tracing as a control strategy of Ebola virus disease
    Authors: T. Berge, A.J. Ouemba Tassé, H.M. Tenkam, J. Lubuma
    Year: 2018
    Citations: 34

  2. Title: Dynamics of host-reservoir transmission of Ebola with spillover potential to humans
    Authors: B. Tsanou, J.M.S. Lubuma, A.J.O. Tassé, H.M. Tenkam
    Year: 2018
    Citations: 22

  3. Title: Ebola virus disease dynamics with some preventive measures: a case study of the 2018–2020 Kivu outbreak
    Authors: A.J. Ouemba Tasse, B. Tsanou, J. Lubuma, J.L. Woukeng, F. Signing
    Year: 2022
    Citations: 6

  4. Title: Nonstandard finite difference schemes for some epidemic optimal control problems
    Authors: A.J.O. Tassé, V.B. Kubalasa, B. Tsanou
    Year: 2025
    Citations: 2

  5. Title: A metapopulation model with exit screening measure for the 2014–2016 West Africa Ebola virus outbreak
    Authors: A.J.O. Tassé, B. Tsanou, J.L. Woukeng, J.M.S. Lubuma
    Year: 2024
    Citations: 1

  6. Title: A mathematical model to study herbal and modern treatments against COVID-19
    Authors: A.J. Ouemba Tassé, B. Tsanou, C. Kwa Kum, J. Lubuma
    Year: 2024
    Citations: 1

  7. Title: Assessment of effective isolation, safe burial and vaccination optimal controls for an Ebola epidemic model
    Authors: A.J.O. Tassé, B. Tsanou, J.M.S. Lubuma, J.L. Woukeng
    Year: 2020
    Citations: 1

  8. Title: Mathematical modelling of the dynamics of typhoid fever and two modes of treatment in a Health District in Cameroon
    Authors: T.J. Tsafack, C.K. Kum, A.J.O. Tassé, B. Tsanou
    Year: 2025

  9. Title: A mathematical model on the impact of awareness and traditional medicine in the control of Ebola: case study of the 2014–2016 outbreaks in Sierra Leone and Liberia
    Authors: A.J. Ouemba Tassé, B. Tsanou, C.K. Kum, J. Lubuma
    Year: 2024

  10. Title: Influence of the co-dynamics Ebola-COVID-19 in the population
    Authors: A.J.O. Tassé, J. Lubuma, B. Tsanou
    Year: 2023

  11. Title: Investigating the impact of isolation, self-isolation and environmental transmission on the spread of COVID-19: case study in Rwanda
    Authors: J.M.S. Lubuma, A.J.O. Tassé, F. Signing, B. Tsanou
    Year: 2023

  12. Title: Modélisation mathématique de la transmission de la maladie à virus Ebola et stratégies de contrôle
    Authors: A.J.O. Tasse
    Year: 2021

  13. Title: Mathematical modeling of contact tracing as a control strategy of Ebola virus disease (Duplicate entry)
    Authors: B. Tsanou, A.J. Ouemba Tassé, H.M. Tenkam, J.M.S. Lubuma
    Year: 2018

  14. Title: Investigating the impact of isolation, self-isolation and environmental transmission on the spread of COVID-19: case study of Rwanda (Duplicate entry)
    Authors: M.S.L. Jean, A.J.O. Tassé, F. Signing, B. Tsanou
    Year: 2023

Rana Raza Mehdi | Machine Learning in Healthcare | Best Researcher Award

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Mr . Rana Raza Mehdi | Machine Learning in Healthcare | Best Researcher Award

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✍

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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.

  • Title: Early works on estimating left ventricle pressure from ventricular strains
    Authors: E.A. Mendiola, R.R. Mehdi, R. Avazmohammadi