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

Prof. Megha Bhushan | Healthcare | Best Researcher Award

Posted on 25/07/202426/07/2024 by Computer Vision Awards

Prof. Megha Bhushan, Healthcare, Best Researcher Award

Megha Bhushan at University of Seville, Spain

Profiles

Scopus

Orcid

Google Scholar

Education:

Dr. Megha earned her Ph.D. in Computer Science and Engineering from Thapar University, Patiala, Punjab, in 2018, with a thesis titled “A Generic Framework for Improving Software Product Line using an Ontological Rule-Based Approach.” She holds a Master’s in Engineering from the same institution, where she focused on improving Software Product Line Engineering using AOP, LEL, and UML. She completed her Bachelor’s of Technology in Information Technology from the University Institute of Information Technology, Himachal Pradesh University, Shimla, HP.

Professional Experience:

Dr. Megha currently serves as an Assistant Professor in the Department of Computer Languages and Systems at the University of Seville, Spain, since April 2024. She also holds the position of Honorary Adjunct Faculty at the School of Computing, Maryam Abacha American University of Nigeria, Kano since July 2024. Her previous roles include Assistant Dean, Research & Consultancy at DIT University, Dehradun, Uttarakhand, India, and various faculty positions at esteemed institutions like Koneru Lakshmaiah Education Foundation and Chitkara University. Dr. Megha has over 9.11 years of teaching and research experience, contributing significantly to the academic community through her various roles and responsibilities.

Research Interests:

Dr. Megha’s research spans various domains including Artificial Intelligence, Machine Learning, Knowledge Representation, Knowledge-based Systems, Rule-based Systems, Software Product Line, Software Quality, Ontologies, and Healthcare. She has been an active research team member in significant projects such as “Data-intensive software product lines” funded by the Ministry of Science and Innovation and “Deficit irrigation programming system for crops Operational Group” funded by the Andalusian Government, Spain.

Grants and Fellowships:

Dr. Megha has been awarded several prestigious grants and fellowships, including the Rajiv Gandhi National Fellowship by the University Grants Commission, New Delhi, for her work on improving Software Product Line using an Ontological Rule-Based Approach. She also received the Student Presenter Scholarship at the Grace Hopper Celebration India (GHCI) 2017.

Achievements:

Dr. Megha has received numerous accolades for her contributions to research and education. She has been an active member of various committees and organizations, serving in roles such as Program Committee Member, Publicity and Sponsorship Committee Member, and Technical Program Committee Member for multiple international conferences. She has also been recognized with awards like “Global Leader in Excellence in Education and Outreach” and “Excellence in Research at the University level” at DIT University.

Memberships and Associations:

Dr. Megha is a member of several professional organizations, including The Society of Digital Information and Wireless Communications (SDIWC) and the Education Research and Development Association (ERDA). She is also a fellow member of Eudoxia Research University and The Research World, International Society of Scientists and Engineers. Additionally, she serves on the advisory panel of The International Forum for Educators and Researchers (IFER).

📖 Publications:

Impact of machine learning and deep learning techniques in autism

Authors: Bhushan, M., Singal, M., Negi, A.
Journal: Future of AI in Medical Imaging
Year: 2024

A comparative study of machine learning and deep learning algorithms for predicting student’s academic performance

Authors: Bhushan, M., Vyas, S., Mall, S., Negi, A.
Journal: International Journal of System Assurance Engineering and Management
Year: 2023

Autonomous navigation of rovers using ML and DL techniques

Authors: Bhushan, M., Singal, M., Layek, S., Negi, A.
Journal: AI-Enabled Social Robotics in Human Care Services
Year: 2023

Classifying breast cancer using transfer learning models based on histopathological images

Authors: Rana, M., Bhushan, M.
Journal: Neural Computing and Applications
Year: 2023

Machine learning and deep learning approach for medical image analysis: diagnosis to detection

Authors: Rana, M., Bhushan, M.
Journal: Multimedia Tools and Applications
Year: 2023

Dr. Weijian Ye | Biomedical | Best Researcher Award

Posted on 08/06/202408/06/2024 by Computer Vision Awards

Dr. Weijian Ye, Biomedical, Best Researcher Award

Doctorate at The First Affiliated Hospital of Jinan University, China

Professional Profile

Summary:

Dr. Weijian YE is a distinguished researcher in the field of Chemical Genomics, with a Ph.D. from Peking University and a Bachelor’s degree from Guangzhou University of Chinese Medicine. With a strong background in Pharmaceutical Engineering, Dr. YE has made significant contributions to biomedical research, particularly in the development of PET tracers and radio pharmacy.

👩‍🎓Education:

Ph.D. in Chemical Genomics (Advisor: Prof. LEE, Chi-Sing)
Peking University, Beijing, China
Sep. 2014 – Jun. 2019
Bachelor’s Degree in Pharmaceutical Engineering (Advisor: Prof. WANG, Tao)
Guangzhou University of Chinese Medicine, Guangzhou, China
Sep. 2010 – Jun. 2014

Work Experience:

Senior Research Fellow
Center of Cyclotron and PET Radiopharmaceuticals, Department of Nuclear Medicine, The First Affiliated Hospital of Jinan University, Guangzhou
Sep. 2021 – Present

Leading research in radio synthesis, PET tracer development, translational medicine, and clinical research.
Developing synthetic routes for complex molecules and managing radiopharmaceutical projects from development to cGMP production.
Collaborating with departments for clinical research on novel PET tracers for neurological disorders and other diseases.

Post-doctoral Research Fellow (Supervisor: Prof. XU, Hao)
Department of Nuclear Medicine, The First Affiliated Hospital of Jinan University, Guangzhou
Jul. 2019 – Jul. 2021

Trained in radiochemistry, focusing on synthesizing and managing radioisotopes for pharmaceutical quality and safety.
Applied research in clinical translational medicine, bridging lab work to clinical applications.

Research Experience

A.Development of PET Tracers/Radio Pharmacy

Norepinephrine Transporter Targeted Tracer 18F-mFBG
- Optimized labeling conditions, achieving a 17.8% radiochemical yield and >97% radiochemical purity within 70 mins.
- Established SOP for clinical production, producing over 30 batches for more than 200 patients.
Multi-functional Imaging Probe for Cu(II)
- Designed and synthesized Cu(II)-specific probes for fluorescence and 19F MR imaging.
- Applied the 18F-isotopologue to PET imaging in cynomolgus macaques.
Vasopressin 1a Receptor Targeted Tracer
- Developed an F-18 labeled PET tracer with high affinity for V1a receptor.
- Conducted PET imaging and kinetic modeling studies in rhesus monkeys.
TSPO Polymorphism-insensitive PET Tracers
- Synthesized ligands for TSPO, demonstrating low sensitivity to TSPO rs6971 polymorphism.
- Conducted PET imaging studies in rhesus monkeys and MCAO animal models.
11C-labeled Tracers for mGlu2 and FAAH
- Contributed to the development and analysis of 11C-labeled PET tracers for mGlu2 and FAAH.
New Synthons and Radiolabeling Strategies
- Synthesized [18F]difluorocarbene and [18F]fluoro-[di-deutero]methyl tosylate, implementing automated radiolabeling programs.

B. Development of Fluorescent Probes

Eu(III) Complex as Fluorescent Probe for Paralytic Shellfish Toxins
- Designed and characterized europium complexes, proposing binding mechanisms using ion mobility mass spectrometry.
Fluorescent Chemosensors for Polyamines
- Synthesized TPE-conjugated pentiptycene derivatives, applied for spermine detection in artificial urine.
Fluorescent Probes and 1H/19F MRI Contrast Agents for EBNA1 and EBV
- Designed and synthesized Eu(III) and Gd(III) complexes for EBNA1, evaluating magnetic resonance properties.

C. Organic Syntheses

Total Synthesis of Natural Product (±)-basiliolide B and Its Analogues
- Performed a 15-step synthesis sequence, including large-scale reactions.
Regioselective Pd-catalyzed (3+2) Cycloaddition of 2-pyrones with Vinylcyclopropanes
- Achieved high yields and regioselectivity, broadening substrate scope for synthesis.

Publications Top Noted:

Paper Title: A PET-based radiomics nomogram for individualized predictions of seizure outcomes after temporal lobe epilepsy surgery

Authors: Huanhua Wu, Kai Liao, Zhiqiang Tan, Chunyuan Zeng, Biao Wu, Ziqing Zhou, Hailing Zhou, Yongjin Tang, Jian Gong, Weijian Ye, Xueying Ling, Qiang Guo, Hao Xu
Journal: Seizure: European Journal of Epilepsy
Volume: 119
Pages: 17-27
Year: 2024

Paper Title: Recent developments on PET radiotracers for TSPO and their applications in neuroimaging

Authors: Lingling Zhang, Kuan Hu, Tuo Shao, Lu Hou, Shaojuan Zhang, Weijian Ye, Lee Josephson, Jeffrey H Meyer, Ming-Rong Zhang, Neil Vasdev, Jinghao Wang, Hao Xu, Lu Wang, Steven H Liang
Journal: Acta Pharmaceutica Sinica B
Volume: 11
Pages: 373-393
Year: 2021
Citations: 114

Paper Title: Positron emission tomography in the neuroimaging of autism spectrum disorder: a review

Authors: Zhiqiang Tan, Huiyi Wei, Xiubao Song, Wangxiang Mai, Jiajian Yan, Weijian Ye, Xueying Ling, Lu Hou, Shaojuan Zhang, Sen Yan, Hao Xu, Lu Wang
Volume: 16
Pages: 806876
Year: 2022
Citations: 14

Paper Title: Sensitive and responsive pentiptycene-based molecular fluorescence chemosensor for detection of polyamines

Authors: Junrong Huang, Weijian Ye, Shuai Zha, Yezi Tao, Min Yang, Ke Huang, Jiqiang Liu, Yan-Ho Fung, Yang Li, Penghao Li, Lizhi Zhu, Chi-Sing Lee
Journal: Journal of Luminescence
Volume: 232
Pages: 117856
Year: 2021
Citations: 12

Paper Title: Recent developments on the application of molecular probes in multiple myeloma: Beyond [18F] FDG

Authors: Shaojuan Zhang, Jingjie Shang, Weijian Ye, Tianming Zhao, Hao Xu, Hui Zeng, Lu Wang
Volume: 10
Pages: 920882
Year: 2022
Citations: 1

Yongkang-Lyu-Biomedical-Best Researcher Award

Posted on 21/05/202421/05/2024 by Computer Vision Awards

Dr. Yongkang-Lyu-Biomedical-Best Researcher Award

Shandong Normal University-China

Author Profile

Scopus
Orcid

Early Academic Pursuits

Dr. Yongkang Lyu's academic journey began in earnest with his enrollment at Shandong Normal University in 2017, where he pursued a Bachelor of Science in Physics. During this period, Lyu demonstrated a profound commitment to both academic excellence and community service. He served as the editor of the college public account from 2017 to 2021, a role that involved managing content, coordinating contributions, and enhancing the visibility of the college's activities and achievements. His dedication extended beyond the confines of the university as he volunteered at the library from 2017 to 2019 and participated in volunteer education programs in western China, underscoring his commitment to education and community development.

Professional Endeavors

Dr. Yongkang Lyu's professional journey is marked by a blend of teaching and hands-on technical roles. In 2020, he gained practical teaching experience at Linqing Foreign Language Middle School in Shandong Province, where he applied his physics knowledge in an educational setting. From 2021 to 2023, he served as a graduate entrance examination professional course teacher, specializing in quantum mechanics, electromagnetism, optics, electrodynamics, thermodynamics, and statistical mechanics. His current role as a Lab Manager at Shandong Normal University, which he has held since 2021, involves overseeing lab operations, configuring high-performance computing servers, and maintaining Linux-based computational chemistry environments. Lyu's technical proficiency extends to the setup and maintenance of advanced computational tools and software, highlighting his versatile skill set.

Contributions and Research Focus

Dr. Yongkang Lyu's research is characterized by the integration of quantum chemistry, first-principles calculations, and molecular dynamics to conduct multi-scale simulations and analyses. His work spans a variety of fields, including biology, environmental science, materials science, chemical engineering, and energy. Notably, his research projects have focused on:

Martini Coarse-Grained Force Field Simulations: Lyu has conducted simulations on multi-component phospholipid membranes to investigate the transmembrane mechanisms of peptides and the dynamic properties of membrane structures under external electric field driving. This research provides mechanistic analysis from both physical and morphological perspectives, offering theoretical guidance for drug design and pathology research.
All-Atom Force Field Simulations: He has studied the dynamic processes of natural and artificial nucleic acid molecules adsorbed on graphene biosensors. By combining quantum chemistry calculations and wave function analysis, Lyu has explored changes in electronic structure and interaction nature due to adsorption, contributing valuable insights to the field of biosensing.
Artificial Bee Colony Algorithm: Lyu has employed this algorithm for hierarchical precise conformational searches, studying the electronic structure characteristics of the lowest free energy conformations of dinitrophenol clusters in solvents. His work reveals interaction features of different solvent coordination numbers through wave function analysis and analyzes changes in infrared vibrational spectra.

Accolades and Recognition

Throughout his academic and professional career, Dr. Yongkang Lyu has been recognized for his contributions to both education and research. His role as an editor of the college public account and his volunteer work have earned him commendations for his leadership and commitment to community service. In the realm of research, his innovative approaches and significant findings in computational chemistry have garnered respect and acknowledgment from his peers and mentors alike.

Impact and Influence

Dr. Yongkang Lyu's work has had a substantial impact on multiple scientific fields. His research in computational chemistry and molecular dynamics provides critical insights that bridge physical methods and chemical theories, influencing areas such as drug design, pathology, and biosensing. His educational endeavors, particularly his teaching roles, have inspired and equipped students with a deep understanding of complex scientific concepts.

The Biomedical Award is a prestigious honor conferred upon individuals who have made significant contributions to the field of biomedical science. This accolade recognizes groundbreaking research, innovative technologies, and exceptional advancements that have a profound impact on healthcare and medical practices. Established to highlight excellence in biomedical research, the award serves as a testament to the dedication and ingenuity of scientists and researchers worldwide.

Legacy and Future Contributions

As Dr. Yongkang Lyu continues his Doctor of Science in Atomic and Molecular Physics at Shandong Normal University, his ongoing research and professional activities are poised to leave a lasting legacy. His current and future contributions are expected to advance the understanding of molecular interactions and dynamics, with potential applications in developing new technologies and therapeutic strategies. Lyu's commitment to combining theoretical and practical approaches ensures that his work will continue to influence and inspire future generations of scientists and educators.

In summary, Dr. Yongkang Lyu's academic pursuits, professional endeavors, and research focus reflect a remarkable blend of technical proficiency, innovative research, and community engagement. His contributions to science and education are noteworthy, and his future work promises to further advance the fields of computational chemistry and molecular physics.