Mohammad Ali Nematollahi | Graph Theory | Best Academic Researcher Award

Assist. Prof. Dr. Mohammad Ali Nematollahi | Graph Theory | Best Academic Researcher Award

Assistant Professor atFasa University, Iran

Dr. Mohammad Ali Nematollahi 📚 is a distinguished Assistant Professor at Fasa University, Iran, known for his significant contributions to mathematics and computer science. He holds a Ph.D. from Sharif University of Technology and has published extensively in top-tier journals, covering topics like spectral graph theory, Seidel energy, and machine learning applications in medicine 🤖🩺. With a strong teaching portfolio, Dr. Nematollahi has inspired many students while maintaining a consistent record of academic excellence 🏆. His numerous awards, including a Silver Medal at the International Mathematics Olympiad for University Students, highlight his dedication and talent. Though his international collaborations could expand further, his interdisciplinary research and exceptional teaching make him a standout candidate for recognition as a Best Academic Researcher 🌐.

Professional Profile 

Education 🎓

Dr. Mohammad Ali Nematollahi began his academic journey at Andisheh Magnet High School in Shiraz, where he excelled with top GPAs in both high school and pre-university studies. He then earned his B.Sc., M.Sc., and Ph.D. in Mathematics from Sharif University of Technology, Tehran—one of Iran’s most prestigious institutions. His graduate research delved into Pfister’s Local-Global Principle and the Spectral Theory of Signed Graphs and Digraphs, under renowned supervisors. This strong educational foundation equipped him with a robust understanding of both pure and applied mathematics, fostering interdisciplinary research and teaching excellence. His academic record, marked by consistently high grades and prestigious scholarships, reflects his dedication and passion for learning, setting the stage for a dynamic and influential academic career. 🌟

Professional Experience 💼

Dr. Mohammad Ali Nematollahi serves as an Assistant Professor at Fasa University’s Department of Computer Sciences, where he bridges mathematics and computer science through teaching and research. His academic roles include teaching courses like Graph Theory, Advanced Programming (Python), Foundations of Combinatorics, and Machine Learning Applications 📊. Earlier, he honed his teaching skills as a Teaching Assistant at Sharif University of Technology, covering Calculus, Linear Algebra, and Differential Equations. Dr. Nematollahi’s experience also extends to supervising undergraduate and graduate students, mentoring them in complex research areas. His diverse experience showcases his versatility, making him a dynamic educator and researcher with expertise that resonates across disciplines. 💼

Research Interest 🔬

Dr. Mohammad Ali Nematollahi’s research interests span a fascinating intersection of graph theory, spectral analysis, combinatorics, and applied machine learning. His work on Seidel energy of graphs, spectral characterizations of signed cycles, and circular zero-sum flows has advanced theoretical understanding of graphs 📈. Additionally, his research extends to machine learning applications, notably in medical diagnostics, disease prediction, and sentiment analysis of social media data. This interdisciplinary approach highlights his ability to tackle both theoretical and practical challenges, positioning him as a forward-thinking researcher whose work benefits academia and society alike. His passion for bridging abstract mathematics with real-world problems drives his continuous exploration of innovative solutions. 🌐

Awards and Honors 🏅

Dr. Mohammad Ali Nematollahi’s academic journey is marked by numerous awards and honors, underscoring his dedication and talent. Early in his career, he ranked in the top 0.1% in the Mathematics Nationwide Entrance Exam and secured admission to Sharif University of Technology. His remarkable achievements continued with a silver medal at the International Mathematics Olympiad for University Students, ranking fifth among top talents. He also won exceptional talent scholarships at both M.Sc. and Ph.D. levels. These accolades reflect his academic excellence and unwavering commitment to advancing mathematical knowledge. Each honor affirms his standing as a distinguished scholar whose contributions resonate globally. 🥇

Research Skills 💻

Dr. Mohammad Ali Nematollahi boasts an impressive skill set, seamlessly blending theoretical mathematics and computational techniques. He is proficient in Python, Mathematica, and SageMath, enabling him to model complex systems and analyze large datasets. His expertise extends to machine learning algorithms, supporting research in medical diagnostics and disease prediction. He is also adept at using LaTeX for professional documentation and Microsoft Office for academic administration 📑. His technical skills empower him to tackle multifaceted research challenges, from graph theory to real-world applications. This diverse toolbox allows him to develop innovative solutions, bridging theory and practice with precision and creativity. 🤖

Publications Top Notes

  • Effective class-imbalance learning based on SMOTE and convolutional neural networks
    Authors: JH Joloudari, A Marefat, MA Nematollahi, SS Oyelere, S Hussain
    Year: 2023
    Citations: 132
    Source: Applied Sciences 13(6), 4006

  • BERT-deep CNN: State of the art for sentiment analysis of COVID-19 tweets
    Authors: JH Joloudari, S Hussain, MA Nematollahi, R Bagheri, F Fazl, …
    Year: 2023
    Citations: 65
    Source: Social Network Analysis and Mining 13(1), 99

  • Prognosis prediction in traumatic brain injury patients using machine learning algorithms
    Authors: H Khalili, M Rismani, MA Nematollahi, MS Masoudi, A Asadollahi, …
    Year: 2023
    Citations: 44
    Source: Scientific Reports 13(1), 960

  • GSVMA: a genetic support vector machine ANOVA method for CAD diagnosis
    Authors: J Hassannataj Joloudari, F Azizi, MA Nematollahi, R Alizadehsani, …
    Year: 2022
    Citations: 26
    Source: Frontiers in Cardiovascular Medicine 8, 760178

  • Proof of a conjecture on the Seidel energy of graphs
    Authors: S Akbari, M Einollahzadeh, MM Karkhaneei, MA Nematollahi
    Year: 2020
    Citations: 25
    Source: European Journal of Combinatorics 86, 103078

  • Spectral characterizations of signed cycles
    Authors: S Akbari, F Belardo, E Dodongeh, MA Nematollahi
    Year: 2018
    Citations: 22
    Source: Linear Algebra and Its Applications 553, 307-327

  • DNN-GFE: a deep neural network model combined with global feature extractor for COVID-19 diagnosis based on CT scan images
    Authors: JH Joloudari, F Azizi, I Nodehi, MA Nematollahi, F Kamrannejhad, …
    Year: 2021
    Citations: 20
    Source: EasyChair 6330

  • Body composition predicts hypertension using machine learning methods: a cohort study
    Authors: MA Nematollahi, S Jahangiri, A Asadollahi, M Salimi, A Dehghan, …
    Year: 2023
    Citations: 18
    Source: Scientific Reports 13(1), 6885

  • CCTCOVID: COVID-19 detection from chest X-ray images using Compact Convolutional Transformers
    Authors: A Marefat, M Marefat, J Hassannataj Joloudari, MA Nematollahi, …
    Year: 2023
    Citations: 13
    Source: Frontiers in Public Health 11, 1025746

  • Association and predictive capability of body composition and diabetes mellitus using artificial intelligence: a cohort study
    Authors: MA Nematollahi, A Askarinejad, A Asadollahi, M Salimi, M Moghadami, …
    Year: 2022
    Citations: 7

  • Mixed paths and cycles determined by their spectrum
    Authors: S Akbari, A Ghafari, M Nahvi, MA Nematollahi
    Year: 2020
    Citations: 5
    Source: Linear Algebra and Its Applications 586, 325-346

  • A short proof of Haemers’ conjecture on the Seidel energy of graphs
    Authors: M Einollahzadeh, MA Nematollahi
    Year: 2024
    Citations: 3
    Source: Linear Algebra and Its Applications 695, 75-78

  • A cohort study on the predictive capability of body composition for Diabetes Mellitus using machine learning
    Authors: MA Nematollahi, A Askarinejad, A Asadollahi, M Bazrafshan, S Sarejloo, …
    Year: 2024
    Citations: 3
    Source: Journal of Diabetes & Metabolic Disorders 23(1), 773-781

  • Improving Prediction of Mortality in ICU via Fusion of SelectKBest with SMOTE Method and Extra Tree Classifier
    Authors: M Maftoun, JH Joloudari, O Zare, M Khademi, A Atashi, MA Nematollahi, …
    Year: 2024
    Citations: 3
    Source: International Work-Conference on the Interplay Between Natural and …

  • Improving a lower bound for Seidel energy of graphs
    Authors: MR Oboudi, MA Nematollahi
    Year: 2023
    Citations: 3
    Source: MATCH Commun. Math. Comput. Chem 89, 489-502

Conclusion ✨

Dr. Mohammad Ali Nematollahi exemplifies excellence in academia through his outstanding education, professional experience, research interests, awards, and technical skills. His dedication to advancing mathematical knowledge and applying it to real-world challenges makes him a leading figure in his field. His passion for teaching and mentoring nurtures the next generation of scholars, while his collaborative spirit and interdisciplinary approach enhance the global impact of his research. As a candidate for the Best Academic Researcher Award, Dr. Nematollahi stands out for his remarkable contributions, innovation, and unwavering commitment to excellence. He is undoubtedly a deserving nominee for this prestigious recognition. 🏆

Muhammad Mudassar Hassan | Graph Theory | Best Researcher Award

Dr. Muhammad Mudassar Hassan | Graph Theory | Best Researcher Award

Researcher at Anhui University, Hefei, China.

Muhammad Mudassar Hassan is a dedicated researcher at Anhui University, Hefei, China, specializing in applied mathematics, chemoinformatics, and structural engineering. His research primarily focuses on the comparative and computational study of Zagreb Connection Indices in chemical graphs, leading to the development of modified indices that enhance structural analysis in graph theory. As the first author of all his research articles, he has contributed to SCI-indexed journals, reflecting the quality of his work. He actively collaborates on various research projects and is a member of the American Mathematical Society. Additionally, he has a patent under process, showcasing his innovative contributions. However, his citation index remains relatively low, and he lacks editorial roles, published books, and industry collaborations. Strengthening these areas could further establish his research impact. With continued publications and broader academic engagement, he has the potential to make significant advancements in his field and become a strong candidate for the Best Researcher Award.

Professional Profile 

Google Scholar
ORCID Profile

Education

Muhammad Mudassar Hassan has a strong academic background in applied mathematics, which forms the foundation of his research expertise. He has pursued higher education with a focus on mathematical modeling, graph theory, and chemoinformatics, equipping him with the necessary skills to analyze complex chemical structures. His academic journey has been marked by rigorous training in computational methods, topological indices, and structural graph theory, allowing him to develop innovative approaches in these areas. Throughout his studies, he has actively engaged in research, contributing to scientific advancements in his field. His commitment to continuous learning and exploration of mathematical applications in chemistry and structural engineering has helped him establish a solid theoretical and practical foundation. By integrating computational techniques with applied mathematics, he has positioned himself as a researcher capable of addressing real-world scientific challenges. His educational background serves as a strong base for his ongoing research and future contributions.

Professional Experience

Muhammad Mudassar Hassan is a researcher at Anhui University, Hefei, China, where he focuses on applied mathematics and graph theory. His professional work revolves around the computational study of Zagreb Connection Indices and other degree-based topological indices, contributing to advancements in chemoinformatics and structural engineering. He has led multiple research projects as the first author of all his published articles, demonstrating his expertise in independent research. Although he does not currently hold editorial positions, he has collaborated with researchers on various projects, broadening his professional experience. Additionally, he is a member of the American Mathematical Society, which reflects his active engagement in the mathematical research community. While his professional experience is centered on academia, expanding into industry-based research or consultancy projects could further enhance his impact. His current work highlights his strong analytical skills and dedication to advancing mathematical applications in chemical graph theory and computational modeling.

Research Interest

Muhammad Mudassar Hassan’s research interests lie at the intersection of applied mathematics, chemoinformatics, and structural engineering. His primary focus is on the development and computational analysis of Zagreb Connection Indices in chemical graphs, which have proven to be essential in understanding structural properties. His work extends to degree-based topological indices, which help in characterizing molecular structures mathematically. His research contributes to both theoretical and practical advancements, as these indices are widely used in cheminformatics, drug discovery, and materials science. He is particularly interested in improving and modifying existing mathematical models to enhance their applicability in real-world chemical and structural problems. His studies involve extensive computational analysis, making his research relevant to data-driven scientific advancements. As a researcher, he continuously seeks to explore new mathematical methodologies that can further improve the understanding and application of graph theory in scientific domains. His work reflects his passion for interdisciplinary mathematical research.

Awards and Honors

Muhammad Mudassar Hassan’s academic and research contributions have been recognized through his membership in the American Mathematical Society, a prestigious organization in the field of mathematics. While he has made valuable contributions to applied mathematics and cheminformatics, he has yet to receive major individual awards or honors in recognition of his research. His publications in SCI-indexed journals demonstrate his credibility as a researcher, and his patent under process indicates his innovative potential. Though he has not yet held editorial positions or received industry-based accolades, his research contributions suggest promising potential for future recognition. Strengthening his academic presence through increased citations, award presentations, and involvement in professional committees could enhance his prospects for receiving distinguished awards. With continued dedication to publishing high-impact research and broadening his academic influence, he is well-positioned to earn honors that acknowledge his expertise in mathematical modeling and computational graph theory.

Conclusion

Muhammad Mudassar Hassan is a dedicated researcher with expertise in applied mathematics, particularly in the computational study of Zagreb Connection Indices and topological indices in chemical graphs. His research contributions, published in SCI-indexed journals, demonstrate his commitment to advancing graph theory applications in cheminformatics and structural engineering. While he has established himself as a promising academic researcher, expanding his influence through higher citation impact, editorial roles, industry collaborations, and additional publications could further strengthen his research profile. His patent under process highlights his potential for innovation, and his membership in the American Mathematical Society reflects his professional engagement. With continued research output and broader academic contributions, he has the potential to achieve greater recognition in his field. By focusing on increasing research impact and networking within the scientific community, he could further establish himself as a leading researcher and a strong candidate for prestigious academic awards in the future.

Publications Top Noted

  • Title: Connection-based modified Zagreb indices of Boron triangular sheet BTS (m, n)

    • Authors: MM Hassan, S Jabeen, H Ali, P Ali
    • Year: 2023
    • Citations: 5
    • Source: Molecular Physics
  • Title: Topological descriptors of molecular networks via reverse degree

    • Authors: MM Hassan
    • Year: 2024
    • Citations: 4
    • Source: Polycyclic Aromatic Compounds
  • Title: Molecular networks via reduced reverse degree approach

    • Authors: MM Hassan, XF Pan, DM Yu, MS Sardar
    • Year: 2025
    • Citations:
    • Source: Journal of Molecular Graphics and Modelling
  • Title: Computation of connection-based Zagreb indices in chain graphs and triangular sheets

    • Authors: MM Hassan, A Waqar, H Ali, P Ali
    • Year: 2024
    • Citations: 3
    • Source: Journal of Coordination Chemistry
  • Title: Connection Number-based Multiplicative Zagreb Indices of Chemical Structures

    • Authors: MM Hassan
    • Year: 2023
    • Citations: 2
    • Source: Current Organic Chemistry
  • Title: Molecular structure of DNA via Zagreb connection descriptors

    • Authors: MM Hassan, XF Pan
    • Year: 2024
    • Citations: 1
    • Source: The European Physical Journal E