Dr. Sufyanv Ghani | Engineering | Best Researcher Award

Assistant Professor at Sharda University, India

Dr. Sufyan Ghani is an accomplished academician and researcher in the field of Civil Engineering. Born on July 4, 1995, in Patna, India, he has consistently demonstrated a strong commitment to higher education and research. He earned his Ph.D. from the National Institute of Technology (NIT) Patna, focusing on advanced topics in Civil Engineering. Dr. Ghani is fluent in English, Urdu, and Hindi, which enhances his ability to communicate effectively with a diverse range of audiences. His personal attributes—positive attitude, self-motivation, and persistence—reflect his dedication to personal and professional growth. Currently, he aims to apply his extensive knowledge and skills as an Assistant Professor in a prestigious academic institution, where he hopes to inspire and mentor the next generation of engineers while continuing his research endeavors.

Professional Profile

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Education

Dr. Ghani’s educational journey showcases his dedication and excellence in the field of Civil Engineering. He completed his Ph.D. at the National Institute of Technology (NIT) Patna, where he focused on cutting-edge research related to Civil Engineering practices and innovations. Prior to this, he earned his Master’s Degree in Soil Mechanics and Foundation Engineering from BIT Mesra in 2019, which provided him with a strong foundation in geotechnical engineering principles. His educational qualifications are complemented by his technical skills in software like MATLAB, AutoCAD, and Python, which are essential for modern engineering research and applications. This combination of formal education and practical skills equips Dr. Ghani with the knowledge required to address complex engineering challenges effectively.

Professional Experience

Dr. Ghani has garnered substantial professional experience in the higher education sector, which complements his academic qualifications. As a researcher and educator, he has been actively involved in various teaching and research roles, contributing to the development of future engineers. His expertise in Soil Mechanics and Foundation Engineering positions him as a valuable resource in the civil engineering department. Dr. Ghani has participated in numerous research projects, collaborating with colleagues and students to explore innovative solutions to engineering problems. His commitment to academic excellence is reflected in his engagement with students, guiding them in their research and practical applications of civil engineering principles. Dr. Ghani’s professional experience not only enhances his profile but also positively impacts the academic community he serves.

Research Interests

Dr. Sufyan Ghani’s research interests lie primarily in the domains of Soil Mechanics and Foundation Engineering. He is particularly focused on advancing the understanding of soil behavior under various loading conditions and its implications for foundation design. His work aims to bridge the gap between theoretical research and practical applications, contributing to safer and more efficient engineering practices. Additionally, Dr. Ghani is interested in exploring sustainable construction materials and techniques, which align with global initiatives for environmentally friendly engineering solutions. By integrating modern computational techniques and experimental methods, he aims to enhance the reliability and performance of civil engineering structures. His commitment to research not only advances the field but also contributes to addressing pressing infrastructure challenges.

Awards and Honors

Throughout his academic and professional journey, Dr. Sufyan Ghani has received recognition for his contributions to the field of Civil Engineering. His outstanding research work has led to several publications in reputable journals, earning him citations and acknowledgment from peers in the academic community. He has participated in various conferences and seminars, where he presented his findings, showcasing his commitment to sharing knowledge and advancing research. Additionally, Dr. Ghani has been involved in collaborative research projects that have received funding and accolades, highlighting his ability to work effectively within teams. His dedication to education and research has positioned him as a respected figure in the civil engineering community, paving the way for future opportunities and recognition in his field.

Conclusion

Dr. Sufyan Ghani is a strong candidate for the Best Researcher Award due to his solid educational background, technical skills, and commitment to research. By focusing on improving the impact of his work, expanding his professional network, and applying his research to community challenges, he can further enhance his contributions to the field of civil engineering. His proactive approach and continuous learning mindset position him well for future success and recognition in academia.

Publication top noted

1. 📖 Advancing earth science in geotechnical engineering: A data-driven soft computing technique for unconfined compressive strength prediction in soft soil
   Authors: Thapa, I., Ghani, S.
   Year: 2024
   Journal: Journal of Earth System Science, 133(3), 159
   Citations: 0
2. 📖 Enhancing unconfined compressive strength prediction in nano-silica stabilized soil: a comparative analysis of ensemble and deep learning models
   Authors: Thapa, I., Ghani, S.
   Year: 2024
   Journal: Modeling Earth Systems and Environment, 10(4), pp. 5079–5102
   Citations: 0
3. 📖 Applying Optimized Machine Learning Models for Predicting Unconfined Compressive Strength in Fine-Grained Soil
   Authors: Thapa, I., Ghani, S.
   Year: 2024
   Journal: Transportation Infrastructure Geotechnology, 11(4), pp. 2235–2269
   Citations: 6
4. 📖 Enhancing bond performance in SRC structures: a computational approach using ensemble learning techniques and sequential analysis
   Authors: Gupta, M., Prakash, S., Ghani, S., Kumar, N., Saharan, S.
   Year: 2024
   Journal: Asian Journal of Civil Engineering, 25(4), pp. 3329–3347
   Citations: 5
5. 📖 Data-driven machine learning approaches for predicting permeability and corrosion risk in hybrid concrete incorporating blast furnace slag and fly ash
   Authors: Kumar, N., Prakash, S., Ghani, S., Gupta, M., Saharan, S.
   Year: 2024
   Journal: Asian Journal of Civil Engineering, 25(4), pp. 3263–3275
   Citations: 7
6. 📖 Enhancing predictive accuracy: a comprehensive study of optimized machine learning models for ultimate load-carrying capacity prediction in SCFST columns
   Authors: Gupta, M., Prakash, S., Ghani, S.
   Year: 2024
   Journal: Asian Journal of Civil Engineering, 25(4), pp. 3081–3098
   Citations: 5
7. 📖 Applications of bentonite in plastic concrete: a comprehensive study on enhancing workability and predicting compressive strength using hybridized AI models
   Authors: Thapa, I., Kumar, N., Ghani, S., Kumar, S., Gupta, M.
   Year: 2024
   Journal: Asian Journal of Civil Engineering, 25(4), pp. 3113–3128
   Citations: 7
8. 📖 Estimation of California bearing ratio for hill highways using advanced hybrid artificial neural network algorithms
   Authors: Thapa, I., Ghani, S.
   Year: 2024
   Journal: Multiscale and Multidisciplinary Modeling, Experiments and Design, 7(2), pp. 1119–1144
   Citations: 12
9. 📖 Enhancing seismic vulnerability assessment: a neural network effort for efficient prediction of multi-storey reinforced concrete building displacement
   Authors: Shrestha, N., Gupta, M., Ghani, S., Kushwaha, S.
   Year: 2024
   Journal: Asian Journal of Civil Engineering, 25(3), pp. 2843–2865
   Citations: 6
10. 📖 Machine learning approaches for real-time prediction of compressive strength in self-compacting concrete
    Authors: Ghani, S., Kumar, N., Gupta, M., Saharan, S.
    Year: 2024
    Journal: Asian Journal of Civil Engineering, 25(3), pp. 2743–2760
    Citations: 6