Hemraj | Algorithms | Best Researcher Award

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Mr. Hemraj | Algorithms | Best Researcher Award

Research Scholar at IIT Guwahati, India.

Dr. Hemraj Raikwar is a Ph.D. research scholar in the Department of Computer Science & Engineering at IIT Guwahati, specializing in theoretical computer science and dynamic graph algorithms. His research focuses on designing incremental, decremental, and fully dynamic algorithms for maintaining approximate Steiner trees in dynamic graphs. With a strong foundation in algorithm analysis, object-oriented programming, and machine learning, he has contributed to top-tier international conferences and journals. His work has been recognized with the Outstanding Paper Award at CANDAR 2023, and he actively reviews for leading computer science journals.

Professional Profile:

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Education Background

Dr. Raikwar is currently pursuing a Ph.D. in Computer Science & Engineering at IIT Guwahati, where he is working under the supervision of Prof. Sushanta Karmakar on developing efficient dynamic algorithms for the Steiner tree problem. He earned his B.Tech in Computer Science & Engineering from Guru Ghasidas Central University, Bilaspur, graduating with an 8.81 CGPA in 2018. His early education was at Jawahar Navodaya Vidyalaya, Khurai, where he excelled in mathematics and computer science, scoring 88.6% in higher secondary.

Professional Development

Dr. Raikwar has been an active reviewer for the American Journal of Computer Science and Technology since April 2024. He has also served as a Computing Lab Teaching Assistant at IIT Guwahati in multiple academic terms, including 2019, 2020, and 2022, where he mentored students in data structures and programming. His experience spans algorithm analysis, machine learning, Linux-based programming, and dynamic algorithm techniques, making him proficient in teaching and research.

Research Focus

Dr. Raikwar’s research primarily focuses on dynamic graph algorithms, with an emphasis on the Steiner tree problem. He works on designing incremental, decremental, and fully dynamic algorithms that maintain efficient approximations of Steiner trees in evolving graphs. His broader interests include algorithm optimization, combinatorial optimization, approximation algorithms, and artificial intelligence, particularly in applications requiring fast and scalable algorithmic solutions.

Author Metrics:

Dr. Raikwar has published extensively in leading IEEE, ACM, and computational science journals. His notable works include:

  • “Fully Dynamic Algorithm for Steiner Tree Using Dynamic Distance Oracle”, ICDCN 2022
  • “Fully Dynamic Algorithm for the Steiner Tree Problem in Planar Graphs”, CANDARW 2022
  • “An Incremental Algorithm for (2−𝜖)-Approximate Steiner Tree”, CANDAR 2023 (Outstanding Paper Award)
  • “Dynamic Algorithms for Approximate Steiner Trees”, Concurrency & Computation, 2025

His research contributions have been recognized in international conferences, earning best paper awards and citations in algorithmic research.

Honors & Awards

Dr. Raikwar has received several prestigious accolades, including the Outstanding Paper Award at CANDAR 2023 for his contributions to dynamic Steiner tree algorithms. He secured a GATE score of 671/1000 with an AIR of 840 and was selected for the Indo-German School for Algorithms in Big Data at IIT Bombay (2019). His academic achievements also include 1st position in the International Science Talent Search Exam (2007) and a 100% score in Logical Reasoning in the Science Olympiad Foundation (2010).

Publication Top Notes

1. Calorie Estimation from Fast Food Images Using Support Vector Machine

Authors: H. Raikwar, H. Jain, A. Baghel
Journal: International Journal on Future Revolution in Computer Science
Year: 2018
Citations: 9

2. Fully Dynamic Algorithm for the Steiner Tree Problem in Planar Graphs

Authors: H. Raikwar, S. Karmakar
Conference: 2022 Tenth International Symposium on Computing and Networking Workshops (CANDARW)
Year: 2022
Citations: 1

3. An Incremental Algorithm for (2-ε)-Approximate Steiner Tree Requiring O(n) Update Time

Authors: H. Raikwar, S. Karmakar
Conference: 2023 Eleventh International Symposium on Computing and Networking (CANDAR)
Year: 2023

4. Fully Dynamic Algorithm for Steiner Tree using Dynamic Distance Oracle

Authors: H. Raikwar, S. Karmakar
Conference: Proceedings of the 23rd International Conference on Distributed Computing (DISC)
Year: 2022

Conclusion

Dr. Hemraj Raikwar has demonstrated outstanding research capabilities, strong academic excellence, and impactful contributions to theoretical computer science. His expertise in dynamic graph algorithms, algorithmic optimization, and AI-driven techniques makes him a deserving candidate for the Best Researcher Award.

With further expansion into global collaborations, industry applications, and high-impact journal publications, he can solidify his position as a leading researcher in algorithmic science.

Wenchao Zhang | Algorithms | Best Researcher Award

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Dr. Wenchao Zhang | Algorithms | Best Researcher Award

Lecturer at Jiangsu Shipping College, China📖

Dr. Wenchao Zhang is a Lecturer at Jiangsu Shipping College, specializing in network science and graph analysis for geotechnical engineering applications. With a Doctorate in Intelligent Transportation Science and Technology from Soochow University and a Master’s in Engineering Mechanics from Northeastern University, his expertise lies in machine learning and data mining, particularly in predictive modeling for excavation deformation and tunneling safety. He has contributed to several innovative approaches in geotechnical research, including Bayesian Evolutionary Trees and gradient boosting for imbalanced regression.

Profile

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Education Background🎓

  • Ph.D. in Intelligent Transportation Science and Technology, Soochow University
  • M.Sc. in Engineering Mechanics, Northeastern University

Professional Experience🌱

Dr. Zhang is currently a Lecturer at Jiangsu Shipping College and is a member of the Jiangsu Underground Space Association. His work focuses on the integration of computational intelligence with geotechnical engineering, particularly in the prediction of excavation deformation and tunneling safety. He has led numerous research projects funded by the National Natural Science Foundation and has extensive experience in machine learning applications in geotechnical contexts.

Research Interests🔬

Her research interests include:

  • Machine Learning and Data Mining
  • Predictive Modeling for Excavation Deformation
  • Tunneling Safety
  • Network Science and Graph Analysis
  • Geotechnical Engineering Applications

Author Metrics

  • Scopus H-index: 2
  • Total Citations: 15
  • Publications: 10 articles (4 SCI-indexed, 1 EI-indexed, 5 Scopus-indexed)
  • Patents Published: 4 (1 invention, 3 utility models)
  • Book Chapter: 1 (ISBN: 978-981-99-4751-5)
Awards and Honors

Dr. Zhang has received recognition for his significant contributions to the field of network science and graph analysis in geotechnical engineering. His work has led to the development of innovative predictive models such as the EB-GLFMR model and Bayesian Evolutionary Trees. Additionally, his interdisciplinary collaborations have advanced both theoretical research and practical applications in the field.

Publications Top Notes 📄

1. Missing Data Analysis and Soil Compressive Modulus Estimation via Bayesian Evolutionary Trees

  • Authors: Zhang, W., Shi, P., Zhou, X., Jia, P.
  • Journal: Lecture Notes in Computer Science (LNAI, including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Year: 2023
  • Volume: 14089
  • Pages: 90–100
  • Citations: 0
  • Abstract: This paper presents a method to address missing data in geotechnical datasets and estimates soil compressive modulus using Bayesian Evolutionary Trees, integrating advanced computational models for more accurate prediction of geotechnical properties.

2. Mechanical Performances and Microscopic Properties of Cemented Backfilling Based on Orthogonal Experiment

  • Authors: Xu, Q., Wang, Y., Zhang, W.
  • Journal: Journal of Mining and Strata Control Engineering
  • Year: 2022
  • Volume: 4(6)
  • Article Number: 063520
  • Citations: 4
  • Abstract: This study investigates the mechanical performance and microscopic properties of cemented backfilling used in mining operations. It uses orthogonal experiments to assess the strength and durability of different backfilling materials, crucial for improving mining safety and efficiency.

3. Study on Settlement Influence of Newly Excavated Tunnel Undercrossing Large Diameter Pipeline

  • Authors: Xu, Q., Zhang, W., Chen, C., Lu, J., Tang, P.
  • Journal: Advances in Civil Engineering
  • Year: 2022
  • Article Number: 5700377
  • Citations: 1
  • Abstract: This research focuses on the settlement effects caused by tunnel excavation under large diameter pipelines, exploring the structural integrity and deformation processes, as well as mitigation strategies for such impacts on urban infrastructure.

4. Research on Deformation Prediction of Diaphragm Wall Based on Improved KNN and Parameters of Subway Deep Excavation

  • Authors: Zhang, W., Shi, P., Liu, W., Jia, P.
  • Journal: Journal of Huazhong University of Science and Technology (Natural Science Edition)
  • Year: 2021
  • Volume: 49(9)
  • Pages: 101–106
  • Citations: 7
  • Abstract: This paper presents an improved K-Nearest Neighbor (KNN) model to predict the deformation of diaphragm walls in deep subway excavations, considering various parameters that affect the stability of underground structures in urban environments.

5. Study of the Mechanical Performance of Excavation Under Asymmetrical Pressure and Reinforcement Measures

  • Authors: Zhang, W., Wu, N., Jia, P., Li, H., Wang, G.
  • Journal: Arabian Journal of Geosciences
  • Year: 2021
  • Volume: 14(18)
  • Article Number: 1834
  • Citations: 9
  • Abstract: The study investigates the mechanical behavior of excavation sites under asymmetrical pressure and explores various reinforcement measures. The findings are crucial for improving excavation methods and ensuring the stability of structures in asymmetrically loaded sites.

Conclusion

Dr. Wenchao Zhang is an exceptional candidate for the Best Researcher Award. His innovative work in network science, machine learning, and geotechnical engineering sets him apart as a leader in his field. His research on predictive modeling, excavation deformation, and tunneling safety has the potential to transform the industry and academic landscapes. With a clear track record of achievements, Dr. Zhang has laid a strong foundation for future contributions. By expanding his reach in practical applications and international collaborations, he could further elevate his impact in the coming years.

In summary, Dr. Zhang’s commitment to advancing geotechnical engineering through computational intelligence and his ability to pioneer new methodologies positions him as a deserving recipient of the Best Researcher Award..