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.

Raheleh Ghouchan Nezhad Noor Nia | Artificial Intelligence | Best Researcher Award

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Dr. Raheleh Ghouchan Nezhad Noor Nia | Artificial Intelligence | Best Researcher Award

Postdoc Researcher at Mashhad University of Medical Sciences, Mashhad, Iran📖

Dr. Raheleh Ghouchan Nezhad Noor Nia is a Senior Data Scientist and Postdoctoral Researcher specializing in the application of machine learning, artificial intelligence, and medical informatics. She is currently based at the Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran. Her extensive academic and professional experience spans multiple domains, including medical informatics, AI in medicine, and data science.

Profile

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Education Background🎓

  • Postdoc in Medical Informatics – Data Science & AI in Medicine (2022 – Present), Department of Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran.
  • Ph.D. in Computer Engineering – Software (2016 – 2022), Department of Computer Engineering, Azad University, Mashhad, Iran.
  • Master’s in Computer Engineering – AI and Robotics (2013 – 2015), Department of Computer Engineering, Azad University, Mashhad, Iran.
  • Bachelor’s in Computer Engineering – Software (2009 – 2013), Department of Computer Engineering, Azad University, Mashhad, Iran.

Professional Experience🌱

Dr. Ghouchan Nezhad Noor Nia currently serves as a Postdoctoral Researcher and Senior Data Scientist at Mashhad University of Medical Sciences, where she is involved in several pioneering research projects related to AI in healthcare. In addition to her role as a researcher, she is a lecturer at various institutions, including the Department of Computer Engineering at Mashhad Azad University, Khayyam University, and Toos University. She is also actively contributing as a reviewer for prestigious journals such as Materials Today Communication and the Medical Informatics Europe conferences.

Her collaborative efforts extend internationally, having worked with prominent researchers at Karlsruhe Institute of Technology, Germany. Dr. Ghouchan Nezhad Noor Nia has also led and contributed to numerous conferences and workshops focused on AI in medical sciences and health technology.

Research Interests🔬

Dr. Ghouchan Nezhad Noor Nia’s research interests include the intersection of AI, machine learning, and medical informatics. Her focus is on big data mining, social mining, graph mining, material science, and AI applications in medical diagnostics, specifically in diseases like lupus nephritis and pulmonary thromboembolism. She is also interested in ontology engineering, metadata management, health social networks, deep learning, and point-of-interest recommendation systems

Author Metrics

Dr. Ghouchan Nezhad Noor Nia has contributed to numerous high-impact publications and has an active research profile with publications in reputed journals and conferences. Her work focuses on innovative solutions and machine learning methods to solve complex challenges in healthcare and material science. She has co-authored papers presented at various prestigious international conferences, including the 12th Neuroscience Congress and International Health Literacy Congress. Her Google Scholar profile reflects her growing influence in the field.

Publications Top Notes 📄

1. A Graph-Based k-Nearest Neighbor (KNN) Approach for Predicting Phases in High-Entropy Alloys

  • Authors: R Ghouchan Nezhad Noor Nia, M Jalali, M Houshmand
  • Journal: Applied Sciences
  • Volume: 12
  • Issue: 16
  • Article ID: 8021
  • Year: 2022
  • DOI: 10.3390/app12168021
  • Summary: This paper introduces a graph-based k-nearest neighbor (KNN) algorithm for phase prediction in high-entropy alloys, leveraging machine learning techniques for material science applications.

2. Machine Learning Approach to Community Detection in a High-Entropy Alloy Interaction Network

  • Authors: R Ghouchan Nezhad Noor Nia, M Jalali, M Mail, Y Ivanisenko, C Kübel
  • Journal: ACS Omega
  • Volume: 7
  • Issue: 15
  • Pages: 12978-12992
  • Year: 2022
  • DOI: 10.1021/acsomega.2c02625
  • Summary: This work focuses on community detection in a high-entropy alloy interaction network using machine learning methods, exploring the structure and relationships between various alloy elements.

3. Non-Alcoholic Fatty Liver Disease Diagnosis with Multi-Group Factors

  • Authors: A Arzehgar, RG Nezhad Noor Nia, V Dehdeleh, F Roudi, S Eslami
  • Journal: Healthcare Transformation with Informatics and Artificial Intelligence
  • Pages: 503-506
  • Year: 2023
  • Summary: This paper proposes a novel methodology for diagnosing non-alcoholic fatty liver disease (NAFLD) by considering multiple influencing factors and utilizing advanced informatics and artificial intelligence.

4. RecMem: Time Aware Recommender Systems Based on Memetic Evolutionary Clustering Algorithm

  • Authors: RG Nezhad Noor Nia, M Jalali
  • Journal: Computational Intelligence and Neuroscience
  • Article ID: 8714870
  • Year: 2022
  • DOI: 10.1155/2022/8714870
  • Summary: The paper presents RecMem, a time-aware recommender system that integrates a memetic evolutionary clustering algorithm, aiming to improve recommendation accuracy in dynamic environments.

5. A Community Detection-based Approach in Social Networks to Improve the Equation Analysis in Material Science

  • Authors: R Ghouchan Nezhad Noor Nia, M Jalali, M Houshmand
  • Journal: Journal of Iranian Association of Electrical and Electronics Engineers
  • Volume: 21
  • Issue: 1
  • Year: 2024 (upcoming)
  • Summary: This study proposes a community detection-based approach within social networks to enhance equation analysis methods used in material science, specifically in the context of high-entropy alloys.

Conclusion

Dr. Raheleh Ghouchan Nezhad Noor Nia is a highly deserving candidate for the Best Researcher Award. Her contributions to AI, machine learning, and medical informatics are transformative and have the potential to address critical healthcare challenges. She exhibits strengths in multidisciplinary research, with a particular focus on medical diagnostics and healthcare innovation. With further growth in clinical and practical applications, Dr. Ghouchan Nezhad Noor Nia’s work could have an even broader and more profound impact on both academia and real-world healthcare solutions. Her dedication to research, teaching, and international collaboration makes her an exemplary figure in her field.