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.

Ruyi Liu | Financial Mathematics | Best Scholar Award

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Dr. Ruyi Liu | Financial Mathematics | Best Scholar Award

Researcher at The Hong Kong Polytechnic University, Australia📖

Dr. Ruyi Liu is a Research Fellow in Financial Mathematics at The Hong Kong Polytechnic University. His research focuses on pairs-trading strategies, financial mathematics, and stochastic analysis. He has contributed to high-impact journals in quantitative finance, stochastic processes, and financial derivatives pricing. With extensive experience in stochastic control and mathematical finance, he collaborates with leading researchers globally and supervises Ph.D. and Master’s students in related fields.

Profile

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Education Background🎓

Dr. Liu obtained his Ph.D. in Statistics from Shandong University, China, in 2020, under the supervision of Prof. Zhen Wu, with a dissertation on optimal pairs-trading strategies and forward-backward stochastic differential equations. He earned his B.S. degree in Applied Mathematics from Shandong University in 2014, establishing a strong foundation in probability theory and stochastic analysis.

Professional Experience🌱

Dr. Liu is currently a Research Fellow at The Hong Kong Polytechnic University (since August 2024), working on pairs-trading strategies and financial mathematics in collaboration with Prof. Zuoquan Xu. Previously, he was a Postdoctoral Fellow (Level A) at The University of Sydney (2021–2024), where he focused on pricing of options and superannuation products with market and credit risk, under the mentorship of Prof. Marek Rutkowski. His research has influenced financial risk management, derivative pricing, and investment strategies.

Research Interests🔬

Dr. Liu’s research interests include pairs-trading strategies, financial derivatives pricing, stochastic control, forward-backward stochastic differential equations (FBSDEs), and market risk modeling. He specializes in developing optimal trading and pricing models for financial markets, incorporating stochastic volatility, credit risk, and Markov-switching models. His work provides mathematical frameworks for hedging, portfolio optimization, and financial risk management.

Author Metrics

Dr. Liu has published in top-tier journals, including Automatica, Quantitative Finance, Applied Energy, and Science China-Mathematics. His research spans financial pricing, stochastic differential equations, and optimization in trading strategies. He has multiple papers under review in high-impact journals, such as Finance & Stochastics and SIAM Journal on Financial Mathematics. His publications address key challenges in quantitative finance, risk management, and mathematical modeling.

Awards & Honors

Dr. Liu received a Chinese Postdoctoral Science Foundation Grant (AUD 70,000) for his research on pairs-trading strategies and stochastic models. He has also co-supervised students who received the University Medal for Outstanding Theses at The University of Sydney. His contributions to financial mathematics have been recognized through international conference presentations and research collaborations with leading experts in stochastic finance.
Publications Top Notes 📄

1. Well-posedness of a class of two-point boundary value problems associated with ordinary differential equations

  • Authors: R. Liu, Z. Wu
  • Journal: Advances in Difference Equations
  • Year: 2018
  • Pages: 1-12
  • Citations: 11
  • Summary: This paper investigates the well-posedness of two-point boundary value problems related to ordinary differential equations (ODEs). The authors analyze the existence, uniqueness, and stability of solutions under specific conditions. The findings contribute to a deeper understanding of differential equation theory and its applications in various mathematical and engineering contexts.

2. Pairs-Trading under Geometric Brownian Motions: An Optimal Strategy with Cutting Losses

  • Authors: R. Liu, Z. Wu, Q. Zhang
  • Journal: Automatica
  • Year: 2020
  • Volume: 115
  • Article ID: 108912
  • Citations: 10
  • Summary: This study proposes an optimal trading strategy for pairs trading under a geometric Brownian motion model. The model incorporates a cutting-loss mechanism to manage risks effectively. The authors develop a framework for determining optimal trade execution and stopping strategies in financial markets, contributing to algorithmic trading and portfolio management.

3. Well-posedness of Fully Coupled Linear Forward-Backward Stochastic Differential Equations

  • Authors: R. Liu, Z. Wu
  • Journal: Journal of Systems Science and Complexity
  • Year: 2019
  • Volume: 32
  • Issue: 3
  • Pages: 789-802
  • Citations: 5
  • Summary: The paper examines fully coupled linear forward-backward stochastic differential equations (FBSDEs). It establishes conditions for the well-posedness of these equations, including existence and uniqueness of solutions. The results are significant for financial mathematics, stochastic control, and applied probability.

4. Continuous-Time Mean-Variance Portfolio Selection under Non-Markovian Regime-Switching Model with Random Horizon

  • Authors: T. Chen, R. Liu, Z. Wu
  • Journal: Journal of Systems Science and Complexity
  • Year: 2023
  • Volume: 36
  • Issue: 2
  • Pages: 457-479
  • Citations: 4
  • Summary: This paper explores a continuous-time mean-variance portfolio selection problem within a non-Markovian regime-switching framework. It introduces a random horizon to reflect uncertain investment periods. The authors develop optimization strategies for asset allocation, with applications in quantitative finance and risk management.

5. Well-posedness and Penalization Schemes for Generalized BSDEs and Reflected Generalized BSDEs

  • Authors: L. Li, R. Liu, M. Rutkowski
  • Journal: arXiv Preprint
  • Year: 2022
  • Article ID: arXiv:2212.12854
  • Citations: 3
  • Summary: This preprint investigates the well-posedness and penalization methods for generalized backward stochastic differential equations (BSDEs) and reflected BSDEs. The authors develop analytical techniques for solving these equations, with implications in stochastic control, financial mathematics, and applied probability.

Conclusion

Dr. Ruyi Liu is an outstanding candidate for the Best Scholar Award due to his high-impact research, technical expertise, global collaborations, and mentorship efforts. His work has significantly contributed to quantitative finance, stochastic analysis, and financial mathematics. While he could further expand his industry collaborations and interdisciplinary research, his current contributions make him a strong contender for this award.