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.

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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.

Xiaoshuai Hao | Multimodal | Best Researcher Award

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Dr. Xiaoshuai Hao | Multimodal | Best Researcher Award

Researcher at Beijing Academy of Artificial Intelligence, China📖

Xiaoshuai Hao is an AI researcher specializing in multimodal learning, large-scale model pretraining, and cross-modal retrieval. He earned his Ph.D. in Information Engineering from the University of Chinese Academy of Sciences, focusing on text-video retrieval and multimodal AI. With professional experience spanning leading AI institutions, he has worked as a researcher at the Beijing Academy of Artificial Intelligence, a senior AI researcher at Samsung Research China, and an applied scientist at Amazon AWS AI Lab. His contributions include innovations in embodied intelligence, robust autonomous driving perception, and high-precision mapping, with multiple patents to his name.

Xiaoshuai has published in top-tier AI conferences such as CVPR, ICCV, and ICRA and serves as a reviewer for premier journals and conferences, including IEEE TCSVT, IEEE TMM, CVPR, AAAI, and IJCAI. He has achieved top rankings in international AI competitions, including 1st place at EPIC-KITCHENS-100 (CVPR 2021) and multiple podium finishes in OOD-CV (ICCV 2023) and The RoboDrive Challenge (ICRA 2024). Recognized for his excellence, he has received the Samsung Research China Outstanding Employee Award and multiple academic honors.

Profile

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Education Background🎓

  • Ph.D. in Information Engineering, University of Chinese Academy of Sciences, China (2017–2023)
    • Research Focus: Text-video cross-modal retrieval, multimodal learning, large model pretraining
  • B.Eng. in Network Engineering, Shandong University of Science and Technology, China (2013–2017)
    • National Scholarship, Outstanding Student of Shandong Province

Professional Experience🌱

  • Beijing Academy of Artificial Intelligence (2024–Present) – Researcher in Embodied Multimodal Large Models
  • Samsung Research China (2023–2024) – Senior AI Researcher in robust autonomous driving perception and BEV-based multimodal fusion
  • Amazon AWS AI Lab (2021–2022) – Applied Scientist (Intern), working on large-scale multimodal pretraining and MixGen data augmentation for vision-language learning
Research Interests🔬
  • Multimodal AI (vision, language, and embodied intelligence)
  • Large-scale model pretraining and fine-tuning
  • Autonomous driving and high-precision mapping
  • Cross-modal retrieval and knowledge fusion
Author Metrics
  • First author of multiple patents on multimodal mapping, visual-language navigation, and robust perception
  • Published in top-tier AI conferences (CVPR, ICCV, ICRA)
  • Reviewer for CVPR, AAAI, IJCAI, ACM MM, IEEE TCSVT, and IEEE TMM
  • Notable Competitions:
    • 1st place: EPIC-KITCHENS-100 2021 Multi-Instance Retrieval (CVPR 2021)
    • 3rd place: The RoboDrive Challenge (ICRA 2024), EPIC-KITCHENS-100 2022, OOD-CV (ICCV 2023), EPIC-Sounds 2023 (CVPR 2023)

Awards & Honors

  • Samsung Research China Outstanding Employee Award (2023)
  • University of Chinese Academy of Sciences Outstanding Student & Student Leader (2021–2022, 2017–2018)
Publications Top Notes 📄

1. MixGen: A New Multi-Modal Data Augmentation

  • Authors: X. Hao, Y. Zhu, S. Appalaraju, A. Zhang, W. Zhang, B. Li, M. Li
  • Conference: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023
  • Citations: 108
  • Summary: Proposes MixGen, a multimodal data augmentation method for vision-language representation learning, improving data efficiency through semantic-based synthetic data generation.

2. The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition

  • Authors: L. Kong, S. Xie, H. Hu, Y. Niu, W.T. Ooi, B.R. Cottereau, L.X. Ng, Y. Ma, W. Zhang, X. Hao, et al.
  • Conference: ICRA 2024 Technical Report
  • Citations: 23
  • Summary: Addresses robustness in autonomous driving through a large-scale benchmark evaluating real-world conditions for perception models.

3. Dual Alignment Unsupervised Domain Adaptation for Video-Text Retrieval

  • Authors: X. Hao, W. Zhang, D. Wu, F. Zhu, B. Li
  • Conference: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
  • Citations: 21
  • Summary: Introduces a domain adaptation framework for video-text retrieval, aligning multimodal representations across different datasets.

4. The End-of-End-to-End: A Video Understanding Pentathlon Challenge (2020)

  • Authors: S. Albanie, Y. Liu, A. Nagrani, A. Miech, E. Coto, I. Laptev, R. Sukthankar, X. Hao, et al.
  • Platform: arXiv preprint arXiv:2008.00744, 2020
  • Citations: 15
  • Summary: A benchmarking challenge for evaluating video understanding models across multiple tasks.

5. Is Your HD Map Constructor Reliable Under Sensor Corruptions?

  • Authors: X. Hao, M. Wei, Y. Yang, H. Zhao, H. Zhang, Y. Zhou, Q. Wang, W. Li, L. Kong, et al.
  • Conference: NeurIPS 2024
  • Citations: 13
  • Summary: Examines the robustness of high-definition map construction models against real-world sensor corruptions.

Conclusion

Dr. Xiaoshuai Hao is a highly deserving candidate for the Best Researcher Award in the field of Multimodal AI. His pioneering research, strong industry-academic footprint, and leadership in AI competitions make him an exceptional candidate. While his research already holds global recognition, further industry collaborations, AI policy engagements, and broader application areas could elevate his influence even more.

Chenru Jiang | Deep Learning | Best Researcher Award

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Dr. Chenru Jiang | Deep Learning | Best Researcher Award 

post doctor, at Duke Kunshan University, China.

Chenru Jiang is a dedicated researcher in artificial intelligence, specializing in deep learning, pattern recognition, and computer vision. He is a Postdoctoral Research Fellow at Duke Kunshan University, contributing to cutting-edge projects in 3D point cloud analysis, human pose estimation, and healthcare applications of AI. With professional experience spanning academia and industry, Chenru has developed advanced algorithms for face detection, tracking, and 3D modeling. A passionate academic mentor, he actively supports students and colleagues in research and teaching activities. Chenru’s work has been featured in prestigious journals and conferences worldwide.

Profile

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Education

🎓 University of Liverpool, UK

  • Ph.D. in Computer Science & Engineering (2023): Dissertation on “Investigation of Human Pose Estimation from 2D to 3D,” supervised by Prof. Kaizhu Huang.
  • M.E. in Multi-media Telecommunication Technology (2017): Thesis on “Siamese Network Based Online Tracking,” co-supervised by Prof. Tammam Tillo and Prof. Kaizhu Huang.
  • B.E. in Digital Media Technology (2015): Focused on “Depth Data Acquisition by One Monocular Camera,” under the guidance of Prof. Tammam Tillo.

Professional Experience

👨‍💻 Postdoctoral Research Fellow – Duke Kunshan University, China (2024–Present)

  • Developed machine learning and deep learning algorithms for healthcare and 3D data processing.
  • Mentored students in advanced computational methodologies.

🧑‍💻 Algorithm Engineer – Suzhou Institute of Nano-Tech and Nano-Bionics, CAS (2017–2019)

  • Designed and implemented face detection and tracking systems.

🚗 Algorithm Engineer – China North Industries Group Co., Ltd (2017)

  • Contributed to driving assistance systems for special vehicles.

Research Interests

🧠 Chenru Jiang’s research focuses on AI for healthcare, including deep learning models for human pose estimation and 3D point cloud analysis. His interests extend to developing innovative algorithms for pattern recognition and machine learning systems, addressing real-world challenges in areas like robotic perception, 3D reconstruction, and zero-shot learning. He is particularly passionate about improving human-computer interaction with advanced vision-based solutions.

Awards

🏆 Chenru Jiang has received numerous accolades for his academic contributions, including recognition for his top-tier publications in CAS-JCR Q1 journals and his presentations at CCF-A conferences like ACM Multimedia and CVPR. His innovative work in algorithm design and pose estimation systems has been praised for its impact in both industry and academia.

Top Noted Publications

📚 Selected Journal Publications

Revisiting 3D Point Cloud Analysis with Markov Process

  • Authors: Jiang C., Ma W., Huang K., Wang Q., Yang X., Zhao W., Wu J., Wang X., Xiao J., & Niu Z.
  • Journal: Pattern Recognition, Volume: 158, Article: 110997, Year: 2025.
  • Cited by: 15.
    Read Article

2. PointGS: Bridging and Fusing Geometric and Semantic Space for 3D Point Cloud Analysis

  • Authors: Jiang C., Huang K., Wu J., Wang X., Xiao J., & Hussain A.
  • Journal: Information Fusion, Volume: 91, Pages: 316-326, Year: 2022.
  • Cited by: 30.
    Read Article

3. Aggregated Pyramid Gating Network for Human Pose Estimation Without Pre-Training

  • Authors: Jiang C., Huang K., Zhang S., Wang X., Xiao J., & Goulermas Y.
  • Journal: Pattern Recognition, Volume: 138, Article: 109429, Year: 2022.
  • Cited by: 20.
    Read Article

Conclusion

Chenru Jiang is a highly qualified candidate for the Best Researcher Award, with a strong track record in impactful AI research, significant publications, and practical algorithm development. Their expertise in deep learning and 3D computer vision positions them at the forefront of innovation in these fields. Addressing the areas of improvement, particularly in independent leadership and community engagement, would further strengthen their case for recognition. Overall, Chenru demonstrates exceptional potential and merit for this award.