Shakila Rahman | Machine Learning | Best Researcher Award

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Ms. Shakila Rahman | Machine Learning | Best Researcher Award

Lecturer at American International University, Bangladesh

Author Profile

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Summary

Shakila Rahman is a dedicated academician currently serving as a Lecturer in the Department of Computer Science at the Faculty of Science and Technology, American International University-Bangladesh (AIUB). She holds a strong academic background in Artificial Intelligence and Computer Engineering, with her research focusing on emerging areas such as UAV networking, wireless sensor networks, optimization algorithms, and machine learning. Shakila is actively involved in mentoring students, guiding projects, and publishing impactful research in reputed platforms.

Educational Details

Shakila Rahman earned her M.Sc. in AI & Computer Engineering from the University of Ulsan, South Korea, in 2023 with an impressive CGPA of 4.00 out of 4.50. She completed her B.Sc. in Computer Science and Engineering from International Islamic University Chittagong (IIUC), Bangladesh, in 2019, securing a CGPA of 3.743 out of 4.00. Prior to her university education, she completed her Higher Secondary Certificate (HSC) from Cox’s Bazar Govt. College and Secondary School Certificate (SSC) from Cox’s Bazar Govt. Girls’ High School.

Professional Experience

Shakila is currently employed as a Lecturer in the Department of Computer Science and Engineering at AIUB, Dhaka, Bangladesh, where she has been working since January 2023. She previously served as a Graduate Research Assistant at the University of Ulsan, South Korea, from September 2020 to December 2022 under Professor Seokhoon Yoon. Additionally, she worked as an Undergraduate Teaching Assistant at IIUC in 2019. She has participated in technical boot camps and workshops and actively contributes to academic supervision, having guided several student projects and a machine learning-based thesis group.

Research Interests

Her research interests span a wide range of cutting-edge topics including UAV Networking, Wireless Sensor Networks, Network Systems, Optimization Algorithms, Machine Learning, Deep Learning, Image Processing, and AR/VR Applications in Artificial Intelligence. These multidisciplinary areas reflect her focus on building intelligent and adaptive systems for real-world applications.

Author Metrics

Shakila Rahman actively maintains a presence on prominent academic platforms. Her ResearchGate profile can be found at https://www.researchgate.net/profile/Shakila-Rahman-3, and her ORCID ID is 0000-0001-6375-4174. She is also available on LinkedIn at Shakila Rahman. Her published works and citation records are regularly updated on these platforms.

Awards and Honors

During her master's studies, Shakila was awarded the prestigious Brain Korea 21 (BK21) Scholarship and a fully funded AF1 scholarship at the University of Ulsan, valued at approximately USD 21,000. She also received funding from Korean Government-supported National Research Foundation (NRF) projects to support her graduate research publications. These accolades recognize her academic excellence and research contributions in the field of computer science and engineering.

Publication Top Noted

1. Bilingual Sign Language Recognition: A YOLOv11-Based Model for Bangla and English Alphabets

Authors: N. Navin, F.A. Farid, R.Z. Rakin, S.S. Tanzim, M. Rahman, S. Rahman, J. Uddin, ...
Journal: Journal of Imaging, Vol. 11, Issue 5, Article 134
Year: 2025
Citation: 1 (as of now)
Summary:
This study introduces a YOLOv11-based deep learning model designed to recognize both Bangla and English sign language alphabets in real-time. The model was trained on a custom bilingual sign dataset and achieved high accuracy and low latency. The contribution is notable in promoting inclusivity for hearing-impaired communities in multilingual regions like Bangladesh.

2. Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11

Authors: R.Z. Rakin, M. Rahman, K.F. Borsa, F.A. Farid, S. Rahman, J. Uddin, H.A. Karim
Journal: Future Internet, Vol. 17, Issue 5, Article 187
Year: 2025
Summary:
This paper proposes an AI model using YOLOv11 to identify infrastructure faults (e.g., road cracks, bridge damage) through image data. Designed with smart city integration in mind, the model is tested in urban environments and demonstrates high efficiency.

3. A Hybrid CNN Framework DLI-Net for Acne Detection with XAI

Authors: S. Sharmin, F.A. Farid, M. Jihad, S. Rahman, J. Uddin, R.K. Rafi, R. Hossan, ...
Journal: Journal of Imaging, Vol. 11, Issue 4, Article 115
Year: 2025
Summary:
This paper presents DLI-Net, a hybrid CNN framework for classifying and explaining acne severity. It incorporates Explainable AI (XAI) techniques to enhance trust and transparency in medical AI systems.

4. A Deep Q-Learning Based UAV Detouring Algorithm in a Constrained Wireless Sensor Network Environment

Authors: S. Rahman, S. Akter, S. Yoon
Journal: Electronics, Vol. 14, Issue 1, Article 1
Year: 2024
Citation: 2 (as of now)
Summary:
This study explores a reinforcement learning-based approach using Deep Q-Learning for UAV navigation in constrained wireless sensor networks. The algorithm optimizes path planning in real-time, even in environments with signal interference or node failures.

5. A Deep Learning Model for YOLOv9-based Human Abnormal Activity Detection: Violence and Non-Violence Classification

Authors: S. Salehin, S. Rahman, M. Nur, A. Asif, M. Bin Harun, J. Uddin
Journal: Iranian Journal of Electrical & Electronic Engineering, Vol. 20, Issue 4
Year: 2024
Citation: 2 (as of now)
Summary:
This paper proposes a YOLOv9-based model to detect abnormal human activity, particularly violent behavior, in real-time video surveillance. The system is trained on public datasets and achieves high detection accuracy.

Conclusion

Ms. Shakila Rahman is a promising and emerging researcher, with an impressive blend of academic excellence, funded research, and contributions to cutting-edge domains like machine learning and UAV networks. Her commitment to mentoring students and publishing research makes her a very strong candidate for the Best Researcher Award, particularly among early-career researchers or those in developing countries.

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.

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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.