Yao Li | Artificial Intelligence | Best Researcher Award

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Mr. Yao Li | Artificial Intelligence | Best Researcher Award

Mr. Yao Li | Artificial Intelligence | postgraduate at National University of Defense Technology | China

Mr. Yao Li is an emerging researcher specializing in emergency response informatics, intelligent decision-support systems, and automated information-requirement generation, with a strong academic foundation developed through advanced postgraduate research training. Mr. Yao Li has built his academic profile through rigorous study in information systems engineering, data-driven modeling, and applied computational analysis, supported by research involvement within recognized academic institutions. His professional experience includes contributing to analytical projects at the National University of Defense Technology, where he supports research on complex emergency scenarios, system automation, and interdisciplinary response frameworks. His research interests span emergency decision-making systems, machine-assisted information extraction, adaptive response models, data analytics for crisis management, and integration of computational tools to strengthen situational awareness during unexpected events. Mr. Yao Li’s research skills include quantitative modeling, system design, simulation-based analysis, algorithm development, data processing, collaborative research coordination, and the application of applied analytics to real-world emergency operations. His scholarly work includes a peer-reviewed article in Applied Sciences, indexed in Scopus, highlighting automated information-requirement generation through computational techniques. Additional contributions include collaborative studies with multidisciplinary teams, participation in institutional research initiatives, and support roles in internationally aligned research programs focusing on intelligent emergency systems. Throughout his academic journey, Mr. Yao Li has demonstrated excellence in both independent and team-based research, receiving recognition for his analytical clarity, methodological discipline, and project commitment. His honors include acknowledgments for research productivity, contributions to institutional research tasks, and active engagement in academic development forums. His future research aims to advance intelligent emergency-response technologies, expand cross-domain collaboration, and contribute to impactful scientific advancements addressing real-world societal challenges. Mr. Yao Li’s growing publication record and increasing engagement with broader academic platforms reflect his potential to emerge as a significant contributor in the fields of emergency informatics and intelligent systems research. His continued dedication to methodological innovation, academic integrity, and professional growth demonstrates his readiness to assume greater research responsibilities and strengthen his contributions to global scientific progress.

Academic Profile: ORCID

Featured Publications:

Li, Y., Guo, C., Lu, Z., Zhang, C., Gao, W., Liu, J., & Yang, J. (2025). Research on the automatic generation of information requirements for emergency response to unexpected events. Applied Sciences.

 

Prerna Chaudhary | Machine Learning | Best Researcher Award

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Ms. Prerna Chaudhary | Machine Learning | Best Researcher Award

Ms. Prerna Chaudhary | Machine Learning | PhD student at IIT DELHI | India

Ms Prerna Chaudhary is an accomplished researcher and scholar specializing in machine learning applications for wireless communication. She earned her Ph.D. from the Indian Institute of Technology-Delhi, where her research focused on advanced channel estimation techniques, adaptive filtering, and signal processing in non-Gaussian environments. Her professional experience includes contributing to international collaborative research projects and working with leading experts such as Prof. Manav R. Bhatnagar and B.R. Manoj, reflecting her strong collaborative and interdisciplinary capabilities. Ms Chaudhary’s research interests encompass machine learning in wireless communications, adaptive signal processing, OFDM systems, and jamming detection. She possesses a diverse set of research skills, including expertise in linear regression models, unscented Kalman filters, algorithm development, data analysis, and experimental design, which have enabled her to address complex problems in modern wireless systems. Throughout her academic career, Ms Chaudhary has achieved recognition for her impactful research contributions, including publications in high-impact IEEE and Scopus-indexed journals, presenting at prestigious international conferences, and receiving institutional awards for excellence in research and innovation. Her notable strengths include methodological rigor, innovative problem-solving, collaborative leadership, and the ability to translate theoretical insights into practical implementations. Areas for development include expanding her research impact through increased citations and assuming leadership in large-scale, multi-institutional projects. Ms Chaudhary is committed to mentoring emerging researchers, participating in professional societies such as IEEE and ACM, and contributing to the global research community through knowledge sharing and international collaborations. Looking ahead, she aims to pursue cross-disciplinary research initiatives and explore opportunities for translating her work into real-world applications, ensuring that her research continues to have a meaningful impact on the field of wireless communication. Ms Prerna Chaudhary’s consistent record of publications, research excellence, and professional engagement establishes her as a leading figure in her domain and a deserving candidate for recognition and awards.

Academic Profile: Google Scholar

Featured Publications:

  1. Chaudhary, P., Chauhan, I., Manoj, B. R., & Bhatnagar, M. R. (2024). Linear Regression-Based Channel Estimation for Non-Gaussian Noise. IEEE 99th Vehicular Technology Conference (VTC2024-Spring). Citation: 2

  2. Chaudhary, P., Manoj, B. R., Chauhan, I., & Bhatnagar, M. R. (2025). Channel Estimation using Linear Regression with Bernoulli-Gaussian Noise.

  3. Srivastava, S., Chaudhary, P., & Bhatnagar, M. R. (2024). Comparative Analysis of Machine Learning Algorithms for Pulse Jammer Detection. IEEE International Conference on Advanced Networks and …. Citation: 0

  4. Chaudhary, P., Manoj, B. R., Patidar, V. K., & Bhatnagar, M. R. (2024). Adaptive Unscented Kalman Filter for Time Varying Channel Estimation in OFDM Systems. IEEE International Conference on Advanced Networks and ….