Dr. Junaid Khan | Engineering | Young Scientist Award

Senior Engineer at Samsung Heavy Industry, South Korea

Dr. Junaid Khan is a distinguished researcher specializing in autonomous navigation systems, intelligent transportation, and deep learning applications. He earned his Ph.D. in Environmental IT Engineering from Chungnam National University, South Korea, focusing on enhancing Alpha-Beta filters with neural networks and fuzzy systems for maritime navigation. Currently, he serves as a Senior Engineer at the Autonomous Ship Research Center, Samsung Heavy Industries. Dr. Khan has made significant contributions to machine learning, maritime traffic analysis, and energy-efficient intelligent systems, reflected in his numerous high-impact journal publications and patents. His research has advanced predictive modeling techniques for vessel trajectory optimization, epileptic seizure detection, and energy consumption reduction. With a strong academic background, international collaborations, and expertise in large language models and digital twins, he continues to drive innovation in intelligent automation and smart mobility. His work bridges theoretical advancements with real-world applications, positioning him as a leading scientist in his field.

Professional Profile 

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Education

Dr. Junaid Khan holds a Ph.D. in Environmental IT Engineering from Chungnam National University, South Korea, where his research focused on enhancing Alpha-Beta filters using neural networks and fuzzy systems for improved maritime navigation. He earned his Master’s degree in Electrical Engineering from the University of Engineering and Technology (UET) Peshawar, Pakistan, specializing in machine learning and intelligent transportation systems. His academic journey laid a strong foundation in artificial intelligence, predictive modeling, and deep learning applications. Throughout his education, Dr. Khan actively engaged in interdisciplinary research, contributing to advancements in autonomous navigation, vessel trajectory optimization, and energy-efficient intelligent systems. His studies also involved extensive work in large language models, maritime traffic analysis, and epileptic seizure detection. With a solid educational background and hands-on experience in cutting-edge research, he has established himself as a leader in AI-driven smart mobility and autonomous systems, bridging theoretical knowledge with practical industry applications.

Professional Experience

Dr. Junaid Khan has extensive professional experience in artificial intelligence, autonomous navigation, and intelligent transportation systems. He is currently contributing to cutting-edge research in AI-driven smart mobility, focusing on vessel trajectory optimization, energy-efficient maritime navigation, and predictive modeling. His expertise spans deep learning, neural networks, and fuzzy logic, which he has applied to real-world problems in environmental IT engineering. Dr. Khan has worked on large-scale projects involving maritime traffic analysis, epileptic seizure detection, and autonomous system development. His industry collaborations and academic research have led to innovative solutions in smart transportation and AI-driven decision-making. Throughout his career, he has been actively involved in publishing high-impact research, mentoring students, and presenting at international conferences. With a strong technical background and hands-on experience in AI applications, Dr. Khan continues to push the boundaries of intelligent mobility, making significant contributions to both academia and industry.

Research Interest

Dr. Junaid Khan’s research interests lie at the intersection of artificial intelligence, autonomous navigation, and intelligent transportation systems. His work focuses on developing AI-driven solutions for smart mobility, including vessel trajectory optimization, energy-efficient maritime navigation, and predictive modeling for transportation networks. He is particularly interested in deep learning, neural networks, and fuzzy logic, applying these techniques to real-world challenges such as maritime traffic analysis, epileptic seizure detection, and autonomous system development. Dr. Khan’s research also explores environmental IT engineering, leveraging AI to enhance sustainability in transportation and logistics. His contributions extend to the design of intelligent decision-making systems that improve safety, efficiency, and energy conservation in autonomous vehicles. With a keen interest in interdisciplinary collaboration, he actively engages in projects that bridge AI with healthcare, maritime operations, and smart city development. Through his research, Dr. Khan aims to advance AI applications in real-world, high-impact domains.

Award and Honor

Dr. Junaid Khan has received numerous awards and honors in recognition of his outstanding contributions to artificial intelligence, autonomous navigation, and intelligent transportation systems. He has been honored with prestigious research grants and fellowships for his innovative work in AI-driven solutions for smart mobility. His contributions to vessel trajectory optimization, deep learning applications, and predictive modeling have earned him accolades from leading academic and professional organizations. Dr. Khan has also been recognized for his exceptional scholarly output, receiving awards for best research papers at international conferences. His work in interdisciplinary research, spanning maritime navigation, healthcare AI, and sustainable transportation, has been acknowledged by esteemed institutions and funding agencies. Additionally, he has been invited as a keynote speaker and session chair at various scientific gatherings, further solidifying his reputation as a leader in his field. Through these honors, Dr. Khan continues to be recognized for his pioneering contributions to AI and intelligent systems.

Research Skill

Dr. Junaid Khan’s research interests lie at the intersection of artificial intelligence, machine learning, and intelligent transportation systems, with a strong focus on autonomous navigation, vessel trajectory optimization, and predictive analytics. His work explores deep learning algorithms, reinforcement learning, and data-driven models to enhance decision-making in maritime and land-based transportation networks. He is particularly interested in developing AI-driven solutions for optimizing vessel routing, minimizing fuel consumption, and improving safety in smart mobility systems. Dr. Khan’s research also extends to healthcare applications, where he leverages machine learning techniques for medical diagnostics and predictive modeling. His interdisciplinary approach integrates AI with real-world challenges, aiming to create sustainable and efficient solutions for global transportation and healthcare industries. With a keen interest in the ethical implications of AI, he also investigates fairness, interpretability, and transparency in automated decision-making systems, ensuring that AI advancements align with societal and industrial needs.

Conclusion

Junaid Khan, Ph.D., is a strong candidate for the Young Scientist Award due to his impressive research contributions, patents, and industry experience. His work in machine learning, maritime navigation, and intelligent transportation systems showcases innovation and impact. Strengthening independent recognition and leadership roles in research projects could further enhance his suitability. Overall, he is a competitive nominee for this award.

Publications Top Noted

1. A higher prediction accuracy–based alpha–beta filter algorithm using the feedforward artificial neural network

   • Authors: J Khan, E Lee, K Kim
   • Year: 2023
   • Citations: 68

2. A comprehensive review of conventional, machine learning, and deep learning models for groundwater level (GWL) forecasting

   • Authors: J Khan, E Lee, AS Balobaid, K Kim
   • Year: 2023
   • Citations: 48

3. An improved alpha beta filter using a deep extreme learning machine

   • Authors: J Khan, M Fayaz, A Hussain, S Khalid, WK Mashwani, J Gwak
   • Year: 2021
   • Citations: 25

4. Secure and fast image encryption algorithm based on modified logistic map

   • Authors: M Riaz, H Dilpazir, S Naseer, H Mahmood, A Anwar, J Khan, IB Benitez, …
   • Year: 2024
   • Citations: 14

5. An efficient feature augmentation and LSTM-based method to predict maritime traffic conditions

   • Authors: E Lee, J Khan, WJ Son, K Kim
   • Year: 2023
   • Citations: 14

6. A performance evaluation of the alpha-beta (α-β) filter algorithm with different learning models: DBN, DELM, and SVM

   • Authors: J Khan, K Kim
   • Year: 2022
   • Citations: 14

7. An efficient methodology for water supply pipeline risk index prediction for avoiding accidental losses

   • Authors: MS Qureshi, A Aljarbouh, M Fayaz, MB Qureshi, WK Mashwani, J Khan
   • Year: 2020
   • Citations: 10

8. Optimizing the performance of Kalman filter and alpha-beta filter algorithms through neural network

   • Authors: J Khan, E Lee, K Kim
   • Year: 2023
   • Citations: 5

9. A Performance Evaluation of the AlphaBeta filter Algorithm with different Learning Modules ANN, DELM, CART and SVM

   • Authors: KK Junaid Khan
   • Year: 2022
   • Citations: 5*

10. Synthetic Maritime Traffic Generation System for Performance Verification of Maritime Autonomous Surface Ships

• Authors: E Lee, J Khan, U Zaman, J Ku, S Kim, K Kim
• Year: 2024
• Citations: 4