Jyoti Katyal | Physics and Astronomy | Best Researcher Award

Dr. Jyoti Katyal | Physics and Astronomy | Best Researcher Award

Assistant Professor atAmity Institute of Applied Science, Amity University, Noida, India

Dr. Jyoti Katyal, an Assistant Professor at Amity University, Noida, holds a PhD from the prestigious Indian Institute of Technology (IIT) Delhi, with expertise in plasmonic nanostructures and their applications in biosensors and SERS substrates. With over a decade of research experience, her work focuses on computational modeling and optimization of metallic nanostructures across the deep-UV to NIR spectrum, aiming to enhance refractive index sensitivity and field enhancement properties. She has an impressive publication record with numerous Scopus-indexed research papers, book chapters, and conference presentations. Dr. Katyal has also received several recognitions, including the International Best Researcher Award (ISSN-2022) and Best Paper Award at ICADMA 2020. Besides her research contributions, she actively participates in academic administration, quality assurance, and mentoring students. Her dedication to advancing plasmonics research and her involvement in organizing scientific events make her a strong candidate for the Best Researcher Award.

Professional Profile 

Education

Dr. Jyoti Katyal has a strong academic background rooted in prestigious institutions and multidisciplinary research. She earned her PhD from the renowned Indian Institute of Technology (IIT) Delhi, specializing in plasmonic nanostructures and their applications in biosensors and Surface-Enhanced Raman Spectroscopy (SERS). Her doctoral research combined computational modeling and experimental techniques to design and optimize metallic nanostructures across various spectral ranges, from deep-UV to near-infrared (NIR). Prior to her PhD, she completed her Master’s and Bachelor’s degrees with a focus on physics and nanotechnology, equipping her with a solid foundation in material science, optics, and sensor development. Throughout her educational journey, Dr. Katyal developed expertise in advanced computational tools, numerical simulation techniques, and analytical characterization methods. Her academic training, enriched by research fellowships and collaborative projects, has laid a strong foundation for her contributions to academia, scientific innovation, and mentoring the next generation of researchers.

Professional Experience

Dr. Jyoti Katyal has built a diverse and impactful professional career, combining academic excellence with innovative research and collaborative projects. Currently, she serves as an Assistant Professor at Netaji Subhas University of Technology (NSUT), Delhi, where she mentors students and leads cutting-edge research in nanotechnology, plasmonics, and sensor development. Her professional journey includes extensive post-doctoral research experience at prestigious institutions, where she worked on interdisciplinary projects involving biosensors, nanomaterials, and advanced computational modeling. Dr. Katyal has also collaborated with leading national and international researchers, contributing to high-impact publications in reputed journals. Her expertise spans both theoretical and experimental research, allowing her to bridge the gap between computational design and real-world applications. In addition to her research, Dr. Katyal actively participates in academic committees, curriculum development, and research grant proposals, making significant contributions to the advancement of scientific knowledge and fostering innovation-driven education in the field of nanotechnology and materials science.

Research Interest

Dr. Jyoti Katyal is primarily interested in plasmonic nanostructures and their applications, with a focus on designing and optimizing metallic nanostructures for biosensing, plasmonic sensors, and SERS substrates. Her research explores computational modeling techniques using advanced simulation tools such as Lumerical’s FDTD software to analyze plasmonic responses across the deep-UV, visible, and near-infrared spectra. She investigates how variations in size, shape, and material composition influence localized surface plasmon resonances, field enhancement, and refractive index sensitivity. By developing novel nanostructured configurations, Dr. Katyal aims to enhance sensing performance and broaden spectral applicability. Her work also extends to optimizing plasmonic multilayered systems and exploring hetero-dimer or -trimer structures, with a keen focus on achieving high figures of merit for biosensing applications. This interdisciplinary research bridges materials science, optics, and nanotechnology, contributing significantly to advanced sensor design and functional nanomaterials. Continuously pushing boundaries, her innovative work promises next-generation diagnostic tools and breakthrough applications.

Awards and Honors

Dr. Jyoti Katyal has been recognized with several prestigious awards and honors that underscore her significant contributions to research and academia. Notably, she received the International Best Researcher Award from the International Society for Scientific Network Awards in 2022 for her work on the theoretical study of Magnetic-Plasmonic Fe-Al core-shell nanostructures for sensing applications. Additionally, she was honored with the Best Paper Award at the International Conference on Advances in Materials Processing & Manufacturing Applications (ICADMA 2020) for her research on localized surface plasmon resonance and field enhancement in metallic nanostructures. Her expertise and innovative work have earned her invitations as a jury member for research evaluations at IIT Delhi Open House 2024 and as an invited speaker at high-profile conferences such as ICRTMD-2023. Further, her active role as a reviewer and editorial board member for renowned journals reflects her esteemed position in the scientific community. Her work remains impactful.

Research Skills

Dr. Jyoti Katyal exhibits exceptional research skills underpinned by a robust foundation in theoretical and experimental methodologies. Her expertise encompasses advanced computational modeling techniques and the proficient use of simulation tools, such as Lumerical’s FDTD software, to analyze and optimize plasmonic nanostructures. Through meticulous design and systematic investigation, she explores size, shape, and material parameters to enhance localized surface plasmon resonances, field enhancement, and refractive index sensitivity. Her work reflects a deep understanding of the interplay between material properties and optical phenomena, enabling her to innovate sensor designs for biosensing and surface-enhanced Raman scattering applications. Dr. Katyal demonstrates strong analytical thinking, attention to detail, and a rigorous approach to hypothesis testing and data interpretation. Her collaborative mindset and leadership in guiding graduate research further amplify her ability to produce high-impact scientific contributions and foster advancements in nanotechnology and materials science. Her relentless pursuit of excellence consistently drives transformative global discoveries.

Conclusion

Dr. Jyoti Katyal’s track record, research focus, publication record, invited talks, peer-review responsibilities, and awards make her a highly deserving candidate for a Best Researcher Award. Her work in plasmonic nanostructures and biosensors is highly relevant to current scientific and technological challenges.

If the award criteria prioritize publication volume, conference participation, and academic engagement, she is highly suitable.
If the focus is on high-impact publications, funded projects, patents, or industry collaboration, some minor gaps exist, but they do not significantly detract from her overall suitability.

Publications Top Noted

  • Katyal, J., & Soni, R.K. (2013). Size- and shape-dependent plasmonic properties of aluminum nanoparticles for nanosensing applications. Journal of Modern Optics, 60(20), 1717–1728.
  • Katyal, J., & Soni, R.K. (2014). Localized surface plasmon resonance and refractive index sensitivity of metal–dielectric–metal multilayered nanostructures. Plasmonics, 9, 1171–1181.
  • Katyal, J. (2021). Localized surface plasmon resonance and field enhancement of Au, Ag, Al and Cu nanoparticles having isotropic and anisotropic nanostructure. Materials Today: Proceedings, 44, 5012–5017.
  • Katyal, J., & Soni, R.K. (2015). Field enhancement around Al nanostructures in the DUV–visible region. Plasmonics, 10, 1729–1740.
  • Katyal, J. (2018). Plasmonic coupling in Au, Ag and Al nanosphere homo-dimers for sensing and SERS. Advanced Electromagnetics, 7(2), 83–90.
  • Katyal, J. (2019). Comparison of localised surface plasmon resonance and refractive index sensitivity for metallic nanostructures. Materials Today: Proceedings, 18, 613–622.
  • Faujdar, S., Pathania, P., & Katyal, J. (2022). Systematic investigation of transition metal nitrides (ZrN, TiN) based plasmonic multilayered core–shell nanoparticle for sensing. Materials Today: Proceedings, 57, 2295–2298.
  • Sharma, C., Katyal, J., Deepanshi, & Singh, R. (2023). Effect of monomers and multimers of gold nanostars on localized surface plasmon resonance and field enhancement. Plasmonics, 18(6), 2235–2245.
  • Katyal, J. (2020). Al-Au heterogeneous dimer–trimer nanostructure for SERS. Nanoscience & Nanotechnology-Asia, 10(1), 21–28.
  • Katyal, J. (2019). Comparative Study Between Different Plasmonic Materials and Nanostructures for Sensor and SERS Application. In Reviews in Plasmonics (pp. 77–108).
  • Sharma, C., Katyal, J., & Singh, R. (2023). Aluminum Nano Stars with Localized Surface Plasmon Resonance and Field Enhancement. Nanoscience & Nanotechnology-Asia, 13(4), 57–64.
  • Sharma, C., Katyal, J., & Singh, R. (2023). Plasmon Tunability and Field Enhancement of Gold Nanostar. Nanoscience & Nanotechnology-Asia, 13(3), 13–18.
  • Faujdar, S., Nayal, A., Katyal, J., & Pathania, P. (2025). Simulation of TiN Nanospheres, Nanoellipsoids, and Nanorings for Enhanced Localized Surface Plasmon Resonance and Field Amplification. ChemistrySelect, 10(9), e202404987.
  • Yashika & Katyal, J. (2024). Detailed Analysis of Size and Shape of TiN Nanostructure on Refractive Index-Based Sensor. Plasmonics, 1–11.
  • Katyal, J. (2022). Plasmonic Properties of Al2O3 Nanoshell with a Metallic Core. Micro and Nanosystems, 14(3), 243–249.

Junaid Khan | Engineering | Young Scientist Award

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 

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