Tianying Chang | Data Engineering | Research Excellence Award

Prof. Tianying Chang | Data Engineering | Research Excellence Award

Professor | Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences | China

Tianying Chang focuses on optical sensing, fiber optic systems, and terahertz spectroscopy. Their research advances high-sensitivity measurement techniques, distributed acoustic sensing, and signal processing methods. With strong contributions to instrumentation and photonics, they develop innovative models and algorithms for real-time monitoring, detection, and analysis in engineering and applied physics domains.

Citation Metrics (Scopus)

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Citations
2361

Documents
162

h-index
29


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Featured Publications

Phase Correction Based on Adaptive Fading Feedback in Distributed Fiber Acoustic Sensing Systems
– IEEE Transactions on Instrumentation and Measurement, 2025 | Citations: 1

Terahertz spectroscopy studies on dielectric and thermal stability properties of polymer resins
– Journal of the Optical Society of America B, 2025 

Distributed Fiber Optic Acoustic Sensing System Based on Fading Mask Autoencoder and Application in Water Navigation Security Events Identification
– Acta Photonica Sinica, 2025 

Tunnel damage detection based on finite element simulation and optical fiber sensing
– Infrared and Laser Engineering, 2024 | Citations: 2

Accurate detection of neotame hydrates and their transformation using terahertz spectroscopy
– Infrared Physics and Technology, 2024 | Citations: 2

Mahendra Gaikwad | Machine Learning | Best Researcher Award

Dr. Mahendra Gaikwad | Machine Learning | Best Researcher Award

Assistant Professor at Veermata Jijabai Technological Institute (VJTI) | Mumbai | India

Dr. Mahendra Uttam Gaikwad is a forward-thinking mechanical and manufacturing engineering professional whose work reflects a deep commitment to advancing modern machining, smart materials research, sustainable manufacturing, and AI-driven optimization in industrial systems. Renowned for his ability to bridge theoretical innovation with practical engineering applications, he has built a strong scholarly footprint through impactful publications in SCI and Scopus-indexed journals, contributions to influential book chapters, and editorial leadership in notable international volumes focused on advanced materials and digital-age manufacturing. His research explores critical themes such as electrical discharge machining, surface integrity analysis, optimization algorithms, additive manufacturing, fatigue modelling, and machine learning applications in production environments, consistently demonstrating an aptitude for tackling complex engineering challenges through empirical investigation and computational modelling. In addition to his academic contributions, he has shown commendable innovation through multiple national and international patents addressing smart systems, sustainable material utilization, and intelligent manufacturing solutions. He has also been an active collaborator with academic institutions, research groups, and industry partners, contributing to advancements in machining automation, performance benchmarking, and data-driven design methodologies. A dedicated mentor, he has guided numerous undergraduate and postgraduate research projects, fostering a research-oriented learning environment and supporting the next generation of engineers. His work as a reviewer, conference contributor, and knowledge disseminator further underscores his commitment to strengthening global engineering discourse. Known for his leadership qualities, professional integrity, and continuous pursuit of technological excellence, Dr. Gaikwad has earned recognition for his contributions to teaching and research, positioning himself as a noteworthy contributor to the evolving landscape of smart and sustainable manufacturing.

Profiles: ORCID | Google Scholar

Featured Publications

Gaikwad, M. U., Somatkar, A. A., Ghadge, M., Majumder, H., Shinde, A. M., & Lohakare, A. V. (2025). Effect of dry and wet machining environments on surface quality of Al6061 using particle swarm optimization (PSO).

Sargar, T., Gautam, N. K., Jadhav, A., & Gaikwad, M. U. (2025). A comparative investigation of kerf width during CO₂ and fiber laser machining of SS 316L material.

Khan, M. A. J., Pohekar, S. D., Bagade, P. M., Gaikwad, M. U., & Singh, M. (2025). CFD analysis of NACA 4415 marine propeller ducts for managing flow separation.

Nishandar, S. V., Pise, A. T., Bagade, P. M., Gaikwad, M. U., & Singh, A. (2025). Computational modelling and analysis of heat transfer enhancement in straight circular pipe with pulsating flow.

Gaikwad, M. U., Gaikwad, P. U., Ambhore, N., Sharma, A., & Bhosale, S. S. (2025). Powder bed additive manufacturing using machine learning algorithms for multidisciplinary applications: A review and outlook.