Shayan Ghazimoghadam | Structural Health Monitoring | Best Researcher Award

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Mr. Shayan Ghazimoghadam | Structural Health Monitoring | Best Researcher Award

PhD Student, Islamic Azad University, Iran

Shayan Ghazimoghadam is a Ph.D. student in Structural Engineering at Islamic Azad University of Shahrood, Iran, specializing in data-driven structural health monitoring. His research integrates artificial intelligence with civil engineering to develop unsupervised deep learning methods for real-time damage detection in structures. Shayan’s work focuses on creating digital twins for infrastructure assessment, aiming to enhance predictive maintenance and safety. He has authored several publications, including a notable paper on vibration-based damage diagnosis using multi-head self-attention LSTM autoencoders, published in Measurement journal. Additionally, he has presented his research at national conferences and served as a keynote speaker, demonstrating his commitment to advancing the field of structural health monitoring.

Profile

Google Scholar​

Education

Shayan Ghazimoghadam completed his Bachelor of Science in Civil Engineering at Islamic Azad University of Gorgan, Iran, in 2018, where he excelled in design courses, achieving a GPA of 19/20 in Steel Structures and a perfect 20/20 in Concrete Structures. He then pursued a Master of Science in Structural Engineering at Lamei Gorgani Institute of Higher Education, Gorgan, graduating in 2022 with a GPA of 19.07/20. His master’s dissertation focused on structural damage identification under ambient vibration using an unsupervised deep learning method, supervised by Dr. Seyed Ali Asghar Hosseinzadeh. Currently, Shayan is a Ph.D. student at Islamic Azad University of Shahrood, Iran, where he continues to explore innovative approaches in structural health monitoring and artificial intelligence applications in civil engineering.​

Experience 

Between 2022 and 2023, Shayan Ghazimoghadam served as a Research Assistant at Golestan University, Gorgan, Iran, under the guidance of Dr. Seyed Ali Asghar Hosseinzadeh. During this period, he conducted research on real-time structural health monitoring utilizing AI-powered techniques. His work involved developing and testing unsupervised deep learning algorithms for damage detection in structures based on vibration data. Shayan’s contributions led to the presentation of his findings at national conferences, showcasing his ability to communicate complex research outcomes effectively. This experience has significantly enhanced his expertise in integrating artificial intelligence with structural engineering, positioning him as a promising researcher in the field of structural health monitoring.​

Awards and Honors 

Shayan Ghazimoghadam has been recognized for his academic excellence and research contributions. He ranked first among M.Sc. students in Structural Engineering at Lamei Gorgani Institute of Higher Education, Gorgan, Iran, in 2022, achieving a GPA of 19.07/20. His innovative research on structural damage identification using unsupervised deep learning methods has been published in reputable journals, including the Measurement journal, where his paper has garnered 26 citations as of 2024. Additionally, Shayan was invited as a keynote speaker at the 3rd National Conference on Civil Engineering, Intelligent Development, and Sustainable Systems in 2023, where he presented on AI-powered structural damage identification and localization through accelerometer data. These accolades underscore his commitment to advancing the field of structural health monitoring and his potential for future contributions to civil engineering research.​

Research Focus

Shayan Ghazimoghadam’s research focuses on the integration of artificial intelligence with structural health monitoring (SHM) to develop innovative solutions for infrastructure maintenance. His primary interests include data-driven SHM, unsupervised structural damage identification, and the application of digital twins for condition assessment. Shayan aims to enhance the accuracy and efficiency of damage detection in structures by employing unsupervised deep learning techniques, particularly multi-head self-attention LSTM autoencoders. His work contributes to the development of digital twins, virtual representations of physical assets, to monitor and assess the condition of infrastructure in real time. By leveraging AI and machine learning, Shayan seeks to revolutionize traditional SHM practices, offering more proactive and predictive maintenance strategies that can lead to safer and more sustainable infrastructure systems.

Publication Top Notes​

📘 1. A Novel Unsupervised Deep Learning Approach for Vibration-Based Damage Diagnosis Using a Multi-Head Self-Attention LSTM Autoencoder

Authors: S. Ghazimoghadam, S.A.A. Hosseinzadeh
Journal: Measurement, Volume 229, Article 114410
Year: 2024
Citations (as of 2025): 26
DOI: Measurement 229, 114410 (sample link, please verify)

🔍 Summary:

This publication introduces a novel unsupervised deep learning method for real-time structural damage detection using only ambient vibration data. The approach combines Long Short-Term Memory (LSTM) autoencoders with multi-head self-attention mechanisms, enabling the system to effectively learn temporal features and focus on critical data patterns without the need for labeled damage data.

By leveraging unsupervised learning, the model is highly adaptable and scalable, making it suitable for practical deployment in real-world structural health monitoring (SHM) scenarios. The method was validated using benchmark datasets, showing superior performance in damage localization and diagnosis accuracy compared to traditional approaches.

📗 2. Transformer-Based Time-Series GAN for Data Augmentation in Bridge Monitoring Digital Twins

Authors: V. Mousavi, M. Rashidi, S. Ghazimoghadam, M. Mohammadi, B. Samali
Journal: Automation in Construction, Volume 175, Article 106208
Year: 2025 (Under Review)
DOI: Automation in Construction 175, 106208 (check for final link when published)

🔍 Summary:

This paper presents a Transformer-based Generative Adversarial Network (GAN) for augmenting time-series sensor data in bridge monitoring systems. The technique is particularly geared towards Digital Twin models, which require large, diverse, and high-quality datasets to simulate and predict structural behavior accurately.

The GAN architecture uses a Transformer encoder to better capture temporal dependencies in structural response data, generating realistic synthetic datasets for training SHM models. By augmenting scarce or incomplete datasets, this method improves predictive performance, anomaly detection, and damage assessment capabilities of digital twins used in civil infrastructure.

Bowen Wang | MEMS Accelerometer | Best Researcher Award

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Dr Bowen Wang | MEMS Accelerometer | Best Researcher Award

PhD student, Aerospace Information Research Institute, Chinese Academy of Sciences, China

Bowen Wang is a dedicated Ph.D. candidate at the State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Beijing, China. He holds a B.Eng in Electronic Science and Technology from Xi’an Jiaotong University (2020). Bowen specializes in Micro-Electromechanical Systems (MEMS), focusing on resonant accelerometers, bulk acoustic wave gyroscopes, and coupled MEMS resonators. His innovative research includes developing mathematical models for mode-localized resonant accelerometers and pioneering empirical response models for weakly coupled resonators. Bowen has co-authored multiple high-impact journal papers and presented at prestigious conferences like IEEE INERTIAL. His work bridges theoretical exploration with practical applications, advancing MEMS technology and its industrial relevance. Passionate about knowledge dissemination, Bowen actively contributes to the academic community, sharing insights that enhance understanding of emerging MEMS technologies.

Profile

Orcid

Education 🎓

Bowen Wang completed his Bachelor of Engineering (B.Eng) in Electronic Science and Technology at Xi’an Jiaotong University, China, in 2020. Currently, he is pursuing his Ph.D. at the State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Beijing, China. His academic journey reflects a strong foundation in electronics and a deep dive into advanced MEMS technologies. Bowen’s Ph.D. research focuses on resonant accelerometers, bulk acoustic wave gyroscopes, and coupled MEMS resonators, combining theoretical modeling with experimental validation. His educational background equips him with a robust understanding of both fundamental principles and cutting-edge innovations in MEMS, enabling him to contribute significantly to the field. Bowen’s academic excellence is further demonstrated through his high-impact publications and active participation in international conferences, showcasing his commitment to advancing MEMS research and applications.

Experience 💼

Bowen Wang has gained extensive research experience during his Ph.D. at the State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Beijing. His work involves designing and optimizing MEMS devices, particularly resonant accelerometers and bulk acoustic wave gyroscopes. Bowen developed innovative mathematical models to predict the behavior of mode-localized resonant accelerometers, establishing the first empirical response model for weakly coupled resonators. He has collaborated on multiple high-impact research projects, resulting in publications in top-tier journals like Micromachines and IEEE Sensors Journal. Bowen has also presented his findings at international conferences, including the IEEE International Symposium on Inertial Sensors and Systems (INERTIAL). His hands-on experience in MEMS fabrication, modeling, and testing has honed his technical expertise, making him a valuable contributor to the field. Bowen’s research bridges theoretical insights with practical applications, driving advancements in MEMS technology.

Awards and Honors 🏆

Bowen Wang has earned recognition for his outstanding contributions to MEMS research. His work on mode-localized resonant accelerometers and weakly coupled resonators has been published in high-impact journals and presented at prestigious conferences like IEEE INERTIAL. Bowen’s innovative research has been acknowledged for its potential to advance MEMS technology, particularly in enhancing sensor sensitivity and performance. His collaborative efforts have resulted in multiple peer-reviewed publications, showcasing his ability to contribute meaningfully to the academic community. While specific awards are not listed, Bowen’s consistent publication record and active participation in international conferences highlight his dedication and excellence in the field. His research has not only deepened the understanding of MEMS principles but also paved the way for practical applications in inertial sensors and systems. Bowen’s achievements reflect his commitment to pushing the boundaries of MEMS technology and its industrial relevance.

Research Focus 🔍

Bowen Wang’s research focuses on advancing Micro-Electromechanical Systems (MEMS), particularly resonant accelerometers, bulk acoustic wave gyroscopes, and coupled MEMS resonators. His work emphasizes mode-localized resonant accelerometers, where he has developed innovative mathematical models to predict device behavior and established the first empirical response model for weakly coupled resonators. Bowen’s research bridges theoretical exploration with practical applications, enhancing sensor sensitivity, performance, and robustness against manufacturing defects. He also investigates novel coupling structures and noise analysis models to optimize MEMS device performance. His contributions have led to high-impact publications and presentations at international conferences, showcasing his ability to address complex challenges in MEMS design and fabrication. Bowen’s research not only deepens the understanding of MEMS principles but also drives advancements in inertial sensors and systems, making significant strides in both academic and industrial applications.

Publication Top Notes📚

  • A Mode-Localized Micro-Electromechanical System Accelerometer with Force Rebalance Closed-Loop Control
  • A Mode-localized Resonant Accelerometer Based on A Novel Micro-lever Coupler Resistant to Manufacture Process Defects
  • Bridging Piezoelectric And Electrostatic Effects: A Novel Pitch/Roll Gyroscope
  • Utilizing Mechanical Micro-lever Coupling Structure to Enhance Sensitivity in Mode-localized MEMS Accelerometer
  • Comparing Different Output Metrics of High-Resolution MEMS Weakly Coupled Resonant Tilt Sensors
  • A Decouple-Decomposition Noise Analysis Model for Closed-loop Mode-localized Tilt Sensors

 

Chhatrasal Gaynerc | Sensor technology | Excellence in Innovation

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Dr. Chhatrasal Gaynerc | Sensor technology | Excellence in Innovation

Dy Manager (Team Lead), Thornton R&D, Mettler Toledo Pvt Ltd, India

Dr. Chhatrasal Gayner is a dedicated postdoctoral researcher in Materials Science and Engineering at the Technion – Israel Institute of Technology since January 2019. With a robust background in thermoelectrics, he specializes in the electrical and thermal transport properties of semiconductors, contributing significantly to the field through innovative research and development. 🌟

Publication Profile

ORCID

Education

Dr. Gayner earned his Ph.D. in Materials Science from the Indian Institute of Technology Kanpur, where he focused on advanced materials. Prior to this, he completed his M.Tech in Materials and Metallurgical Engineering from Visvesvaraya National Institute of Technology and an MSc in Physics from Rashtrasant Tukadoji Maharaj Nagpur University. 📚

Experience

His professional journey includes a research position at Yonsei University in Seoul, South Korea, from January 2017 to September 2018, where he engaged in mechanical engineering research. Dr. Gayner’s expertise spans various facets of materials physics and structural characterization, leveraging techniques like XRD, TEM, XPS, and Raman spectroscopy. 🔬

Research Interests

Dr. Gayner’s research interests revolve around thermoelectric materials, focusing on optimizing electrical and thermal transport properties. He explores the structural characterization of semiconductors, aiming to enhance their performance through innovative synthesis and microstructure manipulation. 🔍

Awards

While specific awards are not detailed, Dr. Gayner’s contributions to the field of materials science are recognized through his impactful publications and active participation in cutting-edge research projects. 🏆

Publications

Here are Dr. Chhatrasal Gayner’s notable publications:

Development of Nanostructured Bi2Te3 with High Thermoelectric Performance by Scalable Synthesis and Microstructure Manipulations
Published in ACS Applied Materials & Interfaces, 2023
DOI: 10.1021/acsami.2c21561

Topologically-Enhanced Thermoelectric Properties in Bi2Te3-Based Compounds: Effects of Grain Size and Misorientation
Published in ACS Applied Materials & Interfaces, 2022
DOI: 10.1021/acsami.2c12843

Effects of Co-doping and Microstructure on Charge Carrier Energy Filtering in Thermoelectric Titanium-Doped Zinc Aluminum Oxide
Published in ACS Applied Materials & Interfaces, 2022
DOI: 10.1021/acsami.1c20300

Effects of Microstructure and Neodymium Doping on Bi2Te3 Nanostructures: Implications for Thermoelectric Performance
Published in ACS Applied Nano Materials, 2021
DOI: 10.1021/acsanm.0c03472

Salah Ud Din | Flexible and Wearable Sensors | Best Researcher Award

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Dr Salah Ud Din | Flexible and Wearable Sensors | Best Researcher Award

Dr Salah Ud Din, Southern University of Science and Technology, China

Dr. Salah Ud Din is a leading researcher in the field of 2D TMDCS materials with a specialization in thin film MoS2. With extensive hands-on experience in AAO substrates and advanced deposition techniques (CVD, PVD, ALD), Dr. Din has made significant contributions to the development of flexible, wearable electronics and strain-insensitive biosensors. His work spans innovative semiconductor-based gas sensors, photocatalysis, and renewable energy solutions. Currently a Postdoctoral Fellow at Southern University of Science and Technology, Shenzhen, Dr. Din is known for his pioneering research, mentorship of students, and development of cutting-edge materials for diverse applications.

Publication Profile

Orcid

Strengths for the Award

Dr. Salah Ud Din has demonstrated a strong commitment to pioneering research in the field of 2D TMDCS materials, particularly focusing on MoS2 thin films. His expertise extends across various advanced material fabrication techniques such as CVD, PVD, and ALD, which are crucial for developing next-generation flexible and wearable electronics. His work on biosensors, photocatalysis, and gas sensors showcases his versatility and dedication to eco-friendly renewable energy solutions. Furthermore, his hands-on experience in mentoring students and leading research projects adds to his credentials as a potential recipient of the award. Dr. Salah’s numerous publications in high-impact journals reflect his significant contributions to the field, making him a strong candidate.

Areas for Improvement

While Dr. Salah Ud Din has a robust background in materials science and engineering, expanding his research to include more interdisciplinary collaborations could enhance his impact. Engaging in more international conferences, workshops, and collaborative research projects may further solidify his global standing in the scientific community. Additionally, focusing on securing more independent funding or leading larger, multi-institutional grants could elevate his profile as a leading researcher in his field.

Education

Dr. Salah Ud Din earned his PhD in Material Science and Engineering from Zhejiang University, China, where he focused on oxide semiconductors and their applications in gas sensors. He completed his MS in Physics at International Islamic University, Islamabad, and his Master’s and Bachelor’s degrees in Physics at Gomal University, Dera Ismail Khan, Pakistan. His academic journey reflects a strong foundation in both theoretical and practical aspects of materials science.

Experience

Dr. Salah Ud Din is currently a Postdoctoral Fellow at Southern University of Science and Technology, Shenzhen, specializing in stretchable and wearable bionic sensors, with a focus on high-performance ion sensing and gesture recognition. Previously, he served as a Postdoctoral Fellow at Westlake University, Hangzhou, where he worked on advanced wearable sensors and CNT-based gesture recognition devices. His earlier role as a Senior Lecturer at Government College University Faisalabad involved teaching advanced physics and guiding students in research projects, showcasing his broad expertise and leadership in academia.

Awards and Honors

Dr. Salah Ud Din has received numerous accolades for his outstanding academic and research contributions. These include Best Performance Certificates from the Election Commission of Pakistan and Government College University Faisalabad, as well as the Golden Category Award for Best Poster Presentation at Zhejiang University. He also earned the Prime Minister of Pakistan’s Scholarship and various merit-based awards. Notable distinctions include the First and Third Prizes in running competitions at Westlake University, reflecting his dedication and excellence both in research and extracurricular achievements.

Research Focus

Dr. Salah Ud Din’s research is at the forefront of materials science, concentrating on 2D TMDCS materials, including MoS2 thin films. His work explores flexible and wearable electronic materials, strain-insensitive biosensors, and innovative applications in eco-friendly renewable energy solutions. His research involves the development of novel gas sensors, advancements in photocatalysis, solar devices, and optoelectronics, driven by his expertise in various deposition and analytical techniques.

Publication Top Notes

  • Design and Synthesis of α-Bi2Mo3O12/CoSO4 Composite Nanofibers for High Performance SO2F2 Sensor at Room Temperature 📰
  • Low-Temperature Detection of Sulfur-Hexafluoride Decomposition Products Using Octahedral Co3O4-Modified NiSnO3 Nanofibers 🧪
  • A Novel Ethanol Gas Sensor Based on α-Bi2Mo3O12/Co3O4 Nanotube-Decorated Particles 📉
  • Development of High-Performance Sensor Based on NiO/SnO2 Heterostructures to Study Sensing Properties Towards Various Reducing Gases 🔬
  • Double Step Modulation and Investigation of Room Temperatures Gas Sensing Performance of SF6 Decomposition Byproduct SO2F2 ⚗️
  • One Step Facile Synthesis, Characterization, and Antimicrobial Properties of Mg-Doped CuO Nanostructures 🧫
  • Ultrasensitive Sensors Based on PdO@SrFe2O4 Nanosphere-Modified Fibers for Real-Time Monitoring of Ethanol Gas 📊

Conclusion

Dr. Salah Ud Din’s extensive experience and notable achievements in material science, particularly in the development of advanced biosensors and renewable energy solutions, position him as a strong contender for the Best Researcher Award. His continued focus on innovation, mentorship, and broadening the scope of his research would further enhance his eligibility and strengthen his bid for this recognition.