Chang He | Composite structures | Best Researcher Award

Mr. Chang He | Composite structures | Best Researcher Award 

PHD student at Tongji University, China

Chang He is a dedicated Ph.D. student in Civil Engineering at Tongji University, Shanghai, where he has distinguished himself through exemplary academic performance and significant contributions to research. With a strong foundation in Civil and Hydraulic Engineering, he has garnered recognition for his innovative approach to integrating smart materials with traditional construction techniques. His commitment to advancing the field of civil engineering is evident in his participation in various high-impact research projects and his proactive engagement in scholarly activities.

Profile

ORCID

Education

Chang He began his academic journey at Shenyang Jianzhu University, where he earned his Bachelor’s degree in Civil Engineering with a commendable GPA of 87.6/100. He was recognized for his academic excellence through several awards, including the Merit Student Award and multiple scholarships. Pursuing further education, he obtained his Master’s degree in Civil and Hydraulic Engineering from Tongji University, achieving a GPA of 84.5/100. Currently, he is advancing his studies as a Ph.D. student in Civil Engineering, where he maintains an impressive GPA of 89.5/100, demonstrating his commitment to academic rigor and research excellence.

Experience

Chang He’s research experience is extensive and multifaceted. He has actively participated in several prominent research projects, including the NSFC Project focused on the integration of spherical piezoelectric smart materials with concrete, and the development of disaster acquisition robot equipment under the National Key R&D Program of China. His involvement in these projects has allowed him to gain hands-on experience in cutting-edge research methodologies and technologies, particularly in the context of structural health monitoring and disaster management. Additionally, he has contributed to the academic community as a reviewer for notable journals, further enhancing his understanding of current research trends and standards.

Research Interest

Chang He’s research interests lie at the intersection of civil engineering and advanced technology. His primary focus includes the application of machine learning and artificial intelligence to analyze and optimize the performance of construction materials and structures. He is particularly interested in exploring how innovative materials, such as fiber-reinforced polymers, can be integrated into traditional concrete structures to enhance their durability and resilience. By leveraging deep learning techniques, Chang aims to develop predictive models that can inform engineering practices and improve the safety and efficiency of civil engineering projects.

Awards

Throughout his academic career, Chang He has received several awards and honors that reflect his dedication to excellence in education and research. Notably, he was awarded the Social Work Scholarship twice, highlighting his commitment to community engagement and social responsibility. Additionally, he received the Second Prize Scholarship twice during his master’s studies, as well as the Third Prize Scholarship and the Merit Student Award during his undergraduate years. These accolades serve as a testament to his hard work, perseverance, and contributions to the academic community.

Publications

Chang He has authored and co-authored several research publications in esteemed journals, demonstrating his commitment to advancing knowledge in his field. His notable works include:

Deep Learning-Based Analysis of Interface Performance between Brittle Engineering Materials and Composites (Expert Systems with Applications, 2024).

Hyperparameter optimization for interfacial bond strength prediction between fiber-reinforced polymer and concrete (Structures, 2023).

Bayesian optimization for selecting efficient machine learning regressors to determine bond-slip model of FRP-to-concrete interface (Structures, 2022).

Semi-supervised networks integrated with autoencoder and pseudo-labels propagation for structural condition assessment (Measurement, 2023).

Application of Bayesian optimization approach for modelling bond-slip behavior of FRP-to-concrete interface (Proceedings of the 12th International Conference on Structural Health Monitoring of Intelligent Infrastructure, 2023).

An acoustic-homologous deep learning method for FRP concrete interfacial damage evaluation (Proceedings of the 12th International Conference on Structural Health Monitoring of Intelligent Infrastructure, 2023).

Conclusion

In conclusion, Chang He embodies the qualities of an exceptional researcher in civil engineering, combining academic excellence with impactful research contributions. His extensive experience, innovative research interests, and notable achievements position him as a strong candidate for the Best Researcher Award. By continuing to push the boundaries of knowledge in his field, Chang He is poised to make significant contributions to civil engineering and society as a whole. His commitment to excellence and passion for research make him a deserving nominee for this prestigious award.

Mostafa Bigdeli | Civil Engineering | Best Researcher Award

Mr.Mostafa Bigdeli | Civil Engineering | Best Researcher Award

Student University of Ottawa  Canada

Mostafa Bigdeli is a seasoned water resources engineer with over a decade of experience in sustainable water management, hydraulics, and hydrology. His expertise spans numerical modeling, hydrotechnical engineering, and designing water infrastructure. With a strong academic background and extensive research experience, Mostafa has contributed significantly to the field of water resources through his work at the University of Ottawa and the National Research Council of Canada.

Profile

Scopus

Education

🎓 Ph.D. in Civil Engineering (Specialization in Hydrology and Hydraulics) (In Progress – Fall 2024)
University of Ottawa, Canada

🎓 M.Sc. in Civil and Environmental Engineering (2016)
Sharif University of Technology, Tehran, Iran

🎓 B.Sc. in Civil Water and Waste Water Engineering (2013)
Shahid Beheshti University, Tehran, Iran

Experience

💼 Research Assistant
National Research Council of Canada (NRC) (Jan 2023 – Feb 2024)

  • Modeled microplastics transport and deposition.
  • Developed CFD models for hydraulic applications.
  • Collaborated with cross-functional teams for data integration.

💼 Research Assistant
University of Ottawa – NRC (Jan 2022 – Jan 2023)

  • Simulated microplastics transport in the Ottawa River.
  • Conducted field studies and laboratory experiments.

💼 Supervisor
Air and Climate Projects, Tehran, Iran (Apr 2019 – Aug 2021)

  • Managed air pollutants and GHG emission inventory projects.
  • Developed emission reduction strategies.

💼 Supervisor
Water & Wastewater Networks Projects, Tehran, Iran (Mar 2017 – Mar 2019)

  • Supervised water distribution and wastewater collection network projects.

Research Interests

Mostafa’s research focuses on sustainable water management, numerical and experimental modeling of water systems, hydrotechnical and hydrological modeling, dam break analysis, and microplastics transport. His work integrates advanced data analysis and computer modeling techniques to improve water resources management.

Awards

  • Ranked in the top 1% of the Nationwide University Entrance Exams for B.Sc.
  • Ranked in the top 0.5% of the Nationwide University Entrance Exams for M.Sc.

Publications

  • Bigdeli, M., Mohammadian, A., Pilechi, A. (2024). “A Laboratory Dataset on Transport and Deposition of Spherical and Cylindrical Large Microplastics for Validation of Numerical Models.” Journal of Marine Science and Engineering, MDPI. https://doi.org/10.3390/jmse12060953 – Cited by 5 articles.
  • Roshani, E., Popov, P., Kleiner, Y., Sanjari, S., Colombo, A., Bigdeli, M. (2024). “Detecting and Locating Chemical Intrusion in Water Distribution Systems Using 9-1-1 Calls.” Journal of Hydroinformatics. https://doi.org/10.2166/hydro.2024.299 – Cited by 3 articles.
  • Bigdeli, M., Taheri, M., Mohammadian, A. (2023). “Numerical Modeling of Dam-Break Flood Flows for Dry and Wet Sloped Beds.” ISH Journal of Hydraulic Engineering. https://doi.org/10.1080/09715010.2022.2052986 – Cited by 4 articles.
  • Bigdeli, M., Mohammadian, A., Pilechi, A. (2022). “Numerical Modeling of Marine Microplastics Deposition Using Coupled CFD-DEM.” 3rd IAHR Young Professionals Congress.
  • Bigdeli, M., Mohammadian, A., Pilechi, A., Taheri, M. (2022). “Lagrangian Modeling of Marine Microplastics Fate and Transport: The State of the Science.” Journal of Marine Science and Engineering, MDPI. https://doi.org/10.3390/jmse10040481 – Cited by 6 articles.
  • Bigdeli, M., Mohammadian, A. (2021). “Numerical Simulation of Dam-Break Flood Flows on Sloping Beds.” CFDSC2021 Conference.
  • Bigdeli, M., Mohammadian, A. (2021). “Numerical Simulation of Turbulent Offset Dense Jet Flow.” CSCE 2021 Annual Conference.

Mate Fazekas | Autonomous Vehicles | Best Researcher Award

Mr. Mate Fazekas | Autonomous Vehicles | Best Researcher Award

Ph.D. student at HUN-REN Institute for Computer Science and Control (SZTAKI), Hungary

Máté Fazekas is a dedicated researcher and senior software developer based in Budapest, Hungary. Since 2017, he has contributed significantly as a research associate at the HUN-REN Institute for Computer Science and Control, focusing on state estimation and model identification for autonomous vehicles. From 2022 to 2024, he expanded his expertise as a senior software developer at HUMDA Lab Kft., specializing in developing control systems for autonomous racecars. Currently pursuing a PhD in Robotics at the Budapest University of Technology and Economics, Máté’s research centers on model calibration for autonomous vehicles integrating machine learning techniques. He holds an MSc in Electrical Engineering and a BSc in Mechatronics Engineering from the same institution, both awarded with highest honors. Proficient in languages including Hungarian and English, he excels in programming languages like Matlab, Python, C/C++, and LabVIEW, complemented by strong skills in CAD software and Microsoft Office applications.

Professional Profiles

Education

Máté Fazekas pursued his academic journey at the Budapest University of Technology and Economics, achieving academic excellence throughout. He completed his Bachelor of Science in Mechatronics Engineering from the Faculty of Mechanical Engineering, graduating summa cum laude in 2017. His undergraduate thesis focused on the development of an automated parking system. Subsequently, he earned his Master of Science in Electrical Engineering and Informatics from the Faculty of Electrical Engineering and Informatics, also graduating summa cum laude in 2019. His master’s thesis centered on state and parameter estimation for car-like robots. Currently, he is continuing his academic pursuits as a PhD student in Robotics at the Faculty of Vehicle Engineering, where his research involves model calibration for autonomous vehicles integrating machine learning techniques.

Professional Experience

Máté Fazekas has gained valuable experience in both research and software development roles. Since December 2017, he has served as a Research Associate at the HUN-REN Institute for Computer Science and Control in Budapest, focusing on state estimation and model identification for autonomous vehicles. Concurrently, from June 2022 to June 2024, he worked as a Senior Software Developer at HUMDA Lab Kft., specializing in the development of control systems for autonomous racecars. His expertise includes integrating GNSS, IMU, and odometry for vehicle localization, along with developing advanced software solutions for autonomous systems.

Research Interest

Máté Fazekas, a Budapest-based researcher and Senior Software Developer at HUMDA Lab Kft., specializes in robotics and autonomous systems. Since 2017, he has contributed significantly as a research associate at the HUN-REN Institute for Computer Science and Control, focusing on state estimation and model identification for autonomous vehicles using GNSS, IMU, and odometry technologies. His current role involves developing control systems for autonomous racecars, enhancing their performance and automation capabilities. Educationally, Máté is pursuing a PhD in Robotics at Budapest University of Technology and Economics, exploring model calibration for autonomous vehicles with machine learning integration. He holds a master’s degree in Electrical Engineering and Informatics, where his research focused on state and parameter estimation for robotic systems, and a bachelor’s degree in Mechatronics Engineering, emphasizing automated parking systems. His technical skills include proficiency in Matlab, Python, C/C++, and LabVIEW, as well as CAD and FEM tools like SolidWorks, Inventor, and Ansys.

Research skills

Mate Fazekas is proficient in multiple languages, with Hungarian as his mother tongue and English at an intermediate B2 level. His computer skills include intermediate proficiency in Microsoft Office tools such as Word, Excel, PowerPoint, Access, and Project. He also possesses intermediate skills in Computer-Aided Design (CAD) and Finite Element Method (FEM) software like SolidWorks, Inventor, and Ansys. In programming, Mate demonstrates intermediate proficiency in languages such as C/C++ and LabVIEW, along with advanced skills in MATLAB and intermediate proficiency in Python. These skills equip him well for his research and development roles, particularly in robotics and autonomous vehicle technologies.

Publications

  1. Wheel odometry model calibration with neural network-based weighting
    • Authors: Fazekas, M., Gáspár, P.
    • Journal/Conference: Engineering Applications of Artificial Intelligence, 2024
    • Citations: 0
  2. LPV-Based Control Design with Guarantees: a Case Study for Automated Steering of Road Vehicles
    • Authors: Nemeth, B., Fazekas, M., Bagoly, Z., Gaspar, P., Sename, O.
    • Conference: European Control Conference, ECC 2023, 2023
    • Citations: 0
  3. Calibration of the Nonlinear Wheel Odometry Model with an Improved Genetic Algorithm Architecture
    • Authors: Fazekas, M., Németh, B., Gáspár, P.
    • Conference: Proceedings of the International Conference on Informatics in Control, Automation and Robotics, 2022
    • Citations: 0
  4. Vehicle Control with Cloud-aided Learning Feature: an Implementation on Indoor Platform
    • Authors: Németh, B., Antal, Z., Marosi, A.C., Fazekas, M., Gáspár, P.
    • Journal: IFAC-PapersOnLine, 2022
    • Citations: 1
  5. Wheel Odometry Model Calibration with Input Compensation by Optimal Control
    • Authors: Fazekas, M., Gáspár, P., Németh, B.
    • Journal: IFAC-PapersOnLine, 2022
    • Citations: 0
  6. Calibration of Front Wheel Odometry Model
    • Authors: Fazekas, M., Gáspár, P., Németh, B.
    • Book: Lecture Notes in Mechanical Engineering, 2022
    • Citations: 0
  7. Implementation of a variable-geometry suspension-based steering control system
    • Authors: Fényes, D., Fazekas, M., Németh, B., Gáspár, P.
    • Journal: Vehicle System Dynamics, 2022
    • Citations: 6
  8. Parameter Identification of the Nonlinear Wheel Odometry Model with Batch Least Squares Method
    • Authors: Fazekas, M., Gáspár, P., Németh, B.
    • Conference: Conference on Control and Fault-Tolerant Systems, SysTol, 2021
    • Citations: 1
  9. Velocity estimation via wheel circumference identification
    • Authors: Fazekas, M., Gáspár, P., Németh, B.
    • Journal: Periodica Polytechnica Transportation Engineering, 2021
    • Citations: 1
  10. Improving the wheel odometry calibration of self-driving vehicles via detection of faulty segments
    • Authors: Fazekas, M., Gaspar, P., Nemeth, B.
    • Conference: IEEE International Conference on Automation Science and Engineering, 2021
    • Citations: 0