Farzaneh Zareian | Machine Learning | Best Researcher Award

Ms. Farzaneh Zareian | Machine Learning | Best Researcher Award

Ms. Farzaneh Zareian – Graduate Student, Amirkabir University of Technology, Iran.

Farzaneh Zareian is a dynamic civil engineering researcher with a specialization in earthquake engineering and machine learning applications in structural analysis. Holding a master’s degree from the prestigious Amirkabir University of Technology and a bachelor’s from the University of Tehran, she has consistently demonstrated academic excellence and innovation. Farzaneh has contributed significantly through teaching, research, and scholarly publications in seismic assessment and structural resilience. With experience in AI-powered modeling, fragility curve generation, and passive control systems, she stands at the intersection of engineering and intelligent computation, contributing to safer, more resilient infrastructure in seismic-prone regions.

Publication Profile

Google Scholar

🎓 Education Background

Farzaneh Zareian earned her M.Sc. in Civil Engineering (Earthquake Engineering) from Amirkabir University of Technology, Tehran (2020–2023) with an excellent-rated thesis supervised by Dr. Mehdi Banazadeh. Her research focused on nonlinear dynamic response estimation using machine learning. Prior to that, she completed her B.Sc. in Civil Engineering at the University of Tehran (2016–2020), with coursework emphasizing earthquake engineering, bridge design, and hydraulic structures. Her academic journey highlights a deep commitment to blending structural theory with advanced computational methods, maintaining strong GPAs and securing top ranks in national entrance exams at both undergraduate and postgraduate levels.

💼 Professional Experience

Farzaneh Zareian has accumulated valuable academic experience through teaching and research roles. She worked as a sessional instructor for the “Soft Computing” course at Shahab Danesh University during 2023–2024 and currently serves as a Teaching Assistant in “Theory of Structural Analysis” at Amirkabir University of Technology. Her practical engagements also include academic projects involving seismic hazard analysis, vulnerability assessment, and AI-driven structural modeling. These roles reflect her dual strength as both an educator and practitioner in earthquake-resistant design and computational engineering, making her a well-rounded and impactful civil engineering professional.

🏅 Awards and Honors

Farzaneh’s academic excellence has been widely recognized through several honors. In 2024, she was selected as a distinguished Ph.D. candidate by Amirkabir University’s Committee of Exceptional Talents. She ranked 1st among her peers in the Earthquake Engineering master’s program in 2022 and was among the top 0.2% in both bachelor’s and master’s national entrance exams in 2016 and 2020, respectively. Additionally, she was the top high school student at NODET. These accolades reflect her exceptional dedication, intelligence, and potential as a future leader in structural and earthquake engineering research.

🔬 Research Focus

Farzaneh’s research focuses on AI-enabled structural design and optimization, particularly in seismic contexts. She specializes in applying machine learning and physics-informed models to estimate structural responses, assess risk and reliability, and enhance infrastructure resilience. Her projects include probabilistic seismic hazard analysis, fragility curve generation, and the use of deep learning for crack detection in masonry. She is deeply committed to integrating data-driven approaches with classical civil engineering practices to improve safety, sustainability, and performance of critical infrastructure under seismic hazards.

🧾 Conclusion

Farzaneh Zareian exemplifies the emerging generation of civil engineers who are leveraging artificial intelligence to redefine structural safety and resilience. Her academic accomplishments, hands-on project experiences, teaching engagements, and scholarly contributions highlight a well-rounded professional profile. As she progresses toward doctoral research, her innovative mindset and strong foundation in both theory and practice make her a prime candidate for research excellence in AI-integrated earthquake engineering. With her interdisciplinary approach, she is poised to make impactful contributions to the global civil and seismic engineering community.

📚 Publication Top Notes

 Prediction of nonlinear dynamic responses and generation of seismic fragility curves for steel moment frames using boosting machine learning techniques
📅 Year: 2024 (Nov.)
📘 Journal: Computers & Structures
🔢 Cited by: 1

 Machine learning-based seismic risk assessment of steel moment structures: a reliability analysis framework
📅 Year: In Preparation (Expected 2025)
📘 Journal: Engineering Structures
🔢 Cited by:

Dr. Aiai Wang | Machine Learning | Best Researcher Award

Dr. Aiai Wang | Machine Learning | Best Researcher Award

Doctoral student, University of Science and Technology Beijing, China

Ai-Ai Wang is a passionate and dedicated young researcher born in March 1998 in Langfang, Hebei Province, China. A proud member of the Communist Party of China (CPC), she is currently based at the University of Science and Technology Beijing (USTB), where she serves as the Secretary of the 16th Party Branch, 4 Zhaizhai. With a solid academic foundation in mining and civil engineering, Ai-Ai has excelled in both academic and research spheres, contributing significantly to digital and intelligent mining technologies. Her work emphasizes physical dynamics in tailings sand cementation and filling, showing strong potential for innovation in sustainable mining practices.

Publication Profile

Scopus

🎓Education Background:

Ai-Ai Wang completed her Bachelor of Science in Mining Engineering from North China University of Science and Technology in 2021. She further pursued her Master’s degree in Civil Engineering at the University of Science and Technology Beijing (2021.09–2024.06), affiliated with the School of Civil and Resource Engineering.

🛠️Professional Experience:

Alongside her academic journey, Ai-Ai has undertaken significant responsibilities, currently serving as Secretary of the Party Branch at USTB. Her leadership extends beyond administration into collaborative research projects, software development, and patent contributions under renowned mentors such as Prof. Cao Shuai. She has played vital roles in developing intelligent systems for mining operations, reinforcing her multidisciplinary strengths.

🏅Awards and Honors:

Ai-Ai Wang has been recognized extensively for her academic and research excellence. Notable accolades include the “Top Ten Academic Stars” at USTB (2023), a National Scholarship for Master’s Degree Students (2022), the prestigious Taishan Iron and Steel Scholarship (2023), and multiple First-Class Academic Scholarships from USTB. She was twice named an Outstanding Three-Good Graduate Student and honored by her school as an outstanding individual. Moreover, she has received scientific awards such as the First Prize from the China Gold Association and the Second Prize from the China Nonferrous Metals Industry for her impactful contributions to green and safe mining.

🔬Research Focus:

Ai-Ai Wang’s research is rooted in advanced techniques of tailings sand cementation, intelligent filling systems, and digital mining. She explores the structural stability of backfills, application of nanomaterials, and CT-based 3D modeling of internal structures. Her work blends civil engineering, environmental safety, and digital innovation, aiming to enhance sustainability and efficiency in modern mining. She also contributes to cutting-edge software systems and patented technologies for mining design and operation support.

📝Conclusion:

Ai-Ai Wang stands out as a promising engineer and researcher whose academic achievements, professional dedication, and innovative research in intelligent mining set a high standard for future civil and mining engineers. Her trajectory reflects not just technical mastery but a deep commitment to sustainable and smart engineering solutions in the mining industry.

📚Top Publications with Details

Effect of height to diameter ratio on dynamic characteristics of cemented tailings backfills with fiber reinforcement through impact loading – Construction and Building Materials, 2022
Cited by: 26 articles
Influence of types and contents of nano cellulose materials as reinforcement on stability performance of cementitious tailings backfill – Construction and Building Materials, 2022
Cited by: 20 articles
Quantitative analysis of pore characteristics of nanocellulose reinforced cementitious tailings fills using 3D reconstruction of CT images – Journal of Materials Research and Technology, 2023
Cited by: 12 articles