Yingyuan Liu | Engineering | Women Researcher Award

Ms. Yingyuan Liu | Engineering | Women Researcher Award

Professor | Shanghai Normal university | China

Dr. Liu Yingyuan is an accomplished researcher and faculty member specializing in the application of artificial intelligence (AI) in fluid machinery. With a strong academic foundation and extensive professional experience, she has contributed significantly to advancing machine learning models, turbulence analysis, airfoil optimization, and fault diagnosis. Currently serving at Shanghai Normal University, Dr. Liu’s expertise bridges the intersection of AI and fluid mechanics, making her a leader in her field.

Profile

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Education

Dr. Liu Yingyuan earned her Ph.D. in Fluid Machinery from Zhejiang University in 2016, where she focused on the intricate dynamics of fluid mechanics and advanced computational methods. Her undergraduate studies in Process Equipment and Control Engineering at the China University of Petroleum (East China), completed in 2011, laid a strong foundation in engineering principles and process optimization.

Experience

Dr. Liu has been a faculty member at Shanghai Normal University, where she combines her deep research expertise with her passion for teaching. Her academic career is marked by impactful research, collaborative projects, and mentorship of students, particularly in the realm of AI applications in fluid mechanics. Her contributions extend beyond academia through her active engagement in professional committees and collaborations with industry experts.

Research Interests

Dr. Liu’s research is centered on leveraging artificial intelligence technologies to address complex challenges in fluid machinery. Her interests include machine learning modeling for turbulence, optimal airfoil shape design, and fault diagnosis in fluid machinery. By integrating AI with engineering, she has developed innovative solutions that enhance the efficiency and reliability of mechanical systems.

Awards

Dr. Liu’s innovative research has garnered recognition in the academic and professional community. Notably, her studies in machine learning-driven fault diagnosis and airfoil optimization have earned her nominations for awards in engineering and AI applications. Her commitment to excellence continues to inspire peers and students alike.

Publications

  1. Liu YY, Shen JX, Yang PP, Yang XW. A CNN-PINN-DRL driven method for shape optimization of airfoils. Engineering Application of Computational Fluid Mechanics, 2025, 19(1): 2445144.
    • Cited by: Researchers developing AI-driven aerodynamics models.
  2. Shen JX, Liu YY, Wang Leqin.* A Deep Learning-Based Method for Airfoil Parametric Modeling. Chinese Journal of Engineering Design, 2024, 31(03): 292-300.
    • Cited by: Articles on parametric modeling techniques.
  3. Liu D, Liu YY. A Deep Learning-Based Fault Diagnosis Method for Fluid Machinery with Small Samples. Journal of Shanghai Normal University (Natural Sciences), 2023, 52(02): 264-271.
    • Cited by: Studies on fault diagnosis in mechanical systems.
  4. Liu YY, Gong JG, An K, Wang LQ. Cavitation Characteristics and Hydrodynamic Radial Forces of a Reversible Pump–Turbine at Pump Mode. Journal of Energy Engineering, 2020, 146(6): 04020066.
    • Cited by: Publications on hydrodynamics and pump-turbine systems.
  5. Liu Y Y, An K, Liu H, et al. Numerical and experimental studies on flow performances and hydraulic radial forces of an internal gear pump with a high pressure. Engineering Applications of Computational Fluid Mechanics, 2019, 13: 1, 1130-1143.
    • Cited by: Research focused on internal gear pump performance.
  6. Liu Y Y, Wang L Q, Zhu Z C.* Experimental and numerical studies on the effect of inlet pressure on cavitating flows in rotor pumps. Journal of Engineering Research, 2016, 4(2): 151-171.
    • Cited by: Studies on cavitation phenomena in rotor pumps.
  7. Liu Y Y, Wang L Q, Zhu Z C.* Numerical study on flow characteristics of rotor pumps including cavitation. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2015, 229(14): 2626-2638.
    • Cited by: Articles on numerical modeling of fluid flows.

Conclusion

Dr. Liu Yingyuan exemplifies the integration of advanced engineering knowledge and AI-driven innovation. Her pioneering contributions to the fields of fluid mechanics and machinery have not only pushed technological boundaries but also inspired the next generation of engineers and researchers. Dr. Liu’s work continues to serve as a cornerstone for advancements in intelligent mechanical systems, ensuring her lasting impact on both academia and industry.

Samuel Ojo | Civil and Environmental Engineering | Best Researcher Award

Mr. Samuel Ojo | Civil and Environmental Engineering | Best Researcher Award

Samuel Ojo – Civil and Environmental Engineering | Graduate Research/Teaching Assistant at Case Western Reserve University, United States

Samuel Tosin Ojo is a highly motivated and innovative civil engineer specializing in sustainable infrastructure and environmental engineering. Currently pursuing a Ph.D. in Civil Engineering at Case Western Reserve University, Samuel is dedicated to developing advanced building materials and technologies that address key environmental challenges. His research spans various interdisciplinary fields, including machine learning applications in environmental engineering, bio-sensing wearables, and materials science for improved air quality. With a deep commitment to improving engineering practices and sustainable building solutions, Samuel brings a unique blend of academic rigor and practical experience to his field.

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Education

Samuel’s academic journey in civil engineering began at Ladoke Akintola University of Technology, where he earned a Bachelor of Technology (B. Tech) degree in Civil Engineering. Graduating with distinction, he achieved a GPA of 4.54 out of 5.0, placing him among the top two students in a cohort of 120. Currently, he is advancing his expertise as a Ph.D. candidate at Case Western Reserve University, focusing on cutting-edge research in civil engineering. This program has provided him with an exceptional platform for deepening his knowledge in sustainable building materials and the development of predictive machine learning models, broadening his understanding of how civil engineering can contribute to environmental health and sustainability.

Experience

Samuel has amassed extensive practical experience, beginning his professional career in Nigeria with FBS Construction Engineering Services, where he served as a site engineer on an ambitious multi-story hotel project. He was responsible for interpreting architectural and structural drawings, managing reinforcements, and supervising concrete batching. His roles required meticulous oversight of structural details, which helped him build a robust foundation in construction management. Later, he worked with Oat Construction and Matrix Resource Limited, where he managed the construction of commercial structures and gained hands-on experience in interpreting complex design specifications. Currently, he is applying his skills as a Research Assistant at Case Western Reserve University, where he delves into the application of innovative materials and machine learning techniques to enhance air quality and structural sustainability.

Research Interest

Samuel’s research centers on sustainable infrastructure, emphasizing the role of innovative materials in improving the built environment. His primary focus is the application of machine learning to enhance organic photocatalysts for indoor air quality management, a project aimed at mitigating pollutants in urban spaces. Additionally, Samuel is exploring bio-sensing wearables, a novel area in civil engineering that integrates biosensors with construction materials to improve environmental monitoring. His multidisciplinary research efforts reflect a forward-looking approach, seeking to integrate sustainable materials and data-driven methodologies to address pressing environmental challenges in urban infrastructure.

Awards

Samuel has received several prestigious awards that acknowledge his dedication to both academic excellence and professional growth. In 2021, he was honored with the Swanger Fellows Award at Case Western Reserve University, followed by a nomination for the Zydane Award later that year. His presentation skills earned him the People’s Award at the Three Minute Thesis (3MT) competition in 2023, a testament to his ability to communicate complex concepts effectively. Samuel was also awarded the Roy Harley Award, recognizing his promise as a graduate student in civil and environmental engineering. Most recently, he received the NCF 2023 Scholarship Award for his outstanding academic performance, further underscoring his commitment to the field of civil engineering.

Publications

“Optimizing Photodegradation Rate Prediction of Organic Contaminants: Models with Fine-Tuned Hyperparameters and SHAP Feature Analysis for Informed Decision Making” (2023) in ACS ES&T Water.

“A Novel Interpretable Machine Learning Model Approach for the Prediction of TiO2 Photocatalytic Degradation of Air Contaminants” (2024) in Scientific Reports.

“Kinetic Studies on Using Plasmonic Photocatalytic Coatings for Autogenously Improving Indoor Air Quality by Removing Volatile Organic Compounds,” presented at the 28th North American Catalysis Society Meeting.

“Innovative Antifungal Photocatalytic Paint for Improving Indoor Environment” (2023) in Catalysts.

Poster presentation on “Photocatalytic Inhibition of Microorganisms” at the Three Minute Thesis Competition.

“Habitable Home,” presented at Innovation Week at Case Western Reserve University.

“Deciphering Fungal Communication,” presented at the Gordon Research Conference.

Conclusion

Samuel Tosin Ojo embodies the qualities of a pioneering researcher, combining deep theoretical knowledge with practical applications that address real-world challenges. His dedication to sustainable building practices, innovative materials research, and application of machine learning in civil engineering positions him as a forward-thinking leader in his field. With a track record of significant contributions and ongoing commitment to improving environmental standards in civil engineering, Samuel is well-deserving of the Best Researcher Award. His vision for sustainable infrastructure and environmental health continues to inspire and influence those around him, marking him as an impactful figure in the future of civil engineering.

Tanaya Mandal | Engineering | Best Researcher Awards

Ms. Tanaya Mandal | Engineering | Best Researcher Awards

PhD Candidate | Texas A&M University | United States

Short Bio 🌟

Tanaya Mandal is a dynamic materials engineer and Ph.D. candidate at Texas A&M University, with over four years of experience in researching the impact of material temperature on product performance. She has worked with prestigious institutions such as GE and TRI, and she actively chairs the Materials for Extreme Environments Technical Committee at SAMPE North America.

Profile

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Education 🎓

Tanaya Mandal is currently pursuing a Ph.D. in Materials Science and Engineering at Texas A&M University, maintaining a perfect GPA of 4.00. She previously earned her M.E. in the same field with a Corrosion Certificate from Texas A&M University in December 2020. Before that, she received her M.HSc from Trinity School of Medicine in May 2019, and her B.S. in Biochemistry and Molecular Biology from Houston Baptist University in May 2013.

Experience 🛠️

Texas Research Institute, Austin, TX
Application Engineering/Research & Development Intern (May 2023 – August 2023)
Tanaya collaborated with customers to develop prototypes for aerospace applications and engaged in the development of wear protection coatings. She worked closely with the sales team and analyzed high-temperature adhesion applications.

Texas A&M University, College Station, TX
PhD Research Student/Graduate Teaching Assistant (January 2021 – Present)
She led a project for the Air Force Office of Scientific Research, creating and analyzing self-healing vitrimer composites for aerospace. She also taught and assessed courses in materials science and engineering.

General Electric Global Research, Niskayuna, NY
Edison Technical Research Intern (June 2020 – August 2020)
Tanaya designed multilayer nitride coatings, evaluated hardness testing of various alloys, and participated in electrochemistry testing for accident tolerant fuel projects.

Research Interest 🔬

Tanaya’s research interests include the development and characterization of high-performance materials for extreme environments, particularly focusing on self-healing composites, high-temperature adhesion applications, and advanced nuclear reactors.

Awards 🏆

  • Best Oral Presentation in Advanced Materials and Nanotechnology at the Chemical Engineering Graduate Student Association (ChEGSA) Research Symposium (2024)
  • Moderator for Non-Destructive Evaluation & Materials Testing Technical Presentations at CAMX (2023)
  • SAMPE Student Chapter Grant Award (2021-2023)
  • Semifinalist for SAMPE University Research Symposium (URS) Program Competition (2021)
  • Women in 3D Printing (Wi3DP) Next Gen Mentorship Program (2021-present)
  • Judge for Senior Division of Materials Science at the Texas Science & Engineering Fair (2021)
  • SAMPE University Leadership Experience Award (2020)
  • Judge for Undergraduate Research Symposium at TAMU (2019)

Publications 📚

  • Mandal, T., Ozten, U., Vaught, L., Meyer, J.L., Amiri, A., Polycarpou, A., Naraghi, M. (2024). Processing and Mechanics of Aromatic Vitrimeric Composites at Elevated Temperatures and Healing Performance. J. Compos. Sci., 8, 252.
  • Mandal, T., Rodriguez-Melendez, D., Palen, B., Long, C.T., Chiang, H., Sarikaya, S., Naraghi, M., Grunlan, J.C. (2023). Heat Shielding Nanobrick Wall for Carbon Fiber Reinforced Polymer Composites. American Chemist Society Applied Polymer Materials, 5(5), 3270-3277.
  • Hoffman, A. K., Umretiya, R. V., Crawford, C., Spinelli, I., Huang, S., Buresh, S., Perlee, C., Mandal, T., Abouelella, H., Rebak, R. B. (2023). The relationship between grain size distribution and ductile to brittle transition temperature in FeCrAl alloys. Materials Letters, 331, 133427.