Ali Nawaz Sanjrani | Big Data Analytics | Global Data Science Award

Dr. Ali Nawaz Sanjrani | Big Data Analytics | Global Data Science Award

Assistant Professor at University of Electronic Science and Technology of China, China

Dr. Ali Nawaz Sanjrani is a dedicated academician and scholar with over 18 years of interdisciplinary experience spanning research, teaching, and industrial project management. His expertise lies in reliability engineering, quality control, health and safety management, and complex machine diagnostics. As a professional with a strong commitment to excellence, Dr. Sanjrani has made significant contributions to engineering education and industrial advancements. His research primarily focuses on reliability monitoring, fault diagnosis, and the application of machine learning in predictive maintenance.

Profile

Orcid

Education

Dr. Sanjrani earned his Ph.D. in Mechanical Engineering from the University of Electronics Science and Technology, Chengdu, China, specializing in reliability monitoring, diagnostics, and prognostics of complex machinery. His doctoral coursework included advanced subjects such as Computer-Aided Manufacturing (CAM), Operations Research (OR), Reliability & Quality Engineering, Automation & Controls, and Finite Element Analysis (FEA). Prior to this, he completed his Master’s degree in Industrial Manufacturing Engineering from NED University of Engineering & Technology, Karachi, with a focus on lean manufacturing. He holds a Bachelor’s degree in Mechanical Engineering from QUEST, Nawabshah, where he developed a strong foundation in mechanical manufacturing and materials engineering.

Experience

Dr. Sanjrani has held several academic and industrial positions, reflecting his diverse skill set and leadership abilities. He served as an Assistant Professor at Mehran University of Engineering and Technology, SZAB Campus, from 2016 to 2020, where he was actively involved in teaching, research, and mentoring students. Additionally, he worked as a visiting faculty member at Indus University, Karachi. His industrial experience includes working as a Quality Assurance Engineer at Descon Engineering Works Limited, Lahore, where he managed quality control processes and implemented international quality management standards.

Research Interests

Dr. Sanjrani’s research interests are centered around machine learning applications in fault diagnosis and predictive maintenance, reliability analysis, and quality engineering. His work integrates artificial intelligence-driven methodologies to enhance the reliability and operational efficiency of high-speed train bearings, microgrids, and other complex mechanical systems. His research also extends to fluid dynamics, heat transfer, and smart manufacturing processes, emphasizing innovative approaches to industrial problem-solving.

Awards and Recognitions

Dr. Sanjrani has been recognized for his academic and research excellence through several prestigious awards. In 2024, he won the 3rd Prize for Academic Excellence at the University of Electronics Science and Technology, China. Additionally, he received the 3rd Prize for Performance Excellence at the same institution. He was also awarded the fully funded Chinese Government Scholarship (CSC) in 2020 for his Ph.D. studies. His industrial contributions have been acknowledged with appreciation certificates from Karachi Shipyard & Engineering Works (KSEW) for achieving multiple international certifications and successful project implementations.

Selected Publications

Sanajrani, A. N. (2025). “High-Speed Train Bearing Health Assessment Based on Degradation Stages Through Diagnosis and Prognosis by Using Dual-Task LSTM With Attention Mechanism.” Quality and Reliability Engineering International Journal, Wiley. DOI: https://doi.org/10.1002/qre.3757

Sanajrani, A. N. (2025). “High-Speed Train Wheel Set Bearing Analysis: Practical Approach to Maintenance Between End of Life and Useful Life Extension Assessment.” Results in Engineering, Elsevier. DOI: https://doi.org/10.1016/j.rineng.2024.103696

Sanajrani, A. N. (2025). “Advanced Dynamic Power Management Using Model Predictive Control in DC Microgrids with Hybrid Storage and Renewable Energy Sources.” Journal of Energy Storage, Elsevier. DOI: https://doi.org/10.1016/j.est.2024.114830

Sanajrani, A. N. (2024). “Dynamic Temporal LSTM-Seqtrans for Long Sequence: An Approach for Credit Card and Banking Accounts Fraud Detection in Banking Systems.” 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). DOI: 10.1109/ICCWAMTIP64812.2024.10873619

Sanajrani, A. N. (2024). “High-Speed Train Health Assessment Based on Degradation Stages and Fault Classification Using Dual-Task LSTM with Attention Mechanism.” The 6th International Conference on System Reliability and Safety Engineering. DOI: 10.1109/SRSE63568.2024.10772528 (EI & Scopus Indexed)

Sanajrani, A. N. (2024). “A C-band Sheet Beam Staggered Double Grating Extended Interaction Oscillator.” IEEE International Conference on Plasma Science (ICOPS). DOI: 10.1109/ICOPS58192.2024.10625809 (EI Indexed)

Sanajrani, A. N. (2023). “Bearing Health and Safety Analysis to Improve the Reliability and Efficiency of Horizontal Axis Wind Turbine (HAWT).” ESREL 2023, Southampton, UK (ISBN: 978-981-18-8071-1).

Conclusion

Dr. Ali Nawaz Sanjrani is a distinguished academic and industry professional with a strong research background in reliability engineering, artificial intelligence, and machine learning applications. His work significantly contributes to the fields of predictive maintenance, fault diagnostics, and industrial automation. With a proven record of academic excellence, numerous international publications, and substantial industrial experience, Dr. Sanjrani continues to drive innovation in engineering and technology. His dedication to bridging the gap between academia and industry ensures impactful contributions to the advancement of modern engineering solutions.

Lixiong Yang | Machine Learning | Best Researcher Award

Prof. Lixiong Yang | Machine Learning | Best Researcher Award

Professor | School of Management, Lanzhou University | China

Dr. Lixiong Yang is a distinguished scholar and professor of economics at the School of Management, Lanzhou University, China. With a strong foundation in econometrics, financial econometrics, and machine learning, he has made significant contributions to advancing quantitative methods in economic research. His work focuses on developing theoretical models and applying them to capital markets, financial warning systems, and macroeconomic policy evaluation. Dr. Yang has authored numerous impactful publications, served as an external reviewer for esteemed journals, and supervised graduate theses. He is also a recipient of multiple awards, including recognition for his doctoral dissertation and academic mentorship.

Profile

Scopus

Education

Dr. Yang received his Ph.D. in Economics from the Jinhe Center for Economic Research at Xi’an Jiaotong University in 2014. His dissertation, “A Method of Nonstationary Time Series Analysis Based on the Degree of Cointegration,” introduced innovative approaches to time-series econometrics. Before that, he earned his B.E. in Financial Mathematics from Sichuan University in 2009. His academic journey reflects a strong inclination toward econometric theory and its practical applications.

Experience

Dr. Yang has held various academic positions at Lanzhou University. He was appointed as a professor in December 2022, following his selection as a Cuiying Scholar in 2020. Earlier, he served as a junior professor (2019–2022) and lecturer (2014–2019). His teaching repertoire includes advanced econometrics, machine learning, and undergraduate econometrics. Additionally, he has actively contributed to the academic community as an external reviewer for prestigious journals such as the Journal of Econometrics and Studies in Nonlinear Dynamics and Econometrics.

Research Interests

Dr. Yang’s research spans econometric theory, panel data models, big data analysis, machine learning, and financial econometrics. His interests also extend to financial warning systems, capital markets, and macroeconomic policy. He has led and contributed to multiple national-level research grants, focusing on time-varying threshold models, high-dimensional data analysis, and fiscal policy effectiveness.

Awards

Dr. Yang’s academic excellence has been recognized through several awards. Notable among them are:

Excellent Supervisor of Lanzhou University Undergraduate Thesis (2021)

Excellent Doctoral Dissertation of Shaanxi Province (2017)

National Scholarship for Doctoral Students (2013)
He has also been commended for his mentorship, winning awards for guiding students in the “Challenge Cup” competition and other academic initiatives.

Publications

Dr. Yang has authored over 20 peer-reviewed articles, focusing on econometrics and its applications. Seven notable publications include:

Yang, L. et al., “Panel Threshold Model with Covariate-Dependent Thresholds and Unobserved Individual-Specific Effects,” Econometrics Review, 2024. Cited by: Advanced Studies in Econometrics.

Yang, L. et al., “Is There a State-Dependent Optimal Interval for Firms’ R&D Investment?” Applied Economics, 2024. Cited by: Industrial Innovation Reports.

Yang, L., “Threshold Quantile Regression Neural Network,” Applied Economics Letters, 2023. Cited by: Computational Finance Insights.

Yang, L., “High-Dimensional Threshold Model with Time-Varying Thresholds,” Studies in Nonlinear Dynamics and Econometrics, 2022. Cited by: Statistical Models Journal.

Yang, L., “Panel Threshold Spatial Durbin Models,” Economics Letters, 2021. Cited by: Urban Economic Analysis.

Yang, L., “Regression Discontinuity Designs with State-Dependent Unknown Discontinuity Points,” Studies in Nonlinear Dynamics and Econometrics, 2019. Cited by: Econometrics Advances.

Yang, L., “Debt and Growth: Is There a Constant Tipping Point?” Journal of International Money and Finance, 2018. Cited by: Global Economic Studies.

Conclusion

Dr. Lixiong Yang embodies the integration of theoretical rigor and practical application in economics. His commitment to advancing econometric methodologies, coupled with his impactful teaching and mentorship, solidifies his status as a leading scholar. Through his extensive research, he continues to shape the future of quantitative economic analysis and inspire the next generation of economists.