Professor at Dalian University of Technology, China
over 20 years of experience, he has led numerous global teams, delivering innovative IoT solutions for monitoring and diagnostics in various industries, significantly enhancing productivity and revenue. Dr. Jiang’s academic accomplishments include co-authoring a book, six book chapters, and over 100 research articles. His research focuses on AI, IoT, predictive modeling, and machine learning, with applications in power engineering and industrial equipment. He has also driven AI innovation in smart manufacturing, filing more than 30 patents. Dr. Jiang has received numerous accolades, including recognition as one of the top 2% of scientists globally by Stanford University. His leadership and contributions have made significant impacts on both academic research and practical industrial applications.
Education
Dr. Xiaomo Jiang has an extensive and impressive educational background. He earned his Ph.D. in Structural Engineering from Ohio State University (2001-2005) and completed his post-doctoral studies at Vanderbilt University (2005-2007). His academic journey began with a Bachelor’s degree in Civil Engineering from Beijing Jiaotong University (1991-1995) and continued with a Master’s degree in Structural Engineering from the National University of Singapore (1999-2000) and another Master’s degree in Structural Engineering from Beijing Jiaotong University (1995-1998).
Professional Experience
Dr. Jiang currently serves as a Professor at Dalian University of Technology (DLUT) in the School of Energy and Power Engineering. He also holds several key leadership roles, including Director of the Provincial Key Lab of Digital Twin for Industrial Equipment and the DLUT Institute of Carbon Neutrality. His previous roles include Vice President and Director of the AI Research Institute at ENN Group and Senior Manager at Mohawk Industries, showcasing his extensive experience in both academia and industry.
Research Interests
Dr. Jiang’s research interests are diverse and cutting-edge, focusing on Artificial Intelligence, IoT, Big Data, Predictive Modeling, and Hybrid Data & Physics Modeling. He is also deeply involved in Reliability and Risk Engineering, Damage Monitoring, and Diagnostics and Prognostics of Turbomachinery. His work integrates advanced statistics, machine learning, and AI to address complex problems in engineering and industrial applications.
Research Skills
Dr. Jiang possesses exceptional research skills, particularly in the areas of AI, IoT, and predictive analytics. He has a proven track record of developing innovative solutions for performance monitoring, diagnostics, and prognostics of industrial equipment. His expertise extends to advanced statistical methods, Bayesian algorithms, and data mining techniques, enabling him to create robust models for predictive maintenance and reliability assessment.
Research Contributions
Dr. Jiang has made significant contributions to the field of engineering and AI. He has coauthored a book, multiple book chapters, and over 100 research articles in prestigious journals. His work on AI and IoT solutions has led to the development of patented technologies and innovative methods for remote monitoring and diagnostics of industrial assets. His contributions have had a substantial impact on improving productivity and reducing costs in various industries.
Geographic Impact
Dr. Jiang’s research and professional activities have had a global impact. He has led projects and collaborated with institutions and industries in the United States, China, Singapore, and other countries. His work on IoT solutions and AI analytics has been implemented in power plants, manufacturing facilities, and other industrial settings worldwide, demonstrating his ability to address global challenges and deliver solutions with far-reaching benefits.
Collaborative Efforts
Throughout his career, Dr. Jiang has demonstrated a strong commitment to collaboration. He has led cross-functional teams, worked with international research institutions, and collaborated with industry partners to develop and implement innovative solutions. His leadership in various research centers and labs highlights his ability to foster collaborative environments and drive collective success.
Applied Research
Dr. Jiang’s applied research has led to practical and impactful solutions in various industrial sectors. His work on predictive maintenance and remote monitoring has resulted in significant cost savings and improved efficiency for power plants, manufacturing facilities, and other industries. His focus on integrating AI and IoT technologies into real-world applications underscores the practical relevance and value of his research.
Specific Projects and Publications
Dr. Jiang has been involved in numerous high-impact projects and has a prolific publication record. His projects include the development of AI-driven predictive maintenance solutions for LNG and combined cycle plants, the integration of IoT technologies in the textile industry, and the creation of digital twin models for industrial equipment. His publications in top-tier journals and conferences reflect his deep expertise and contributions to advancing knowledge in his field.
Environmental Health, Vector Control, Parasitology, and Infectious Diseases
While Dr. Jiang’s primary focus is on engineering, AI, and industrial applications, his work on predictive modeling and diagnostics can have implications for environmental health and other related fields. The methodologies and technologies he develops could potentially be adapted for use in vector control, parasitology, and infectious diseases, demonstrating the broader applicability of his research skills.
Awards and Recognition
Dr. Jiang has received numerous awards and recognitions for his outstanding contributions to research and technology. He was named among the top 2% of scientists in the world by Stanford University in 2022 and has received several talent program awards in Dalian, Liaoning Province, and at the national level. His accolades also include GE Executive and Innovative Awards, Ford Patent Award, and the SAE Arch T. Colwell Merit Award, among others.
Conclusion
In conclusion, Dr. Xiaomo Jiang is a highly accomplished researcher with a strong educational background, extensive professional experience, and significant contributions to the fields of AI, IoT, and engineering. His research skills, collaborative efforts, and applied research have had a global impact, making him a suitable candidate for the Research for Best Researcher Award. His work demonstrates excellence in innovation, practical application, and leadership in advancing technology and knowledge.
Publications Top Notes
- Application of the fast 3D simplified simulation method for the large CAP1400 nuclear island evaporator based on the coupled source term method
- Authors: Guo, Z., Chen, Y., Lu, Y., Wang, X., Jiang, X.
- Year: 2024
- Bearing fault feature extraction method: stochastic resonance-based negative entropy of square envelope spectrum
- Authors: Zhao, H., Jiang, X., Wang, B., Cheng, X.
- Year: 2024
- Citations: 1
- An orbit-based encoder–forecaster deep learning method for condition monitoring of large turbomachines
- Authors: Jiang, X., Wang, Z., Chen, Q., Yang, S., Meng, J.
- Year: 2024
- Citations: 4
- OrbitDANN: A Mechanism-Informed Transfer Learning Method for Automatic Fault Diagnosis of Turbomachinery
- Authors: Jiang, X., Li, Y., Wang, Z., Hui, H., Cheng, X.
- Year: 2024
- Citations: 1
- Identifying the dominant influencing factors of secondary lining cracking risk in an operating mountain tunnel
- Authors: Peng, Z., Fang, Q., Ai, Q., Huang, X., Yuan, Y.
- Year: 2024
- Design of UDE-based finite-time fault-tolerant control for DP vessels with complex disturbances and input constraints
- Authors: Zhou, B., Hu, C., Jin, G., Jiang, X., Zhang, G.
- Year: 2023
- Citations: 1
- Deep reinforcement transfer learning of active control for bluff body flows at high Reynolds number
- Authors: Wang, Z., Fan, D., Jiang, X., Triantafyllou, M.S., Karniadakis, G.E.
- Year: 2023
- Citations: 4
- Learning Multitask Gaussian Process over Heterogeneous Input Domains
- Authors: Liu, H., Wu, K., Ong, Y.-S., Jiang, X., Wang, X.
- Year: 2023
- Citations: 0
- An artificial viscosity augmented physics-informed neural network for incompressible flow
- Authors: He, Y., Wang, Z., Xiang, H., Jiang, X., Tang, D.
- Year: 2023
- Citations: 9
- Towards smart monitoring of systems: an integrated non-parametric Bayesian KDE and LSTM approach for anomaly detection of rotating machinery under uncertainties
- Authors: Zhao, H., Jiang, X., Wang, B., Cheng, X., Xu, S.
- Year: 2023
- Citations: 2