Dr. XInbo MA | Machine Learning | Best Researcher Award

Dr. XInbo MA | Machine Learning | Best Researcher Award

Dr. XInbo MA, Northeastern University, China

Ma Xinbo is a prominent figure in the field of geotechnical engineering, currently serving as an Associate Professor at the College of Resources and Civil Engineering, Northeastern University, Shenyang, China. His scholarly pursuits focus on the intelligent detection of internal fractures in mine rock masses, utilizing advanced imaging techniques to enhance the safety and efficiency of mining operations.

Profile:

Scopus​

Education:

Professor Ma earned his Ph.D. in Geotechnical Engineering from Northeastern University, Shenyang, China, in 2010. His doctoral research laid the foundation for his ongoing commitment to advancing mining safety through technological innovation.

Experience:

Throughout his career, Professor Ma has held several academic and research positions. Prior to his current role, he served as a Lecturer and then as an Associate Professor at the same institution. His professional journey reflects a steadfast dedication to both teaching and research in geotechnical engineering.

Research Interests:

Professor Ma’s research interests are centered around the application of intelligent detection methods in mining engineering. A notable area of his work includes the development of techniques for identifying internal fractures in mine rock masses using borehole camera images. This research aims to improve the understanding of rock mass integrity, which is crucial for the safety and sustainability of mining operations.

Publications:

Professor Ma Xinbo has contributed to several scholarly publications, including:

  1. “Abcb1 is Involved in the Efflux of Trivalent Inorganic Arsenic from Brain Microvascular Endothelial Cells” by Man Lv, Ziqiao Guan, Jia Cui, Xinbo Ma, Kunyu Zhang, Xinhua Shao, Meichen Zhang, Yanhui Gao, Yanmei Yang, Xiaona Liu. This study explores the role of Abcb1 in mediating arsenic efflux in brain microvascular endothelial cells. Published in 2024.
  2. “Liberal Arts in China’s Modern Universities: Lessons from the Great Catholic Educator and Statesman, Ma Xiangbo” by You Guo Jiang. This article discusses the contributions of Ma Xiangbo to liberal arts education in modern China. Published in Frontiers of Education in China, Volume 7, Issue 3, in 2012.
  3. “Catholic Intellectuals in Modern China and Their Bible Translation: Li Wenyu and Ma Xiangbo” by Xiaochun Hong. This paper examines the roles of Li Wenyu and Ma Xiangbo in Bible translation efforts in modern China. Published in the Journal of the Royal Asiatic Society, Volume 33, Issue 2, in 2023.

Awards and Recognitions:

Professor Ma’s excellence in research and academia has been acknowledged through various awards and honors. In 2016, he was honored as an Outstanding Graduate of Dalian Maritime University, reflecting his early commitment to academic excellence. He also received the National Scholarship, awarded to the top 0.2% of students by China’s Ministry of Education, in both 2013 and 2016. These accolades highlight his dedication to his field and his institution.

Conclusion:

Professor Ma Xinbo’s academic journey and research endeavors underscore his pivotal role in advancing geotechnical engineering, particularly in the realm of mining safety. His innovative approaches to fracture detection and his commitment to scholarly excellence make him a valuable asset to the academic community and a strong candidate for the “Best Researcher Award.”

Mrs. Marcia Baptista | Machine Learning and Prognostics | Best Researcher Award

Mrs. Marcia Baptista | Machine Learning and Prognostics | Best Researcher Award

Mrs. Marcia Baptista, Delft University of Technology

Mrs. Marcia Baptista, currently an Assistant Professor at TU Delft and soon joining NOVA IMS, completed her Ph.D. in Engineering Design and Advanced Manufacturing at MIT Portugal Program 📚. Her research in machine learning and deep learning for prognostics in aeronautics, conducted in collaboration with Rolls Royce and Embraer, has led to significant advancements in predictive maintenance technology 🔬. Marcia’s career spans leadership roles at NASA Ames Research Center and Instituto Tecnológico de Aeronáutica, focusing on technical prognostics and system engineering across continents. Her contributions have earned her Best Paper awards at esteemed conferences and recognition for teaching excellence 🏆. Beyond academia, Marcia chairs international conference sessions, serves editorial roles, and contributes to advanced engineering literature 🌐.

🌐 Professional Profile:

Orcid

Scopus

📚 Education & Academic Path

I completed my Ph.D. in Engineering Design and Advanced Manufacturing at MIT Portugal Program, focusing on machine learning and deep learning for prognostics in aeronautics. This research involved collaborations with Rolls Royce and Embraer, resulting in significant advancements in predictive maintenance technology.

🔬 Research & Professional Experience

Currently serving as an Assistant Professor at TU Delft and starting soon at NOVA IMS, I’ve been actively involved in teaching, research, and leadership roles. My work spans multiple continents, including positions at NASA Ames Research Center and Instituto Tecnológico de Aeronáutica, where I contributed to cutting-edge projects in technical prognostics and system engineering.

🏆 Achievements & Recognition

Throughout my career, I’ve been honored with numerous awards, including Best Paper accolades at prestigious conferences like WCE 2019 and ISM 2019. I’ve also received recognition for my teaching contributions and was awarded a Doctorate Scholarship from the Foundation for Sciences and Technology in Portugal.

🌐 Contributions & Outreach

Beyond academia, I’ve chaired sessions at international conferences and served as a web chair for the Intelligent Transport Systems Conference. My editorial roles include being a special issue editor for prominent journals and authoring chapters on advanced engineering topics.

Publication Top Notes:

  • Aircraft Engine Bleed Valve Prognostics Using Multiclass Gated Recurrent Unit
    • Year: 2023
    • Citations: 2
  • 1D-DGAN-PHM: A 1-D denoising GAN for Prognostics and Health Management with an application to turbofan
    • Year: 2022
    • Citations: 4
  • Relation between prognostics predictor evaluation metrics and local interpretability SHAP values
    • Year: 2022
    • Citations: 57
  • A self-organizing map and a normalizing multi-layer perceptron approach to baselining in prognostics under dynamic regimes
    • Year: 2021
    • Citations: 14
  • Classification prognostics approaches in aviation
    • Year: 2021
    • Citations: 15