Background

Xin Sui received the M.Sc. degree in from Chinese Academy of Sciences, Beijing, China, in 2018 in electrical engineering. In 2022, she received the Ph.D. degree in machine learning for battery state of health estimation from Aalborg University, Aalborg, Denmark. She was a visiting researcher with the Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, in 2023, and with the Department of Mechanical Engineering, Imperial College London, London, UK, in 2024. She is currently an Assistant Professor in the Department of Energy, Aalborg University, Denmark. Her research expertise lies in smart battery management, covering 1) Lithium-ion battery modeling; 2) Battery diagnostics and prognostics including state estimation, health assessment, and lifetime prediction; 3) Advanced battery management techniques, including pulse charging strategies, and balancing; 4) Feature engineering, machine learning, and AI application in battery system; 5) Smart battery technology.

Motivation

My motivation stems from both a sense of responsibility and a passion for supporting early-stage researchers. Having faced the challenges of initiating research topic myself, I understand how difficult it can be for young data scientists to find a clear direction amidst concerns about innovation, and time management, etc. Over the past few years, I have had the opportunity to supervise both PhD and Master’s students and observed that many have strong capabilities, but often struggle to identify the key focus of their work. I believe my experience can guide them more efficiently toward impactful outcomes. I also see this as a mutually beneficial opportunity. Mentoring helps me improve my own leadership and pedagogical skills through reflection and feedback. Moreover, I believe that collaboration often sparks creativity and fresh perspective. I hope to engage in meaningful discussions that could lead to new research ideas. I am excited about the potential of building a supportive, collaborative environment where knowledge flows both ways and new insights emerge naturally.

Skills, expertise, and interests

• • data science
  • machine learning
  • deep learning
  • data analytics
  • statistical and data modelling
  • PhD advice
  • time series analysis
  • data mining
  • digitalisation