Hello and welcome to my homepage! My name is Jianqi Yang, 杨建祺 in Chinese.

I am going to get my undergraduate degree in Industrial Engineering, but I actually have a broad background in Data Science, Engineering and Environmental Science. I completed a series of research projects during my undergraduate studies - mainly based on data science and optimisation theory - and discovered my interest in atmospheric science and computer science. Currently, my interests are focused on the intersection of data science and atmospheric science, including the use of machine learning and other novel data science methods for analysing atmospheric observation data, and re-inventing techniques such as data assimilation, which are commonly used in the field of atmospheric science. These crossovers have given birth to a lot of very interesting new theories, such as AI4Science and Data Learning, and I’ve already seen them garnering attention within the broader field of research.

My work and research

Interests

• Statistics, Data Science and Machine Learning, etc
• AI4Science, especially in Atmospheric Science
• Interaction of scientific and technological development with society and the environment

Current research priorities

I am currently working at the intersection of machine learning and signal processing, developing a novel algorithm to leverage observational data generated by sensors mounted on vehicles to study the distribution patterns of pollutants within urban areas. I anticipate that this algorithm will identify baseline pollutant concentrations within cells of varying scales and investigate how these baselines appropriately couple with those of other cells (of the same or different scales) and with local sources.

Why atmospheric science?

I think atmospheric science is really a meeting point between physics, earth science and data science, and even other broader fields. At all scales of atmospheric science research, physics and statistics alternately dominate: concise physical rules in simple systems are obscured by chaotic systems on larger scales, and then complex phenomena on even larger scales evoke statistical laws. As a result, atmospheric science has become the intersection of two currently dominant modelling paradigms: the purely physically-driven, CFD-mediated solution; and the data-centric solution, where the complexity of the phenomena obscures the underlying physical principles. Furthermore, the atmosphere, as the only medium that physically connects all humans, all nations, and temporally connects the present to the past through artefacts, rocks and ice cores, naturally holds special significance in sociology, economics and politics. This vast picture is sure to excite any curious mind.

Atmospheric science has an irreplaceable role to play in saving lives, avoiding economic losses and protecting vulnerable communities, and an understanding of the atmospheric environment helps us to reflect on the problems of economic development and industrialisation and to actively search for ways to achieve sustainable development. Atmospheric science is exciting both in terms of satisfying the curiosity of researchers and in terms of its practical benefits to society.

My background and history

If you are curious about why I abandoned my research on multi-agent algorithms for UAVs, it is because I indeed recognized the potential for these technologies to be used in warfare.

My intellectual communities

I have organized several informal seminars and discussion sessions on how to apply for patents and publish research findings. I have also been responsible for introductory courses on basic computer knowledge and programming. Additionally, I serve as a reviewer for an academic journal.