Yang Tian
To explore and discover.
About Me
I am a researcher in the fields of statistical physics, machine learning, and computational science. My work spans these seemingly distinct areas due to my focus on understanding the physical principles governing complex and heterogeneous systems, and exploring how to control and optimize these systems. My research centers on topics such as renormalization group theory, randomized algorithms, operator theory, percolation, and neural networks, with a particular emphasis on identifying their applications across various scientific and technological challenges.
Education
Ph.D. in Cognitive Science (although I barely learn)
Tsinghua Laboratory of Brain and Intelligence, Tsinghua University. During my time at the THBI, I shift my focus towards the study of statistical physics and computational physics, and begin conducting research in these related areas.
2020 - 2024
Bachelor of Science
Tsinghua University. During my undergraduate studies, I focus primarily on computer science and mathematics, with a keen interest in neuroscience. I have explored theoretical neuroscience and attempted to conduct research in this field.
2016 - 2020
Work Experience
Researcher
Infplane AI Technologies Ltd. I serve as the CEO and a researcher at Infplane AI Technologies Ltd. My primary research focuses on optimal control of complex, integrated hardware systems and high-performance computing.
July 2024 - Present
Researcher
Laboratory of Computational Biology and Complex Systems, City University of Macau. During this period, my research focuses on the development of high-performance computing tools related to computational physics and computational biology.
August 2024 - December 2024
Research Intern
Central Research Institute, 2012 Laboratories, Huawei Technologies Co. Ltd. I am primarily responsible for developing systems related to scientific computing. In this role, I develop neural operators and various differential equation-based neural network solvers, focusing on improving the efficiency and accuracy of computational methods for solving complex mathematical problems.
2021 - 2023
Research Work
Analytical methods for complex systems
My first research direction is the analytical methods for complex systems. I explore theories and computational tools such as renormalization group theory, percolation, and stochastic networks, aiming to provide a foundation for characterizing, understanding, and predicting the behaviors of complex systems.
Computational science for everything
My second research direction is scientific computing and high-performance computing. I explore how to combine tools from machine learning, randomized algorithms, and operator theory to design high-performance computational tools. These tools aim to solve a wide range of scientific and industrial problems more efficiently, using fewer computational resources and less time.
Computational statistics for complex data
My third research direction is computational statistics. I explore new statistical methods and their computational implementations, aiming to provide tools for statistical inference and hypothesis testing in complex scenarios.