Dr Hao Ni

Dr Hao Ni joined UCL in 2016, and is a Senior Lecturer in Financial Mathematics, as well as being a fellow of the Alan Turing Institute for Data Science.

Before this, Dr Ni was a visiting postdoctoral researcher at ICERM and the Department of Applied Mathematics at Brown University, and continued her postdoctoral research at the Oxford-Man Institute of Quantitative Finance before joining UCL. She completed her D.Phil in mathematics in 2012 under the supervision of Prof. Terry Lyons at the University of Oxford. Her research has covered themes such as stochastic analysis and machine learning, and her work in Oxford focussed on modelling the evolution of complex systems that are impacted by noise using the theory of rough path and its applications.

Her undergraduate studies took place in China and Germany, followed by a masters degree in Computational and Mathematical finance at Oxford. After this, she interned briefly in the financial industry, before going on to complete her PhD on the rough paths theory. With regards to her interest in mathematics, Hao says:

“Starting from a young age, I have felt obliged to make a positive impact on our society by helping others. It has never occurred to me that there would be any conflict between being a scientist and a woman at the same time; instead it defines who I am and makes me feel special and proud. In mathematics, there are only absolute truths and falsehoods, everything is clear-cut, and there is no ambiguity. The “good” mathematical objects stem out of the abstraction of realities. To me, these are the mystical wonders of the mathematical world.”

Hao’s current work has involved research into machine learning and statistics. When asked about this, she replied:

“I believe the development of machine learning or artificial intelligence has traditionally focused more on the engineering side, which has led to huge successes in various domains, such as image recognition and robotics. But it is yet unclear why those methods were so successful. I believe mathematics can offer an answer to this question, and provide a solid theoretical foundation to data science as a whole.”

Outside of work, she enjoys travelling, fine art, and food tasting. When asked about those she has worked with, Hao responded:

“I am grateful that I am generously supported in life by my parents and loving friends, who have given me lots of courage and care – and in my professional life, I have had great mentors, colleagues and collaborators”.