Dr Lanpeng Ji
- Position: Lecturer in Actuarial/Financial Maths
- Areas of expertise: risk theory; actuarial mathematics; applied probability; extreme value theory; gaussian random fields; quantitative risk management.
- Email: L.Ji@leeds.ac.uk
- Phone: +44(0)113 343 5891
- Location: 9.312 Physics Research Deck
- Website: Googlescholar | Researchgate | ORCID
I have been a Lecturer in Actuarial/Financial Maths at the University of Leeds since Jan 2018. I completed my Ph.D. studies in Actuarial Science at University of Lausanne (UNIL) under the supervision of Prof Enkelejd Hashorva; I was awarded "Prix de la Fondation Nicolas et Helene Porphyrogenis 2014" for my Ph.D. thesis in Dec 2014. After my Ph.D. I worked as Junior Lecturer and Senior SNF researcher at UNIL between Feb 2014 and April 2016. From May 2016 to Dec 2017, I worked as a postdoc in Machine Learning for HEIG-VD, University of Applied Science of Western Switzerland, under the supervision of Prof Stephan Robert.
My research has focused on the study of ruin probabilities and related quantities of various insurance risk models. The class of models that I have explored is highly diverse, including renewal models with two classes of claims and taxes, compound Poisson models with dependence, perturbed heavy-tailed models, gamma-reected models, (time-changed) Gaussian models and multidimensional Gaussian models. All of the research is related to studies of renewal processes, Levy processes, Gaussian processes and copulas. Recently, I have become interested in the Extreme Value Analysis (EVA) of Gaussian and related fields (GRFs), which has important applications in insurance, queueing and statistics. Additionally, data mining and machine learning techniques for insurance pricing, fraud detection and financial time-series prediction are also of interest to me.
- PhD, Actuarial Science, University of Lausanne
- MSc, Probability Theory, Nankai University
- BSc, Information and Computing Science, Hebei Agriculture University
Research groups and institutes
- Probability and Financial Mathematics