Fei Huang
School of Risk ＆ Actuarial - PhD, Australian National University | MPhil, University of Hong Kong | BSc, Xiamen University
Fei joined the School of Risk and Actuarial Studies in July 2020 as a Senior Lecturer. She received her BSc. in Mathematics from Xiamen University in 2009, MPhil in Actuarial Science from the University of Hong Kong in 2011, and PhD in Actuarial Studies from the Australian National University in 2015. Before joining UNSW, she worked at the Australian National University as a Lecturer (2015-2018) and Senior Lecturer (2019-2020).
Fei's main research interest lies in predictive modelling and data analytics. She applies machine learning techniques and statistical methods for various actuarial applications, such as mortality modelling and forecasting, macroeconomic and investment forecasting, and general insurance pricing.
Fei is also a dedicated educator. Her educational excellence has been recognized by winning the ANU Vice Chancellor’s Award for Teaching Excellence in the Early Career Category (2018) and ANU College of Business and Economics Award for Teaching Excellence in the Early Career Category (2017).
From This Author
Beyond black box AI: Pitfalls in machine learning interpretability
New research exposes the hidden dangers of black box AI models, highlighting their vulnerability to manipulation and raising concerns about fairness and transparency
This is why your insurance premiums keep going up
Insurance premiums have been on the rise – and for many households, they're contributing to cost-of-living stress, writes UNSW Business School's Fei Huang
If you get a genetic test, could a life insurance firm use it against you?
There are a number of serious ethical and legal questions that need to be addressed about life insurance firms discriminating on the basis of genetic testing results
Home insurance is on the rise. Is there an affordable solution?
The effects of climate change put affordable insurance, people, their homes and businesses at risk. Solving this problem requires a multi-disciplinary approach
How insurers can mitigate the discrimination risks posed by AI
Research reveals insurers can use fairness criteria alongside AI to minimise bias and discrimination risks
Pricing fairness: tackling big data and COVID-19 insurance discrimination
Research highlights the need for regulators, consumer advocate groups and industry associations to be involved in determining what insurers can – and cannot – discriminate against