Dr Yan Wang is a senior lecturer in Statistics at RMIT. She has extensive experience in both teaching and consultation with her research focusing on ecological modelling.
Yan joined RMIT in 2009, and works in Statistics and Analytics with the School of Science.
From teaching first year statistics to a wide range of students coming from business, finance, economics, chemistry, bioscience, biochemistry, applied science, physics, enviromental science, food science and mathematics she has also developed and delivered advanced courses in statistical inference, multivariate analysis, predictive modelling and statistical learning/machine learning.
Due to the necessity of statistical techniques, she has carried out multi-disciplinary research with collaborators from a variety of disciplines: ecology, insurance, meteorology, public health, marketing, epidemiology and social science.
Her research interest has focused on ecological statistics and modelling, such as species distribution modelling and capture-recapture studies and her published papers sit within the top 5% of journals in the areas of ecological modelling and landscape conservation.
She is currently supervising 3 PhD students and 2 postdoctoral fellows on ecological modelling and consults across a range of universities in the statistics and modelling fields.
My recent teaching responsibilities have covered 3 areas:
- Course coordinator for Work Intergrated Learning (WIL) subject for Master of Analytics students. Students will be allocated to real industry projects, and apply their analytics skills to solve real industry problems. Our past industry partners have included Yarra Valley Water, City West Water, Coles Analytics, Industry Beans, NSW Trainlink, Novartis Pharmacy etc.
- First year statistics service teaching to non-mathematics background students. The service teahing normally has large size class around 200-300 students. The first-year statistics covers elementary statistics, for example, descriptive statistics, basic probability theories, inferenfential statistics in one or two samples estimation and hypothesis testing, regression, multiple sample mean comparsion.
- Advanced statistics courses to high year undergraduate students and postgraduate students, such as multivariate analytics, predicitive modelling and statitsical learning/machine leanring.
- Program Manger for BH119 Bachelor of Analytics (Honours). With data-driven decisions now a fundamental part of business operations, this program provides students with complementary skills in analytics, tapping into studies in statistics, operations research, data science, information technologies, business, finance and marketing. Students can earn a SAS Joint Certificate by completing required courses
- The working member of Athena Swan Working Party committee member, helping RMIT to promote gender equity in academia.
- Course coordinator for WIL subject of Master of Analytics, liasing with industry orgainsations and insitututions, coordination of project allocation, design of WIL course assessment, and assessing student performance.
- PhD (Statistics), the University of Hong Kong
- Master of Engineering (Econometrics), Tianjin University
- Bachelor of Engineering (Industrial Economics), Tianjin University
- Elective member of International Statistical Institute (ISI) (2007)
- Member of International Biometrics Society
- Member of Statistical Society of Australia
- Flint, I.,Wang, Y.,Xia, A. (2023). On the Conway-Maxwell-Poisson point process In: Communications in Statistics - Theory and Methods, , 1 - 25
- Wang, Y.,Samarasekara, C.,Stone, L. (2022). A machine learning method for estimating the probability of presence using presence-background data In: Ecology and Evolution, 12, 1 - 25
- Flint, I.,Golding, N.,Vesk, P.,Wang, Y.,Xia, A. (2022). The saturated pairwise interaction Gibbs point process as a joint species distribution model In: Journal of the Royal Statistical Society. Series C: Applied Statistics, 71, 1721 - 1752
- Kodikara, S.,Demirhan, H.,Wang, Y.,Stone, L. (2021). Inferring extinction date of a species using non-homogeneous Poisson processes with a change-point In: Methods in Ecology and Evolution, 12, 530 - 538
- Hogg, S.,Wang, Y.,Stone, L. (2021). Effectiveness of joint species distribution models in the presence of imperfect detection In: Methods in Ecology and Evolution, 12, 1458 - 1474
- Kodikara, S.,Demirhan, H.,Wang, Y.,Solow, A.,Stone, L. (2020). Inferring extinction year using a Bayesian approach In: Methods in Ecology and Evolution, 11, 964 - 973
- Garrard, G.,Kusmanoff, A.,Faulkner, R.,Samarasekara, C.,Gordon, A.,Johnstone, A.,Peterson, I.,Torabi, N.,Wang, Y.,Bekessy, S. (2020). Understanding Australia’s national feral cat control effort In: Wildlife Research, 47, 698 - 708
- Wang, Y.,Stone, L. (2019). Understanding the connections between species distribution models for presence-background data In: Theoretical Ecology, 12, 73 - 88
- Khot, R.,Azra, E.,Kurra, H.,Wang, Y. (2019). FoBo: Towards Designing a Robotic Companion for Solo Dining In: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, Scotland, United Kingdom, 04/05/2019 - 09/05/2019
- Li, Z.,Chen, W.,Wang, Y.,Hoang, T.,Wang, W.,Boot, M.,Greuter, S.,Mueller, F. (2018). Heatcraft: Playing with ingestible sensors via localised sensations In: Proceedings of the Annual Symposium on Computer-Human Interaction in Play Companion, Melbourne, Australia, 28-31 October 2018
6 PhD Current Supervisions7 PhD Completions
- Modelling dynamics in spatial ecology (led by The University of Melbourne). Funded by: ARC Discovery Projects via Other University Grant 2022 from (2022 to 2025)
- Integrating Niches, Interactions & Dispersal in Species Distribution Models (administered by University of Melbourne). Funded by: ARC Discovery Projects 2019 from (2019 to 2023)
- New statistical approaches for analysing foodwebs and species distributions. Funded by: ARC Discovery Projects 2015 from (2015 to 2020)