I am actively seeking a full-time position.
>_Tech stack
| Programming |
Python, R, SQL, Bash / Shell |
| Cloud services |
Microsoft Azure (Azure Cloud Services, AzureML) |
| Others |
Linux, Git, Power BI, QGIS, PyTorch, YOLO |
Hello world! I'm a PhD student at UNSW Sydney. My current research focuses on applying computer vision tools to process wildlife camera trap images in Australia.
I'm supervised by Richard Kingsford, Will Cornwell, and Arcot Sowmya from UNSW, Shinichi Nakagawa from the University of Alberta, and Hideo Saito from Keio University.
Prior to this, I completed my BSc in Biology and BSc (Hons) at UNSW Sydney, under the supervision of Shinichi Nakagawa and Will Cornwell.
My graduate thesis focused on quantifying biases in the scientific literature of all mammals, which was later published.
I have 6+ years of experience programming in R (tidyverse, ggplot2, brms, etc.), Python (pandas, sklearn, Pytorch, etc.), and SQL.
I enjoy solving problems with statistical methods, as well as visualising data.
To see examples of my figures, please refer to my first-author publications below.
Peer-reviewed works
Martinig, A. R., Burk, S. L. P., Drobniak, S. M., Perry, I., Morrison, K., Petersohn, M., Pottier, P., Nakagawa, S., Pollo, P., Ricolfi, L., Williams, C., Mizuno, A., Chhen, A., Tam, J., Yang, Y., de Jong, J., Ceccacci, A., Cuadros, A., Lagisz, M. (2026). Dragon Kill Points: applying a transparent working template to relieve authorship stress. BMC biology, 24, 48. https://doi.org/10.1186/s12915-026-02521-x.
Tam, J., Saito, H. (2026, January). Diffusion-based free-viewpoint synthesis for dataset expansion and widlife classification [Poster]. AI for Environmental Science (AI4ES) Workshop @AAAI, Singapore.
Pollo, P., Martinig, A. R., Mizuno, A., Morrison, K., Pottier, P., Ricolfi, L., Tam, J., Williams, C., Yang, Y., Drobniak, S. M., Lagisz, M. & Nakagawa, S. (2025). Harnessing meta-analyses’ insights in ecology and evolution research. Royal Society Open Science, 12, 2507592. https://doi.org/10.1098/rsos.250759.
Tam, J., Cornwell, W. (2025, October). Simple edge-guided wildlife classification with classical detectors [Poster]. Sustainability with Earth Observation & AI (SEA) Workshop @ICCV, Honolulu, Hawaii.
Pottier, P., Lagisz, M., Burke, S., Drobniak, S., Downing, P., Macartney, E., Martinig, A., Mizuno, A., Morrison, K., Pollo, P., Ricolfi, L., Tam, J., Williams, C., Yang, Y. & Nakagawa, S. (2024). Title, abstract, and keywords: a practical guide to maximise the visibility and impact of academic papers. Proceedings of the Royal Society B: Biological Sciences, 291, 20241222. https://doi.org/10.1098/rspb.2024.1222.
Tam, J., Kay, J. (2024, June). Comparing fine-grained and coarse-grained object detection for ecology [Poster]. 11th Fine-Grained Visual Categorization (FGVC) Workshop @CVPR, Seattle, USA. https://doi.org/10.48550/arXiv.2407.00018.
Nakagawa, S., Lagisz, M., Francis, R., Tam, J., Li, X., Elphinstone, A., Jordan, N., O'Brien, J., Pitcher, B., Van Sluys, M., Sowmya, A. & Kingsford, R. (2023). Rapid literature mapping on the recent use of machine learning for wildlife imagery. Peer Community Journal, 3, e35. https://doi.org/10.24072/pcjournal.261.
Tam, J., Lagisz, M., Cornwell, W. K. & Nakagawa, S. (2022). Quantifying research interests in 7,521 mammalian species with h-index: a case study. Gigascience, 11, giac074. https://doi.org/10.1093/gigascience/giac074.
