Hello, I'm Jess Tam! 👾

🐧LinkedIn

I am looking for a full-time infrastructure position, e.g. DevOps Engineer / SysAdmin / HPC Engineer / Systems Engineer!!

>_Tech stack

Programming	R, Python, shell / bash, SQL (basic)
Infrastructure	CI/CD, GitHub Actions, Docker, Terraform, Prometheus, Grafana
Cloud & Systems	Linux, Git, Azure (Container Registry, Machine Learning Studio), AWS (VPCs, gateways, etc.), APIs, networking basics
Others	PyTorch, YOLO, QGIS

Hello world! I'm a PhD graduate from UNSW Sydney. My previous work focused on exploring AI tools to process wildlife camera trap images in Australia. Prior to that, I completed my BSc in Biology and BSc (Hons) at UNSW Sydney, focusing on quantifying biases in the scientific literature of all mammals using an R package that I wrote, which was later published.

Although my background is in research, I am keen to work in the industry and apply the technical and soft skills I have learnt independently in the past few years, such as programming in R and Python, using Azure, writing technical doeumentation, etc. Currently, I am aiming to work as a DevOps Engineer / Linux SysAdmin / HPC Engineer since I enjoy building pipelines and working on the infrastructure side of things. In order to fill in my knowledge gap, I am actively learning and building with the tools required for the role (see below).

📢 Projects 📢

1. Linux media server to run various media services with Docker to browse various forms of media from my collectionm. I'm also running Prometheus and Grafana for monitoring.

2. Book Tracker application using FastAPI and a PostgreSQL database to display books from my reading list. I first provisioned Azure infrastructure using Terraform, including an Azure Container Registry and PostgreSQL server. I then developed a simple FastAPI application, containerised it using Docker, and configured GitHub Actions to build, test, and publish container image. Finally, I deployed the application to Azure Kubernetes Service, where it connects to the managed PostgreSQL database hosted on Azure.

3. D20 application written in Lua to roll a dice. I implemented a CI/CD pipeline using GitHub Actions to containerise the app with Docker, and then deployed it on Azure.

4. specieshindex R package to extract bibliometric information from literature databases, e.g. Scopus, using their APIs. I implemented a CI/CD pipeline with GitHub Actions to conduct unit testing of the package using testthat and codecov to check for code coverage of the tests.

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.