I am a Postdoctoral Research Fellow with a background Statistical Learning (Machine Learning) and research focus on deep learning explainability and environmental data science. My professional journey includes prior experience as a Design Automation Engineer at a major semiconductor multinational corporation, where I honed my technical and problem-solving skills. Passionate about technology and education, I enjoy sharing knowledge and empowering others through teaching and mentorship. My strong technical foundation is complemented by hands-on experience in research and industry. In my leisure time, I indulge in photography, paper craft, music, and exploring mountains. While I am an avid fan of gaming, I stick to strategy and role-playing games, which align with my analytical mindset. I also engage deeply with topics such as philosophy, politics, and social justice, advocating for the welfare of children and equity in society.
| Website | https://www.nicklim.com |
|---|---|
| Address | FG2.07, AI Institute, University of Waikato, 3210 Hamilton East, Hamilton, New Zealand |
Conducting research on deep learning explainability and uncertainty estimation. Leading projects involving weak supervision for object localization and environmental data analysis.
Supervising postgraduate students on topics like satellite image super-resolution and machine learning for ecological datasets.
Developed design methodologies for transistor-level simulations and verification. Worked closely with design engineers to debug and resolve issues, earning peer-level recognition awards.
Identified critical design flaws, including the $700M Cougarpoint PCH SATA recall issue, showcasing my technical expertise.
Methodology Development and Process Migration (2007, 2009-2011)Played a key role in automating the translation of circuit designs between process nodes. Contributed to a skunkworks team that earned a division-level award for innovation.
Development and Maintenance of Mathematical Models (2007-2010)Automated the generation of IBIS/.LIB models for signal integrity and timing analysis, bridging gaps between transistor designs and mathematical models under specification constraints.
Graduate Intern Trainer and Department Trainer (2007-2010)Trained new hires on Intel design flow, circuit design, simulations, and soft skills. Delivered department-level training on UNIX and proprietary environments.
University of Waikato
My doctoral research focused on "Ensemble Learning in High Dimension Datasets," supervised by Dr. Robert Durrant. I explored the impact of random subspace projections on classifier accuracy and ensemble diversity. Awarded Best Conference Presenter at the 2017 New Zealand Mathematics and Statistics Postgraduate Conference for my talk "Meeting the Modern Prometheus: An Introduction to Deep Learning from a Mathematics and Statistics Perspective." Recipient of the University of Waikato Doctoral Scholarship.
University of Waikato
My master's research focused on the decomposition of bipartite graphs into stars within combinatorics. Recipient of the A Zulauf Scholarship and admitted to the Golden Keys Honour Society. Graduated with First-Class Honours and a GPA of 8.25/9.00.
Multimedia University
Achieved multiple entries to the Dean's List and was runner-up in the Intervarsity Engineering Competition in my second year. Graduated with First-Class Honours and a GPA of 3.69/4.00.
G. Cassales, S. Salekin, N. Lim, D. Meason, A. Bifet, B. Pfahringer, E. Frank. Published in Ecological Informatics, 103014, 2025.
A. Dwivedi, N. Lim, A. Bifet, E. Frank, B. Pfahringer. Presented at the International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, 2024.
N. Lim, A. Bifet, D. Bull, E. Frank, Y. Jia, J. Montiel, B. Pfahringer. Published in the Journal of the Royal Society of New Zealand, 53(1), 69-81, 2023.
Y. Jia, E. Frank, B. Pfahringer, A. Bifet, N. Lim. Presented at the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021.
N. Lim, R.J. Durrant. Presented at the International Conference on Artificial Intelligence and Statistics, 2020.
N. Lim, R.J. Durrant. Preprint available at arXiv:1705.06408.
K.L. Yeoh, J.S. Lim, K.L. Goh, S.M. Tee. Presented at the SoC Design Conference, 2009.
N. Lim. (2019). Doctoral thesis, University of Waikato. Available at hdl:10289/13422.
Conducted a tutorial on AI techniques and practical applications during the AI Hackathon Bootcamp at the University of Waikato.
Delivered a beginner-friendly workshop on Scikit-Learn at the IndigiData Aotearoa Wānanga.
Presented an applied workshop on machine learning techniques for flood prediction at the Waikato Regional Council.
Led publicity efforts for AUSDM'23, enhancing visibility and participation in the conference.
Contributed as a graduate student in the working committee, assisting in organizing logistics and session management.
Played a key role in the central organizing committee, overseeing event planning and coordination for the conference.
Some of my photos taken during my travels.