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Lecturer at Auckland University of Technology
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
3D-Medical-Generative-Survey
August 9, 2022 – July 15, 2024
3D-Medical-Generative-Survey — GitHub repository
View Projecttri-M-ICCV
October 16, 2021 – October 22, 2021
A multi-mode modulator for multi-domain few-shot classification (ICCV)
View ProjectLSMI-Sinkhorn
June 25, 2021 – June 27, 2021
Code for ECML/PKDD paper: "LSMI-Sinkhorn: Semi-supervised Mutual Information Estimation with Optimal Transport"
View ProjectSCOT
July 8, 2020 – October 16, 2021
CVPR 2020, Semantic Correspondence as an Optimal Transport Problem, Pytorch Implementation.
View ProjectTPN-pytorch
May 4, 2019 – July 26, 2021
Pytorch Code for ICLR19 paper: Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning.
View ProjectTPN
December 22, 2018 – May 4, 2019
Code for ICLR19 paper: Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning.
View Projectseqvlad-pytorch
May 5, 2018 – June 20, 2019
The implementation of Sequential VLAD in Pytorch
View ProjectFew-shot-Meta-learning-papers
April 6, 2018 – June 15, 2018
Recent few-shot meta-learning papers
View ProjectCultural Fit Analysis
The candidate's projects are heavily research-oriented and academic, focusing on advanced machine learning topics. While this demonstrates strong technical depth, the lack of diverse project types (e.g., production systems, team-based projects, business applications) makes it difficult to assess cultural fit for a typical industry Data Scientist role. The experience level is 0, suggesting a recent graduate or someone transitioning from academia, which might require more mentorship and integration into a corporate environment.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. No psychometric test results or interview feedback are available.