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PhD Student in Computer Science (AI for Environmental Risks), University of Cambridge
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Assessing your cultural and operational fit
University of Cambridge
Data Scientist
June 28, 2026 – Present
CAMP3D
September 12, 2025 – September 16, 2025
Cambridge Arboreal Modelling Panoptic 3D: Pipeline and Dataset
View ProjectSPREAD
July 11, 2024 – August 8, 2025
SPREAD is a large-scale synthetic dataset for image- and point-cloud- based tasks in forestry.
View ProjectPILA
May 22, 2024 – Present
PILA: Physics-Informed Low Rank Augmentation for Interpretable Earth Observation
View Projectai-refined-rtm
April 17, 2023 – May 22, 2024
Self-supervised learning refined radiative transfer modelling to retrieve biophysical variables in forests
View Projectfast-hierarchical-learning-for-fsod
December 10, 2022 – December 10, 2022
Fast hierarchical learning for few-shot object detection
View ProjectHoloInspect
January 13, 2021 – April 29, 2021
HoloInspect is a Mixed Reality BIM software on HoloLens, which extends traditional BIM software with mixed reality experience and partial multi-user capabilities in presentation, management, and on-site inspection.
View ProjectCIL_Road_Segmentation
September 28, 2020 – September 28, 2020
CIL_Road_Segmentation — GitHub repository
View Projectyolo-teacher
May 6, 2020 – September 28, 2020
Main repository for the AR Language Teacher for the 3D vision class, spring 2020.
View ProjectCultural Fit Analysis
The candidate's project portfolio is heavily focused on academic and research-oriented personal projects, primarily in computer vision and environmental science. While demonstrating strong technical capabilities, the lack of diverse project types (e.g., industry applications, team-based projects with clear business objectives) and a single current role as 'Data Scientist' at a university makes it difficult to fully assess cultural fit for a typical industry Data Scientist role. The experience level is listed as 0, which contradicts the current role, suggesting a potential mismatch in how experience is categorized or a very recent entry into the role.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions suggest a strong focus on technical execution and research, but collaboration, communication, and problem-solving approaches are not detailed.