Hi! My name is Chen Jin, and I’m a research scientist at the Centre for Artificial Intelligence (CAI) at Data Science & Artificial Intelligence, AstraZeneca (UK). My research focuses on Large Multimodal Generative Models, Vision-Language Human-Machine Interactions, Computer Vision and Natural Language Processing for Healthcare, and Predictive Models.

Previously, I was a research assistant at the Centre for Medical Image Computing (CMIC) at UCL’s AI Centre, where I worked on Multimodal-Multiscale-Multitask representation learning, image generation and mapping. I have also developed attention-based learnable sampling methods for either data efficient or computation efficient machine learning, and applied on segmentation of large volume high-resolution images.

Externally, I got the privilege to collaborate closely with Dr Ryutaro Tanno from Google DeepMind, Prof. Daniel Alexander from CMIC UCL and Prof Mihaela van der Schaar from Artificial Intelligence and Medicine at the University of Cambridge.

I obtained my PhD in Computational Geoscience at the Institute of GeoEnergy Engineering, Heriot-Watt University. My thesis was about developing a Hierarchical Digital Rock Analysis Workflow, involving machine learning-based multi scale-modal image fusion, reconstruction and fluid simulation.

Download my CV (ast updated 14/03/2022).

News:

• July 2024: Our work Local Diffusion is accepted by ECCV 2024, which explored Tackling Structural Hallucination in Image Translation.
• May 2024: Our work Multi-Concept Prompt Learning is accepted by ICML 2024, which explored language-driven concepts learning during human-machine interactions.
• Jan 2022: Our work on Learning to downsample is accepted by ICLR 2022, which aiming to adapt the computation/sampling budget to the difficulty of segmented pixel/region.
• July 2021: We are organizing the foveation project on MedICSS Summer School 2021.
• Sep 2020: a shorter version of our work on Foveation for Segmentation is accepted at MICCAI 2020, also check out full extensive version.
• Aug 2020: Our attempts on Disentangling human annotation error is accepted at NeurIPS 2020.