Charles Nicklas Christensen

PhD
Computer Vision & AI

University of Cambridge

Biography

Charles Nicklas Christensen is a computer vision researcher with experience in deep learning, super-resolution, image segmentation and image classification. During his PhD at University of Cambridge, he researched computer vision methods in the context of optical microscopy using deep learning. Examples of methods he has contributed are ML-SIM , ERnet and VSR-SIM .

ML-SIM is a method to train neural networks to reconstruct structured illumination microscopy images based on physically modelled synthetic training data. ERnet is an image segmentation model of endoplasmic reticulum images that leverages an architecture inspired by the vision transformer. He has also worked on large-scale image classification to enable product recognition in a retail setting.

Other interests include mathematical modelling, artificial intelligence and deep learning.

Download Charles Christensen’s resumé.

Interests

Computer Vision
Image Analysis
Deep Learning
Super-resolution
Computational Imaging

Education

PhD in Computational Imaging, 2022

University of Cambridge
MRes in Image Processing and Sensing, 2018

University of Cambridge
MScEng in Mathematical Modelling and Computation, 2017

Technical University of Denmark
BScEng in Physics and Nanotechnology, 2015

Technical University of Denmark

Experience

Data Scientist

Oxford Nanoimaging, ONI

Apr 2022 – Present London, UK

Developed computational methods for spectral demixing towards a new product offering. This touched on statistical techniques applied to 3D data and deep nerual networks for processing image data. In addition to this, I have also contributed to a web-based acquisition and analysis software, called CODI, which involves several technologies spanning front-end, back-end and deployment.

Python
C++
Image processing
Deep learning
Pytorch
CNNs

Co-founder & CTO

Wizion AI

Oct 2019 – Sep 2022 Cambridge, UK

Together with a colleague from my research group, I co-founded Wizion AI, a company offering image processing products utilising deep learning methods inspired by research in our lab. In 2020, we were selected for the Cambridge Accelerate accelerator programme at the Judge Business School. After three years of efforts towards the development of two products, we decided to shift our focus to other opportunities.

Computer vision
MLOps
Image processing

Computer Vision Research Scientist

Tesco

Jul 2021 – Oct 2021 London, UK

Within the Tesco Data Science department, I worked with the computer vision team on large-scale image classification for product recognition at the till to prevent theft.

Metric learning
Transformers
Pytorch
Pandas
TensorRT

Co-founder & Head of Data

Open-seneca

Jun 2018 – Present Cambridge, UK

Part-time involvement in non-profit organisation. Started as a research project doing my MRes and spun out in 2019 with five other PhD students.

Open-source hardware design of air quality monitor.
Deployment of sensor networks in several countries across South America, Africa, Europe and Asia.
More information https://open-seneca.org

Big data
Data visualisation
Python
Scikit-learn
MongoDB

Research Associate

Physical Computation Lab

Jul 2018 – Jan 2019 Department of Engineering, Cambridge University, UK

I worked with Professor Phillip Stanley-Marbell, head of the Physical Computation Lab. In the research project, I modelled lightness illusions in the human visual system by attempting to reproduce them with artificial neural networks.

Research Assistant

Danish Fundamental Metrology

May 2017 – Aug 2017 Lyngby, Denmark

Researched and developed a machine learning system for a novel laser based fire detector. This involved optical engineering and signal processing using machine learning techniques.

Signal processing
Time-series classification
Support vector machines

Data Consultant

VisionTrace

Sep 2017 – Dec 2016 Lyngby, Denmark

Alongside my MSc studies, I worked part-time on a number of consultant jobs including:

Developed a system for image analysis of a continuous video feed at a production facility overseen by food industry technology provider GEA.
Performed data mining on a big dataset for Danish television broadcaster TV2 with results used in a program about fitness level.

Video tracking
Data mining

Research Assistant

Department of Photonics Engineering

Sep 2017 – Jul 2017 Technical University of Denmark

Further researched the topic of my BSc thesis and wrote it up as a publication.

Markov processes
Quantum mechanics
Algebraic computation
Mathematica

Publications

Charles Nicklas Christensen, Edward N. Ward, Meng Lu, Pietro Lio, Clemens F. Kaminski (2021). ML-SIM : Universal reconstruction of structured illumination microscopy images using transfer learning. In Biomedical Optics Express.

PDF Cite DOI GitHub

Charles Nicklas Christensen, Yevgen Zainchkovskyy, Salvador Barrera-Figueroa, Antoni Torras-Rosell, Giorgio Marinelli, Kim Sommerlund-Thorsen, Jan Kleven, Mikael Lassen (2021). Simple and robust speckle detection method for fire and heat detection in harsh environments. In Applied Optics.

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Charles Nicklas Christensen, Jake Iles-Smith, Torkil S. Petersen, Jesper Mørk, Dara P. S. McCutcheon (2018). Driving-induced population trapping and linewidth narrowing via the quantum Zeno effect. In Physical Review A.

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