Ringo Chu

Aloha this is Ringo and welcome to my page😬! I have completed my undergraduate degree in Computer Science at University College London a while ago. Currently I am taking a 'gap-year' but will come back very soon. During my time at university I did researches, and they emphasize on scene understanding for visual tracking and relation tagging, Kalman-Bucy filtering. I am previously a robotic research intern at Dyson working on monocular visual odometry with sparse depth for Eye 360. I am also funded by Intel working on probabilistic finance inflation models, sim-to-real robotic control for non-trivial tasks. Prior to that, I was a renowned researcher at Imperial College London Custom Computing, where I worked on fast 3D Convolution neural networks for NASA AVRIS imaging and inference acceleration.

Apart from being a nerd, I am a bookworm🐛, a tennis player🎾 as well as a part-time traveller🛫️; If you want to know more about me, you can look at my Google Scholar, Facebook, LinkedIn , my résumé📚 or even contact me in person☎️. Even though I am away, I remain reachable and always keen to chat and answer your queries :)

Publications and Projects

Below is the collection of my researches and some of my interesting projects!


Ringo Chu, Ho-Cheung Ng, Xiwei Wang, Wayne Luk
IEEE Symposium On Geoscience and Remote Sensing (IGARSS), 2019
[Paper] [Code] [Bibtex]

A new deep-learning model to extract 3D features at spatial and spectral levels simultaneously to address the classic problem of high dimensionality on HSIs. Experiments were conducted using scenes acquired by NASA Airborne Visible/Infra-Red Imaging Spectrometer and have shown competitive performance.

{Steven Liu*, Ringo Chu*}, Xiwei Wang, Wayne Luk
Applied Reconfigurable Computing (ARC), 2019
[Paper] [Code] [Bibtex]

An FPGA accelerator for hyperspectral imagery with an optimized architecture that is faster than GPUs and consume less power. The proposal method is suitable and ideal for real time inference deployment.


Martin Ferianc, Hongxiang Fan, Ringo S.W. Chu, Jakub Stano, Wayne Luk
Applied Reconfigurable Computing (ARC), 2020
[Paper] [Bibtex]

Featured here!!! -> this
Abstract: A method for exploring the design space and latency prediction for FPGAs using a Gaussian process parametrized by an analytic approximation and coupled with runtime data. Experiments were conducted on three CNNs accelerator targeting Intel Arria 10 GX 1150, demonstrating a 30.7% improvement in accuracy w.r.t MAE against a standard analytic method in leave-one-out cross-validation.

(A Closer Look at) Neural RGB+𝔻 Object Tracking with Depth Estimation


This project introduces the first neural approach to support 2D tracking using approximated depth information. We assume that objects should have small displacement and small change in depth between frames, and apply this assumption to a visual object tracker and a multiple object tracker using depth information generated from a monocular depth estimation network. Based on preliminary quantitative results, the proposed approach has shown promise in occlusion detection for tracking of single and multiple generic objects.

Task transformation from Image to Video Visual Relation Detection


Current Project

Multiple Agents Artificial Stock Market Modelling


A research project to model a stock market with agents of many behaviours. A probabilistic method is used to analyse the stochasticity of processes and collectives of agents. The model is also benchmarked on Intel Xeon multi-core CPU and Altera FPGA.

Mindstorm Robots


My childhood, and then recently

First prototype born in 2015(old photo and rare footage). This is a 35cm x 35cm x 40cm sized robot built in Lego only! The robot was initially constructed with a Sony phone attached as an explorer and as a Mars rover capable of broadcasting live motions. Later I experimented some Reinforcement learning algorithms with the robot for solving maze and learning to escape from being stuck.

LSTM verses Gaussian Process Regression
Me, 2018 Summer
Short Summary

A very very very brief comparison between Long-Short term memory and Gaussian Process Regression on stock predictions based on their interpretabilities.

ringo.chu.16 at ucl.ac.uk /

1st floor, 90 High Holborn,
London, WC1V 6LJ