Ringo Chu

Aloha my name 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 plenty amount of researches, and they emphasize on scene understanding for visual tracking and visual relation detection, as well as their optimisation. I am previosly a research intern with Intel, where I worked on Sim2Real and probabilistic finance models. Before that, I was a robotic research intern at Dyson working on monocular visual odometry aided by sparse depth for the Eye 360. 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 temporarily 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!

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

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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.


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Ho-Cheung Ng, Steven Liu, Izaak Coleman, Ringo Chu, Man-Chung Yue, Wayne Luk
[Paper] [Bibtex]

Abstract:Recent advancements in the throughput of nextgeneration sequencing machines pose a huge computational challenge in analyzing the massive quantities of sequenced data produced. A critical initial step of genomic data analysis is short read alignment, which is a bottleneck in the analysis workflow. This paper explores the use of a reconfigurable architecture to accelerate this process, based on the seed-and-extend model of Bowtie2. In the proposed approach, complete information available in sequencing data is integrated into an FPGA alignment pipeline for biologically accurate runtime acceleration. Experimental results show that our architecture achieves a similar alignment rate compared to Bowtie2, mapping reads around twice as fast. Particularly, the alignment time is reduced from 50 minutes to 26 minutes when processing 300M reads.


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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.


Multiple Agents Artificial Stock Market Modelling

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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.

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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.


Task transformation from Image to Video Visual Relation Detection

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Current Project


Mindstorm Robots

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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.

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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.



Emails:
ringo.chu.16 at ucl.ac.uk /
ringochu1997@gmail.com


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


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