Shing Chan
Verified Expert in Engineering
Machine Learning Developer
Asuncion, Paraguay
Toptal member since October 27, 2021
Shing is a researcher and developer of machine learning methods with experience across diverse domains, including physics-informed machine learning for fluid simulations, generative AI for geomodelling, clinical risk scores for patient screening, customer lifetime value models for eCommerce, and models for sports betting and algorithmic trading. Shing currently works at the University of Oxford, researching methods to leverage wearable sensor data for health analytics.
Portfolio
Experience
Availability
Preferred Environment
Linux, Git, PyTorch, Keras, Bash, Fortran, Vim Text Editor, Amazon Web Services (AWS), Python
The most amazing...
...thing I've developed is a generative AI for geomodelling, published in CompGeosci 2019.
Work Experience
Researcher
University of Oxford
- Developed algorithms for wearable devices to recognize sleep, walking, cycling, and other activities.
- Applied time-to-event analysis (e.g., Cox PH, survival forests) to model time to hospitalization or death based on patient characteristics and alternative data such as wearable sensor data.
- Developed a deep learning model for data-driven information extraction from high-res and unstructured wearable sensor data for health and behavioral insights.
- Created Python packages to process and analyze wearable sensor data for health analytics.
- Built data pipelines to perform time-series analyses on terabytes of healthcare data.
Machine Learning Expert
KEG Systems LLC
- Researched novel features (e.g., player-player, player-team, team-team interaction features) to predict game outcomes for sports betting (e.g., money line, over-under, and spread), emphasizing calibration to inform bet sizing and risk management.
- Created reproducible pipelines for daily retraining, including feature selection, fine-tuning, and pruning.
- Oversaw deployment and decision-making, betting with real money and tweaking metamodels based on feedback.
PhD Candidate
Heriot-Watt University
- Developed a physics-informed machine learning model to speed up computationally expensive Monte Carlo fluid simulations.
- Developed a novel framework for geological reconstruction based on generative models (e.g., GANs, VAEs) to enhance geological realism for improved accuracy of oil production forecasts in Bayesian history matching.
- Created Python packages for subsurface fluid simulations.
Engineering Intern
FAdeA
- Assisted in the maintenance and repair of aircraft components.
- Assessed the capabilities of aircraft repair stations, making sure tools and procedures were in order according to technical manuals.
- Issued reports documenting deviations from technical manuals, including changes in procedures, the use of original equipment manufacturer (OEM), or refurbished parts.
Research and Development Intern
Instituto Universitario Aeronáutico
- Contributed to an in-house software for viscous flow simulation, extending it with the arbitrary Lagrangian-Eulerian formulation on unstructured grids.
- Identified bottlenecks in the simulation software and parallelized them with OpenMP where possible.
- Ported code sections with CUDA Fortran to enable GPU acceleration, resulting in more than 10 times the speed-up.
Experience
Package for Processing and Analysis of Wearables' Data for Health Analytics
https://github.com/activityMonitoring/biobankAccelerometerAnalysisNumerical Optimization with Natural Evolution Strategies
https://github.com/chanshing/xnesSynthesis of Geological Images
https://github.com/chanshing/geoconditionPhysics-informed Machine Learning for Accelerated Simulations
https://www.sciencedirect.com/science/article/abs/pii/S0021999117307933?utm_medium=emailEducation
PhD in Petroleum Engineering
Heriot-Watt University - Edinburgh, United Kingdom
Engineer's Degree in Aerospace Engineering
Instituto Universitario Aeronáutico - Cordoba, Argentina
Certifications
Financial Markets
Yale University | via Coursera
Heterogeneous Parallel Programming
University of Illinois | via Coursera
Programming Mobile Applications for Android Handheld Systems
University of Maryland | via Coursera
Skills
Libraries/APIs
PyTorch, Keras, Scikit-learn, TensorFlow, OpenMP
Tools
Git, MATLAB, Vim Text Editor
Languages
Python, Java, Fortran, Bash, C, SQL, R
Platforms
Linux, CUDA, Android, AWS
Paradigms
Parallel Programming
Storage
PostgreSQL
Other
Physics Simulations, Machine Learning, Deep Learning, Time Series Analysis, Generative Adversarial Networks (GANs), Artificial Intelligence, Time Series, Predictive Modeling, Data Science, Algorithms, Algorithms, Signal Processing, Data Mining, NLP, Numerical Methods, Numerical Analysis, Physics, GPU Computing, Computational Fluid Dynamics (CFD), Computer Vision, Data Science, Scientific Software, Finance, Aerodynamics, Numerical Simulations, Optimization, Convolutional Neural Networks (CNNs), Wearables, Fitness Trackers, Risk Models, Variational Autoencoders, Recurrent Neural Networks (RNNs), Health, Aerospace & Defense, Aircraft & Airlines, Engineering, Trading, Gambling, Sports
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