Liam Bui, Deep Learning Developer in Toronto, ON, Canada
Liam Bui

Deep Learning Developer in Toronto, ON, Canada

Member since October 24, 2019
Liam is passionate about building data products that help organizations extract insight from data. He has worked with operations research, computer vision, and machine learning/deep learning projects, from data collection, data processing to model training, evaluation, and deployment. He has attained the Certified Machine Learning Specialty award by AWS and is a Certified Analytics Professional (CAP) awarded by the Institute for Operations Research and Management Sciences (INFORMS).
Liam is now available for hire

Portfolio

  • Toptal
    SciPy, Jupyter Notebook, Scikit-learn, Python
  • Fugro
    Amazon Web Services (AWS), Amazon SageMaker, AWS, Object Detection...
  • Terramera
    Keras, TensorFlow, OpenCV, Scikit-learn, Python

Experience

Location

Toronto, ON, Canada

Availability

Part-time

Preferred Environment

Git, Bitbucket, Spyder, Eclipse, Ubuntu, Windows

The most amazing...

...project I've worked on used computer vision to automatically count dead versus live worms in microscopic images, eliminating the need for manual counting.

Employment

  • Data Scientist

    2020 - PRESENT
    Toptal
    • Implemented Liquid chromatography-mass spectrometry data processing and machine learning models to detect targeted proteins in blood samples, facilitating faster disease diagnosis.
    • Researched time-domain/frequency-domain signal processing and machine learning algorithms for fall detection based on biomedical signal data collected from wearable sensors using Python and Scikit-learn.
    • Provided advice on data science use cases and data collection protocols for machine learning model development/evaluation.
    Technologies: SciPy, Jupyter Notebook, Scikit-learn, Python
  • Machine Learning Engineer (Computer Vision)

    2020 - PRESENT
    Fugro
    • Developed deep learning object detection models and object tracking prototypes with Python and TensorFlow on Amazon SageMaker to detect and track traffic objects, reducing manual processing cost by 50%.
    • Architected data processing workflow with multiple parallel compute jobs using AWS Batch and Step Functions, reducing processing time ten times.
    • Implemented computer vision algorithms in C++ and OpenCV to improve automated pavement distress extraction.
    • Improved data processing pipeline and implemented bundle adjustment to estimate a road object’s GPS coordinate based on vehicle’s position/orientation, inertial measurement unit (IMU) data, and camera intrinsic/extrinsic parameters.
    • Standardized team’s DevOps and MLOps processes with Jenkins, Docker, and MLflow.
    Technologies: Amazon Web Services (AWS), Amazon SageMaker, AWS, Object Detection, Computer Vision, MLflow, Docker, Jenkins, OpenCV, Python, C++
  • Senior Machine Learning Engineer

    2018 - 2019
    Terramera
    • Researched deep learning models for object detection (Mask RCNN, U-Net) using Python and TensorFlow to automate pest counting processes.
    • Implemented multispectral image processing pipelines for plant health evaluation using computer vision and machine learning techniques in Python, OpenCV, and Scikit-learn.
    • Developed a multispectral imaging prototype and implemented machine learning models to estimate grape sugar content based on multispectral reflectance, in Python, OpenCV, and Scikit-learn.
    • Implemented machine learning models for drug dose-response modeling, drug synergy analysis, and prediction using cheminformatics and machine learning libraries (Scikit-learn, RDKit) to accelerate the drug discovery process in plant health research.
    • Provided advice on experimental design and implemented statistical analysis pipelines with Python, Rpy2, and StatsModels to automate various statistical analyses for plant health research.
    Technologies: Keras, TensorFlow, OpenCV, Scikit-learn, Python
  • Data Scientist

    2017 - 2017
    Phemi Systems
    • Developed distributed data processing and analytics prototypes using Spark (Scala), Hive, and Zeppelin to demonstrate fast query and analytics on terabytes of clinical data.
    • Proposed machine learning and deep learning demos using Python, Scikit-learn, and TensorFlow to show how medical imaging data can be analyzed to support the diagnosis.
    • Implemented a natural language processing pipeline in Scala and Apache cTAKES - a library with both rule-based and machine learning techniques, to extract clinical information from unstructured medical text.
    • Developed term partitioned index mechanism in Java to enable fast document search in Accumulo.
    Technologies: Scikit-learn, SciPy, Python, Scala, Zeppelin, Apache Hive, Spark, Hadoop
  • Data Analytics Engineer

    2012 - 2016
    DBS
    • Developed SAS code to extract data from Teradata SQL databases and perform statistical analysis for the card and unsecured lending sales and marketing.
    • Liaised with the modeling team to deploy predictive models (recommender system, location analytics) for targeted marketing, leading to two times the lift in the customer response rate in digital campaigns.
    • Proposed experimental design and hypothesis testing on different content factors to improve customer engagement in email marketing.
    • Developed Java analysis reports and QlikView dashboards to provide technology and operations teams with insight on process improvement and risk control.
    • Performed process mapping and simulation modeling to optimize business processes, resulting in a 10% reduction in operating costs.
    • Led infrastructure design and deployment for an enterprise data management system.
    Technologies: QlikView, SQL, SAS, Java
  • Business Intelligence Developer

    2011 - 2011
    Hutcabb Consulting
    • Implemented inventory modeling and demand forecasting function in a Decision Support System with Java Servlet to facilitate optimal inventory decision making.
    • Supported infrastructure design and deployment for the Decision Support System.
    • Streamlined data processing from multiple data sources by developing Data Extract, Transform, and Load (ETL) pipeline with Microsoft SQL Server Integration Services (SSIS).
    Technologies: SQL Server Integration Services (SSIS), SQL, Java

Experience

  • Automated Worm Counting with Deep Learning

    Built an image processing pipeline and deep learning model to automatically count dead and live worms in microscopic images, reducing the worm counting time from two days (manual counting) to one hour (automated counting with Computer Vision).

  • Automated Plant Health Evaluation with Computer Vision

    Built an image processing pipeline to automatically segment out individual plants in a greenhouse and classify the plants' health.

  • Grape Sugar Content Estimation with Multispectral Imaging

    Built a multispectral imaging prototype and machine learning models to predict grape sugar content based on multispectral reflectance.

  • Plant Traits Estimation with 3D Imaging

    Collaborated with the robotics team to build a 3D imaging prototype and implement an image processing pipeline to perform 3D reconstruction of plants and calculate leaf count and angles.

  • Drug Efficacy Prediction

    Built a chemo-informatics pipeline to extract 1D, 2D, and 3D molecular descriptors of chemical molecules and predict their efficacy against certain diseases in plants.

  • Fall Detection with Signal Processing

    Implemented signal processing algorithms and built machine learning models to detect when an elder falls based on biomedical signal data collected from wearable sensors.

  • Skin Lesion Classification

    Implemented a prorotype to demonstrate the use of Computer Vision and Machine Learning to assist diagnosis by classifying whether a skin lesion is malignant vs benign.

  • Clinical Text Extraction

    Implemented a prorotype to demonstrate the use of Natural Language Processing to extract relevant clinical information from clinical text.

Skills

  • Libraries/APIs

    Keras, TensorFlow, Scikit-learn, OpenCV, SciPy
  • Paradigms

    Data Science
  • Other

    Computer Vision, Machine Learning, Deep Learning, Image Processing, Statistics, Big Data, Image Recognition, Artificial Intelligence (AI), MLflow, Object Detection, AWS, Statistical Analysis, Analytics, Operations Research, Inventory Management, Forecasting, Financial Analysis, Technology, Computer Science
  • Languages

    Python, Java, SQL, SAS, C++, Scala
  • Frameworks

    Spark, Flask, Hadoop
  • Tools

    Scikit-image, Spyder, Bitbucket, Git, Jenkins, Amazon SageMaker, Tableau
  • Platforms

    Ubuntu, Eclipse, Zeppelin, Docker, Jupyter Notebook, Linux, Windows, Amazon Web Services (AWS), QlikView
  • Storage

    Apache Hive, SQL Server Integration Services (SSIS), MySQL, PostgreSQL, Cassandra, MongoDB

Education

  • Master's Degree in Computer Science
    2016 - 2017
    Simon Fraser University - British Columbia, Canada
  • Master's Degree in Technology Management
    2012 - 2013
    National University of Singapore - Singapore, Singapore
  • Bachelor's Degree in Computer Science, Business Analytics (Dual degree)
    2008 - 2012
    Nanyang Technological University - Singapore, Singapore

Certifications

  • AWS Certified Machine Learning Specialty
    SEPTEMBER 2021 - SEPTEMBER 2024
    Amazon Web Services
  • HIPPA Business Associate II (Non-Clinical) v11
    MAY 2017 - PRESENT
    Litmos
  • Certified Analytics Professional (CAP)
    MAY 2017 - MAY 2023
    Institute for Operations Research and the Management Sciences
  • SAP Certified Business Associate with SAP ERP 6.0
    APRIL 2010 - PRESENT
    SAP

To view more profiles

Join Toptal
Share it with others