Liam Bui Email & Phone Number

San Francisco, California, US

San Francisco, California, US

San Francisco, California, US

• Lead the research, development and deployment of reranking models in recommender systems, using Databrick/Spark and AWS Sagemaker, to improve user engagement
• Drive the productionalization and improvement of embedding-based retrieval in recommender systems, leading to 20% increase in user engagement
• Define recommender system use cases and requirements to guide Machine Learning Platform’s scalable feature store and vector database solutions
• Collaborate with Machine Learning Platform team to standardize the organization’s MLOps processes

Leidschendam, South Holland, NL

• Developed deep learning object detection models and object tracking prototypes with Python and Tensorflow on AWS SageMaker to detect and track road objects
• Architected data processing workflow with multiple parallel compute jobs using AWS Batch and Step Functions, reducing processing time by 10X
• 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 coordinates based on Vehicle’s position/orientation IMU data and Camera intrinsic/extrinsic parameters
• Standardized team’s DevOps and MLOps processes with Jenkins, Docker, MLflow

Work From Anywhere, Remote, OO

• 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 accelerometers
• Provided advice on data science use cases and data collection protocols for machine learning model development/evaluation

Vancouver, British Columbia, CA

Vancouver, British Columbia, CA

• Researched deep learning models for object detection and semantic segmentation using Python and Tensorflow to automate pest counting and disease evaluation processes
• Collaborated with software team to implement multispectral image processing pipelines for plant health evaluation using computer vision and machine learning techniques, including geometric transformation & keypoint.
• Implemented machine learning models for drug dose-response modelling, drug synergy analysis and prediction using cheminformatics and machine learning libraries (Scikit-learn, RDKit, PubChemPy) to accelerate drug.
• Provided advice on experimental design and implement statistical analysis pipelines with Python, Rpy2 and Statsmodels to automate various statistical analyses for plant health research

Vancouver, British Columbia, CA

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

Singapore, SG

• Liaised with modelling team to deploy predictive models (Recommender System, Location Analytics) for targeted marketing, leading to 2x lift in customer response rate in digital campaigns
• Proposed experiment design and hypothesis testing on different content factors to improve customer engagement in email marketing
• Performed process mapping and simulation modelling to optimize business processes, resulting in 10% reduction in operating cost
• Developed SAS code to extract data from Teradata SQL databases and perform statistical analysis for Card & Unsecured Lending sales and marketing
• Developed Java analysis reports and Qlikview dashboards to provide technology and operations teams with insight on process improvement and risk control
• Led infrastructure design and deployment for an enterprise data management system

• Implemented inventory modelling and demand forecasting functions in a Decision Support System with Java Servlet to facilitate optimal inventory decision making
• Streamlined data processing from multiple data sources by developing Data Extract, Transform and Load (ETL) pipeline with Microsoft SQL Server Integration Services (SSIS)

Master'S Degree, Computer Science (Big Data, Machine Learning)

Master'S Degree, Technology Management

Bachelor'S Degree, Computer Science, Business Analytics (Dual Degree)