Liang Kuang
Verified Expert in Engineering
Data Scientist and Developer
Liang has a Ph.D. with a strong background in numerical computation, machine learning, deep learning, neural network, big data mining, visualization, and multiple programming. He developed the largest financial regulatory database in the world and a Consolidated Audit Trail (CAT), handling up to 400 billion records per trade day. He brings deep technical insights in designing algorithms and end-to-end analytic platforms, including data lakes and predictive ML/AL models for system optimization.
Portfolio
Experience
Availability
Preferred Environment
Amazon Web Services (AWS), Generative Pre-trained Transformers (GPT), GPT, Natural Language Processing (NLP), Scikit-learn, PyTorch, Keras, Docker, SQL, ETL, Spark, Scala, Python
The most amazing...
...project I built was a large-scale operational hurricane forecast warning system for NOAA and a graph-based fraud detection analytic data solution for FINRA.
Work Experience
Big Data, AI Developer
Financial Industry Regulatory Authority
- Developed the largest financial regulatory database in the world. Consolidated Audit Trail (CAT) handling up to 400 billion records per trade day.
- Developed and implemented a graph-based algorithm to link all market events and track its life cycle on the scale of billions of records using Spark and AWS.
- Created an end-to-end graph-based analytic solution for recommendation and fraud detection and an end-to-end people's analytics recommendation system using machine learning.
Senior Data Scientist
GEICO
- Build a state-of-art end-to-end machine learning solution for the second-largest insurance company for 17 million customers.
- Delivered an end-to-end machine learning tracking and verification pipeline using blockchain for better machine learning model lifecycle management.
- Oversaw model deployment and designed an integrated pipeline for continuously monitoring model performance and online learning.
Data Scientist
IHS Markit
- Drove cultural change in engineering for the advanced analytic team to experiment and adopt more efficient analysis methodologies and tools.
- Collaborated with the energy and maritime team to develop creative analytic solutions to their unique business challenges.
- Streamlined the data mining process and standardized all methodologies for sharing and validating analysis. Automated daily data analysis pipeline, SQL search, and R code review with web-based applications.
- Designed and experimented with various popular machine learning models for predicting oil price, major finance events using ARIMA, VAR, state-space model, regression, neural network, random forest, elastic neural net, RBM, and other similar methods.
- Translated billions of maritime trip data into valuable business insight by pattern recognition and modeling on AWS environment.
- Provided in-team technical assistance and knowledge-sharing on best machine learning and coding practices.
Operational Storm Surge Model Developer
NOAA: National Oceanic & Atmospheric Administration
- Built a national hurricane database and perform category analysis.
- Developed and maintained risk scoring for regions with different levels of flooding risk.
- Designed, developed, implemented, and validated a deterministic and ensemble storm surge model for the North Atlantic Ocean.
- Developed statistics metrics and visualization in Python for evaluating model performance.
- Designed an algorithm to deploy an operational storm surge model on Unix cloud clusters and code in Perl and Shell Scripts.
- Delivered a Python-based opensource library for automatically generating model grids, pre-processing, and post-analyzing model results.
- Developed signal processing algorithms for short and long-term water level time series using sophisticated statistic methods: Fourier transform, PCA, multivariate dimensional analysis, and regression analysis, to name a few.
Numerical Modeler and Data Scientist
Environmental Resource Management
- Developed and quantitatively validated the coupled four-dimensional numerical coastal ocean models and water quality model for global oceans.
- Designed algorithms for four dimensional fluid dynamic models and deployed it for various water-bodies, from ponds, rivers, to ocean waters.
- Worked on international projects for oil & gas, mining, and the hydro power industries, where my role was to use various sophisticated hydrodynamic, environmental models, and data analytic tools to assess its impact on the receiving environment.
- Deployed a sophisticated four-dimensional operational hydrodynamic modeling system for the Bohai Sea (www.euler-tech.com) using Java, JavaScript, PHP, HTML5, SQL, and Amazon EC2.
Experience
1000 Faces
https://github.com/eulertech/1000FacesWeb-based Application for Auto-configuring Spark Jobs
https://github.com/eulertech/spark-submitAutoConfigMachine Learning Pipeline: Challenges and Verification with Blockchain
https://github.com/eulertech/machine_learning_blockchain_verification_frameworkADCIRC: Python-based Library for Ocean Model Pre-post Processing
https://github.com/eulertech/ADCIRCSmart Ocean Platform
Skills
Paradigms
Data Science, ETL
Other
Machine Learning, Computational Fluid Dynamics (CFD), Forecasting, Computational Physics, Fluid Dynamics, Operations Research, Data Engineering, Neural Networks, Analytics, Unix Shell Scripting, Natural Language Processing (NLP), Deep Learning, Dash, GPT, Generative Pre-trained Transformers (GPT)
Languages
Python 3, Scala, SQL, Python, R, Fortran
Frameworks
Spark
Libraries/APIs
Spark ML, Keras, PyTorch, Scikit-learn, PySpark, TensorFlow, XGBoost, D3.js, Microsoft HPC
Tools
Spark SQL, Plotly, MATLAB
Platforms
Docker, Azure, Linux, Amazon Web Services (AWS)
Industry Expertise
Cybersecurity
Storage
Microsoft SQL Server
Education
Doctor of Philosophy Degree (Ph.D) in Ocean Engineering
Stevens Institute of Technology - Hoboken, New Jersey, USA
Certifications
Application Security and Secure Coding Training
CODEBASHING, LTD.
Strategic Thinking
Hadoop: Data Analysis
Neural Network for Machine Learning
Coursera
Big Data Analysis with Apache Spark
edX
Machine Learning
Standford University
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