skill_col_size = 5 #publication_url -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- linkedin_logo = ''' ''' github_logo = ''' ''' # personal info (for main page) -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- info = {'brief': """ Accomplished Data Scientist and Technical Advisor with over 25 years of experience in infrastructure and environmental data analytics. Dr. Wei Liu is renowned for leveraging data science and artificial intelligence to drive innovation, optimize performance, and deliver actionable insights in complex engineering and technology landscapes. His work spans across multiple continents, contributing to significant advancements in predictive modelling, asset management, and strategic decision-making. """, 'name':'Dr Wei Liu', 'location':'Hamilton, New Zealand', 'interest':'Everything about data and AI', 'skills':['Python','R','Javascript','Typescript','Shell','HTML & CSS','PySpark','FME','Docker','Kafka','Kubernetes','MongoDB','PostgreSQL','MySQL','SQLite','AWS','Azure','Digital Ocean','Github','PowerBI','Tableau'], } # Experience -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- #[[header, subheader, date, location, content, link, link_url], [...], etc.] Experience = [ ["GHD Digital", "Senior Digital Intelligence Specialist", "Sep 2021 – June 2024", "Hamilton, New Zealand", """ - Development and Deployment of R Shiny Cloud Web Appplications for Various Spatial Multi-Criteria Analysis Web Portal for Site Selection, Route Selection and Asset Risk Assessment Projects. - Development and Implementation of an End-to-End Computer Vision Solution for Assessment of Street Leaf Debris using Deep Learning Semantic Segmentation Models for ACT Healthy Waterways program Australia. - Undertaking Big Data Analytics for Road Network Speed Zone Clustering Analysis and Mapping through Collecting and Integration of Various Relevant Data Sources for Mainroads West Australia. - Automating of Offshore Environmental Monitoring Survey Data Analytics, Visualistion and Reporting for North Oil Company Qatar. - Automating of ESRI Survey123 Field Inspection Reports Generation for Puhoi to Warkworth Motorway Construction Project from 20 Gigabites of Structured and Unstructured Data of over 4,000 Field Inspection Forms for New Zealand Transport Agency. - Conducting Independent Technical Assessments including Remote Sensing Work Technical Reviews and Data Products QA for All Project Milestones Under Hydrometric Networks and Remote Sensing Program for Murray Darling Basin Authority Australia. """, "Company website", "https://www.ghd.com/en/expertise/digital"], ["GHD Digital", "Data Science Lead", "August 2018 – Sep 2021", "Hamilton, New Zealand", """ - Conducting Bridge Condition Big Data Predictive Analytics with Automated Machine Learning and Development Bridge AI Portal for Pennsylvania Department of Transport (PennDOT) USA. - Development and Implementation of Short-Term Electricity Price Forecasting Models for Sun Metals Australia and River Flow Forecasting Models for Tauranga City Council New Zealand using Machine Learning and Time Series Algorithms. - Live Internet of Things (IoT) Data Management, Analytics and Visualisation and Development of Air Quality Monitoring Dashboard for Port of Tauranga New Zealand. """, "Company website", "https://www.ghd.com/en/expertise/digital"], ["GHD Advisory", "Executive Advisor - Asset Management", "July 2017 – August 2018", "Waltham, MA", """ - School Infrastructure Portfolio Capital Investment Optimisation and Cloud Computing Web App Development for NSW Department of Education Australia. - Development of Decision Trees and Pavement Performance Models & Support of the Agile EAM Implementations. - Road Infrastructure Forecast Modelling & dTIMS Road Asset Management System Implementations for Brisbane City Council. - Pavement Performance Predictive Modelling & dTIMS Road Asset Management System Implementations for Pennsylvania Department of Transport (PennDOT) USA. - Infrastructure Capital Project Multi-Criteria Prioritisation and Multi-Contraints Programme Optimisation Tool Development for Moreton Bay Regional Council Australia. """, "Company website", "https://www.ghd.com/en/expertise/advisory"], ["GHD", "Principal Asset Management Engineer", "Jan 2011 – July 2017", "Hamilton, New Zealand", """ - Conducting New Zealand Long-Term Pavement Performance (LTPP) Big Data Analysis and Visualisation using Data mining and Knowledge Discovery Approaches. - Conducting Big Data Analysis and Visualisation and Business Intelligence Analytics for National Pavement Performance Reporting for New Zealand Transport Agency. - Management of RAMM Databases and Extracting and Using RAMM data for Network Performance Management Reporting for Various Local Government Clients in New Zealand. - Conducting dTIMS Road Deterioration Modelling and Developing Long-term Optimised Road Maintenance and Rehabilitation Programs for Various State Highway Networks and Local Road Networks in New Zealand. """, "Company website", "https://www.ghd.com/en"], ["Pavement Management Services", "Senior Engineer", "Jan 2007 – Jan 2011", "Hamilton, New Zealand", """ - Network Level Condition Survey, Data Processing and Validation, and Database Management for Pavement Management System or Road Asset Maintenance Management System. - Project Level Testing and Evaluation for Pavement Design and Construction. - Calibration and Validation of High-Technique Data Collection Equipment including High-Speed Laser Profilometer with GPS, Norsemeter ROAR Skid Resistance (Friction) Tester, Falling Weight Deflectometer (FWD), and Multiple-View Road Right-of-Way Video Logging System, Scanning Laser System etc. - Carrying out R&D work for development of computer softwares for processing and analysis of data collected for high-speed laser profilometer FWD and Friction Tester and Video Logging System. - Writing research grant proposals and conducting research project for New Zealand Transport Agency. """, "Company website", "https://www.pavement.com.au/"] ] # Portfolio -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- # {'project1':[HEADER, CONTENT] # 'project2':[HEADER, CONTENT] # ...} Portfolio = { 1:['Shiny Data Platform', """ - An automated machine Learning platform for Everyone to develop trusted and accurate machine learning models. - Seamlessly transforming data into actionable predictive models that are used as remote services or, locally, embedded into applications to make predictions. - Easy-to-use and powerful functionalities for data exploration, data quality assessment, data visualization, feature engineering, machine learning model development and deployment. """], 2:['Easy Chart Maker', """ - Self-Service Data Visaulization Tool - Tableau-like Drag and Drop GUI Visualization. - Rich Chart Themes and Styles - Quick Interactive Analysis and Insights from Data Sources """], 3:['New Zealand Commuter Insights Portal', """ - Commuting significantly influences the temporal and geographic distributions of non-commuting travel, as trips to and from work often define an individual’s or household’s travel schedule. Work trips shape peak transportation service and infrastructure capacity needs that define the design capacity requirements of road and public transport investments. ain-specific inquiries than ChatGPT. - The main purpose of this New Zealand Commuter Insights Portal is to provide a self-service analytics platform to get useful information and insights on the patterns of commuting trips made between different areas of New Zealand. - Analysis and information presented in this Portal are based on the Statistics New Zealand 2018 Census commuter view dataset. """], 4:['3D Map Creator', """ - Generating 3D Map from Digital Terrain Model (DTM) data. - Rendering High Quality 3D Visuals with Vivid Textures and Shades. - Making and Saving a 3D Print File """], 5:['Automated Data Quality Explorer', """ - Import Data File in Various Format. - Expore the Data Quality Issues Interactively. - Automatically Generate Data Quality Report in One Click. """], 6:['WebVR Data Visualisation Portal', """ - Web-based virtual reality experiences for immersive, cross-platform data visualizations. - True 3-D data exploration with the HTC Vive. Stereo vision with positional tracking provide a link to human spatial understanding that cannot be imitated on the desktop monitor, and hand controls provide an intuitive experience. Together, these allow for rapid data exploration and insight discovery. - Data Visualisation Portal WebVR provides an online and shared experiences so that colleagues from across the globe could join each other to explore data together. WebVR is also multi-platform, so colleagues or clients could join with mobile VR or even desktop monitors for you to show them what you've discovered. """], 7:['NZ Traffic AI Portal', """ - Time series forecasting is one of the most commonly encountered problems with various applications such as demand prediction, weather forecasting, price prediction, real estate predictions. Recently, deep learning techniques have been applied to solve this class of problems. - Time series prediction (forecasting) has experienced dramatic improvements in predictive accuracy as a result of the data science machine learning (ML) and deep learning (DL) evolution. As these ML/DL tools have evolved, businesses and financial institutions are now able to forecast better by applying these new technologies to solve old problems. The Long Short Term Memory (LSTM) network is a type of Recurrent Neural Networks (RNN). The RNN model processes sequential data. It learns the input data by iterating the sequence of elements and acquires the state information regarding the observed part of the elements. Based on the learned data, it predicts the next item in the sequence. - In this portal, we showcase the use of LSTM model to build a forecast model to predict the traffic volumes of over 100 traffic count sites in New Zealand State Highway network based on data from New Zealand State Highway traffic volume monthly reports 2008–17 published in New Zealand Transport Agency (NZTA) Website: https://www.nzta.govt.nz/resources/state-highway-traffic-growth/ """], 8:['Covid19 Response Prioritisation and Programme Optimisation Tool', """ - This tool is developed to demontrate a data-driven appoach to help public agencies to make better emergency/crisis (e.g. Covid-19) response investment decisions. - It provides functiionalities to prioritise response work/project list in a consistent and logical way through multi-criteria decsion analysis. - It provides functiionalities to optimise investments by deriving the optimal programme with optimisation modelling with the objective and constraints defined by user. """] } # Contacts -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- phone = "(0)27-430-2016" email = "clw1031@gmail.com" linkedin_link = "https://www.linkedin.com/in/drweiliu/" github_link = "https://github.com/DrRoad?tab=repositories" # iframes -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- figma_iframe = '' figma_link = "https://www.figma.com/embed?embed_host=share&url=https%3A%2F%2Fwww.figma.com%2Ffile%2FlMYyNOptCmZb5JlYXmKkif%2FCourseEvaluation%3Ftype%3Ddesign%26node-id%3D160%253A1249%26mode%3Ddesign%26t%3DEj6BVdYEZCLgxthB-1" StoryMap_iframe = "https://storymaps.arcgis.com/stories/dfb9689618e343cf9f6ef36d9a8329a7?header" Evaluation_html = '''

''' # Certificates -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- # {'certificate1':[HEADER, CONTENT] # 'certificate2':[HEADER, CONTENT] # ...} Certificate = { 1:['Neo4j Certified Professional', """ - Date issued: April 20, 2024 - Issued by: Neo4J GraphAcademy """], 2:['Neo4j Graph Data Science Certification', """ - Date issued: April 17, 2024 - Issued by: Neo4J GraphAcademy """], 3:['Introduction to Generative AI', """ - Date issued: Apr 28, 2024 - Issued by: Google Cloud """], 4:['Responsible AI: Applying AI Principles with Google Cloud', """ - Date issued: Apr 30, 2024 - Issued by: Google Cloud """], 5:['FME Certified Professional', """ - Date issued: May 31, 2023 - Issued by: FME Academy """], 6:['FME Flow Certified Professional', """ - Date issued: June 14, 2023 - Issued by: FME Academy """], 7:['Intel Edge AI Certification', """ - Date issued: December 07, 2022 - Issued by: Intel """], 8:['ESRI MOOC Training Certificate on Image in Action', """ - Date issued: Sep, 2021 - Issued by: ESRI """], 9:['Amazon Web Services (AWS) Certified', """ - Date issued: May, 2020 - Issued by: Udemy """], 10:['A-Z Machine Learning using Azure Machine Learning (AzureML) ', """ - Date issued: May, 2020 - Issued by: Udemy """], 11:['Advanced Microsoft Power BI', """ - Date issued: May, 2020 - Issued by: Project Management Institute """], 12:['Azure Administrator: AZ-103/AZ-104', """ - Date issued: May, 2020 - Issued by: Udemy """], 13:['Tableau Data Scientist', """ - Date issued: 03 May, 2020 - Issued by: Tableau Software """], 14:['Tableau Analyst', """ - Date issued: 04 May, 2020 - Issued by: Tableau Software """], 15:['Tableau Developer', """ - Date issued: 04 May, 2020 - Issued by: Tableau Software """] }