Khoa Nguyen Email and Phone Number

Acceleration is WPP’s data and technology consultancy, operated globally by leading media company GroupM. By utilizing data science and ML/AI, we provide data driven insights and solutions that help our clients make more effective marketing decisions.

Developed ML/AI and data science solutions to grow capabilities in business transformation, digital twins and predictive analytics in different application domains.- Generative AI for business transformation: different Proof of Concept (PoC) projects on using generative AI and large language models to automate and accelerate business processes (using both Azure OpenAI and open-source models).- Freight modelling: using connected vehicle data for freight modelling, in collaboration with Queensland Department of Transport and Main Roads (DTMR) and Compass IoT.- Video analytics for retails: a PoC on using video camera footages to understand staff efficiency.- Space Tech: using satellite imaging to audit forest restoration activities.- Fatigue analysis: using driver shift and crash/infringement data to analyse key factors for driver fatigue in personalized transport, in collaboration with Translink.

Contributed to solution development and led a team of data and research scientists working on predictive analytics for asset management, digital twins, structural health monitoring (SHM) and energy demand forecasting using ML/AI. - Damage identification in Structural Health Monitoring for monitoring the iconic Sydney Harbour Bridge and Gateway Bridge in Brisbane, in collaboration with government entities and private companies such as Transport for New South Wales and Transurban.- Electricity demand forecasting (part of Australia's National Energy Analytics Research Program - NEAR), in collaboration with Australian Energy Market Operator (AEMO). We investigated ML/AI techniques for operational and long-term energy demand forecasting. We developed capabilities in energy forecasting in state level, building level and household level for different sources of energy (electricity, gas, solar, battery). - Digital Twins with applications for industrial robot arms: fault detection and diagnostics for robot arms in a digital twin context, in collaboration with a major global aerospace company.

Developed solutions and led the research and project delivery on Structural Health Monitoring (SHM) and anomaly detection for asset management. The SHM projects investigate ML/AI techniques for damage identification on civil structures using sensing data. - Damage identification in SHM- Fault Detection and Diagnosis (FDD) for HVAC systems

Developed ML/AI techniques for damage identification on civil structures such as bridges and buildings using sensing data. - SHM systems to monitor the structural health of jack arches on the Sydney Harbour Bridge, and two other small bridges in Sydney.- Road condition assessment using vehicle mounted sensors.

Investigated the use of data mining and machine learning techniques to predict the proper antidepressant medications for patients with major depressive disorder. This is a short pilot project for iSPOT-D (the International Study to Predict Optimized Treatment for Depression) which was funded by BRC.

Investigated the use of data mining and machine learning techniques, together with domain knowledge in insurance industry to risk score insurance claims with a view to identify claims that may be fraudulent, and to predict cost and duration of insurance claims.

Worked as a lecturer and data mining researcher in Faculty of Computer Science and Engineering. I was in charge of undergraduate courses "Informatics" and "Algorithms Analysis and Design". Moreover, I participated in the following research projects:- Applying Data Mining Techniques to Build an Intelligent Search Engine: funded by HCMUT.- Applying Graph Coloring Algorithms to University Exam Scheduling: funded by HCMUT.