About Me

Experience

Postdoctoral Research Associate in Net Zero

School of Engineering, Newcastle University | August 2024 – Present

• Quantify and evaluate the value of low-carbon technologies, such as solar panels, electrical and thermal storage systems, and heat pumps, within the smart home energy management system (SHEMS) framework.
• Review and evaluate the roof-rental mechanisms in the UK and internationally and propose potential research directions and technologies for further improvement.

Data & Analytics Intern

Essex County Council | April 2024 – June 2024

• Analysed the dependence between the demand for services (i.e., Waste and Recycling, Children and Adults Social Care, and Highways) and corresponding complaints received by the Essex County Council using Granger Causality Test and Distance Correlation. The data preprocessing and analysis are implemented in R and RStudio.
• Examined and visualised different measures for each district in Essex, such as Business, Economy and Industry, Education and Skills, Population and Community Information, Health and Social Care, and Road and Transportation, using Power BI.

Research Officer

University of Essex, School of Mathematics, Statistics and Actuarial Science | April 2023 – July 2023

• Developed dynamic programming and Dijkstras-based algorithms using Python to solve the shortest path problem with multiple destinations.

Assistant Lecturer

University of Essex, School of Mathematics, Statistics and Actuarial Science | October 2021 – April 2023

• Taught machine learning techniques (e.g., regression, classification clustering) in Statistical Methods, Modelling Experimental Data and Applied Statistics.
• Taught to code each algorithm using R and RStudio and marked assignments.

Research Assistant

University of Essex, School of Mathematics, Statistics and Actuarial Science | April 2022 – July 2022

• Developed a novel mixed-integer linear programming (MILP) model for optimal energy allocation and pricing under the uncertainty of renewable energies using MATLAB. It solved it using a genetic algorithm (GA).

Lecturer

Office of Student (OfS) & University of Essex, School of Mathematics, Statistics and Actuarial Science & School of Computer Science and Electronic Engineering | February 2022 – April 2022

• Worked with a multi-disciplinary team to develop Data Science and AI Short Courses for data analysts at local companies, including Introduction to Statistical Analysis and Data Science in R, Visualisation in R, and Predictive Modelling.

Graduate Lab Assistant, Data Science Bootcamp

University of Essex, Department of Social Sciences | September 2021

• Taught students to code various statistical and machine learning algorithms, such as regression, classification, and clustering in Python and R. Introduced various Python scientific libraries, such as NumPy, Pandas, SciPy, and Matplotlib.

Education

Ph.D. in Data Science

University of Essex, School of Mathematics, Statistics and Actuarial Science | October 2020 – July 2024

• Developed a smart hierarchical transactive energy system that considers multi-energy, renewable energies and demand side management using bilevel optimisation models and metaheuristics approaches, such as GA, particle swarm optimisation (PSO) and simulated annealing (SA) algorithms.
• Developed a Transformer-based long-term time-series forecasting model for multi-energy load prediction using PyTorch.

M.Sc. in Banking and Finance

University of Sussex, Sussex Business School | September 2018 – September 2019

• Grade: Distinction (79%)
• The magnitude of the impact of crude oil prices’ shock on different commodity markets: evidence from DCC-GJR-GARCH model.

M.Sc. in Project Management

University of Sussex, Sussex Business School | September 2017 – September 2018

• Grade: Merit (62%)

B.Eng. in Chemical Engineering and Technology

Hebei University of Engineering | September 2012 – June 2016

Publications

A smart hierarchical transactive energy system in the presence of renewable energies, and demand-side management | University of Essex

• PhD thesis which 1) analyses a game-theoretic decision-making model for energy retailers’ strategic bidding and offering problem while considering customers’ switching behaviour; 2) introduces a customised multi-energy pricing scheme; 3) develops an innovative forecasting model named Patchformer, based on Transformer-based architectures and patch embedding method, for the prediction of long-term multi-energy loads.

Advancing Long-Term Multi-Energy Load Forecasting with Patchformer: A Patch and Transformer-Based Approach | arXiv

• The proposed Patchformer is a novel model that integrates patch embedding with encoder-decoder Transformer-based architectures for long-term time-series forecasting to predict multi-energy loads using PyTorch. Numerical analysis shows it outperforms many state-of-the-art models for multivariate and univariate forecasting tasks.

A bilevel game-theoretic decision-making framework for strategic retailers in both local and wholesale electricity markets | Applied Energy

• Proposed a bilevel optimisation model to support energy retailers’ decision-making. The bilevel model was solved by diagonalisation algorithm and coded in Python and its scientific libraries, such as Pyomo, NumPy, Pandas, and Matplotlib.

Customised Multi-Energy Pricing: Model and Solutions | Energies

• Proposed a bilevel MILP model to formulate the energy retailer’s customised multi-energy pricing decisions, solved by GA, PSO and SA algorithms in MATLAB.

Customized Multi-energy Pricing in Smart Grids: A Bilevel and Evolutionary Computation Approach | Advances in Intelligent Systems and Computing, Springer

Conferences

PES Summer School– Future Energy Systems: Advances in OR and AI

The Technical University of Denmark | June 2023

The 21st UK Workshop on Computational Intelligence (UKCI 2022)

University of Sheffield | September 2022

• Presented the paper: Customized Multi-energy Pricing in Smart Grids: A Bilevel and Evolutionary Computation Approach.

Putting Net Zero into Action: addressing the implementation gap (UKERC 2022)

UK Energy Research Centre, Manchester | June 2022

• Nominated to present in the poster session.

Skills

Python | PyTorch | TensorFlow | Pyomo | MATLAB | R | LaTex | Machine Learning | Deep Learning | Transformers | Time Series Forecasting & Analysis | Natural Language Processing (NLP) | Operational Research | Metaheuristic Algorithms | Mathematical Optimisation | Convolutional Neural Networks (CNN) | Recurrent Neural Network (RNN) | Energy Markets

Achievements

Best Student Award

• Accounting & Finance prize for the Best Student in MSc. Banking and Finance