Hamid Nejadghorban

Biography

* Research Fellow in Econometric Modelling for Energy and the Environment - Bartlett School Env, Energy & Resources - Faculty of the Built Environment - UCL Hamid is a Research Fellow at the UCL Institute for Sustainable Resources and a Doctoral Researcher in the Department of Economics at the University of Essex. Hamid focuses on machine learning, econometrics modelling, and, specifically, quasi-experimental analysis. He has been involved in several policy evaluation projects funded by BEIS. In particular, he is involved in the impact and economic analysis for the Green Homes Grant Voucher (GHGVS), the scoping of impact and economic analysis for the Cross Cutting evaluation of GHGVS, Local Authority Delivery (LAD) and Social Housing Decarbonisation Fund Demonstrator (SHDF(D)), the scoping of impact and economic analysis for the Energy Bills Support Scheme (EBSS) and the Energy Price Guarantee (EPG), the scoping and evaluation of the UK Emissions Trading Scheme (ETS), and the scoping and assessment of the Social Housing Decarbonisation Main Fund (SHDF). In his research, Hamid is also developing deep reinforcement learning (DRL) algorithms such as Twin Delayed Deep Deterministic Policy Gradients (T3D) to forecast high volatility markets. Previous work experience focused on applying machine learning algorithms to determine the optimal setting for energy-consuming appliances, air conditioners and boilers using big data. Hamid is an experienced Data scientist, and in his analyses, he employs complex algorithms and statistical methods using professional programming in different languages, including Python, R and SQL, and several statistical and engineering software like Stata and Matlab.

Qualifications

• MRes in Economics (with Distinction) University of Essex (2019)

• MSc in Economics (with Distinction) Sharif University of Technology (2018)