Maitha is a DPhil in Engineering Science candidate at the University of Oxford. She holds double bachelor’s degrees in ‘Computer Engineering’ and ‘Electrical and Electronics Engineering’ and double master’s degrees in ‘Mechanical Engineering’ and ‘Electrical Engineering’ - all from the United States. Her research interest lies in deploying advanced technological solutions (Data Science and AI) in the energy sector to enable renewable integration towards a net-zero transition. Her thesis is titled “Machine Learning for Improving the Role of Demand Response Aggregators”.
Maitha is a fellow of the Atlantic Council’s Women Leaders in Energy and Climate. She is the Founder and CEO of a technology startup located in the UAE that deploys advanced technological solutions in the sustainability, energy, and climate fields. She is the Founder and President of the Emirati Women in Engineering Network – a non-profit organization that supports UAE female high school and university students pursuing engineering majors and professional engineers working in various engineering disciplines across private, academic, and government sectors. Maitha is a technology entrepreneur, futurist, policy and strategy professional, technology consultant, AI and Data Science expert, innovation leader, and an advocate for women in STEM. She has accumulated over 13 years of working experience in the fields of consultancy, engineering, nuclear energy, management, artificial intelligence, government, and diplomacy. She holds professional certifications from international bodies as she is a certified Scrum Master, Innovation Manager, and Agile Manager.
Maitha served as President of the Oxford Women in Engineering Network Committee (Oxford WiE) and as the EDI Committee member for two consecutive years (2021 and 2022).
Research Interests
- Artificial Intelligence
- Data Science
- Net-zero Transition
- Climate Change
- Energy Aggregators
- Renewable Energy
- Smart Grids
- Demand Response
- Energy Policy, Governance, Infrastructure, and Technology
Current Project
- Machine Learning for Improving the Role of Demand Response Aggregators
Related Academics
- M. Al Shimmari and D. Wallom, "Short-term load forecasting using UK non-domestic businesses to enable demand response aggregators’ participation in electricity markets," 2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge), San Diego, CA, USA, 2023, pp. 1-5, doi: 10.1109/GridEdge54130.2023.10102712.
