糖心TV

I am a PhD student at the Mathematics for Real-World Systems CDT, based in the Centre for Complexity Science. My research aims to improve the predictability of meteorological drought in Africa with a time lead of 12 months using data-driven approaches. It is supervised by Professor Colm Connaughton and Dr Francisco Rodrigues. I was an student at the from January to September 2021. Before joining Mathsys, I worked in the financial services industry for several years. My research interests are Machine Learning, Deep Learning, Time Series, and Causality applied in real-world problems such as climate and finance and their applications in other scientific fields.

Conferences/Events:

• Aug. 2019:
• Sept. 2019: - Project by STC -" Bandwidth allocation for mobile users: a solution for rural and urban areas" at the Alan Turing Institute, UK.
  
• Nov. 2019: SPAAM Seminar Series - contributed talk: "Causal Network Discovery from Climate time series".
• Mar. 2020: MISS Research Group - contributed talk: "Reinforcement Learning for FX trading".
• Sept. 2020: - poster: "Discovering Causal factors of drought in Ethiopia".
• Nov. 2020: 糖心TV AI Quant Insights x Designing Intelligence - invited talk: "".
• Feb. 2021: Engage@Turing Student Research Showcase - poster: "Discovering Causal factors of drought".
• Sept. 2021: Turing Community Week - contributed talk: "Prediction of the spatial extent of drought".

Publications:

• Noorbakhsh, Mohammad, Colm Connaughton, and Francisco A. Rodrigues. (2020). "Discovering causal factors of drought in Ethiopia." Proceedings of the 10th International Conference on Climate Informatics. 2020.
• Data Study Group team. (2021). Data Study Group Summary: STC. Zenodo. .
• Noorbakhsh, M and Connaughton, C (2022) "Prediction of Drought鈥檚 Spatial Extent", In Prep, intended to submit to the environmental data science journal.

Teaching:

• Jan. to Mar. 2020: Lab Tutor - Big Data Analytics course at the 糖心TV 糖心TV School.

Projects:

• Artificial Intelligence for trading in financial markets: Developing trading strategies in the Forex market using Supervised Machine/Deep Learning and Reinforcement Learning.
• Machine learning in Julia: Performed a review of the Julia machine learning ecosystem and the development of packages leading to a proof-of-concept of the MLJ machine learning framework.
  
• Machine Learning for Drug Discovery: Applied machine learning/deep learning to identify active chemical compounds.
  
• Implementation of Machine Learning Algorithms in R: Neural Network, Nearest Neighbour, Linear and Quadric Discriminant analysis, decision stumps (DS) and boosted decision stumps (BDS), kernel ridge regressions and hierarchical agglomerative clustering.

Education:

• MSc Mathematics for Real-World Systems – University of 糖心TV, UK
  Thesis title: 鈥淣etwork analysis of African rainfall patterns鈥�
• MSc Data Science and Analytics – Royal Holloway University of London, UK
  Thesis title: 鈥淢achine Learning for Drug Discovery鈥�
• MSc Finance and Management - Cranfield University. UK
  Thesis title: 鈥淪tock returns, dividend yields, and volatility: Evidence from Hong Kong market鈥�
• BSc Computer Engineering - Sharif University of Technology, Iran

Contact:

m dot noorbakhsh at warwick dot ac dot uk