Key Data Science projects

Sensor output forecasting  [GitHub link]

Keywords: Time-series forecasting, XGBoost, Python, Feature Engineering, Uncertainty quantification

Forecast of sensor output based on the past sensor time-series data.

• Extract periodicity and seasonality information via EDA to perform efficient feature engineering

• Data cleaning, predictive modelling based on an optimised gradient boosting algorithm

• Statistical analysis on the residuals to quantify uncertainty of the predictions

Price Forecasting  [GitHub link]

Forecast of UK wholesale energy prices for the next 24 hour period to inform bidding strategies.

• Data acquisition via API calls to NESO (National Energy Systems Operator)

• Data cleaning, EDA, and addition of lag features to inform forecasting

• Implementation of LSTM model using TensorFlow. Optimisation using Optuna. Deployment on Azure

Optimisation framework for resource allocation

A constrained optimisation-based matching engine built in Python to match 800+ MSc students seeking their dissertation projects to 200+ academic supervisors.

• Created pipeline for data acquisition from multiple sources (operations, IT, HR), pooling data and handle mismatch, data cleaning, ensure robustness.

• Optimisation using multi-criteria inputs while managing operational constraints.

• Direct and significant positive impact on operational efficiency supporting a £24M+ annual revenue stream

• Due to confidentiality reasons, the source code can't be shared openly.

Scientific Computing:

During most of my scientific career, I have focussed on developing computational solutions to engineering problems. Below are some recent projects. I have published over 30 scientific papers which can be downloaded from here and can be seen on Google Scholar. 

Continuous learning:

I have completed the following certifications in Machine Learning and Data Science

• Transformer Models and BERT models [certificate link]

• Trading, Machine Learning and GCP [certificate link]

• Using Machine Learning in Trading and Finance [certificate link]

• Financial Markets, Yale [certificate link]

• Portfolio Construction and Analysis with Python, EDHEC business school [certificate link]

• Inferential Statistical Analysis with Python, Michigan [certificate link]

• Deep Learning with PyTorch: Generative Adversarial Network [certificate link]

• Machine Learning, Stanford Online [certificate link]