👋
Hi! My name is Edwin, and I'm currently an ML engineer at Hazy working on improving a privacy-preserving synthetic data generation platform for enterprise data analytics.
I studied at the University of Edinburgh, with an MSc in Statistics with Data Science at the School of Mathematics, and a BSc in Computer Science at the School of Informatics.
I normally work with Python, R and sometimes Ruby, mainly doing machine learning or data science related things in Python and R, and any general purpose scripting, task automation or web development in Python and Ruby – but I'm always interested in learning new things!
Right now, I'm learning about:
- Docker and containerisation
- Gaussian processes
- AWS services
I'd like to learn more about:
- C++
- Graphs: general graph theory concepts, spectral graph theory, graph ML
- Bayesian methods: variational inference, probablistic graphical models, Bayesian optimization
- Statistical time series: autocorrelation, forecasting models (ARIMA, GARCH etc.)
- Ensemble classifiers: bagging and boosting (with AdaBoost, XGBoost, LightGBM etc.)
I'm very familiar with:
- Common ML methods: GLM, logistic regression, kNN, mixture models etc.
- Neural networks: mainly feed-forward and recurrent architectures, but also some knowledge and practice with CNNs
- Sequential modelling: HMMs, RNNs, DTW
- Natural language processing: word embeddings, attention, sentiment analysis
- Statistical methodology: likelihood-based inference (MLE, CIs, etc.), Bayesian statistics, hypothesis testing
I am confident with these languages, tools and systems:
|
|
|
|
|
|
|
|---|---|---|---|---|---|---|
|
|
|
|
|
|
|