Benjamin Thomas Email & Phone Number

London, England, United Kingdom

At IMU we are creating an extraordinary platform to map and decode the human immune system. We turn 2ml of blood into 5M data points using spectral flow cytometry, facilitating unprecedented resolved insight into the cell populations and states that govern our biological immune defense. With machine learning we are vastly simplifying these complex data.

Austin, Texas, United States

• My role as a postdoctoral fellow involved three core responsibilities.The first was using supervised machine learning to assist with evaluating data quality:
• Multiple supervised machine learning projects to evaluate data quality. Heavily depended on by collaboration of ~100 globally-distributed team members working on $42M experiment.
• Delivered innovative models and algorithms to meet the requirements of the collaboration using advanced Python scripting (NumPy… Show more My role as a postdoctoral fellow involved three core responsibilities.The first.
• Delivered innovative models and algorithms to meet the requirements of the collaboration using advanced Python scripting (NumPy, scikit-learn, keras, PyTorch) to analyse large volumes of data (TBs).
• Excellent interpersonal and communication skills as demonstrated by consistently translating stakeholder domain knowledge into defined data science tasks.
• Developed auto-encoder-based supervised machine learning pipeline to classify complex and high-dimensional image data to evaluate quality of ~2M data samples in Python.

Portsmouth, United Kingdom

• I used Python and its various numerical and scientific libraries to clean and analyse complex data sets from the Dark Energy Survey to learn about superluminous supernovae. I designed a novel set of algorithms in.
• Good… Show more I used Python and its various numerical and scientific libraries to clean and analyse complex data sets from the Dark Energy Survey to learn about superluminous supernovae. I designed a novel set of.
• Good knowledge of SQL for accessing and combining data from multiple sources as demonstrated by obtaining time-series data on a rare type of "superluminous" supernova.
• Led a team of researchers to manually classify a new sample of superluminous supernova.
• Performed complex statistical analysis to determine the rate of superluminous supernovae across the cosmos.
• Unsupervised machine learning to identify rare (less than 1 in a million) objects.

Doctor Of Philosophy - Phd, Astrophysics

Mphys, Physics With Astrophysics, First Class (Hons)