Since 2014, Matthew has been working professionally in the fields he loves, software and data—culminating in him co-founding the Rubota corporation in 2017. Before that, he spent the past decade at Cornell University conducting scientific research specifically in statistical and biological physics. All in all, Matthew is an engaging, intense communicator with a passion for knowledge and understanding.
Amazon Web Services (AWS), Graphics Processing Unit (GPU), Jupyter, Pandas...
Amazon Web Services (AWS), Graphics Processing Unit (GPU), Jupyter, Pandas...
Amazon Web Services (AWS), Graphics Processing Unit (GPU), Jupyter, Pandas...
Part-time
Amazon Web Services (AWS), Python, Agile, Pandas, PyTorch, Data Science, Convolutional Neural Networks (CNN), Docker, Containerization, Generative Pre-trained Transformers (GPT)
...project I've developed was an advanced energy market trading algorithm with multiple machine learning (predictive) and optimization (prescriptive) models.
Active learning is a special case of machine learning in which a learning algorithm is able to interactively query the user (or some other information source) to obtain the desired outputs at new data points. In statistics literature, it is sometimes also called optimal experimental design. [Wikipedia: https://en.wikipedia.org/wiki/Active_learning_(machine_learning)]
Designed and developed Greensmith Energy's first real-time energy market trading process that makes use of advanced optimization models. Successful 30-day operational test by the world's largest producer of electricity, Électricité de France (EDF: https://en.wikipedia.org/wiki/Électricité_de_France) in a CAISO five-minute real-time energy market simulator. [https://en.wikipedia.org/wiki/California_Independent_System_Operator]
