Speaker "Sourav Dey" Details Back



Applications of Mixed Effect Random Forests


Clustered data is all around us. The best way to attack it? Mixed effect models. Inspired by the models Sourav and his team build at Manifold, they developed an open source-implementation package for the Python community to use — and build upon: Mixed Effects Random Forests. Sourav will explain how the mixed effects random forests model marries the world of classical mixed effect modeling with modern machine learning algorithms, and how it can be extended to be used with other advanced modeling techniques like gradient boosting machines and deep learning. He will provide examples of use cases of mixed effects random forests, and demonstrate MERF performance on synthetic and real data.


As Managing Director and head of Machine Learning, Sourav is responsible for the overall delivery of data science and data product services to make clients successful. Before Manifold, Sourav led teams to build data products across the technology stack, from smart thermostats and security cams (Google / Nest) to power grid forecasting (AutoGrid) to wireless communication chips (Qualcomm). He holds patents for his work, has been published in several IEEE journals, and has won numerous awards. He earned his PhD, MS, and BS degrees from MIT in Electrical Engineering and Computer Science.