I am a third year PhD student in Operations Research and Information Engineering at Cornell University coadvised by Christina Lee Yu and Siddhartha Banerjee. I completed a BSc in Mathematics and Computer Science at McGill University where I worked on a project with Tony Humphries. Before returning to graduate school I spent two and a half years teaching mathematics, science, and English in a small community in rural Ghana with the Peace Corps, and after worked at National Life as a financial analyst.
In general, I am interested in machine learning, statistics, and differential equations. My current work is on the theoretical underpinnings of reinforcement learning (RL) in metric spaces. These are natural models for systems involving real-time sequential decision making over continuous spaces. To facilitate RL's use on memory-constrained devices there are many challenges. The first is learning an "optimal" discretization - trading off memory requirements and algorithmic performance. The second is learning the metric when it is not clear what metric the problem is optimal to learn in. This balances the two fundamental requirements of implementable RL - approximating the optimal policy and statistical complexity for the number of samples required to learn the near optimal policy. During undergraduate studies at McGill I worked on studying numerical methods for distributed delay differential equations. In these equations, the definition of the differential equation itself involves an integral of the solution over the past. This integration can be finite, unbounded, or state dependent. The current MATLAB solver only solves these equations with discrete delays. I worked on developing simple first and second order MATLAB solver for a class of distributed delay differential equations using Runge-Kutta methods. I will be a visiting graduate student at the Simons Institute for the Theory of Reinforcement Learning program in the Fall of 2020. Office: 294 Rhodes Hall Email: srs429 at cornell.edu Github Google Scholar |

Sequential Fair Allocation of Limited Resources under Stochastic Demands |
Working Paper |

Sean R. Sinclair, Gauri Jain, Siddhartha Banerjee, and Christina Lee Yu. | |

Adaptive Discretization for Model-Based Reinforcement Learning [arXiv] [video] |
Accepted to NeurIPS 2020 (Poster) |

Sean R. Sinclair, Tianyu Wang, Gauri Jain, Siddhartha Banerjee, and Christina Lee Yu. | |

Adaptive Discretization for Episodic Reinforcement Learning in Metric Spaces [arXiv] [ACM] [video] |
ACM POMACS |

Sean R. Sinclair, Siddhartha Banerjee, and Christina Lee Yu. | |

Normal and pathological dynamics of platelets in humans [arXiv] [springer] |
Journal of Mathematical Biology |

GP Langlois, M Craig, AR Humphries, MC Mackey, et all. |