Here's a list of some of the events I've spoken at in the past or upcoming.

One of my favorite ways to share my ideas is through conversations with other people.


Bayesian Optimization: An Approach for Optimal Hyperparameter Tuning

RE-WORK: AI in Finance 2023

Bayesian optimization is an approach for optimizing objective functions that are expensive to evaluate. “Vanilla” Bayesian optimization is typically best-suited for optimization over continuous domains of less than 20 dimensions and is tolerable to noisy function evaluations. It works by fitting a surrogate model to the objective function and quantifying uncertainty in the surrogate using a Bayesian machine learning technique, Gaussian process regression; we then build an acquisition function from this surrogate to decide where to sample. Although not limited to hyperparameter tuning, we will discuss how Bayesian Optimization is used to find optimal hyperparameters for machine learning models.


Strategies for Non-myopic Bayesian Optimization

INFORMS Annual Meeting 2022 - Flash Presentation

Several strategies for myopic Bayesian optimization have been proposed where the immediate reward is maximized. Myopia isn’t inherently bad; rather than under-weighting future consequences, we ignore them altogether. Nonmyopic Bayesian optimization aims to resolve these issues by using “lookahead” algorithms that maximize a reward over a finite horizon. In this work, we provide a novel formulation for constructing non-myopic heuristics using well-tested myopic heuristics as building blocks. Our formulation creates a family of non-myopic acquisition functions that is highly parametric; the choice of “base acquisition function” and horizon creates this familial space.



Graduate Gang Podcast

In this episode I speak with a Darian Nwankwo who is a graduate student pursuing a Ph.D in Computer Science at Cornell University. We talk about his life, the intersection of Ai and medicine, and about what it's like to be a student at Cornell. Enjoy Grad Gang!