Draft Schedule

Room: Ballroom

Abstracts

Keynote 1: Data Science Africa - Lessons from a grassroots initiative

Since 2015, Data Science Africa has organised annual machine learning summer schools and research workshops aimed at increasing the level of expertise in data science and machine learning in Africa and creating a cohesive African data science community tackling local problems. Initially held in the three East African countries of Kenya, Uganda and Tanzania, DSA Abuja in November 2018 marked our entry into West Africa. In this talk I will describe the DSA journey, the lessons we have learned, and highlight the achievements and future plans for DSA.

Keynote 2: Statistical and machine learning challenges from genetics to CRISPR gene editing

Molecular biology, healthcare and medicine have been slowly morphing into large-scale, data driven sciences dependent on machine learning and applied statistics. Many of the same challenges from other domains are applicable here: causality vs association; covariate shift; hidden confounders; heterogenous target space; model validation; (multiple) hypothesis testing; feature engineering (owing to relatively small data sets). In this talk, I will go over domain-specific instantiations of some of these problems, along with proposed solutions. In particular, I will start by presenting modelling challenges in finding the genetic underpinnings of disease, which is important for screening, treatment, drug development. Assuming that we have uncovered genetic causes, genome editing—which is about deleting or changing parts of the genetic code—will one day let us fix the genome in a bespoke manner. Editing will also help researchers understand mechanisms of disease, enable precision medicine and drug development, to name just a few more important applications. I will close this talk by discussing how we have advanced CRISPR gene editing with machine learning.

Keynote 3: Stein’s Method, Inference, and Learning (SMILE)

Stein’s method is a powerful tool from probability theory for bounding the distance between probability distributions. In this talk, I’ll describe how this tool designed to prove central limit theorems can be adapted to assess and improve the quality of practical inference procedures. I’ll highlight applications to Markov chain sampler selection, goodness-of-fit testing, variational inference, and nonconvex optimization and close with several opportunities for future work.