May Meetup - Probabilistic Programming using PyStan

Join us for our monthly virtual PyDistrict. We'll have one great speaker who'll be presenting: Saran Ahluwalia.

Saran Ahluwalia - Probabilistic Programming using PyStan: Quantifying the business impact of adverse events on networks using device telemetry

Causal Inference has been greatly popularized in the past several years in both contemporary machine learning and in statistics. Applications include advertising campaigns, economic analysis and disaster recovery.

But what exactly is "causal inference"? From a statistician's perspective, it is the study of the consequences of the effects of individuals' actions. This is really important as one causal phenomena in your data can be more meaningful than dozens of correlation patterns in your data.

In this talk we will briefly discuss an application of causal inference to cybersecurity. Objectives in this talk include:

1. An introduction to Bayesian structural time series models
2. An introduction to probabilistic programming using Stan
3. How to construct hierarchical models that can capture more complex dynamics of systems
4. An introduction to network traffic analysis and its utility in impacting both cybersecurity and the science of causal inference

About Saran

Saran Ahluwalia is currently a Data Scientist working within the Advanced Security Initiatives group in Cisco. You can learn more about Saran here. You can also follow him and his ramblings on 1990s and 2000-era NBA basketball, COVID-19 policy and statistics (among other topics) on Twitter: @SaranAhluwalia.