A listing of a few projects I’ve worked/am working on:
Worklytics – Worklytics is a data-driven Employee Experience solution. We combine traditional “active listening” via surveys with real-time predictive analytics, to provide rich, continuous insight into employee experience. Using data from common SaaS productivity tools (G Suite, Office 365, GitHub, Asana, JIRA, Slack, etc), Worklytics provides ongoing prediction of employee satisfaction and leading indicators of employee outcomes.
Pipelines 2.0 for Google App Engine – still very preliminary, but vision is to provide Beam-like semantics for “adhoc” pipelines. Distinct from Cloud Dataflow in that each pipeline is not its own distinct JAR with a static DAG. Rather, the JAR execution bundle may execute a diverse set of pipelines, with DAGs that may be built dynamically at run-time. Each invocation of a pipeline from application code (a job) can use logically isolated persistent storage, to support multi-tenant use-cases.
Amazon S3 API binding for Google Apps Script – Google’s provided ScriptDB limits objects to 100KB. I needed more, so I wrote a (very simple) library to store things to S3 via the Amazon Web Services REST API.
Economic Hierarchical Q-Learning – My senior thesis (pdf), advised by David C Parkes, applied economic approaches to the problem of hierarchical reinforcement learning. The thesis won a Thomas T. Hoopes prize for outstanding undergraduate research. We published a conference paper based on the work, entitled Economic Hierarchical Q-Learning, at AAAI’08.