Projects

As one of my works at Mu Sigma Labs, I was part of a research project on the High Velocity Time Series on early 2019. One of the goals was to create a high velocity trading app using Pair Trading. The Requisite terms Long and Short trades Long trades are buying a security. Short is selling a security even when you don’t own it. It generally means that you are borrowing someone’s securities and selling them in the hopes of buying it back for lower cost later and returning it and hence, making a profit....

Serving recommendation to 200+ millions of users for thousands of candidates with less than 100ms is hard but doing that in Python is harder. Why not add some compiled spice to it to make it faster? Using Cython you can add C++ components to your Python code. Isn’t all machine learning and statistics libraries already written in C and Cython to make them super fast? Yes. But there’s still some optimizations left on the table....

So the ask is to do 3 Million Predictions per second with as little resources as possible. Thankfully its one of the simpler model of Recommendation systems, Multi Armed Bandit(MAB). Multi Armed bandit usually involves sampling from distribution like Beta Distribution. That’s where the most time is spent. If we can concurrently do as many sampling as we can, we’ll use the resources well. Maximizing Resource utilization is the key to reducing overall resources needed for the model....

At Mu Sigma Labs, I led a significant project focused on BPMN-based analytics automation and pipeline orchestration. Using the open-source platform Activiti, I owned, developed, tested, and maintained a system serving about 3,000 internal users, handling critical reporting and data pipelines. Technologies Used The core technologies employed were: Backend: Java and Spring Boot Scripting: Python and R for analytics tasks Frontend: Angular for user interface Understanding BPMN Business Process Model and Notation (BPMN) provided the foundation for our automation approach....