I bring a unique combination of technical and programming depth along with an extensive experience across industries and business functions in AI, optimization and machine learning. I grasp the nuances of a business decision quickly, am adept at breaking complex business requirements, and implementing actionable algorithms and models.
I am doing cutting edge research in machine learning algorithms - developing a probabilistic distance clustering algorithm in Python that converges fast and seems to be an alternative to the well-known EM methods for de-mixing distributions. For my research in large scale nonlinear optimization, I am building LSNLOPT, an extensible and robust framework in C++20 that exploits super-sparsity, uses graph theoretic algorithms for fill-in reduction and addresses common pitfalls.
I was a hands-on Manager, Decisions Sciences at Kohl’s; where, I used Python and tools like GAMS, GUROBI to build models for assortment planning and improving supply chain across 1100 stores nationwide. My expertise in building complex scalable algorithms helped me to prototype faster and get business approvals for the tradeoffs involved between decision granularity and model complexity.