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. With my business school background I grasp the nuances of a business decision quickly, adept at breaking complex business requirements, and able to translate to model and algorithm specifications.
Currently in machine learning, I am 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. Planning to test this approach to multidimensional data sets in healthcare.
In addition, presently, I am building LSNLOPT, an extensible framework for large scale optimization in C++20 and graph theoretic algorithms that exploits super-sparsity, stable by addressing many of the pitfalls associated with nonlinear optimization like scaling, error checking etc.
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. For instance, at Kohl’s our first round of assortment planning prototype was intractable.
I am focused on advancing optimization and machine learning, as demonstrated my current work - both on stable large scale optimization algorithms, and developing new probabilistic distance clustering algorithms.