Ravindra Gajulapalli, PhD

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.

• Modeling: Machine Learning | Statistical Modeling | Math Programming | Discrete Event Simulation
• Algorithmic: Online Algorithms | Graph Algorithms | Vehicle Routing | Large Scale Nonlinear Optimization Algorithms
• Technologies: Python, C++, R, SAS, Java, FORTRAN | GUROBI, CPLEX, COIN-OR | GAMS, AMPL, SIGMA, SLAM | Oracle PL/SQL, Microsoft Transact SQL, Teradata SQL, HDFS – Hive QL.

• A Fast Tabu Search Implementation for Large Asymmetric Traveling Salesman - OPSEARCH, Springer (2012).
• Implementing Tabu Search to Exploit Sparsity in ATSP instances. W.P. No. 2008-10-02, Indian Institute of Management, Ahmedabad (2008).
• HCL Technologies: Employee First, Customer Second - Case Study UVA-OM-1366, Darden Business Publishing, University of Virginia (2008).
• Scaling Sparse Constrained Nonlinear Problems for Iterative Solvers - W.P. No. 2006-08-06, Indian Institute of Management, Ahmedabad (2006).
• Scaling Sparse Matrices for Optimization Algorithms - W.P. No. 2006-08-05, Indian Institute of Management, Ahmedabad (2006).
• Computational experience with a safeguarded barrier algorithm for sparse nonlinear programming - Computational Optimization and Applications, vol 19, pgs 107-120, Kluwer Academic Publishers (2001).
• A single server queue with cyclically indexed arrivals and service times - Queuing Systems: Theory and Applications, vol 15, pgs165-198, JC Baltzer AG, Science Publishers.
• INTOPT: an interior point algorithm for large scale nonlinear optimization - Doctoral Dissertation, The University of Texas at Austin.
• A textbook for managers on using quantitative methods - Mathematical Programming for Managers - TBD.

• Detecting wasteful data collection - US 8,825,609 · Issued Sep, (2014).
• Resource management using environments - US 8,635,624 · Issued Jan, (2014)

• Personal Interests:
  • Travel, Reading, Hiking, and supporting a Delhi non-profit Niramaya in addressing preventable blindness
• Fun Facts:
  • I go by initials GS. 
  • I have been serial entrepreneur – once in Austin, TX and twice in Silicon Valley.
  • I have done three trans-continental relocations.
  • I grew up in Kipling country – central India.
  • Along with English, I can speak four Indian languages - Hindi, Telugu, Bengali, and Bhojpuri