Internship:

My Master's research used Reinforcement Learning (RL) techniques to learn complex behaviors from human demos (Thesis, Slides, Video). Subsequently, I joined SAP America, Inc. as an Intern at the Logistics Planning & Procurement Team. Here I developed an RL model for supply chain production-line plan generation (TensorFlow). Extending this work, I implemented an ML-based 'Pattern Optimizer' for Tyson Foods, which can detect rules-based production line patterns and dynamically recommend revenue-optimized production plans (SAP IBP for Response).

As an intern, I enhanced Supply Chain Forecasting in SAP Integrated Business Planning (IBP) by leveraging ML techniques from HANA Predictive Analysis Library (PAL) on customer data. I developed an SAP Business Technology Platform (BTP) App and SAP Intelligent Robotic Process Automation (IRPA) Bot to perform parameter optimization for a customer's data and choice of forecast algorithm. The work mostly involved programming in SAP UI5 (Frontend), Python (Backend), and HANA SQL (Database). Outside the SAP framework, I am familiar with algorithms and frameworks for ML/RL, and do most of my programming in Python. I have also developed a recent interest in data science & visualization, familizarizing myself with related tools. Full-TIme:

I have had the opportunity to implement projects and PoCs for industries such as pharmaceuticals (J&J), auto & parts (GM), energy (Suncor), consumer goods (Alicorp), and beverages (The Coca-Cola Company). I also look forward to leveraging my background in Machine Learning to work closely with our in-house Joule AI to automate and further enhance SAP IBP.