I am a 2nd year Master student, advised by Prof. Dr. David Ebert. My research is in the areas of optimization and machine learning, with applications in Supply Chain, Finance and Agriculture. My current focus is on research topics like Stochastic Optimization, Bayesian Inference and Deep Learning. Currently I am working on developing and deploying Deep learning and Bayesian inference models for effective irrigation Management to growers in California.

I am pursuing Masters in Industrial Engineering with specialization in Operation Research at Purdue Univeristy

I graduated from Sardar Patel College Of Engineering (class 2015) with a Btech degree in Mechanical Engineering.

Research Assistant


Visual Analytics Lab, Purdue

• Working on seed funding project for developing and testing a first-of-its-kind visual analytics software tool that integrates in situ and remotely sensed data at multiple scales of resolution
• Develop Bayesian decision-making and deep learning models in Python, to enable real time multi-criteria decision making
• Built web app using python Django framework for growers in California which would help them save 8% of irrigation water







Quality Analytics Intern


Oerlikon Fairfield, Lafayette

• Dramatically reduced production scrap and waste by implementing SPC software on the shop floor to analyse real-time data.
• Proposed predictive maintenance of gear grinding machine using application of IOT and anomaly detection machine learning algorithm
• Implement Six Sigma methodologies over various problems on the shop floor.





Project Engineer


Kalpataru Power Transmission Ltd, Mumbai

• Managed project budget of $51 M+ with workforce team of 20+ subcontractors and performed budget allocation
• Leveraged SAP to Integrate various business modules by creating BOMs, Sales Order, PR & POs and Shipment tracking
• Documented the Annual Operating plan (AOP) for Transmission line projects for cost and revenue analysis.

More information is available in my CV, LinkedIn profile and GitHub account.