Application of Bayesian Deep Learning for Manufacturing Assembly Diagnosis & Correction
Phone: +44 (0) 7570200029
Address: IMC, WMG, University of Warwick, Coventry, CV4 7AL, United Kingdom
Sumit is a doctoral student in the Digital Lifecycle Management (DLM) group at WMG, University of Warwick (U.K). His doctoral research focuses on the development and application of deep learning models such as Bayesian 3D Convolutional Neural Networks (CNN) for Root Cause Analysis (RCA) and the application of deep reinforcement learning such as Deep Deterministic Policy Gradient (DDPG) for Corrective and Preventive Action (CAPA) in multi-station assembly systems. He obtained his Bachelor’s Degree in Industrial and Systems Engineering from the Indian Institute of Technology (IIT) Kharagpur (2012-2016).
CORPORATE & RESEARCH EXPERIENCE
He has worked as a Business Analyst at ZS Associates where he used machine learning models to solve problems in the pharmaceutical sales and marketing domain. Previously he worked briefly as a Research Assistant at the University of Hong Kong to develop a multi-variate time-series model for risk minimization in the retail industry. He also worked briefly at Chongqing University (China) to build a dynamic warehouse space pricing model using artificial neural networks.
As a student of IIT Kharagpur, he worked on various real-world projects. He worked to build a forecasting model for the Reserve Bank of India. The developed forecast model helped in forecasting currency demand across the nation. He worked with the Orissa State Medical Corporation to build a supply chain model for drug demand prediction and distribution. As his bachelor’s project, he worked on building resilient supply chains using stochastic search heuristics.