Biography
Shanthan Kandula is a PhD candidate pursuing design science research in Information Systems at the Indian Institute of Management, Ahmedabad. The primary aim of his research is to build design artifacts such as algorithms, models, and frameworks to solve business problems. His current research focusses on solving operational and platform design problems that arise in the E-commerce industry. In particular, he is developing artifacts that address order delivery, packaging design and review information overload. His research primarily draws from classical machine learning, deep learning, natural language processing, reinforcement learning and optimization theory.Prior to joining his PhD, Shanthan worked as an ETL developer consultant at the J.P. Morgan Chase & Co. under the paryroll of Tata Consultancy Services. His work involved in developing ETL applications that support and move data across diverse systems of the organization. He was also the Subject Matter Expert of MCP (Mission Control Process) system.
Education
- Ph.D. in Management (Information Systems), IIM Ahmedabad (Expected 2023)
- B.Tech in Electrical & Electronics Engineering, Kakatiya University, 2016
- TEP (Technology Entrepreneurship Program), ISB Hyderabad, 2015
Work Experience
- ETL Developer, TCS-JPMC (July, 2016 to February, 2018)
Awards & Honors
- INFORMS Innovative Applications of Analytics Award (IAAA), 2022
- Industrial Finance Corporation of India (IFCI) Award for Thesis Proposal, 2021
- Chaudhary-Padmanabhan-Pant Award for Scholastic Performance, 2020
- Outstanding Performer-Junior, 2017
- Star of the Learners Group, 2017
- University 1st Rank in Electrical & Electronics Engineering, 2016 (more than 900 students appeared from more than 10 colleges)
- Podduturi Ganga Reddy Gold Medal for Academic Excellence, 2016
- Best Outgoing Student - Electrical & Electronics Engineering, 2016
- Best Project Award - Electrical & Electronics Engineering, 2016
Journal Publications
Kandula, S., Krishnamoorthy, S., & Roy, D. (2021). A prescriptive analytics framework for efficient E-commerce order delivery. Decision Support Systems, 113584. Access: doi.org/10.1016/j.dss.2021.113584
