Saptarshi Sengupta
About Saptarshi Sengupta
Saptarshi Sengupta is a Graduate Teaching Assistant at Vanderbilt University, specializing in Electrical Engineering and Computational Swarm Intelligence Algorithms.
Current Position
Saptarshi Sengupta is currently a Graduate Teaching Assistant at Vanderbilt University in Nashville, Tennessee, United States. His responsibilities include taking classes, proctoring and grading tests, instructing lab sessions, and holding office hours for courses such as EECE 2112 Circuits I and EECE 2116 Digital Logic.
Previous Roles
Saptarshi Sengupta has held various positions in multiple organizations. In 2018, he worked as a Guest Lecturer at Vanderbilt University. He was a Biomedical Engineering Intern at Medica Superspecialty Hospital in Kolkata, India, in 2014, and an Undergraduate Researcher at Netaji Subhash Engineering College from 2013 to 2015. In 2013, he served as a Summer Trainee at the Computer Society of India. Earlier in his career, he engaged with NASA Ames Research Center on Toroidal Space Settlements and Active Shielding from 2008 to 2009.
Educational Background
Saptarshi Sengupta has a comprehensive educational background. He pursued his Master's and PhD at Vanderbilt University from 2015 to 2020, studying Electrical Engineering, Philosophy, and Teaching. Additionally, he attended the Indian Institute of Technology, Kharagpur, in 2014 for Computer Science, Business, and Communication studies. From 2013 to 2014, he studied Energy at Stanford University. Furthermore, he earned a Bachelor's degree in Communication, Engineering, and Electronics from the West Bengal University of Technology, Kolkata. His early education was completed at South Point High School, Kolkata.
Notable Contributions
Saptarshi Sengupta co-created the Quantum Double Delta Swarm (QDDS) Optimization Algorithm. He serves as the Vanderbilt Engineering Ambassador for Electrical Engineering and collaborates with Dr. Richard Alan Peters II on Computational Swarm Intelligence Algorithms aimed at solving complex, real-world optimization problems.