Tamoghna Bose

Doctoral Researcher in the Department of Economics

Two-sided matching with applications to ridesharing and platform competition

Tamoghna Bose's current research focuses on using game theory and network economics to construct the model of ride-sharing platform and platform competition.

Tamoghna Bose

Supervisors: Professor Siddhartha Bandyopadhyay and Professor Aditya Goenka

Contact details

Email address: txb000@student.bham.ac.uk

Google view: https://sites.google.com/view/tamoghna-bose/home

LinkedIn: http://linkedin.com/in/tamoghna-bose-8a369b143

Biography

Mr Tamoghna Bose graduated in 2019 from Cardiff University. He worked as a MRes Economics candidate under the prestigious funding of the Cardiff Business School, in which Cardiff University awarded to only four students in the Business School.

He started his research in microeconomic theory as a funded doctoral research candidate at University of Birmingham, UK, where he works on Spatial Pricing in the context of Ride-Sharing Platforms. It is here where he works on applied game theory and networking problems like decision making between ride-sharing and hailing cabs, price-setting and matchmaking of the platforms and how consumer surplus for riders, driver’s earnings and profits of platform goes up.

Mr Bose taught various tutorials in Cardiff University to both undergraduates and post-graduates as a part-time graduate teaching assistant.

Mr Bose, along with his PhD, is also a part-time Research Fellow in the Birmingham Business School.

Qualifications

  • MRes Economics (Cardiff University)
  • MSc Economics (Cardiff University)
  • MA Economics (Jadavpur University)
  • BSc Economics (University of Calcutta)

Teaching Responsibilities

Postgraduate Teaching Assistant for the following modules (September 2021 - present)

  • Mathematics for economics (UG-1)
  • Industrial Organisation (UG-3)

Professional Memberships

Royal Economic Society (July 2021 - present)

Research interests

  • Game-Theory
  • Labour Economics
  • Network Economics
  • Machine-Learning