Aashay Mehta

Aashay Mehta

Member of Technical Staff

Edgeverve Systems Ltd.

About Me

I am primarily interested in the field of reinforcement learning (RL) and its real-world applications. For my undergraduate thesis in this domain, I was fortunate enough to have been advised by Dr. Falk Lieder at Max Planck Institute for Intelligent Systems.

Since graduation, I’ve been working at EdgeVerve. My current project involves designing and implementing the new microservices architecture for payments in Finacle, our industry-leading digital banking solution used by banks in 100+ countries.

I’ve previously interned at the University of Alberta through the Mitacs GRI program (2019). There, my project under Dr. Hamzeh Khazaei involved using ML methods to predict the performance of workloads on different virtual machines. Before that, I worked in the labs of Prof. Poonam Goyal and Dr. Kamlesh Tiwari, assisting on projects related to NLP and Computer Vision. Even before that, in my summer internship at Happiest Minds Technologies, I developed an end-to-end system that generated highlights from videos of soccer matches.

Interests
  • Reinforcement Learning
  • AI for Social Good
  • Algorithmic Robotics
Education
  • BE in Computer Science, 2020

    Birla Institute of Technology & Science (BITS), Pilani

Experience

 
 
 
 
 
Member of Technical Staff
Edgeverve Systems Ltd.
Nov 2020 – Present WFH
 
 
 
 
 
Bachelor’s Student Intern
Max Planck Institute for Intelligent Systems
Jan 2020 – Aug 2020 Tübingen, Germany
 
 
 
 
 
Mitacs Globalink Research Intern
University of Alberta
May 2019 – Jul 2019 Edmonton, AB, Canada
 
 
 
 
 
Undergraduate Research Assistant
BITS Pilani
Aug 2018 – May 2019 Pilani, India
 
 
 
 
 
Analytics Intern
Happiest Minds Technologies
May 2018 – Jul 2018 Bengaluru, India

Publications

(2022). Leveraging machine learning to automatically derive robust decision strategies from imperfect models of the real world. Preprint.

PDF Project DOI

(2020). Leveraging Machine Learning to Automatically Derive Robust Planning Strategies from Biased Models of the Environment. In CogSci 2020.

PDF Project