My research project is focused on developing a digital twin of the financial markets that can be used by various participants (such as banks, asset managers, investors, etc.) to assess the impact of deploying AI systems into the markets and by financial regulators to define better market rules. My research interests are centred around ensuring scalable and safer deployment of AI models in the financial markets. Given the black box nature of machine learning models, I’m interested in researching avenues to better stress test and explain actions of ML agents before they can be used for decision making in the financial markets.
I chose to pursue a PhD with the STAI CDT since my research interests closely aligned with the goals of the CDT towards ensuring safe and trusted AI systems. I believe that the cohort experience will enrich my PhD journey by providing a platform to collaborate with like-minded researchers in the field of safe and trusted AI. The training sessions and modules organised by the CDT provide a solid foundation on areas such as symbolic AI, explainability, etc. which I believe will be very useful to my PhD journey.
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Undergraduate Qualification: Bachelor of Technology (B-Tech), Electrical Engineering, Indian Institute of Technology Bhubaneswar
Masters Qualifications:
- Master of Business Administration (MBA), Indian Institute of Management Bangalore
- Master of Science (MS), Data Science, University of Texas at Austin
Work Experience:
- Quantitative Analyst (~5.5 years) at Goldman Sachs/JP Morgan/Barclays
- Equities Trader (~2 years) at Citigroup
