Anand Deo joined SUTD as a researcher in March 2021. He holds a Bachelor’s degree in Electronics Engineering from the University of Mumbai, and a Masters in Computer Science from the Tata Institute of Fundamental Research, Mumbai. His research focuses mainly on applied probability, with a specific interest at the interface of finance, operations research and machine learning. Prior to joining SUTD, he was a research scholar at Tata Institute of Fundamental Research, Mumbai. His webpage is https://sites.google.com/view/anands-webpage/home.
- PhD in Computer and System Science, Tata Institute of Fundamental Research (thesis submitted, expected in 2021)
- Masters in Computer Science, Tata Institute of Fundamental Research, 2016.
- Bachelors in Electronics Engineering, Mumbai University, 2015.
- Applied Probability, Operations Research, Simulation.
- Machine Learning, Optimisation.
- Study of Large Networks.
- Deo, A., & Juneja, S. (2021). Credit Risk: Simple Closed-Form Approximate Maximum Likelihood Estimator. Operations Research, 69(2), 361-379
- Deo, A., & Murthy, K. (2020, December). Optimizing tail risks using an importance sampling based extrapolation for heavy-tailed objectives. In 2020 59th IEEE Conference on Decision and Control (CDC) (pp. 1070-1077). IEEE.
- Deo, A., & Murthy, K. (2021). Achieving Efficiency in Black Box Simulation of Distribution Tails with Self-structuring Importance Samplers. arXiv preprint arXiv:2102.07060.
- Best Paper Award at the CRISIL Doctoral Symposium, for the research paper Simple Closed-Form Approximate Maximum Likelihood Estimator.