Bikramjit Das’s research is in the investigation and analysis of rare and extreme events using tools from applied probability, statistics, and optimization. The application domain for his research spans insurance, finance, telecommunication, and more recently social and economic networks. He received his Ph.D. from the School of OR&IE at Cornell University. He was a post-doctoral researcher at the RiskLab at ETH Zurich before joining SUTD.
He teaches undergraduate lectures in Probability, Statistics, Simulation, and Analytics, and graduate lectures in Statistics and Stochastic Modeling.
- PhD in Operations Research, Cornell University, USA
- B. Stat, M.Stat, Indian Statistical Institute, India
Recent publications and preprints
- B. Das (2023+), Inference for heavy-tailed data with Gaussian dependence.
- B. Das and V. Fasen-Hartmann (2023+), On heavy-tailed risks under Gaussian copula: the effects of marginal transformation.
- B. Das and V. Fasen-Hartmann (2023+), Aggregating heavy-tailed random vectors: from finite sums to Lévy processes.
- B. Das, T. Wang, and, G. Dai (2022), Asymptotic behavior of common connections in sparse random networks, Methodology and Computing in Applied Probability, 24, 2071–2092.
- B. Das, V. Fasen-Hartmann, and, C. Klüppelberg (2022), Tail probabilities of random linear functions of regularly varying random vectors, Extremes, 25 (4), 721-758.
- B. Das, A. Dhara, and, K. Natarajan (2021), On the heavy-tail behavior of the distributionally robust newsvendor, Operations Research, 69(4), 1077-1099.