Research Interests

I am interested in convex optimization and machine learning techniques for large-scale stochastic systems. My research to-date has focused on applying the conditional-value-at-risk (CVaR) risk measure to model uncertainty in convex optimization, as well as efficient and data-driven algorithms to solve these problems.

Publications

  • Madavan, Avinash N. & Bose, Subhonmesh, 2019. Subgradient Methods for Risk-Sensitive Optimization. arXiv e-prints, p.arXiv:1908.01086. Available at: https://arxiv.org/abs/1908.01086.
  • Madavan, A.N. et al., 2019. Risk-Sensitive Security-Constrained Economic Dispatch via Critical Region Exploration. Power and Energy Society General Meeting.
  • Madavan, A.N. & Bose, S., 2019. Risk-Sensitive Energy Procurement with Uncertain Wind. GlobalSIP.
  • Shish, K. et al., 2016. Aircraft mode and energy-state prediction, assessment, and alerting. Journal of Guidance, Control, and Dynamics, 40(4), pp.804–816.
  • Shish, K.H. et al., 2015. Trajectory Prediction and Alerting for Aircraft Mode and Energy State Awareness. In AIAA Infotech@ Aerospace. p. 1113.

Presentations

  • Risk-Sensitive Energy Procurement with Uncertain Wind. Presented at: GlobalSIP 2020 [file]
  • Risk-Sensitive Security-Constrained Economic Dispatch via Critical Region Exploration. Presented at: IEEE Power and Energy Society General Meeting 2019 [file]
  • Short-Step Interior Point Algorithm. [file]