Email : milind [at] stanford [dot] edu
350 Serra Mall, # 340 (Packard), Stanford, CA 94305
Statistical Learning Theory and High-Dimensional Statistics
Stochastic control and optimization
M. Rao, T. Javidi, Y.C. Eldar, & A. Goldsmith, “Estimation in Autoregressive Processes With Partial Information,” accepted at ICASSP, 2017
N. Farsad, Y. Murin, M. Rao, & A. Goldsmith, “On the Capacity of Diffusion-Based Molecular Timing Channels With Diversity,” Asilomar, 2016
M. Rao, A. Kipnis, T. Javidi, Y.C. Eldar, & A. Goldsmith, “System Identification from Partial Samples: Non-Asymptotic Analysis,” Conference on Decision and Control, 2016
M. Chowdhury, M. Rao, Y. Zhao, T. Javidi & A. Goldsmith, “Benefits of Storage Control for Wind Power Producers in Power Markets,” IEEE Transactions on Sustainable Energy, 2016
G. Malysa, M. Hernaez, I. Ochoa, M. Rao, K. Ganesan & T. Weissman, “QVZ: lossy compression of quality values,” BMC Bioinformatics, 2015
M. Rao, M. Chowdhury, Y. Zhao, T. Javidi & A. Goldsmith, “Value of Storage for Wind Power Producers in Forward Power Markets,” American Control Conference, 2015
M. Rao, F. J. Lopez-Martinez, M. S. Alouini & A. Goldsmith, “MGF Approach to the Analysis of Generalized Two-Ray Fading Models,” IEEE Transactions on Wireless Communications, 2015
M. Rao, F. J. Lopez-Martinez, M. S. Alouini & A. Goldsmith, “MGF Approach to the Capacity Analysis of Generalized Two-Ray Fading Models,” IEEE International Conference on Communications, 2015
M. Rao, F. J. Lopez-Martinez & A. Goldsmith, “Statistics and System Performance Metrics for the Two Wave with Diffuse Power Fading Model,” CISS, 2014.
PhD in Electrical Engineering, Stanford University, 2013-
MS in Electrical Engineering, Stanford University, 2013-2015
B. Tech in Electrical Engineering, Indian Institute of Technology Madras, 2009-2013
Statistical Learning Theory, Probabilistic Graphical Models, Stochastic Control, Reinforcement Learning, Convex Optimization, Wireless Communication, Information Theory, Statistical Signal Processing
Learning with Domain Adaptation
Worked with Farzan Farnia and Nishal Shah on extending results on fundamental performance bounds while performing statistical learning with domain adaptation. This was part of a course project for EE377: Information Theory and Statistics with Prof. John Duchi.
Adapting Reinforcement Learning Algorithms to Episodic Learning of POMDPs
Worked with Stephen Ragain on learning the state and model of a partially observed process. Course project for MS&E 338: Reinforcement Learning with Prof. Ben Van Roy.
cvx_fit: From Data to a Convex Model
Worked with Alon Kipnis and Mainak Chowdhury on implementing an algorithm that fits a piecewise linear convex function to any input convex function for use in disciplined convex programming. Course project for EE364B: Convex Optimization with Prof. Stephen Boyd.
Stability and Hopf Bifurcation Analysis of the Delay Logistic Equation
Worked with Preetish and Prof. Gaurav Raina on characterizing non-linear population dynamics.
Machine Learning Techniques in Non-Linear Receivers for Interference Mitigation
Worked with Prof. Giridhar on using clustering techniques to model interference in cellular communications.