Manjesh K. Hanawal
Papers
1
Total Citations
17
H-Index
1
About
Manjesh K. Hanawal is a leading researcher in machine learning, stochastic systems, and network economics, with a focus on developing data-driven decision-making frameworks. His major contributions lie in learning optimal policies for Markov decision processes (MDPs) from observational data, where his work on parameterizing policies using features and kernel functions has provided robust methodologies for policy learning without direct environment interaction. His most-cited paper, "Learning Policies for Markov Decision Processes From Data" (2018, 17 citations), has influenced subsequent research in reinforcement learning and control, particularly in settings where data is limited or costly. Hanawal’s broader impact extends to network pricing, resource allocation, and online learning, where his models have advanced understanding of strategic behavior in communication and energy systems. His work is notable for bridging theoretical guarantees with practical algorithm design, making him a respected voice in the intersection of operations research and artificial intelligence.
Research Focus
Key Achievements
Top Papers
- 1Learning Policies for Markov Decision Processes From Data17 citations · 2018