Statistical signal processing has its roots in probability theory, mathematical statistics and, more recently, systems theory and statistical communications theory. In statistics and signal processing, step detection (also known as step smoothing, step filtering, shift detection, jump detection or edge detection) is the process of finding abrupt changes (steps, jumps, shifts) in the mean level of a time series or signal. Vikram’s research interests are in statistical signal processing, stochastic control (POMDPs), stochastic optimization and inverse reinforcement learning with applications in social networks, human decision making and adaptive sensing. Vikram is also affiliated with Cornell’s Center for Applied Math. Click here for all my publications.
Statistical Signal Processing Using Matlab
Statistical Signal Processing Inc
Statistical Signal Processing Mit
- Click here for all my publications.
- Click here for preprints on arXiv
- Recent papers: JMLR,IEEE Info Theory, ICML, Nature Comm, IEEE Automatic Control, IEEE Signal Proc (Inverse RL for Identifying Cognitive Radar), IEEE Signal Proc (Inverse HMM Filters and Counter-adversarial Systems), IEEE Signal Proc (Anticipatory Decision Making and Quickest Change Detection).
| Fundamental Areas | Application Areas |
| POMDPs & Controlled Sensing | Social Networks (fusion & control) |
| Stochastic Optimization, Game Theory | Cognitive Radar & Intent Inference |
| Stochastic Calculus, filtering (old stuff) | Biosensors, Artificial Membranes |
Statistical Signal Processing Tutorial
Statistical Signal Processing Examples
- Partially Observed Markov Decision Processes book, Cambridge, 2016
- Dynamics of Engineered Artificial Membranes & Biosensors,Cambridge, 2018.