Adam M. Johansen

About

Adam M. Johansen is a leading figure in computational statistics, whose work has fundamentally shaped modern approaches to Bayesian inference and sequential Monte Carlo methods. His primary research areas include particle filtering, smoothing, and the development of scalable algorithms for complex state-space models. Johansen’s most significant contribution is his seminal tutorial, "A Tutorial on Particle Filtering and Smoothing: Fifteen years later," which has amassed over 1,400 citations. This comprehensive work demystified the theory and practice of particle methods for non-linear, non-Gaussian systems, becoming an essential resource for both new researchers and seasoned practitioners. Beyond this landmark paper, his research has advanced the theoretical foundations of Monte Carlo methods, making them more robust and applicable to high-dimensional problems. Johansen’s work has had a profound impact on fields ranging from robotics and signal processing to econometrics and systems biology. His ability to translate complex mathematical ideas into accessible, practical tools has cemented his reputation as a key innovator in computational statistics, guiding a generation of researchers in tackling real-world inference challenges.

Research Focus

Key Achievements

Top Papers

1. 1
   A Tutorial on Particle Filtering and Smoothing: Fifteen years later

   1,407 citations · 2008
2. 2
   Particle Filtering and Smoothing: Fifteen years later

   10 citations · 2008

Key Collaborators

Contact & Links