I am a PhD student at University of Cambridge (with Zoubin Ghahramani) and Max Planck Institute for Intelligent Systems (with Bernhard Schölkopf) as a part of the Cambridge-Tübingen PhD programme.
My research focus is on probabilistic programming, in particular on exploiting composition of Bayesian inference algorithms. I am also broadly interested in functional programming and machine learning.
Consistent kernel mean estimation for functions of random variables
Carl-Johann Simon-Gabriel*, Adam Ścibior*, Ilya Tolstikhin, Bernhard Schölkopf
* joint first authors
NIPS 2016
Fabular: regression formulas as probabilistic programming
Johannes Borgström, Andrew D. Gordon, Long Ouyang, Claudio Russo, Adam Ścibior, and Marcin Szymczak
POPL 2016
Practical probabilistic programming with monads
Adam Ścibior, Zoubin Ghahramani, and Andrew D. Gordon
Haskell 2015
Building inference algorithms from monad transformers
Adam Ścibior, Yufei Cai, Klaus Ostermann, and Zoubin Ghahramani
PPS 2017
Modular construction of Bayesian inference algorithms
Adam Ścibior and Zoubin Ghahramani
AABI 2016
Parameterized probability monad
Adam Ścibior and Adrew D. Gordon
PPS 2016
Reproducing kernel Hilbert space semantics for probabilistic programs
Adam Ścibior and Bernhard Schölkopf
PPS 2016
Probabilistic programming with effects
Adam Ścibior and Ohad Kammar
HOPE 2015
Compositional inference in probabilistic programming
Microsoft Research Cambridge 2016
Practical Probabilistic Programming with Monads
Haskell Symposium 2015
Effects in Bayesian inference
HOPE 2015