Sensing, Learning & Inference Group

About SLI

Our research focuses on theory and algorithms for extracting actionable information from complex data streams. We are particularly interested in decision-making under uncertainty for autonomy in a variety of application areas. SLI is led by John Fisher and is part of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology.

For more information about our current research and collaborations, see our Research and Publications pages.

Recent News

12/10/20 - Sue presented Sequential Bayesian Experimental Design With Variable Cost Structure at NeurIPS 2020. [pdf]
12/10/20 - Michael submitted his M.Eng. thesis Bayesian Scene Understanding with Object-Based Latent Representation and Multi-Modal Sensor Fusion.
12/8/20 - Genevieve presented Belief-dependent macro-action discovery in pomdps using the value of information at NeurIPS 2020. [pdf]
10/6/20 - Genevieve, Sue, and Christopher presented at the 2020 ETI Annual Workshop. [etiworkshopday1] [etiworkshopday2]
9/25/20 - Sue’s paper Sequential Bayesian Experimental Design With Variable Cost Structure has been accepted to NeurIPS 2020! [pdf]
9/25/20 - Genevieve’s paper Belief-dependent macro-action discovery in pomdps using the value of information has been accepted to NeurIPS 2020! [pdf]
8/26/20 - Christopher and John presented Bayesian Modeling and Inference at the ETI Summer School. [presentation] [hdpcollab]
6/17/20 - David presented his Nonparametric oobject and parts modeling with lie group dynamics at CVPR 2020. [pdf] [conf]
2/26/20 - David’s paper Nonparametric oobject and parts modeling with lie group dynamics has been accepted as an oral presentation at CVPR 2020! [pdf]

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