News

Fri, 08/12/2022

Sai Ravela gave a talk at GFD Summer school at Woodshole on August 12,
"A Stochastic Dynamical Perspective forOptimizing Learning Machines for Climate Applications"

Mon, 06/27/2022

The CREWSnet leadership selects Sai Ravela to lead the Tropical Cyclone and Heat Stress related activity including modeling, quantifying risk, decision support, and interventions in the Climate Grand Challenges project led by Prof. Elfatih, Dr. Aldridge, and Dr. Campbell. 

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Mon, 06/13/2022

For the summer of 2022, we are pleased to welcome Vivian Ngyuen and Diego Luca Gonzalez Gauss who will respectively be working on the Animal Biometrics for Conservation and Machine Learning for Extreme Flood modeling projects.

Thu, 04/21/2022

The Earth Signals and Systems Group is honored to contribute to MIT's flagship Climate Grand Challenges projects: "Preparing for a new world of weather and climate extremes"
Sai Ravela will serve as the Machine...

Wed, 05/19/2021

Sai Ravela gives a Data Science for Atmospheric Sciences colloquim at  Ludwig Maximilian University of Munich (LMU)  on a Bayesian Theory for Coherent Fluids that non-parametrically incorporates amplitude and geometry in for robust inference, uncertainty quantification, sampling, and reduced...

Fri, 02/12/2021

Sai, together with collaborators from Applied Research Associates (ARA) in a team comprising of Nakata and Ravela from EAPS, presents an overview of our lab and the methods we develop for application to MINEM.

Wed, 02/10/2021

Sai speaks at the LMU Colloquia in Data science for Atmospheric and Ocean Applications in the first of a three part lecture, with focus on

Informative Neural Ensemble Learning.

The other two to follow are
Tractable Information Theoretic Ensemble Data Assimilation 
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Tue, 01/12/2021

Sai presents work on informative ensemble learning at AI for Environmental Sciences
101st American Meteorological Society Annual Meeting (confex.com)

 

 

Wed, 12/16/2020

On 16th Dec 2020, a presentation was given at the IndoML conference on an ensemble approach to informative deep learning. Here is a video:

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