News

Grace Jiang, ML-Accelerated Climate Risk Assessment: Storm Surges in Bangladesh

Thu, 08/31/2023

Grace, our super super UROP presented her work on learning surrogate models for cyclone-induced flood risk assessment.

Thu, 04/06/2023

Dr. Jiangchao Qiu, a coastal modeler with an interest in machine learning, joins the ESSG! Welcome, Dr. Qiu!

GFD Summer School 2022: Stochastic Dynamics to Optimize Learning

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"

MIT CGC: Ravela selected to Lead Cyclone and Heat Stress research

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.

...

Amazing High School Interns Join ESSG

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.

MIT CGC: Ravela selected as Machine Learning lead for Climate Extremes

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...

Bayesian Theory for Coherent Fluids

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...

AFRL Machine Intelligence for Nuclear Explosion Monitoring (MINEM) Meeting

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.

Patterns, Information, and Informative Learning for States and Structure: Deep connections between ML and DA

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
...

Variational and Ensemble Approaches for Stable Hybrid Dynamical Systems

Tue, 01/12/2021

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