IEEE GRSS IITI and IEEE SB IITI cordially invites the student community to join for the upcoming Tech Talk series on Dec 7,2022 for an interesting talk by Dr.Zachary Labe on climate model projections and visualizations using Machine Learning.
The details of the Tech-Talks:
Title: Machine learning for evaluating climate model projections
Speaker: Dr.Zachary Labe, Postdoctoral Research Associate at Princeton University and NOAA GFDL,
Date and time:Wednesday Dec 7,2022 18:30 IST (poster attached)
Registration Link: https://iiti.webex.com/weblink/register/r4580ea40572d2f54864c9d034cb97089
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Lucky participants would receive IEEE merchandise goodies. Please fill out the feedback form after the webinar to claim this offer (limited to IITI students only)
Kindly find the abstract and speaker bio below for reference.
Abstract : The popularity of machine learning methods, such as neural networks, is rapidly expanding in nearly all areas of science. The interest in these tools also coincides with a growing influx of big data and the need for high efficiency in solving prediction problems. However, there is also some hesitancy in adopting the use of neural networks due to concerns about their reliability, reproducibility, and interpretability.
In climate science, we often consider signal-to-noise problems to help disentangle human- caused climate change from natural variability. These applications typically involve complicated relationships between different feedbacks at play in the ocean, cryosphere, land, and
atmosphere. Recent work has shown that neural networks can be a promising tool for solving these types of statistical problems when combined with explainability techniques developed by the fields of computer science and image processing. Interestingly, these methods have revealed that neural networks often leverage regional patterns of climate change in order to make their predictions. In this webinar, I will share examples from climate science that use a few of these visualization methods to peer into the “black box” of neural networks, which help us to better understand their decision-making process while also learning new science. The same machine learning visualization methods can be easily adapted for a wide variety of applications and other scientific fields of study.
Bio: Dr. Zachary Labe is a postdoc at NOAA’s Geophysical Fluid Dynamics Laboratory and the Atmospheric and Oceanic Sciences Program at Princeton University. His current research interests explore the intersection of climate variability, extreme events, decadal prediction, and explainable machine learning methods. In addition to academic research, he is very passionate about improving science communication, accessibility, and outreach through engaging climate change visualizations.