Neural networks have begun finding use in glaciology, becoming a critical new tool in the study of climate change and its effects on receding glaciers. A recent publication in The Cryosphere releases and evaluates a new neural network known as the Calving Front Machine (CALFIN), a program capable of automatically identifying the calving fronts of ocean-terminating glaciers from decades of satellite imagery. Programs such as CALFIN could make it possible to catalog and monitor the rates of glacier loss all around the world, even at sites that scientists haven’t been able to study manually.

Read the full story at Phys.org.

The Association for Computing Machinery has selected UCI computer scientist Michael Franz to receive the ACM Charles P. “Chuck” Thacker Breakthrough in Computing Award. Franz was recognized for his development of just-in-time compilation techniques that enable fast and feature-rich services on the internet, including such widely used applications as Gmail and Facebook.

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UCI researchers have harnessed digital technology and deep-learning neural networks to develop a next-generation eye prosthetic that can match the pupil movements of the natural eye.

Read the full story at UCI Health.

Many people who experience long-term symptoms from the coronavirus did not feel sick at all when they were initially infected, according to a new study that adds compelling information to the increasingly important issue of the lasting health impact of Covid-19.

Read the full article at The New York Times.

Interdisciplinary group to combine research, clinical care to empower people

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Deep learning, a family of machine-learning methods based on artificial neural networks, has revolutionized applications such as image interpretation, natural language processing and autonomous driving. In a study published recently in Science Advances, UCI researchers describe how the technique can also be successfully used to observe gene regulation at the cellular level. Until now, that process had been limited to tissue-level analysis.

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Sameer’s work centers on large-scale and interpretable machine learning applied to information extraction and natural language processing. We caught up with Sameer right after he was awarded the best paper award at ACL 2020 for his work on Beyond Accuracy: Behavioral Testing of NLP Models with CheckList. In our conversation, we explore CheckLists, the task-agnostic methodology for testing NLP models introduced in the paper. We also discuss how well we understand the cause of pitfalls or failure modes in deep learning models, Sameer’s thoughts on embodied AI, and his work on the now famous LIME paper, which he co-authored alongside Carlos Guestrin.

Listen below or at the TWIML AI Podcast.