Sangeetha Abdu Jyothi’s Work on Solar Superstorms Raises Concerns of Internet Apocalypse

A recent Wired headline read, “A Bad Solar Storm Could Cause an ‘Internet Apocalypse.’” A few days later, Forbes ran the article, “Why America Should Suddenly Prepare For A Billion-Dollar ‘Internet Apocalypse’ Caused By The Sun.” Both publications were sharing concerns raised by Sangeetha Abdu Jyothi, an assistant professor of computer science at UCI’s Donald Bren School of Information and Computer Sciences. Abdu Jyothi presented her research, “Solar Superstorms: Planning for an Internet Apocalypse,” at SIGCOMM 2021, the annual conference of the ACM Special Interest Group on Data Communication.

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Harnessing the Data Revolution: ICS Researchers and Collaborators Receive $1.5M Data Science Grant

Babak Shahbaba
Babak Shahbaba

When Statistics Professor Babak Shahbaba took over as director of the Data Science Initiative, coordinating UCI faculty and students involved in data science activities, the plan was to broaden the initiative’s reach in terms of education, research and external relations. This plan motivated the creation of the Californian Data Experience Transformation (CADET) program, which aims to offer data science training via curriculum development, hands-on experiences, and close interactions with both academic and non-academic organizations.

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Wired: “A Bad Solar Storm Could Cause an ‘Internet Apocalypse'” (Sangeetha Abdu Jyothi quoted)

At the SIGCOMM 2021 data communication conference on Thursday, Sangeetha Abdu Jyothi of the University of California, Irvine presented “Solar Superstorms: Planning for an Internet Apocalypse,” an examination of the damage a fast-moving cloud of magnetized solar particles could cause the global internet. Abdu Jyothi’s research points out an additional nuance to a blackout-causing solar storm: the scenario where even if power returns in hours or days, mass internet outages persist. 

Read the full story at Wired.

Marco Levorato Receives NSF Grant for Mobile Autonomous Systems (MAS) Research

Assistant Professor of Computer Science Marco Levorato was awarded $415,000 from the National Science Foundation (NSF) for his grant, “Reliable Task Offloading in Mobile Autonomous Systems Through Semantic MU-MIMO Control.”

“Mobile autonomous systems (MASs) such as self-driving vehicles and drones have a pivotal role in critical applications such as urban mobility, precision agriculture and remote surveillance,” explains the grant abstract. “To achieve their tasks, MASs increasingly rely on high-throughput low-latency streaming of computer vision tasks (e.g., object detection) to edge servers. However, ephemeral environmental factors such as blockages, congestion and fading may erratically interrupt the flow of tasks to the edge servers.”

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Commit the Change: Students Creating Software for Nonprofits Gain Real-World Experience

Looking back, it might seem an untimely decision to start a new student club just before the COVID-19 pandemic shut down the UCI campus and surrounding community. Yet Commit the Change, started in March 2020 by a group of students in the Donald Bren School of Information and Computer Sciences (ICS), pressed on with their goal of developing high-quality software for nonprofit organizations while creating hands-on learning opportunities for UCI students. Now, more than a year’s worth of Zooms later, the club has close to 60 active members, a portfolio of four project collaborations with local nonprofits, and a 2021 Anteater Award for Best New Organization.

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From Theory to Practice: UCI’s Machine Learning Hackathon Delivers

For decades, UCI has been known for its impressive Machine Learning Repository. With more than 580 publicly accessible datasets, the repository serves as a tremendous resource for empirical and methodological research in machine learning. It also serves as a great learning tool for UCI students, thanks to the second annual ML Hackathon, a student competition funded by the National Science Foundation. The UCI research community donated nine challenge datasets from the repository for the event and the Research Cyberinfrastructure Center (RCIC) provided the computational resources.

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Sandy Irani and Sameer Singh Receive Distinguished Faculty Awards

Each year, the Academic Senate presents Distinguished Faculty Awards to recognize those who have brought distinction to UCI through their research, teaching, mentorship or service. Two faculty members from the Donald Bren School of Information and Computer Sciences (ICS) received awards for the 2021-22 academic year: Sandy Irani received the Distinguished Faculty Award for Teaching, and Sameer Singh received the Distinguished Early-Career Faculty Award for Research.

“These awards are conferred by the Academic Senate to recognize sustained and distinguished contributions to teaching and research,” says ICS Dean Marios Papaefthymiou. “They are among the most prestigious awards University of California faculty members can receive from their colleagues.”

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Graduate Student Spotlight: Daniel Cheng’s Computer Science Research Has Real-World Impact

When Daniel Cheng first came to UCI in 2013, he wanted to pursue a bachelor’s degree in computer science because of its “applicability to many other fields.” Now, as a Ph.D. student in the Donald Bren School of Information and Computer Sciences (ICS), he’s practicing that applicability with real-world impact. Specifically, he has led the development of the Calving Front Machine (CALFIN), a program that uses artificial intelligence, machine learning and neural networks to help Earth science researchers monitor glacier loss. He collaborated on CALFIN with researchers from UCI, the University of Washington, and NASA’s Jet Propulsion Laboratory (where he’s an intern), and the team outlined their work in a paper published by The Cryosphere. Their efforts were further spotlighted in “An Artificial Neural Network Joins the Fight Against Receding Glaciers,” an article pointing out that this “neural network is capable of recognizing and measuring the edges of glaciers in satellite images of Earth’s surface nearly as well as a trained scientist, except it’s autonomous, quick, and can reliably process countless more glaciers than any human being ever could.” Here, Cheng talks about his interdisciplinary research and how computer science can help us better understand climate change.

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