Given the forecast that “by 2021, users will view 3 trillion internet videos per month, or about a million video minutes every second,” it’s clear that data compression remains critical to the field of computer science. In an effort to develop innovative ways to store and distribute information, Professor Stephan Mandt of UCI’s Donald Bren School of Information and Computer Sciences (ICS) is exploring fundamentally new approaches for compressing video and images to unprecedentedly small file sizes while preserving visual quality.[Read more…]
Computer Science Professors Ardalan Amiri Sani and Gene Tsudik recently started work on a new project, “Verifiable Provenance and Subject Awareness for Photos and Videos.” Funded through the National Security Agency (NSA), the goal is to investigate secure, user-friendly techniques for establishing the origin and authenticity of digital photographs and video clips, and for ensuring the subject was aware of the process and context of the recording.[Read more…]
Researchers in the Donald Bren School of Information and Computer Sciences (ICS) have been awarded a National Science Foundation (NSF) grant on machine learning explanations in collaboration with colleagues from Harvard University. The three-year, $450,000 grant, “Post hoc Explanations in the Wild: Exposing Vulnerabilities and Ensuring Robustness,” will support new research into machine learning interpretability that focuses on understanding how adversaries can manipulate explanation techniques. The goal is to then better defend against such attacks.[Read more…]
Sharad Mehrotra and his associates at the University of California, Irvine are developing a way to trace contacts that accounts for both challenges. Their approach uses the school’s Wi-Fi network and specially designed software to collect data from sensors that can track individuals via their connected devices. When someone tests positive and shares their diagnosis, the system warns other students who have been near the carrier—all while keeping each person’s identity private.
Read the full article at Workflow.
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.
Growing up in the San Francisco Bay Area, Adarsh Pachori was surrounded by technology, but it wasn’t until an unlikely experience with a sudoku puzzle that he realized his passion for programming. Today, the second-year computer science major is working diligently as a student at UCI’s Donald Bren School of ICS and as a co-founder of Enrole, an exciting new app designed to streamline the hiring process for the next-generation workforce. His fellow co-founders include childhood friends Neel Griddalur, Aishwarva Suresh and Aarti Vellimedu.[Read more…]
At the University of California, Irvine, campus managers will keep an eye on how crowded buildings are, using a system that has been under development for years with a grant from the US Defense Advanced Research Projects Agency. As students’ phones and laptops search for Wi-Fi signals in buildings, they generate ‘probe events’ that will be used to estimate how many people are in each area.
Read the full story at Nature.
Computer Science Professor Ardalan Amiri Sani from UCI’s Donald Bren School of Information and Computer Sciences and Professor Zhiyun Qian from UC Riverside’s Department of Computer Science and Engineering have received a $500,000 grant from the National Science Foundation (the Secure & Trustworthy Cyberspace program) for a project on “Deep and Efficient Dynamic Analysis of Operating System Kernels.” The objective of this project is to improve the security of operating system (OS) kernels through deep analysis and testing. OS kernels are the foundation of computer systems such as personal computers, smartphones, servers, as well as the internet infrastructure in general. Modern OS kernels are enormously complex and contain a large number of security vulnerabilities that slip through the testing phase onto end devices. Unfortunately, the state-of-the-art testing solution is insufficient as deeper parts of the OS remain hard-to-reach and therefore largely untested. The project aims to solve this precise issue by developing a set of innovative dynamic analysis and testing techniques that will greatly improve the security and quality of OS kernels.
At the recent Usenix Security Symposium, Computer Science Professor Qi Alfred Chen and his research team presented a first-of-its-kind study revealing a “take-over vulnerability” in self-driving car systems that could result in serious accidents.[Read more…]
At the University of California, Irvine, a team of researchers is hoping to put those [privacy] concerns to rest by developing a web-based app that uses data the school already collects. Instead of requiring students and employees to turn over troves of personal information, the system works by tracking the number of devices connected to the campus Wi-Fi. To avoid overcounting people, it uses machine learning techniques to determine if two or more devices are moving together, suggesting they belong to the same person.
Read the full story at Education Dive.