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  • October 2, 2023
  • HDSIComm

Fireside Chat: Theory in the age of modern AI

TILOS Fireside Chat on “Theory in the age of modern AI“, which will be a conversation led by TILOS team members: Misha Belkin (UCSD), Arya Mazumdar (moderator, UCSD), Tara Javidi (UCSD), Visheeth […]

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  • August 17, 2023
  • Sheetal Srivastava

Causal Inference symposium

Causality is increasingly a part of AI, data science, robotics, and more, but it is not always clear how we can learn causality from data. This symposium will be featuring leading HDSI Faculty who will be providing an introductory overview on these methods, followed by domain-specific talks and open discussion.

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  • May 1, 2023
  • Kaleigh O'Merry

Security and Privacy in an Everchanging System Landscape

Abstract: From AI and IoT to AR/VR and Web 3.0, computer systems are evolving at an unprecedented rate. While this evolution has given rise to exciting applications and opportunities, it has also brought about novel security and privacy challenges within these systems and across their interactions with existing platforms. In this talk, I will discuss how system security researchers can keep up with this everchanging landscape and showcase some of my lab’s recent work on understanding and detecting malicious web bots. I will explore how we can build and roll out research infrastructure to measure web bot activities and later use our newfound understanding to develop practical solutions to counter them. I will highlight how we can apply similar research principles to areas such as AI and IoT. Finally, I will conclude my talk by previewing some of my ongoing work and outlining my research roadmap toward achieving “security at inception” for emerging systems.

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  • April 7, 2023
  • Kaleigh O'Merry

Decoding Nature’s Message Through the Channel of Artificial Intelligence

Abstract: Nature contains many interesting physics we want to search for, but it cannot speak them out loud. Therefore physicists need to build large particle physics experiments that encode nature’s message into experimental data. My research leverages artificial intelligence and machine learning to maximally decode nature’s message from those data. The questions I want to ask nature is: Are neutrinos Majorana particles? The answer to this question would fundamentally revise our understanding of physics and the cosmos. Currently, the most effective experimental probe for Majorana neutrino is neutrinoless double-beta decay(0vββ). Cutting-edge AI algorithms could break down significant technological barriers and, in turn, deliver the world’s most sensitive search for 0vββ. This talk will discuss one such algorithm, KamNet, which plays a pivotal role in the new result of the KamLAND-Zen experiment. With the help of KamNet, KamLAND-Zen provides a limit that reaches below 50 meV for the first time and is the first search for 0νββ in the inverted mass ordering region. Looking further, the next-generation 0vββ experiment LEGEND has created the Germanium Machine Learning group to aid all aspects of LEGEND analysis and eventually build an independent AI analysis. As the odyssey continues, AI will enlighten the bright future of experimental particle physics.

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  • April 3, 2023
  • Kaleigh O'Merry

Chatting GPT

Artificial Intelligence (AI) systems have made astonishing progress in the last year. In particular, Large Language Models (LLMs) — AI systems trained on massive amounts of text — have reached […]

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  • March 1, 2023
  • Kaleigh O'Merry

Intelligent mobile systems for equitable healthcare

Access to even basic medical resources is greatly influenced by factors like an individual’s birth country and zip code. In this talk, I will present my work on designing AI-based mobile systems for equitable healthcare. I will showcase three systems that are not only interesting from an AI standpoint but are also having real-world medical impact. The first system can detect ear infections using only a smartphone and a paper cone. The second system enables low-cost newborn hearing screening using inexpensive earphones. Lastly, I will present an ambient sensing system that employs smart devices to detect emergent and life-threatening medical events such as cardiac arrest. Through these examples, I will demonstrate how new applied machine learning and sensing approaches that generalize across hardware and work in real-world environments can help to address pressing societal problems.

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