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Data Science

  • August 17, 2023
  • HDSIComm

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 31, 2023
  • HDSIComm

Samuel Lau | Instructor-Centered Design of Tools to Support Teaching Programming and Data Science At Scale

Instructors of technical subjects like programming and data science use a wide array of software tools that enable them to create sophisticated and engaging lessons at scale. Although there are many such tools available, instructors often find themselves repurposing software originally designed for other people, like professional software engineers. To address these issues, this dissertation takes an instructor-centered approach. It surfaces previously unmet needs through studies of instructors, their goals, and their software tools.

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  • May 12, 2023
  • HDSIComm

UC San Diego & MathWorks | Research & Curriculum Micro Symposium

A series of lightning talks will be presented highlighting collaborative efforts between UC San Diego and MathWorks via series of supported research and curriculum projects. Join us to learn how each project team are crossing boundaries and using MATLAB & Simulink in their work in areas such as Data Science, Oceanography, and various Engineering disciplines!

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  • May 12, 2023
  • HDSIComm

Earth & Ocean Image Processing Made Easy with MATLAB – Lunch and Learn

Join us for a lunch-n-learn technical seminar from our MathWorks team on image processing with MATLAB! MathWorks is looking to create a connection point for conversations about the landscape of computational languages – and the broader impact that software has in academia and industry today. This session explores the basics of pixel-level image processing and high-level machine learning models in MATLAB for images.

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  • March 16, 2023
  • Kaleigh O'Merry

Uncovering the algorithmic foundations of language learning and processing

Human language is extraordinarily complex. Nevertheless, we readily acquire language as children, when we are most cognitively limited, and we comprehend language as adults with striking efficiency. My research seeks to understand the mental algorithms that allow us to accomplish this feat, with particular focus on how memory and prediction mechanisms are recruited to overcome the bottlenecks of real-time language processing. In this talk, I will review results from three of my lines of inquiry into this question. First, using diverse naturalistic reading datasets, I will show evidence that prediction is a central concern of the human language processing system. Second, using fMRI measures of naturalistic story listening, I will show evidence that memory and prediction processes are dissociable in the brain’s response to language, that syntactic structure building plays a major role in ordinary language comprehension, and that the neural resources that are responsible for structure building are largely specialized for language. Third, I will show evidence from computational modeling that memory and prediction pressures independently encourage discovery of phonological regularities from natural speech. Together, these results support an intricate coordination of memory and prediction abilities for language learning and comprehension. I will conclude by outlining planned directions for my future lab, integrating neuroimaging, behavioral methods, natural language processing, and computational modeling to study language learning and processing.

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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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