Title: 2+2 Program Work-in-progress, presented by the Education RTPresenter(s): Education RTDate: April 27, 2022 DescriptionContinue Reading
Title: Racial and Gender Homophily in Classroom Discussion NetworksPresenter(s): Regan Harper (UAF DART GRA)Date: MarchContinue Reading
Title: Prediction and learning for designing materials for solar energy conversionPresenter(s): Rob CoridanDate: February 23,Continue Reading
October 27, 2021 (Xintao Wu)
As big data and AI technologies are deployed to make critical decisions that potentially affect individuals (e.g., employment, college admissions, credit, and health insurance), there are increasing concerns from the public on privacy, fairness, safety, and robustness issues of data analytics, collection, sharing and decision making. In this talk, we first overview our social awareness research, in particular, on how to mitigate side effect of enforcing one social concern on another, and how to address multiple social concerns simultaneously. We then focus on robustness of machine learning under two representative scenarios, distribution shift and adversarial attack. In the former scenario, we present robust learning based on kernel reweighing and Heckman model. In the second scenario, we present adaptive defense that purposely leverages multiple types of adversarial samples to learn the context information in the training. We conclude the talk with some future research directions.
Covid-19 has become a global pandemic, and recently Arkansas has seen a dramatic increase in number of cases, mainly due to a new variant (“Delta”). Using a population genomics approach, we are in the third wave with the current Delta variant accounting for about 83% of the strains sequenced. This has been preceded by the Alpha variant, which peaked in March of this year (2021), and another less characterized variant (Janus), which peaked in September 2020. Each of these variants has become better adapted for infecting and spreading within the human population.
Through DART, we plan to implement a wide range of professional development and data science education activities to engage K20 learners in Arkansas. Our vision is that Arkansas will have a statewide educational ecosystem, where learners of any age can receive a designed, consistent, scaffolded education in data science, with further educational opportunities or job opportunities at appropriate points in their careers. To accomplish this, our mission is to create a model data science and analytics program for Arkansas schools that will promote problem-based and experiential-based pedagogy in critical thinking and analysis, technology familiarity, and a foundation in math and statistics.
A major challenge in building secure and widely adopted deep learning systems is that they sometimes make wrong, unexplainable, and/or unpredictable misclassifications. This talk overviews initial efforts towards techniques using large-scale deep learning with multi-source integrated data sets. In addition, we introduce the integration of statistical learning approaches with learning-based frameworks.