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Students will analyze images from optical microscopes that reveal features of materials and microstructures using machine learning techniques. These images have been collected by geologists, who use them to study the rock samples that they collect in the field and determine their properties and origins. We have a baseline system already implemented, and the goal is to improve it with new machine learning techniques, guided by the insights of our collaborating geologists.

With the recent enthusiasm about algorithmic fairness and responsible AI, many conferences are encouraging or requiring a broader impact section to assess societal harms and benefits of the AI research being presented. In this project, we will analyze the themes of these sections, with a particular focus on the ethical issues being addressed and acknowledged. We will develop tools and methods to evaluate the harms and benefits of the presented research. The goal is to see how is the community helping AI research to be less harmful but more beneficial for society. For more background on work in this area, please review this workshop.

The COVID-19 pandemic has reinvigorated calls to close the digital divide in the US and elsewhere. Without adequate Internet access, households are at a disadvantage in education, jobs, health, and other key dimensions of wellbeing. While local broadband markets are increasingly concentrated, there is also increased interest in exploring the role those small local operators (“microtelcos”) can play in serving in low-income and rural communities. These microtelcos range from small wireless cooperatives to mom-and-pop private ISPs to municipal-backed operators. This project seeks to a) map and identify the characteristics of communities where microtelcos are present in CA, and b) to examine whether microtelcos presence affects broadband service quality and adoption by businesses and households in the community. The project will use broadband deployment data collected by the CPUC (California Public Utilities Commission) and socioeconomic data from the Census Bureau.

Developing models to analyze images is a demanding task that requires significant time, resources, and effort. Recently, companies such as Amazon and Google are providing services to make the modeling process easier so even users with little machine learning expertise can enjoy deep learning technologies. Based on our prior work in object detection and classification for smart city applications, we would like to compare and evaluate the process and performance of commercial services using our training datasets. This project will be a good practice to understand the image machine learning modeling process and the advantages/limitations of commercial services for customized learning.

Students will analyze genomic and environmental data collected through the lifetime of individuals to investigate which genes and external conditions could be associated with aging. The goal of the project will be to reproduce an existing published paper and improve on its results.

This project will assemble a team to participate in the Biomarker and Endpoint Assessment to Track Parkinson’s Disease (BEAT-PD) DREAM Challenge by the Michael J. Fox Foundation and Sage Bionetworks. The challenge is designed to benchmark new methods to predict Parkinson’s disease progression. Teams participating in the Challenge will have access to raw sensor data that can be used to predict individual medication state and symptom severity. Specifically, teams are asked to develop methods to predict on/off medication status, dyskinesia severity, and/or tremor severity.

The DETER cybersecurity testbed has been running experiments for several years, collecting information about intrusions, vulnerabilities, and mitigation strategies. This project will capture cybersecurity experiments as a knowledge graph that can be browsed, queried, and mined to find patterns and create models of cyberattacks.

Paleoclimate data is highly diverse, requiring different sets of metadata to describe the various datasets. In this project, you will help build an interface to assist researchers in annotating their paleoclimate datasets according to an evolving reporting standard (PaCTS) and download them in the Linked Paleo Data (LiPD) format. The interface should be highly interactive (wizard) to accommodate the diversity of the data as well as offer editing capabilities for existing datasets (upload LiPD files) and check their compliance with PaCTS, plot location information and the time series, download into the LiPD format, upload to a semantic wiki and/or an SQL database. In addition, the interface should support the use of a recommender system (to be build) to help researchers in annotating their datasets.

Top climate scientists post their findings and views regularly on social media. These very scientists are met with tweets from those with opposing views, often containing vitriolic and false information. It is important that we can identify and characterize these tweets to understand the counter-narratives of climate change. We will address topics including false information, bot campaigns, and harassment.