This one-day course will be offered via Zoom only. Attendance is required as it will not be recorded.

Course Summary:
Statistical machine learning is an interdisciplinary research area which is closely related to statistics, computer sciences, engineering, and bioinformatics. Many statistical machine learning techniques and algorithms have proven to be very useful for various scientific areas. This course will cover a number of unsupervised learning techniques for finding patterns and associations in Big Data. These include dimension reduction techniques such as principal components analysis and non-negative matrix factorization, clustering analysis and significance analysis, and network analysis with graphical models. The main emphasis will be on the analysis of real data sets from various scientific fields. The techniques discussed will be demonstrated in R.

This course is intended for researchers who have some knowledge of statistics and machine learning, and want to be introduced to relatively more advanced statistical machine learning topics.

Prerequisite:
Participants should be familiar with matrix linear algebra, linear regression and basic statistical and probability concepts, as well as some familiarity with R programming.

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The Office of the Vice Chancellor for Research (OVCR) invites all members of the research community to a once-per-semester live update session.

What to Expect for the April Session:
IRB Process Updates: Senior Associate Vice Chancellor Andy Johns and Director of the Office of Human Research Ethics Carley Emerson will provide the latest information and changes to IRB procedures.
Research Enterprise Updates: Vice Chancellor Penny Gordon-Larsen will share general news and developments impacting Carolina’s research activities.
Q&A: There will be a dedicated Q&A session for participants to ask questions to any member of the OVCR leadership team.

Please submit your questions in advance to facilitate a smooth discussion. You can submit them during registration or by email to This email address is being protected from spambots. You need JavaScript enabled to view it..

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Optimizing Data Collection and Creating Maps with Drones

This course is being offered in collaboration between the Odum Institute and the Center for Urban & Regional Studies. Attendance is required - this course will not be recorded.

This one-day short course, led by the Carolina Drone Lab, will cover drone data collection, planning, and analysis. Small unoccupied aircraft systems (sUAS or drones) are a common mapping and 3D-modeling tool in many organizations. To fully leverage the technology and its benefits, there needs to be a foundation in understanding how to collect quality images and process those images into usable information. The course discusses the best flight parameters for different environments along with advanced data analysis in a GIS environment. Attendees will be introduced to: mission planning, creating automated flights for data capture, processing software for drone imagery (Pix4dMapper), and working with drone imagery in GIS. Commonly used tools and resources will be shared.

Students will learn the technical capabilities and limitations of the drones available for use, and why selection of the right drone and sensor combination is important to obtaining the right data for a project. This course offers participants the chance to learn about a broad spectrum of techniques to take the next steps on their own.

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Value of Knee OA Treatments

Join the UNC School of Medicine Thurston Arthritis Research Center for a UNC Core Center for Clinical Research (CCCR) Speaker Series seminar featuring Elena Losina, PhD. Losina is the Robert W. Lovett Professor of Orthopedic Surgery, Director of the PIVOT Center, and Director of the OrACORe in the Department of Orthodedic Surgery at Brigham and Women's Hospital.

Knee osteoarthritis is a debilitating painful condition affecting quality of life. Currently, there are no disease-modifying treatments to halt the progression of knee OA. Treatments are focused on symptom alleviation. Provision of value-based care is centered on efficacy and economic efficiency. The cost-effectiveness measures both efficacy and effectiveness in a single outcome measure. In this talk, Losina will highlight the assessments of value for non-pharmacologic, pharmacologic, and surgical treatments for knee OA.

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This course is being offered in collaboration between the Odum Institute and the Center for Urban & Regional Studies.

Urbanization has been a fundamental trend of the past two centuries and a key force shaping the development of the modern world. While urbanization in rapidly growing nations is helping lift hundreds of millions of people out of poverty, it is also creating immense societal challenges by increasing greenhouse-gas emissions, destabilizing fragile ecosystems and creating new demands on public services and infrastructure that pose significant challenges on the environment. Despite the importance of understanding the drivers of urban growth, we are still unable to quantify the magnitude and pace of urbanization in a consistent manner at a high resolution and global scale.

The revolution in geospatial data has transformed how we study cities. Since the 1970s, terrestrial Earth observation data have been continuously collected in various spectral, spatial and temporal resolutions. As improved satellite imagery becomes available, new remote-sensing methods and machine-learning approaches have been developed to convert terrestrial Earth-observation data into meaningful information about the nature and pace of change of urban landscapes and human settlements. But until recently, most remote sensing studies focused on local settings. Mapping land cover at a national or regional scale is challenging because of the lack of high-resolution global imagery, the heterogeneous and complex spectral characteristics of land, the small and fragmented spatial configuration of many cities, and importantly, computational constrains (for storage and processing). Emerging cloud-based computational platforms now allow for scaling analysis across space and time. Google Earth Engine (GEE) is one platform that leverages cloud-computing services to achieve planetary-scale utility. GEE leverages cloud-computational services for planetary-scale analysis and consists of petabytes of geospatial and tabular data, including a full archive of Landsat, Sentinel-2, Sentinel-1, and MODIS, together with a JavaScript, Python based API (GEE API), and algorithms for supervised image classification.

This hands-on course will focus on the use of Google Earth Engine (GEE) for urban research applications. It will demonstrate how free and open-source satellite imagery – including electro-optical (EO) and Synthetic Aperture Radar (SAR) imagery – can be utilized to map urban areas and urbanization trends and patterns, across space and time, and to perform a qualitative analysis of the impacts of urban expansion on the landscape. In addition to analyzing existing classification schemes of urban areas to understand how cities expand and evolve, the course will provide a brief introduction to concepts in Remote Sensing Machine Learning, with a focus on supervised pixel-based image classification. Students will learn how to automatically map built-up land cover based on publicly available satellite imagery (e.g., Landsat and Sentinel). In addition, the course will demonstrate recent innovations in the use of remotely sensed nighttime light observations to understand variations in economic activity within and between cities – all utilizing data and tools that are available in GEE. The course will include PowerPoint slides, group hands-on coding (in JavaScript) and short exercises. Prior coding knowledge is not required.

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In this session, participants will have the opportunity to ask questions and seek feedback on their qualitative analysis from facilitators and other attendees. This session serves as a workshop in which participants get a chance to discuss and review qualitative analysis techniques while also learning from others.

To attend, participants must be working on a qualitative data analysis project and can seek feedback on any step in the process (e.g., coding, creating matrices/diagrams/other products, developing categories or themes, summarizing data). Participants must have attended an Introduction to Qualitative Data Analysis training offered by NC TraCS (either the session offered on April 16, 2024 or a previously offered session).

Please note: We will not be presenting information about qualitative analysis in this session; instead, we will ask participants to share where they are in their analysis process, ask questions about their analyses, and collectively discuss strategies for moving our analyses forward.

Facilitators:
MaryBeth Grewe, MPH, Program Manager, Qualitative and Engaged Research
Simone Frank, MPH, Project Manager, Community Engagement in Research
Milenka Jean-Baptiste, MPH, Qualitative Research Specialist

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