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As therapies for cancer become more available world-wide, there is a need to develop screening tools to diagnose potential patients overcoming geographical and economic limitations. In order to improve the quality of patient categorization and diagnosis, the Diagnostic Augmented Intelligence for Global Health (DIAG) team seeks students to join us on two main projects:

1. Artificial Intelligence-enabled Diagnostics (AID): Develop image-based screening tools for rapid disease categorization and diagnosis support

2. Spatial Analytics for the Cancer Ecosystem (SPACE): Using modern tools from environmental monitoring, climatology, space sciences and geographical AI; we aim to fundamentally rethink the interpretation of cancer imaging data for beter diagnostics.

These start-up projects will develop software-based tools to facilitate diagnosis and interpretation of cancer image data. The team currently has the following ongoing projects:

AID: This tool under development is envisioned as an AI-based image analysis tool, deployed on the cloud that will enable disease categorization and diagnosis regardless of economic and geographical constraints. With an eye towards Low and Medium Income Countries (LMIC), this tool will support clinical users who upload standard-of-care histology images to the cloud and obtain rapid feedback to support their diagnoses, even in scenarios where the accessibility to expert pathologists is limited. This project will draw students from EECS, CS/CE, BME and just about any major interested to contribute to the goal of bringing AI-enabled quality clinical decision-making to everyone on this planet.

SPACE: This project aims to integrate geographical and spatial modeling tools with AI methods like deep learning. We invite all non-linear thinkers coming from various disciplines as diverse as quantitative geography, environmental monitoring and even climate/disaster modeling to work alongside machine learning/AI enthusiasts to build a new paradigm. Students from all engineering disciplines interested in applying spatial modeling ideas to interpreting biomedical image data are welcome.

TEAM Details 

Meeting time and location: Our MDP team meets Tuesdays at 4-5:30 pm ET using video conferencing and in-person meetings. Online meeting options are available as needed. Each subteam arranges a separate convenient time to meet and work together following university guidelines. A two-term commitment will begin January 2022.

Team organization: This team has flexible sub-teams that allow students to deepen their learning for the AID and SPACE projects. The project teams will draw from roles such as System Design, Data Engineering, Modeling and AI, as well as a Software Design and Prototyping. Each subteam will also have a student team leader that reports to the faculty PI and is supported by other doctoral students in the lab.

More information

First-year undergraduates through master’s graduate students are welcome to apply, and all will be encouraged to stay on the team for more than the two-semester minimum. Leadership roles are available in the lab, and experienced students will be a natural fit for these positions as their knowledge grows over time.

System Design (3 Students)

Preferred Skills: Systems & Engineering Thinking

Likely Majors: EECS, Computer Science /Computer Engineering (CS/CE), Biomedical (BME), Industrial and Operational Engineering (IOE), School of Public Health (SPH), ANY

Data Engineering, Modeling and AI Design (5-7 Students)

Preferred Skills: Computer Vision, AI, Machine Learning, image processing, general programming, Data Sciences

Likely Majors: Electrical Engineering, Computer Science (CSE/CS-LSA), BME, Information, Data Science

(for the SPACE project, any majors interested in spatial modeling, including students from Environmental Science, School for Environment and Sustainability (SEAS), Program in the Environment (PiTE), Space Science, Climate & Meteorology are welcome to apply)

Software Design & Prototyping (3 Students)

Preferred Skills: Developing user-interfaces

Likely Majors: Computer Science (CSE/CS-LSA), Information, ANY

Apprentice Researchers (3 Students)

Requirements: Interest in project material, willingness to develop skills. Open to first- and second-year undergraduate students ONLY.

Likely Majors: ANY

Faculty Sponsor

Arvind Rao
Associate Professor, Department of Computational Medicine & Bioinformatics
Associate Professor, Radiation Oncology
Arvind Rao is an Associate Professor in the Department of Computational Medicine and Bioinformatics, Radiation Oncology, Biostatistics and an affiliate association with Biomedical Engineering at the University of Michigan. Arvind received his PhD in Electrical Engineering and Bioinformatics from the University of Michigan, specializing in transcriptional genomics, and was a Lane Postdoctoral Fellow at Carnegie Mellon University, specializing in image informatics. He was previously on the faculty in Bioinformatics and Computational Biology at the University of Texas MD Anderson Cancer Center. His research group uses image analysis and AI/machine learning methods to mine and integrate different kinds of data sources for health applications like drug repurposing, spatial systems biomedicine, imaging diagnostics and health informatics. His interest in Single Cell Spatial Analysis involves developing bioinformatics and data science approaches for the interpretation and integration of data from spatial transcriptomics, proteomic & imaging platforms.

Number of Students: 9 – 13

Likely Majors: Any, BME, CSE/CS-LSA, Environemental Sciences, EE, IOE, ME, PiTE, Public Health, SEAS,

Summer Opportunity: Summer research fellowships may be available for qualifying students

Citizenship Requirements: This project is open to all students on campus

IP/NDA: Students who successfully match to this project team will be required to sign an Intellectual Property (IP) Agreement prior to participation in January 2022

Course Substitutions: Honors

Location: In Person Only: This project requires in person participation on the Ann Arbor campus for the entire project period. Remotely based students may not participate in this project at this time.