New sensing and cloud computing capabilities in mobile platforms are creating game changing opportunities for mobile health research. A significant drawback is the lack of precision in emerging off-the-shelf mobile health products and apps. Advances in synergistic sensor and machine learning (ML) algorithm design are key to improving precision in mobile health and wearables. The aim of the IRES project is to embed students in cutting edge research that will provide integrated hardware and algorithm/software solutions for the next generation wearables and mobile health monitoring technologies.  

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This Interdisciplinary international program will address challenges at the overlap of sensor design, big data analytics, signal processing (SP), and Machine Learning L. The IRES Track 1 program will provide opportunities for US students to collaborate internationally with researchers at the Dublin City University (DCU) Insight center. Students will be mentored by ASU and DCU faculty and will focus on solving application-driven problems involving integrated sensor devices and ML algorithms. The entire IRES program is 8 weeks and the IRES will support undergraduate and graduate students to travel for 6 weeks annually to DCU Dublin, Ireland.

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Intensive pre-training and post-engagement plans are provided to ensure successful outcomes. Provisions will be made (if necessary) to operate the IRES project under COVID-19 restrictions. The ASU Co-PI team brings expertise from Professors G. Raupp (sensors), E. Forzani (biomarkers), A. Spanias (ML, SP), and N. Kellam (Eng. Education). Core DCU collaborators are Professors S. Daniels (sensors), S. Little (big data, ML), N. Dune (nanosensors), and N. O’Connor (Vision, ML). The project will also engage Nobel Laureate Leland Hartwell as a thought leader for mobile health solutions. Additional faculty from both institutions will provide research mentorship and crosscutting training.

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Intellectual Merit:  This IRES project will focus on research and projects at the overlap of sensor device design and ML algorithm development. IRES projects will be planned in several areas including flexible sensors and ML, sensor information management and visualization, efficient deep learning, and big data analysis.

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Sample projects described include:

• biomarker detection,
• big data processing,
• gait detection
• deep neural networks
• quantum computing and machine learning
• reduction of ML bias

Without exceptions, applicants must be:

• US citizens or permanent residents.
• Full-time students in good academic standing.
• STEM Students that have at least one more semester after the experience at their university.
• At least 18 years old by May 15 and must have a valid Passport
• Available for overseas Travel (EU COVID protocols apply)