Development of data science course and summer bootcamp for ADRD researchers

NIH RePORTER · NIH · T32 · $80,735 · view on reporter.nih.gov ↗

Abstract

Abstract Given the rising prominence of data science in biomedical and Alzheimer’s research, fueled by recent developments in artificial intelligence (AI), especially deep learning, and high- performance computer hardware; a need has emerged to broadly train the predoctoral students in data science, in both its fundamental principles, and its applications to Alzheimer’s disease and related dementia (ADRD). To this end, we will develop a spring 2022 course with seven modular units in the following areas: 1. Fundamentals and overview (Lai and C. Wang); 2. Deep learning (G. Wang and Lai); 3. Bioinformatics (Bystroff and Fraser); 4. Medical imaging and ADRD (G. Wang and Yan); 5. Systems engineering (Hahn); 6. Multi-omics (Hurley and Fraser); 7. AI and drug discovery (C. Wang, Dordick and Fraser). During the summer of 2022, we will develop a data science bootcamp to provide the students with hands-on experience in data science, featuring a week-long intensive training in R, followed by a coached project to analyze an ADRD dataset, e.g., from AD knowledge portal (https://adknowledgeportal.synapse.org). The course will culminate in a mini-conference where the students present their research results to a panel of domain experts. Both the new course and summer camp will be developed and taught in person and will also be recorded and made available via an online platform to the NIA T32 trainees and graduate students from the Departments of Architecture, Biological Sciences, Biomedical Engineering, Chemical Engineering and Chemistry at Rensselaer. All the training materials will be regularly updated and made freely available to the scientific community through Rensselaer’s web portal and as new training modules through the institution’s online training platform Percipio. Using online quizzes, surveys and in-person interviews, the newly developed course and bootcamp will be evaluated for efficacy, effectiveness and sustainability.

Key facts

NIH application ID
10405365
Project number
3T32AG057464-05S1
Recipient
RENSSELAER POLYTECHNIC INSTITUTE
Principal Investigator
Chunyu Wang
Activity code
T32
Funding institute
NIH
Fiscal year
2021
Award amount
$80,735
Award type
3
Project period
2017-09-15 → 2023-08-31