REU in Data Science at Harvey Mudd College
NSF-Funding Has Ended. The HMC Data Science REU Will Not Be Offered in 2021.
Overview:
Data science is one of the fastest growing professional fields of work and research. The REU in Data Science at Harvey Mudd College was a 10-week summer research program operating from 2018-2020 for undergraduates interested in data science methods and data-heavy STEM careers. Student participants worked on research projects and received additional training in data science methods and professional skills. Part of a larger group of summer research students, REU participants had opportunities to get to know the Claremont Colleges, the larger Los Angeles metropolitan region, and enjoyed social events on and off campus.
What we offered:
A fully funded 10-week summer research program (limited cost of travel support was available)
Mentoring and research guidance by experienced and passionate faculty in the life and environmental sciences, mathematics, computer science, and engineering
Supplementary training workshops in data science and professional skills such as time and project management, public speaking, and more
Advising on graduate schools and STEM careers
A rich social program with peers in and around the Claremont Colleges
Contact:
The PIs on the NSF grant were Lisette de Pillis (depillis@g.hmc.edu) (2018-2020), Tanja Srebotnjak (2018-2019) and Susan Martonosi (martonosi@g.hmc.edu) (2020).
Past Projects:
Swarming Locusts: Deducing Insect Interactions from Field Data
Rethinking Digital Microscope Design to Leverage the Computer in the Loop
Data analysis in magnetic resonance imaging (MRI)
Data analysis in type 1 diabetes modeling
Invisible cyclists and road network analysis
Computing for active transportation
Sports analytics
Brain tumor detection
Predicting human behavior from smartphone data
Data dimensionality reduction methods
Prevalence and propagation of “fake news”
Analysis of RNA-seq data
Spatial modeling of the climatic ecology and geographic range of a desert lizard
Dimension reduction and pseudo time analysis of large scale biological cell differentiation data