November 9-10, 2019
1104 NCSA building
1205 W. Clark Street
Urbana, Illinois
The second NCSA-NVIDIA AI Hackathon of the semester is co-organized by the Gravity Group, Innovative Systems Lab, and NCSA Industry, and co-sponsored by NCSA SPIN and NVIDIA. The main goal of the hackathon is to let talented U of I students, postdocs and staff showcase their skills in a friendly competition while working on challenging problems involving deep learning on a state-of-the-art compute platform designed for AI. This will be an intensive 2-day experience culminating in the final presentation of results on Sunday afternoon. Courtesy of NVIDIA, the winning team will receive two Titan V GPU cards. The second-place team will receive one Titan V GPU card.
To participate, we ask interested students and staff to sign up at https://forms.illinois.edu/sec/7957390 by Tuesday, November 5 and indicate which of the three projects described below they would like to work on. Students accepted to participate in the event will be notified on Thursday, November 7.
Hackathon Schedule
Saturday, November 9
8:30am — Teams registration and light breakfast
9:00am — Overview of the Hackathon rules, challenge problems, and a brief intro to HAL computing environment
9:30am — Teams assemble in break-out rooms and start working on the problems
Noon — Lunch (pizza will be provided)
1:00pm — Teams continue to work on the challenge problems (snacks will be provided)
5:00pm — Teams meet in 1104 for a brief status update
Sunday, November 10
8:30am — Teams continue working on the problems (light breakfast will be provided)
Noon — Lunch (pizza will be provided)
1:00pm — Teams continue to work on the challenge problems (snacks will be provided)
4:30pm — Teams present results
Wednesday, November 13
Winning teams are announced and presented with prizes
Hackathon Projects
Project 1: Fall Detection with Deep Neural Nets
Problem: Develop a model to perform human activity recognition, specifically to detect falls. Falls are an important health problem worldwide and reliable automatic fall detection systems can play an important role to mitigate negative consequences of falls. For more details, refer to https://wiki.illinois.edu/wiki/display/~kindrtnk/Fall+detection.
Datasets: Data will be provided at the time of the competition on HAL cluster.
Project 2: Semantic Amodal Segmentation
Problem: Train a model to perform amodal segmentation of humans. Results will be evaluated on accuracy of predicted regions that describe visible and occluded human body parts. For more details, refer to sailvos.web.illinois.edu.
Datasets: Data will be provided at the time of the competition on HAL cluster.
Project 3: Feature Recognition in Digital Elevation Maps
Problem: Develop and train a model to recognize features in Digital Elevation Maps (DEMs). The provided dataset contains pre-processed DEM tiles with labeled regions of interests. The features of interest are soil erosion regions. More details, refer to https://wiki.illinois.edu/wiki/display/~kindrtnk/DEMs.
Datasets: Data will be provided at the time of the competition on HAL cluster.
