This will be a project-based IAP course that aims to develop new AI for a series of problems. Students will work closely with MIT faculty/staff in small teams and will be provided with data, project ideas, and computing resources. There will be numerous opportunities for successful and interested students to continue their research with ongoing research projects after the IAP course through UROPs, theses projects, etc.

email vijayg@ll.mit.edu if you are interested in attending this course.

AI challenges such as ImageNet, CIFAR, Graph Challenge, Moments in Time have resulted in major advances in image recognition, graph processing, and video action recognition. These and many other challenge problems are characterized by: 1) open datasets, 2) clear problem statements and 3) baseline implementations. Inspired by these challenges, through the USAF-MIT AI Accelerator, we are developing challenge problems to bring AI innovations to domains such as:

1) Datacenter Monitoring: Develop AI that can detect failures and workload characteristics in an operational datacenter
2) Reinforcement Learning Applications: Develop AI for aerial vehicles for games and novel environments
3) Magnetic Navigation: Develop AI for aerial vehicles for navigation in GPS denied environments by leveraging novel ML techniques alongside physical modelling.
4) Flight Maneuvers: Develop AI to detect good and bad flight paths from a flight simulator.

This will be a project-based IAP course and our team will provide significant guidance to students in developing AI capabilities for the above domains. Students will work closely with MIT faculty/staff in small teams and will be provided with data, project ideas, and computing resources. There will be numerous opportunities for successful and interested students to continue their research with ongoing research projects after the IAP course through UROPs, theses projects, etc.