Are you up to the challenge?

You’ve seen Rosie, now it’s time to demonstrate your skills in using MSOE’s supercomputer!

MSOE Regent and alumnus Dr. Dwight Diercks is challenging MSOE students to make use of Rosie. If you’re taking your course work to the next level, developing a side project or completing something else notable using Rosie, this is for you! First-time Rosie users are encouraged to enter as well. Create a poster, short paper, or short video presentation on what you did and how you used Rosie.

• What problem did you solve using Rosie?
• Is there a solution you’ve developed with Rosie to improve a process, or answer to a difficult question?

The opportunities are endless! Share your best work for a chance to win amazing prizes. Open to any MSOE student or team of MSOE students.

How to Submit an Entry

To submit your entry, please email Rosie_S.lqe6tdwasggor3ys@u.box.com and include your submission as an attachment. You should receive a confirmation message from Box that the file was uploaded successfully. If you have any issues uploading or if your file size exceeds the email attachment limit, feel free to email riley@msoe.edu. 

Deadline to Submit Entries

The deadline to submit entries for the Rosie Supercomputer Super Challenge is 11:59 p.m. on Friday, March 27, 2026.

Awards

• 1st place: $10,000
• 2nd place: $6,000
• 3rd place: $4,000  
• Multiple NVIDIA GPUs and DGX Spark Computers will be awarded by judges at the finals

Judges

• Dr. Dwight Diercks ’90, NVIDIA senior VP of software engineering, MSOE Regent
• Nick Haemel ’02, NVIDIA VP of medical imaging and system software, MSOE Regent
• Dr. Jeremy Kedziora, PieperPower Endowed Chair of AI
• Dr. Derek Riley, MSOE computer science program director

Final Presentations

Poster Session: 4 p.m.
Finalist Presentations: 5-6:30 p.m.
Join us on Tuesday, April 21 at Dwight and Dian Diercks Computational Science Hall for a poster session with this year's entrants and the finalists' presentations before the judges, followed by an award ceremony.

Finalists

Presented in order of project submission:

SkyNet: Belief-Aware Planning in Partially Observable Stochastic Games
Adam Haile, computer science and machine learning
This project uses reinforcement learning to demonstrate how an AI agent can be trained to play a game called SkyJack that incorporates hidden cards and random chance. 

SMEARGLE: Sketch the Draft, Skip the Attention
Dylan Norquist, computer science and machine learning
This project introduces a more efficient mechanism for the transformer architecture (the AI model structure that underpins most major generative AI models).

Proactive Urban Forestry Management: A Machine Learning Approach to Predicting and Prioritizing Tree Pruning in Milwaukee
Josh Myers, computer science and machine learning; Xander Ede, computer science and machine learning; Eddy Chukwuma, computer science; Dylan Norquist, computer science and machine learning
This project explores how satellite data can be combined with forestry data from the City of Milwaukee to help re-prioritize how the forestry department manages the hundreds of thousands of trees across the city in a proactive and more cost-efficient manner.  

From Revit to Robot: BIM-Driven Simulation for Autonomous Building Operations
Owen Pacetti, computer engineering; Steven Thomas, biomedical engineering; Joseph Loduca, software engineering; Nicolas Picha, software engineering; Tanner Cellio, computer science and machine learning; Diego Gonzalo, computer science; Delsoro Some, computer science; Adrian Manchado, computer science and machine learning
This project explores the use of a digital twin that was constructed for the new engineering building at MSOE from the blueprints. The digital twin was used to train a robot to help it navigate and understand the nuances of the new building prior to the building existing. 

Teaching Agents how to Bargain at Settlers of Catan
Mazen Hamid, computer science and machine learning
This project explores the use of reinforcement learning to train an AI agent to play Settlers of Catan, including the bargaining components of the game.  

TerraCare: GPU Accelerated healthcare
Alhagie Boye, computer science and machine learning; Wilfred Tapsoba, computer engineering
This project explores how satellite and other data can be used to intelligently identify medical deserts and place medical facilities strategically across Africa to improve accessibility and patient care. 

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