Dear Students,
We have a WONDERFUL opportunity for you all to participate in an Artificial Intelligence challenge this semester!
The Center for AI in Business at Smith, in collaboration with the NourishNet NSF Convergence Accelerator and Artificial Intelligence Institute of Maryland, is launching the first university-wide AI case competition for undergraduate and master’s students at the University of Maryland College Park.
The broad goal of this challenge is to understand how AI can help alleviate food insecurity within communities. Food insecurity is the focus of Governor Moore’s recent efforts on the ENOUGH Act and related Neighborhood Impact Grant to alleviate the food insecurity. The theme also aligns well with UMD’s First Year Book - Poverty by America.
This competition is open to ALL undergraduates and master’s students at the University of Maryland. Why participate in the competition? Here are five reasons:
- There will be prize money for the winning teams (undergraduate teams will be judged separately from master’s teams - i.e. there will be separate prizes for undergraduate and master’s teams)!
- Capital Area Food Bank (the case sponsor) is interested in hiring students as interns and has indicated that they are open to continuing these projects over the summer by hiring some interns from participating the teams as well
- We will circulate the best solutions in each case to several of our industry advisory board members, increasing the chances of students securing summer internships
- All students who participate and submit entries will receive a certificate of completion
- Participating in the “AI for Food Insecurity” challenge can be a tremendous component of your resume in which to signal your ability to work with teams, think outside the box on pressing issues facing society, and willingness to take on new challenges.
We are delighted that one of the largest food banks in the area, Capital Area Food Bank, has signed on as the main sponsor of the tasks in the challenge, providing questions, data and support. Your team will need to select among one of three available cases. Initial descriptions of the cases are provided below although further details will be shared on March 4th 2025, at the beginning of the competition.
Brief Case Descriptions - more detail on each case will be provided on March 4th. Each team will be assigned ONE case to work on, but in the Google Form submission below we require each team to indicate at least two cases that are of interest. We will assign teams to cases to ensure approximately equal competition across cases.Case 1: Food insecurity is a complex issue. There are many who need help, but do not seek it. There are those who need food but do not know where to go for it. This case asks teams to design or develop an AI-based solution that can help those who need food to understand where to go to get it.
Case 2: Capital Area Food Bank has many partners such as grocery stores or farmers who provide food (vegetables, meat, etc.) that can be distributed to food pantries. Many of these partners (grocery stores, farmers etc) have questions that need to be answered (i.e. “where can we deliver food, what types of food are needed” etc). Currently the organization opens “customer service tickets” to answer these questions, and this is a time- and person-intensive process. Can AI help in improving the efficiency of this process?
Case 3: Capital Area Food Bank spends a large amount of time developing grant proposals, reports, presentations, blog posts, and social media content regarding the work they do. Can generative AI help do this more easily, better, faster?
The above information gives you an idea of what kinds of problems you will work on (your team has to pick one). We will provide detailed information on all of these on March 4th 2025. Student teams can “design” or “develop” AI-based solutions for these. We will also share the judging criteria after we release the full cases, but do want to emphasize that we are open to creative ideas that may or may not include prototypes built to “solve” these cases. That is, we are open to getting solutions that are “designs” of AI solutions even if student teams have not implemented them. That said, we are certainly open to submissions where students can use AI tools to actually develop prototypes as well for these problems. Once the cases are released, we will have zoom webinars where we provide more information on the cases, expectations and answer any questions.
Timelime:
March 4th 2025: Detailed information of the cases released to all participant teams
April 15th 2025: Teams submit their solutions
April 24th 2024: Teams selected for the finals present their solutions to judges between 5pm-9pm and winners are announced.
Interested in Participating? Fill out the Google Form below before March 4th (but do so as early as you can since we will cap the participation once we have sufficient teams for all the cases). Note that:
- Teams have to be between 2-4 students.
- Teams have to be either completely undergraduate students, or completely master’s students (this is because we will select “winners” separately from undergraduate and master’s teams - i.e. each case will have one undergraduate team winner and one master’s team winner).
ONE person from each team needs to fill out the Google Form below to indicate your commitment to participating in this challenge (the form requires you to enter names/email addresses of all the team members). All teams that have submitted the form will receive further information on the cases by March 4th, 2025.
https://forms.gle/jTnt64yBfzjvGeZLA
We strongly encourage interdisciplinary teams, but leave it up to student teams to form teams to compete in this!
We hope to see great interest from our undergraduate and master’s students in this competition. It’s not always about winning, but to use this as a reason to try and make a difference! Once we have the final teams we will provide all case information directly to the emails on March 4th. But in the meanwhile if there are any questions about this please email Hanwen Shi at hwshi@umd.edu.