AI for Good Projects

The Nittany AI Alliance is facilitating partnerships with industry, nonprofits, and Penn State colleges and faculty to enable experiential learning opportunities for Penn State students called AI for Good projects. These projects use artificial intelligence to make a positive impact in the areas of education, the environment, health, or humanitarianism through proofs of concept that show the feasibility of an AI solution to a real-world problem using real data. Students experience hands-on, experiential learning that can lead to networking and job opportunities while empowering them to help make the world a better place.

Project Title Description Client Partner
AI–Powered Seasonal Forecasting of Hurricane Activity and Intensity  Develop an AI proof of concept focused on the application of machine learning methods to seasonal forecasting of Atlantic basin tropical cyclone/hurricane activity. By drawing more potentially predictive information from the available data sources used by this ML–powered model, we may be able to go beyond forecasting just the frequency of seasonal hurricanes and predict other critical factors like storm intensity and the likelihood of a landfall event as well. Michael Mann, Director, Earth System Science Center, and Steven Greybush, Associate Director, Center for Advanced Data Assimilation and Predictability Techniques IBM 
Childhood Abuse Risk Factor Identification Identify risk factors for childhood abuse in Pennsylvania using various data analysis techniques. Center for Medical Innovation, Penn State College of Medicine Gramener
Enhanced Hurricane Forecasting  Develop an AI proof of concept focused on the application of ML methods to seasonal forecasting of Atlantic basin tropical cyclone/hurricane activity. IBM College of Earth and Mineral Sciences
GeoThermal Earthquake Prediction  Use machine learning to develop an algorithm, from various parameters, that can predict earthquakes resulting from the injection of fluids into the earth for heat transfer purposes. The goal of the predictive model is to control the fluid injection process such that earthquakes can be prevented. Dr. Parisa Shokouhi, Penn State  Dataiku 
Goodwill Inventory Automation Use computer vision to automate the capture of donated goods such as shoes. Capture images and extract data such as brand, condition, color, etc. Goodwill  Microsoft
High School Transcript Processing Process high school transcripts using AI and natural language processing (NLP). Penn State Admissions N/A
Lost Hiker Location  Apply an AI proof of concept application that utilizes unmanned aerial vehicles equipped with lidar to provide a detailed map of terrain using one or more aerial platforms to deploy the existing codebase to the NVIDIA Jetson hardware platform. Lockheed Martin Lockheed Martin 
Mitigating Negative Behaviors in Youth Social Media Use  Explore how AI and machine learning could help reduce the problem of negative behavior on social media among youth. Community Anti-Drug Coalitions of America (CADCA) Leidos
Nonprofit Volunteer Matching Match volunteers to nonprofit organization opportunities based on interest and availability using AI, machine learning (ML), and NLP. The United Way Pennsylvania Association of Nonprofit Organizations (PANO)
Philadelphia Smart City  Help SEPTA safety officers identify areas where their help may be needed. Use computer vision to automate the identification of circumstances and transit platform areas that require the attention of a SEPTA safety officer, such as overcrowding, potential falls on the tracks, and unattended bags. SEPTA (Southern Pennsylvania Transportation Authority)  AWS (Amazon Web Services)
Public Interest Technology (PIT)
 Provide the City of Philadelphia with an AI proof of concept focused on how AI/ML technology can be used to create innovative solutions in the public interest technology space. The City of Philadelphia Law, Policy, and Engineering (LPE) at Penn State
Smart OCR  Automate the manual self-reported academic record (SRAR) using AI to interpret the contents of high school student transcripts, requiring only verification and slight corrections of text and use of Optical Character Recognition (OCR) and Natural Language Processing (NLP).  Penn State Undergraduate Admissions  

Past Projects