AI & Machine Learning

A few hundred years ago only a privileged few had access to education. The general people had to depend on these chosen few to guide them to the right path. Then, we democratized education, allowing everyone to access literacy and enlightenment. This democratization helped the human race to prosper exponentially in every field of knowledge. Andrew Ng, a co-founder of Google Brain, said that, right now, Artificial Intelligence and Machine Learning (AI & ML) are almost what education was two hundred years ago. A crystal ball that only a chosen few understand and take advantage of, and everyone else depends on this chosen few to help the rest make an informed decision. However, what if we democratize AI? What if we allow AI & ML to be pervasive, not just limited to the big tech, but also helping the small businesses benefit from its enormous capabilities?

The GVSU Applied Computing Institute is trying to bridge this very gap. Our focus primarily lies in applied machine learning, where we take a business problem and try to tackle it through state-of-the-art AI & ML techniques. Some of our research projects are the following: Use audio ML detection to better oral care regimen; use NLP techniques to predict the potential success of an online course or a fundraiser campaign; use transformers, knowledge graphs, and information retrieval techniques to auto-generate software tests from software requirements; build a chatbot using NLP to scale your customer service experience, etc. Whether you are a university researcher looking for assistance in AI & ML, or an industry partner looking for help prototyping an AI concept, we can help you.

AI & Machine Learning Team

Students discussing an Machine Learning problem



  • Astha Thapa
  • Azizul Hakim

Example Projects and Case Studies

Android Phone with BullyAlert App Displayed

BullyAlert App

Helping parents detect Cyberbullying

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Improving oral care with edge ML

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Cyberbullying Research

Scalable and Timely Detection

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Representative Publications

  • Esteban Echeverri Jaramillo, Griffin Going, Rahat Ibn Rafiq, Jonathan Engelsma, Venu Vasudevan, YouBrush: Leveraging Edge-Based Machine Learning In Oral Care
  • Rahat Ibn Rafiq, Homa Hosseinmardi, Richard Han, Qin Lv, Shivakant Mishra, Identifying Differentiating Factors for Cyberbullying in Online Social Networks, SIMBig 2020
  • Rahat Ibn Rafiq, Richard Han, Qin Lv, Shivakant Mishra,BullyAlert, A Mobile Application for Adaptive Cyberbullying Detection, 11th EAI International Conference on Mobile Computing, Applications and Services (MobiCASE) 2020, To Appear
  • Rahat Ibn Rafiq, Homa Hosseinmardi, Richard Han, Qin Lv, Shivakant Mishra, Scalable and Timely Detection of Cyberbullying in Online Social Networks, 33rd ACM/SIGAPP SAC, 2018
  • Rahat Ibn Rafiq, Homa Hosseinmardi, Sabrina Mattson, Richard Han, Qin Lv, Shivakant Mishra, Analysis and Detection of Cyberbullying Instances in Vine, SNAM, 2016.
  • Rahat Ibn Rafiq, Homa Hosseinmardi, Sabrina Mattson, Richard Han, Qn Lv, Shivakant Mishra, Careful what you share in six seconds: Detecting cyberbullying instances in Vine, in ASONAM 2015

Do you have an AI / Machine Learning problem?

If you have a challenging computing problem that Machine Learning and AI could help solve, don’t hesitate to get in touch.

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Page last modified September 28, 2023