UNCW Applied AI Research Lab 

Research in areas of Artificial Intelligence, Data Science, Internet of Things, Machine Learning, Motion Analytics, and Learning Analytics are conducted in our lab.

Applied AI Reseach Lab consists of undergraduate, graduate students and faculty from Computer Science Department of UNCW. Our lab is always looking for new students to join us. We have funds to support UNCW graduate/undergraduate students. We collaborate with Age Facing Group Lab of Dr.Karl Ricanek, SECRETS lab of Dr.Sudip Mittal, Dr.Yang Song and with other UNCW CS faculty.

We hold weekly research meetings during the academic year that are open to visitors. You are welcome to join us to find out what we are working on. Please email your CV to Dr.Gulustan Dogan at dogang@uncw.edu if you are interested in AI. 

Applied AI Research Lab is founded by Gulustan Dogan, an assistant professor at University of North Carolina Wilmington . She did her PhD at City University of New York, where she was advised by Ted Brown and funded by the NS-CTA. She worked at NetApp and Intel in Silicon Valley, CA. She did her bachelors at the Middle East Technical University, Ankara, Turkey. She is a strong advocator of encouraging women in Computer Science. She is the founder of Anita Borg Institute TWIC Women In Computing group and she is a Stanford Women In Data Science Ambassador. . 

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  • Nouran Alotaibi
  • Elif Sahin
  • Dillon Roy Harless
  • Morgan Glisson
  • Brian Alvarez Gabriel
  • Blythe Katherine Layne
  • Irem Naz Cay
  • Sinem Sena Ertas
  • Seref Recep Keskin


Representative papers highlighting the research done are highlighted. 


Where are you? Human activity recognition with smartphone sensor data 

Gulustan Dogan, Iremnaz Cay, Sinem Sena Ertas, Şeref Recep Keskin, Nouran Alotaibi, and Elif Sahin.

This research is mainly focused on recognizing modes of transportation (Still, Walk, Run, Bike, Car,Bus, Train, Subway) in a user-independent manner with an unknown phone position. The goal was to recognize the user transportation (activities) from data coming from the phone of the “test” user while the location of that phone on the “test” user is not specified. 


Analysis of Course Dependency Trees: A Data-driven, Evidence-based Approach 

Gulustan Dogan, Yang Song, Yusuf Ozturk 

We use the power of big data on historical educational data in assisting departments in restructuring their course trees because big data analytics can reveal very important information that human eyes cannot detect. We aim to optimize the course dependency to help student graduate on time, reduce the number of dropouts, better support minority students. .


A Data-Driven Approach to Kinematic Analytics of Spinal Motion 

Seref Recep Keskin, Aysenur Gencdogmus, Gulustan Dogan, Yusuf Ozturk

We employ deep learning and machine learning methods to study spine motion and postural stress using two sensors attached to lower back of a healthy subject while the subject is performing regular daily activities. 


A Deep Learning Approach to Predict Learners' Primary Language in MOOCs

Ismail Duru, Ayse Saliha Sunar, Gulustan Dogan, Su White, Banu Diri

In this study, we aim to exploit deep learning for course dropout prediction of second language English speakers by using their posts to discussions gathered from a range of MOOCs without performing feature extraction.


Sentiment Analysis of Twitter Data with Deep Learning 

Gozde Merve Demirci, Gulustan Dogan, Sule Itir Satoglu

We train a deep neural network on twitter data for sentiment analysis. We aim to see whether social media can help disaster management. Our findings indicate that social media can be used to understand sudden situations like disasters and may help to decide how to act. 


Energy Efficient Smart Buildings: LSTM Neural Networks for Time Series Prediction 

Idil Sulo, Seref Recep Keskin, Gulustan Dogan, Theodore Brown

We use the Long Short Term Memory (LSTM) neural network model to analyze the energy expenditures of the buildings that reside in the campuses of the City University of New York (CUNY). 


Protru: a provenance-based trust architecture for wireless sensor networks 

Gulustan Dogan

We design a distributed trust‐enhancing architecture using only local provenance during sensor fusion with a low communication overhead. Our network is cognitive in the sense that our system reacts automatically upon detecting low trust.