Gulustan Dogan Demirhan

I am an assistant professor at University of North Carolina Wilmington , where I work on Artificial Intelligence, Data Science and Internet of Things.

I did my PhD at City University of New York, where I was advised by Ted Brown and funded by the NS-CTA. I worked at NetApp and Intel in Silicon Valley, CA. I did my bachelors at the Middle East Technical University, Ankara, Turkey.

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Research

I'm interested in Data Science, Internet of Things, Machine Learning, Motion Analytics, and Learning Analytics. Much of my research is about inferring analytics from data. Representative papers are highlighted.

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.