A. Cagla Acun

A. Cagla Acun

Assistant Professor in Artificial Intelligence and Data Engineering at Hacettepe University

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

A data science pipeline for educational data: A case study using learning catalytics in the active learning classroom

Published in M.S. thesis, University of Louisville, 2017

Master thesis developing a data science pipeline for classroom response system data collected in an active learning classroom.

Recommended citation: Acun Sener, A. C. (2017). "A data science pipeline for educational data: A case study using learning catalytics in the active learning classroom." M.S. thesis, University of Louisville. https://ir.library.louisville.edu/etd/2758/

Using a Data Science Pipeline for Course Data: A Case Study Analyzing Heterogeneous Student Data in Two Flipped Classes

Published in ASEE Annual Conference and Exposition, 2019

A four-stage data science pipeline applied to classroom response system, homework, exam and demographic data from two flipped engineering-mathematics sections.

Recommended citation: Acun Sener, A. C., Hieb, J. L., & Nasraoui, O. (2019). "Using a Data Science Pipeline for Course Data: A Case Study Analyzing Heterogeneous Student Data in Two Flipped Classes." ASEE Annual Conference and Exposition. https://peer.asee.org/using-a-data-science-pipeline-for-course-data-a-case-study-analyzing-heterogeneous-student-data-in-two-flipped-classes

A Data Science Approach to Flagging Non-Retention in Engineering Enrollment Data

Published in 2020 ASEE Virtual Annual Conference, 2020

Proposes an enrollment-gap criterion, three consecutive semesters not enrolled, as a data-driven short-term measure of non-retention in engineering.

Recommended citation: Boujelbene, M., Damak, K., Acun Sener, A. C., Hieb, J. L., Bego, C. R., Ralston, P. A., & Nasraoui, O. (2020). "A Data Science Approach to Flagging Non-Retention in Engineering Enrollment Data." 2020 ASEE Virtual Annual Conference. https://peer.asee.org/a-data-science-approach-to-flagging-non-retention-in-engineering-enrollment-data

In-training explainability frameworks: A method to make black-box machine learning models more explainable

Published in 2023 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2023

Introduces pre-hoc and co-hoc explainability, preserving black-box prediction accuracy while drawing explanations from an inherently interpretable white-box model.

Recommended citation: Acun, C., & Nasraoui, O. (2023). "In-training explainability frameworks: A method to make black-box machine learning models more explainable." 2023 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 230-237. https://ieeexplore.ieee.org/document/10350149

GAI-Enhanced Assignment Framework: A Case Study on Generative AI Powered History Education

Published in NeurIPS 2023 Workshop on Generative AI for Education, 2023

A case study of ChatGPT role-play assignments in an undergraduate History of Science course, introducing the Reference-Check Protocol as a safeguard for accuracy and academic integrity.

Recommended citation: Acun, C., & Acun, R. (2023). "GAI-Enhanced Assignment Framework: A Case Study on Generative AI Powered History Education." NeurIPS 2023 Workshop on Generative AI for Education. https://gaied.org/neurips2023/files/48/48_paper.pdf

ChatGPT for conversational recommendation: Refining recommendations by reprompting with feedback

Published in arXiv preprint arXiv:2401.03605, 2024

Evaluates ChatGPT as a top-n conversational recommender, showing that reprompting with user feedback improves relevancy and that popularity bias can be mitigated through prompt engineering.

Recommended citation: Spurlock, K. D., Acun, C., Saka, E., & Nasraoui, O. (2024). "ChatGPT for conversational recommendation: Refining recommendations by reprompting with feedback." arXiv preprint arXiv:2401.03605. https://arxiv.org/abs/2401.03605

In-training explainability frameworks to make black-box machine learning models more explainable

Published in Ph.D. dissertation, University of Louisville, 2024

Doctoral dissertation introducing pre-hoc and co-hoc explainability, with global and local variants evaluated on credit risk scoring, movie recommendation and robotic grasp failure detection.

Recommended citation: Acun, A. C. (2024). "In-training explainability frameworks to make black-box machine learning models more explainable." Ph.D. dissertation, University of Louisville. https://ir.library.louisville.edu/etd/4375/

Enhancing Robotic Grasp Failure Prediction Using A Pre-hoc Explainability Framework

Published in 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE), 2024

Adds pre-hoc explainability to robot grasp failure prediction with Factorization Machines, learning the explainable model before the black-box model rather than after it.

Recommended citation: Acun, C., Ashary, A., Popa, D. O., & Nasraoui, O. (2024). "Enhancing Robotic Grasp Failure Prediction Using A Pre-hoc Explainability Framework." 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE), 1993-1998. https://ieeexplore.ieee.org/document/10711481

Robot failure mode prediction with deep learning sequence models

Published in Neural Computing and Applications, 2025

Recurrent neural network models for robot grasp failure prediction, introducing generative-interpretive grasp prediction and adaptive time to failure analysis.

Recommended citation: Damak, K., Boujelbene, M., Acun, C., Alvanpour, A., Das, S. K., Popa, D. O., & Nasraoui, O. (2025). "Robot failure mode prediction with deep learning sequence models." Neural Computing and Applications, 37(6), 4291-4302. https://link.springer.com/article/10.1007/s00521-024-10856-1

Comparative Analysis of Post Hoc Explainable Methods for Robotic Grasp Failure Prediction

Published in Electronics, 2025

A comparison of Tree-SHAP, LIME and TreeInterpreter on grasp failure predictions, covering agreement between methods, dependence on model type and computational efficiency.

Recommended citation: Alvanpour, A., Acun, C., Spurlock, K., Robinson, C. K., Das, S. K., Popa, D. O., & Nasraoui, O. (2025). "Comparative Analysis of Post Hoc Explainable Methods for Robotic Grasp Failure Prediction." Electronics, 14(9), 1868. https://www.mdpi.com/2079-9292/14/9/1868

Pre Hoc and Co Hoc Explainability: Frameworks for Integrating Interpretability into Machine Learning Training for Enhanced Transparency and Performance

Published in Applied Sciences, 2025

Pre hoc and co hoc frameworks that embed an inherently interpretable white-box model into black-box training, improving global and local fidelity over LIME without sacrificing accuracy.

Recommended citation: Acun, C., & Nasraoui, O. (2025). "Pre Hoc and Co Hoc Explainability: Frameworks for Integrating Interpretability into Machine Learning Training for Enhanced Transparency and Performance." Applied Sciences, 15(13), 7544. https://www.mdpi.com/2076-3417/15/13/7544

Task Allocation for Nursing Robots Using Explainable Machine Learning with Factorization Machines

Published in 2025 IEEE 21st International Conference on Automation Science and Engineering (CASE), 2025

An explainable pre-hoc Factorization Machine framework that predicts optimal task allocation between nurses and robotic nursing assistants with 94% accuracy.

Recommended citation: Acun, C., Ashary, A., Popa, D. O., & Nasraoui, O. (2025). "Task Allocation for Nursing Robots Using Explainable Machine Learning with Factorization Machines." 2025 IEEE 21st International Conference on Automation Science and Engineering (CASE), 1044-1049. https://ieeexplore.ieee.org/document/11164103

talks

teaching