Fairness-aware Machine Learning
Educational Data Mining
Responsible AI
Generative AI
Deep Learning
Time Series Prediction.

Conference Facilitation

▪ Session Chair, PAKDD 2026
▪ Session Chair, PAKDD 2025
▪ Volunteering, KDD 2022
▪ Volunteering, ECML/PKDD 2020

Journal Peer Review

▪ ACM Transactions on Knowledge Discovery from Data (TKDD), AI and Ethics (AIET), Applied Sciences, Applied Computational Intelligence and Soft Computing, Applied Intelligence (APIN), Education and Information Technologies (EAIT), IEEE Signal Processing Magazine (SPM), Information Sciences (INS), Information Systems (INFOSYS), Journal of Data Science and Analytics, Knowledge and Information Systems (KAIS), Neural Computing and Applications (NCAA), SN Computer Science, Scientific Report, The Journal of Supercomputing (SUPE)

Conference Peer Review

▪ CIKM 2020 (rank A CORE)
▪ AIES 2022 , AICS 2022, ECML/PKDD 2022 (rank A CORE), PAKDD 2022 (rank A CORE), IJCAI 2022 (rank A* CORE)
▪ AIED 2023 (rank A CORE), ECML/PKDD 2023 (rank A CORE)
▪ AIED 2024 (rank A CORE), ECML/PKDD 2024 (rank A CORE), EDM 2024 (rank B CORE), ECAI 2024 (rank A CORE)
▪ AAAI 2025 (rank A* CORE), AIED 2025 (rank A CORE), ECIR 2025 (sub reviewer, rank A CORE), EDM 2025 (rank B CORE), ICML 2025 (rank A* CORE), IJCNN 2025 (rank B CORE), KDD 2025 (ADS and research tracks, rank A* CORE), PAKDD 2025 (rank A CORE), The ACM Web Conference 2025 (rank A* CORE), ECAI 2025 (rank A CORE)
▪ AAAI 2026 (rank A* CORE), AIED 2026 (rank A CORE), EDM 2026 (rank B CORE), ICLR 2026 (rank A* CORE), PAKDD 2026 (rank A CORE), IJCAI-ECAI 2026 (rank A CORE)

Foundation Models
Exercises for Foundation Models
Human-Centered AI
Exercises for Human-Centered AI

Introduction to Web Science
Exercises for Introduction to Web Science
Exercises for Recommender Systems

Foundation Models
Exercises for Foundation Models
Seminar: AI in Finance: Threats and Challenges (kick-off meeting)

Introduction to Web Science
Exercises for Recommender Systems
Research Lab: Exploring Bias and Fairness in Large Language Models (kick-off meeting)

Machine Learning - Supervised learning (Q1, 2024)
Machine Learning - Unsupervised learning and feature engineering (Q1, 2024)
Algorithms, data structures and programming languages (Q1, 2024)
Introduction to Data Science (Q1, 2024)
Data Science (DLMBDSA01) (Q1, 2024)
Introduction to Reinforcement Learning (Q1, 2024)
Programming with Python (Q2, 2024)
Artificial Intelligence - DLBDSEAIS1(Q2, 2024)
Artificial Intelligence - DLMAIAI01 (Q2 + Q3, 2024)
Analytical software and frameworks (Q2 + Q3, 2024)
Introduction to Programming with Python (Q3, 2024)

Artificial Intelligence - DLBDSEAIS1(Q2, 2023)
Introduction to Computer Science (Q2, Q4 2023)
Introduction to Programming with Python (Q3, 2023)
Artificial Intelligence - DLMAIAI01 (Q3, 2023)
Machine Learning - Supervised learning (Q3, 2023)
Machine Learning - Unsupervised learning and feature engineering (Q3, 2023)
Programming with Python (Q4, 2023)
Object Oriented and Functional Programming with Python (Q4, 2023)

Our paper "An Experimental Study on Fairness-aware Machine Learning for Credit Scoring Problems" has been accepted to the Digital Finance Journal. Congrats Huyen and Thang! The paper is online now!

Our paper "Feedback Moves and Engagement Dynamics in AI Tutoring" has been accepted to the The 26th IEEE International Conference on Advanced Learning Technologies (ICALT 2026) as a short paper. Congrats Stefania and Shokooh!

Our paper "'I Don't Know' as a Learning Signal: Uncertainty-Aware Routing for Human--LLM Support in Visualization Practice" has been accepted to the The 26th IEEE International Conference on Advanced Learning Technologies (ICALT 2026) as a full paper. Congrats Stefania!

I am invited to serves as a Reviewer for the Conference on Neural Information Processing Systems (NeurIPS) 2026

Our paper "Scaffolding-First Constraint Design for LLM Tutors in Data-Science Problem Solving" has been accepted to the AIED 2026 conference. Congrats Stefania and Shokooh!

Our paper "FairFinGAN: Fairness-aware Synthetic Financial Data Generation" has been accepted to the Special Session: Data Science: Foundations and Applications (DSFA) at the PAKDD 2026 conference.

I am invited to serve as a Reviewing Editor within Springer Nature’s Reviewer Communities.

Our paper "A Deep Latent Factor Graph Clustering with Fairness-Utility Trade-off Perspective" has been accepted to the IEEE Big Data 2025 conference. Congrats Siamak!!

Our paper "Bias in Recruitment Systems Utilizing Large Language Models" has been accepted to the 9th ICAAI 2025 conference. Congrats Nupur, Sharayu, Lakshmi and Jayani!!

Our paper "Constraint-Reduced MILP with Local Outlier Factor Modeling for Plausible Counterfactual Explanations in Credit Approval" has been accepted to the NICE-TEAS ASIA 2025 conference. Congrats Trung Nguyen!!

Our paper "FACROC: a fairness measure for FAir Clustering through ROC curves" has been accepted to the Special Session: Data Science: Foundations and Applications (DSFA) at the PAKDD 2025 conference.

I have joined the Institute for Web Science and Technologies , University of Koblenz as a Postdoctoral Researcher.

Our paper A survey on datasets for fairness-aware machine learning received enough citations to be one of the Top-cited articles of WIREs Data Mining and Knowledge Discovery published in 2022 and 2023, according to Web of Science

I successfully defended my PhD thesis, entitled "Fairness-aware Machine Learning in Educational Data Mining", at the L3S Research Center - Leibniz University Hannover, with magna cum laude (presentation, dissertation).

Our paper "Multi-fair Capacitated Students-Topics Grouping Problem" has been published in the proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2023).

I got the student registration award of the PAKDD 2023 conference.

Our book chapter "A Review of Clustering Models in Educational Data Science Toward Fairness-Aware Learning" (a part of the book "Educational Data Science: Essentials, Approaches, and Tendencies") is now available on SpringerLink. A preprint version of this book chpater could be found here.

Our paper "Multi-fair Capacitated Students-Topics Grouping Problem" has been accepted to The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2023).

Our article "A survey on datasets for fairness-aware machine learning" received enough citations to be a top cite darticle in WIREs Data Mining and Knowledge Discovery journal in 2021-2022.

Our article "A survey on datasets for fairness-aware machine learning" in the WIREs Data Mining and Knowledge Discovery was one of the top 10 downloaded in 2022.

Our paper "Evaluation of group fairness measures in student performance prediction problems" has been accepted at SoGood 2022 workshop, held in conjunction with ECML/PKDD 2022

I got the KDD 2022 Student Volunteers (co-organized with Student Travel Award)

Our article A survey on datasets for fairness-aware machine learning has been published in Wiley Online Library.

I got the IEEE BigData 2021 Student Travel Award

Our research paper Fair-capacitated clustering has been accepted in the 14th International Conference on Educational Data Mining 2021.