21 citations · 24 across the 11 of their papers we have counts for
15 papers
Beyond Uniform Compression: Budgeted Transmission Allocation for Extreme Federated Learning
Pengfei Li, Mohammad Khalil
Federated learning faces severe communication bottlenecks when clients upload high-dimensional model updates. Existing methods often compress these updates uniformly across all lay…
PEG-Tab: Sampling-Time Record Repair and Release Control for Tabular Synthesis
Pengfei Li, QinYi Liu, Mohammad Khalil
Pretrained tabular generators can reproduce training records even when aggregate utility remains high. When retraining is unavailable or too costly, sampling and release are the re…
MIND: Marginal-Invariant Neural Dependency Diffusion for Mixed-Type Tabular Generation
Pengfei Li, Mohammad Khalil
This paper proposes MIND, a marginal-invariant neural dependency diffusion model for mixed-type tabular data. MIND does not directly learn the joint distribution in the original he…
Decoupled Learning and Selection in Slate Recommendation for Privacy and Stability Under Noisy Scores
Sam Urmian, Qinyi Liu, Mohammad Khalil
We formalize slate recommendation as a randomized score learner followed by deterministic selection. First, an appropriately scoped differential-privacy guarantee passes through se…
Fed-CausalDiff: Decoupled Synchronization for Federated Do-Simulation and Policy Evaluation
Pengfei Li, Mohammad Khalil
While federated learning enables collaborative modelling on decentralised data, standard methods merely fit historical observations. This purely observational approach is fundament…
Causal Pre-training Under the Fairness Lens: An Empirical Study of TabPFN
Qinyi Liu, Mohammad Khalil, Naman Goel
Foundation models for tabular data, such as the Tabular Prior-data Fitted Network (TabPFN), are pre-trained on a massive number of synthetic datasets generated by structural causal…
Brief but Impactful: How Human Tutoring Interactions Shape Engagement in Online Learning
Conrad Borchers, Ashish Gurung, Qinyi Liu +3
Learning analytics can guide human tutors to efficiently address motivational barriers to learning that AI systems struggle to support. Students become more engaged when they recei…
Measuring the Impact of Student Gaming Behaviors on Learner Modeling
Qinyi Liu, Lin Li, Valdemar Švábenský +2
The expansion of large-scale online education platforms has made vast amounts of student interaction data available for knowledge tracing (KT). KT models estimate students' concept…
Quality Degradation Attack in Synthetic Data
Qinyi Liu, Dong Liu, Sam Urmian +2
Synthetic Data Generation (SDG) can be used to facilitate privacy-preserving data sharing. However, most existing research focuses on privacy attacks where the adversary is the rec…
Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning
Mohammad Khalil, Ronas Shakya, Qinyi Liu
The increasing adoption of data-driven applications in education such as in learning analytics and AI in education has raised significant privacy and data protection concerns. Whil…
A Showdown of ChatGPT vs DeepSeek in Solving Programming Tasks
Ronas Shakya, Sam Urmian, Mohammad Khalil
The advancement of large language models (LLMs) has created a competitive landscape for AI-assisted programming tools. This study evaluates two leading models: ChatGPT 03-mini and…
Creating Artificial Students that Never Existed: Leveraging Large Language Models and CTGANs for Synthetic Data Generation
Mohammad Khalil, Sam Urmian, Ronas Shakya +1
In this study, we explore the growing potential of AI and deep learning technologies, particularly Generative Adversarial Networks (GANs) and Large Language Models (LLMs), for gene…
Advancing privacy in learning analytics using differential privacy
Qinyi Liu, Ronas Shakya, Mohammad Khalil +1
This paper addresses the challenge of balancing learner data privacy with the use of data in learning analytics (LA) by proposing a novel framework by applying Differential Privacy…
Can Synthetic Data be Fair and Private? A Comparative Study of Synthetic Data Generation and Fairness Algorithms
Qinyi Liu, Oscar Deho, Sam Urmian +3
The increasing use of machine learning in learning analytics (LA) has raised significant concerns around algorithmic fairness and privacy. Synthetic data has emerged as a dual-purp…
Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning Analytics
Qinyi Liu, Mohammad Khalil, Ronas Shakya +1
Privacy poses a significant obstacle to the progress of learning analytics (LA), presenting challenges like inadequate anonymization and data misuse that current solutions struggle…