publications

2026

1. UNSURE ’26

   Beyond Uncertainty: Generalizable Failure Monitoring for Surgical Segmentation under Acquisition Degradation

   Hieu D. Pham, Dang P. M. Cao, and Thanh Trung Huynh

   In UNSURE Workshop, MICCAI, 2026

   Abs arXiv Code

   We propose TCSR-Monitor, a post-hoc failure monitoring framework that goes beyond uncertainty estimates by incorporating complementary observable signals such as shape, temporal consistency, and image quality. The framework operates without access to model internals or ground truth during deployment and generalizes to unseen acquisition degradations, helping make AI systems more reliable in real surgical environments.

2. FAIMI/BRIDGE/EPIMI ’26

   When Oracle Conditioning Misleads Deployment: Conditioning-Availability Bias in Echocardiographic Segmentation

   Dang P. M. Cao, Hieu D. Pham, and Hieu Pham

   In FAIMI/BRIDGE/EPIMI Workshop, MICCAI, 2026

   Abs arXiv Code

   This work highlights a deployment issue that often goes unnoticed: models are frequently evaluated using auxiliary information that will not actually be available in practice. We introduce a simple audit framework to expose this conditioning-availability bias, showing how oracle-conditioned evaluation can substantially overestimate real-world performance and fairness.

3. LREC ’26

   

   VietJobs: A Vietnamese Job Advertisement Dataset

   Hieu Pham Dinh, Hung Huy Nguyen, and Mo El-Haj

   In Language Resources and Evaluation Conference, 2026

   Abs arXiv Code

   VietJobs is a large-scale corpus of 48,092 Vietnamese job advertisements containing more than 15 million words and spanning all 34 provinces and municipalities of Vietnam. It includes structured information such as job titles, categories, salaries, skills, employment conditions, occupational domains, and work types, supporting research in Vietnamese NLP and labour-market analytics.

4. LREC ’26

   

   Structured Prompting for Arabic Essay Proficiency: A Trait-Centric Evaluation Approach

   Salim Al Mandhari, Hieu Pham Dinh, Mo El-Haj, and Paul Rayson

   In Language Resources and Evaluation Conference, 2026

   Abs arXiv Code

   This work introduces standard, hybrid, and rubric-guided prompting strategies for trait-specific Arabic automatic essay scoring. Eight language models are evaluated on the QAES dataset across organization, vocabulary, development, and style in zero-shot and few-shot settings.