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Drexel University

Academic institutionnorthamerica · us
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Research library256linked papers
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Selected work

Representative Papers

Where Do AI Coding Agents Fail? An Empirical Study of Failed Agentic Pull Requests in GitHub

Jan 21, 2026

This study addresses the high rejection rate of pull requests (PRs) submitted by AI coding agents on GitHub, a phenomenon whose underlying causes remain poorly understood. Through a large-scale empirical analysis of 33,000 AI-generated PRs, combining data mining, quantitative statistics, and manual qualitative coding, the work presents the first hierarchical taxonomy of reasons for PR rejection. The findings uncover socio-technical factors—such as insufficient reviewer engagement and misalignment with project goals—that are often invisible to conventional metrics. PRs related to documentation, continuous integration (CI), and build tasks exhibit the highest merge rates, whereas those targeting performance optimization and bug fixes are least likely to be accepted. Unmerged PRs typically involve larger code changes, affect more files, and frequently fail CI validation.

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Multi-modal wound classification using wound image and location by Xception and Gaussian Mixture Recurrent Neural Network (GMRNN)

May 12, 2025

To address insufficient diagnostic accuracy for acute and chronic wounds in clinical practice, this paper proposes a multimodal intelligent classification method integrating wound images and anatomical location information. We introduce a novel collaborative architecture combining Xception for deep visual feature extraction and a Gaussian Mixture Recurrent Neural Network (GMRNN) — the first to explicitly model temporal semantic relationships among anatomical locations. Multimodal features are fused via concatenation and jointly optimized through end-to-end training, overcoming limitations of single-image modality. Evaluated on four wound types—diabetic, pressure, surgical, and venous ulcers—the method achieves classification accuracies ranging from 78.77% to 100%, significantly outperforming conventional deep learning models. This work pioneers the incorporation of anatomical location modeling into intelligent wound diagnosis, empirically validating the efficacy and clinical applicability of location-aware multimodal representation learning.

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MVFNet: Multipurpose Video Forensics Network using Multiple Forms of Forensic Evidence

Mar 26, 2025

Existing video forensic methods typically target a single manipulation type (e.g., deepfakes or inpainting), rendering them inadequate for real-world scenarios where manipulation types are unknown and often co-occur. This paper introduces the first end-to-end, multi-purpose video forensic network capable of jointly detecting diverse manipulations—including deepfakes, inpainting, splicing, and editing—without prior knowledge of the manipulation type. Our method features a novel multi-scale hierarchical Transformer module that jointly models spatiotemporal anomalies and precisely localizes forged regions of arbitrary shape and size across scales. Additionally, it integrates multimodal forensic cues with multi-scale spatiotemporal features. Evaluated on a comprehensive multi-manipulation benchmark, our approach achieves state-of-the-art performance, while also matching or surpassing specialized detectors on single-type manipulation tasks—demonstrating significantly improved generalization and practical applicability.

1 citationsRead paper

Learning-Augmented Robust Algorithmic Recourse

Oct 02, 2024arXiv.org

Dynamic updates of machine learning models frequently invalidate historical counterfactual explanations (recourse), undermining user actionability. To address this, we propose a Learning-Augmented Robust Explainable Decision Framework—the first to integrate learning-augmentation into recourse design. Our method forecasts model evolution trends to jointly optimize consistency (minimizing adjustment cost under accurate predictions) and robustness (bounding worst-case cost increase under prediction errors). We formally characterize the consistency–robustness trade-off, derive theoretical bounds linking prediction error to cost inflation, and unify robust optimization, online learning-based calibration, and minimum-cost counterfactual generation into a two-stage algorithm with provable performance guarantees. Experiments show that when prediction accuracy exceeds 80%, our framework reduces average recourse cost by 37% compared to baselines, while worst-case cost growth remains tightly aligned with theoretical upper bounds—significantly outperforming purely robust approaches.

1 citationsRead paper
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