Institution profile

Instituto Tecnológico y de Estudios Superiores de Monterrey

Academic institutionnorthamerica · mx
Official website
Research library50linked papers
Opportunities0open roles
Selected work

Representative Papers

Ensemble of Radiomics and Convnext for Breast Cancer Diagnosis

Jun 18, 20252025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS)

This study proposes an integrated approach combining radiomics and the ConvNeXt deep learning model to improve the accuracy of early breast cancer diagnosis from mammographic images. The method uniquely integrates radiomic features with the ConvNeXtV1-small architecture and incorporates a prediction calibration strategy, achieving robust cross-dataset performance on two independent cohorts—RSNA and TecSalud. Experimental results demonstrate that the ensemble model attains an AUC of 0.87 on testing data, significantly outperforming standalone ConvNeXt (AUC: 0.83) and radiomics-based models (AUC: 0.80). These findings underscore the efficacy and generalizability of multimodal fusion in enhancing breast cancer screening.

1 citationsRead paper

Strategic Technical Debt: A Real Options Approach to Early-Stage Software Experimentation

Aug 17, 2026

This study addresses the challenge of rationally quantifying early-stage technical debt by reframing it as a real option. Leveraging finite-horizon dynamic programming and risk-neutral valuation, we propose the strategic debt boundary, shadow price, and refactoring pivot theorem. The research establishes a falsifiable model for predicting refactoring bursts, quantifies debt overhang thresholds, and empirically validates the impact of salvage value on strategy through preregistered analysis. By elucidating rational pricing mechanisms and optimal repayment timing under high uncertainty, this work facilitates a cognitive paradigm shift from viewing technical debt as an engineering pathology to recognizing it as a strategic financial instrument.

0 citationsRead paper

Prompt engineering using order-of-addition experiments: An application to generating two-level fractional factorial designs

Jul 06, 2026

This study addresses the pronounced sensitivity of large language models (LLMs) to prompt phrasing order when generating two-level fractional factorial designs, demonstrating that the sequence of phrases in a prompt significantly affects output quality. To tackle this issue, the authors introduce, for the first time, a sequential addition experimental design framework into prompt engineering. This approach systematically quantifies the ordering effects of individual prompt components and automatically identifies the optimal prompt configuration. The proposed method not only elucidates the underlying mechanisms by which LLMs respond to structural variations in prompts but also substantially enhances both the performance and stability of LLMs in statistical experimental design tasks.

0 citationsRead paper
Recent publications

Latest Papers

Strategic Technical Debt: A Real Options Approach to Early-Stage Software Experimentation

Aug 17, 2026

This study addresses the challenge of rationally quantifying early-stage technical debt by reframing it as a real option. Leveraging finite-horizon dynamic programming and risk-neutral valuation, we propose the strategic debt boundary, shadow price, and refactoring pivot theorem. The research establishes a falsifiable model for predicting refactoring bursts, quantifies debt overhang thresholds, and empirically validates the impact of salvage value on strategy through preregistered analysis. By elucidating rational pricing mechanisms and optimal repayment timing under high uncertainty, this work facilitates a cognitive paradigm shift from viewing technical debt as an engineering pathology to recognizing it as a strategic financial instrument.

0 citationsRead paper

Prompt engineering using order-of-addition experiments: An application to generating two-level fractional factorial designs

Jul 06, 2026

This study addresses the pronounced sensitivity of large language models (LLMs) to prompt phrasing order when generating two-level fractional factorial designs, demonstrating that the sequence of phrases in a prompt significantly affects output quality. To tackle this issue, the authors introduce, for the first time, a sequential addition experimental design framework into prompt engineering. This approach systematically quantifies the ordering effects of individual prompt components and automatically identifies the optimal prompt configuration. The proposed method not only elucidates the underlying mechanisms by which LLMs respond to structural variations in prompts but also substantially enhances both the performance and stability of LLMs in statistical experimental design tasks.

0 citationsRead paper

ROBOCYCLE: Autonomous Dual-Arm Robotic Manipulation and Coordination for Recycling Applications

Jul 03, 2026

This work addresses the challenge of automating urban waste sorting by tackling the limitations of current robotic systems in perceiving and manipulating transparent, deformable, or cluttered waste items. The authors propose a dual-arm autonomous sorting system tailored to Tokyo’s recycling standards, integrating a novel dual-arm coordination mechanism, multi-view RGB-D instance segmentation based on RF-DETR, and six-degree-of-freedom grasp planning via AnyGrasp. This integrated approach enables robust manipulation of irregular and deformable objects and supports fine-grained tasks such as unscrewing PET bottle caps. Evaluated in real-world conditions, the system achieves a 90.3% grasp success rate and an overall task success rate of 84.3%, offering a scalable solution for automated waste management in complex human environments.

0 citationsRead paper