Husni Almoubayyed
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Husni Almoubayyed has a total of 1000 co-authors. Here are the top co-authors by number of shared publications:
- Steve Ritter
Carnegie Learning, Pittsburgh, USA
6 shared publications - Stephen E. Fancsali
Carnegie Learning, Pittsburgh, USA
5 shared publications - Rachel Mandelbaum
McWilliams Center for Cosmology and Astrophysics, Department of Physics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
4 shared publications - April Murphy
Carnegie Learning, Pittsburgh, USA
3 shared publications - Kole Andreas Norberg
Carnegie Learning, Pittsburgh, PA, USA
3 shared publications A. A. Shah
NASA Marshall Space Flight Center
2 shared publications
Latest Publications
- Open Access
- 3 December 2025
- Published by Oxford University Press (OUP) in Monthly Notices of the Royal Astronomical Society
- Vol. 545 (2)
Forecasting the impact of source galaxy photometric redshift uncertainties on the LSST 3 × 2pt analysis
Photometric redshifts of the source galaxies are a key source of systematic uncertainty in the Rubin Observatory Legacy Survey of Space and Time (LSST)’s galaxy clustering and weak lensing analysis, i.e. the $3\times 2$pt analysis. This paper introduces a Fisher forecast code fishera2z for the LSST Yr 10 (Y10) $3 \times 2$pt and cosmic shear analyses, utilizing a 15-parameter redshift distribution model, with one redshift bias, variance, and outlier rate per tomographic bin. fishera2z employs the Core Cosmology Library ccl to compute the large-scale structure power spectrum and incorporates a four-parameter non-linear alignment model for intrinsic alignments. We evaluate the impact of marginalizing over redshift distribution parameters on weak lensing, forecast biases in cosmological parameters due to redshift errors, and assess cosmological parameter sensitivity to redshift systematic parameters using decision trees. The sensitivity study reveals that for LSST $3 \times 2$pt analysis, $S_8$ is most sensitive to the mean redshift of the fourth out of the five source tomographic bins, while other cosmological parameters possess different sensitivities. Additionally, we provide cosmological analysis forecasts based on different scenarios of spectroscopic training data sets. We find that the figures-of-merit for the cosmological results increase with the number of spectroscopic training galaxies, and with the completeness of the training set above $z=1.6$, assuming the redshift information comes solely from the training set galaxies without other external constraints.- 3 December 2025
- Published by Oxford University Press (OUP) in Monthly Notices of the Royal Astronomical Society
- Vol. 545 (2)
Open AccessForecasting the impact of source galaxy photometric redshift uncertainties on the LSST 3 × 2pt analysis
Photometric redshifts of the source galaxies are a key source of systematic uncertainty in the Rubin Observatory Legacy Survey of Space and Time (LSST)’s galaxy clustering and weak lensing analysis, i.e. the $3\times 2$pt analysis. This paper introduces a Fisher forecast code fishera2z for the LSST Yr 10 (Y10) $3 \times 2$pt and cosmic shear analyses, utilizing a 15-parameter redshift distribution model, with one redshift bias, variance, and outlier rate per tomographic bin. fishera2z employs the Core Cosmology Library ccl to compute the large-scale structure power spectrum and incorporates a four-parameter non-linear alignment model for intrinsic alignments. We evaluate the impact of marginalizing over redshift distribution parameters on weak lensing, forecast biases in cosmological parameters due to redshift errors, and assess cosmological parameter sensitivity to redshift systematic parameters using decision trees. The sensitivity study reveals that for LSST $3 \times 2$pt analysis, $S_8$ is most sensitive to the mean redshift of the fourth out of the five source tomographic bins, while other cosmological parameters possess different sensitivities. Additionally, we provide cosmological analysis forecasts based on different scenarios of spectroscopic training data sets. We find that the figures-of-merit for the cosmological results increase with the number of spectroscopic training galaxies, and with the completeness of the training set above $z=1.6$, assuming the redshift information comes solely from the training set galaxies without other external constraints. - 21 July 2025
- Published by Springer Nature
- p. 356-363
Exploring Student Identity in Adaptive Learning Systems Through Qualitative Data
- 21 July 2025
- Published by Springer Nature
- p. 356-363
Exploring Student Identity in Adaptive Learning Systems Through Qualitative Data
- 21 July 2025
- Published by Springer Nature
- p. 309-316
An Agentic Framework for Real-Time Pedagogical Plot Generation
- 21 July 2025
- Published by Springer Nature
- p. 309-316
An Agentic Framework for Real-Time Pedagogical Plot Generation
- Open Access
- 20 July 2025
- Published by Springer Nature
- p. 188-195
Using Generative AI to Foster Student Sense of Belonging in Mathematics
- 20 July 2025
- Published by Springer Nature
- p. 188-195
Open AccessUsing Generative AI to Foster Student Sense of Belonging in Mathematics
- 20 July 2025
- Published by Springer Nature
- p. 341-348
Evaluating an AI Tutor for Bias Across Different Foundation Models
- 20 July 2025
- Published by Springer Nature
- p. 341-348
Evaluating an AI Tutor for Bias Across Different Foundation Models
- Open Access
- 3 March 2025
- Published by Association for Computing Machinery (ACM)
- p. 631-641
Predicting Long-Term Student Outcomes from Short-Term EdTech Log Data
- 3 March 2025
- Published by Association for Computing Machinery (ACM)
- p. 631-641
Open AccessPredicting Long-Term Student Outcomes from Short-Term EdTech Log Data
- Open Access
- 19 July 2024
- Published by Elsevier in International Journal of Artificial Intelligence in Education
- Vol. 35 (2) , 587-626
Rewriting Content with GPT-4 to Support Emerging Readers in Adaptive Mathematics Software
- 19 July 2024
- Published by Elsevier in International Journal of Artificial Intelligence in Education
- Vol. 35 (2) , 587-626
Open AccessRewriting Content with GPT-4 to Support Emerging Readers in Adaptive Mathematics Software
- Open Access
- 18 March 2024
- Published by Association for Computing Machinery (ACM)
- p. 210-218
Hierarchical Dependencies in Classroom Settings Influence Algorithmic Bias Metrics
Measuring algorithmic bias in machine learning has historically focused on statistical inequalities pertaining to specific groups. However, the most common metrics (i.e., those focused on individual- or group-conditioned error rates) are not currently well-suited to educational settings because they assume that each individual observation is independent from the others. This is not statistically appropriate when studying certain common educational outcomes, because such metrics cannot account for the relationship between students in classrooms or multiple observations per student across an academic year. In this paper, we present novel adaptations of algorithmic bias measurements for regression for both independent and nested data structures. Using hierarchical linear models, we rigorously measure algorithmic bias in a machine learning model of the relationship between student engagement in an intelligent tutoring system and year-end standardized test scores. We conclude that classroom-level influences had a small but significant effect on models. Examining significance with hierarchical linear models helps determine which inequalities in educational settings might be explained by small sample sizes rather than systematic differences.
- 18 March 2024
- Published by Association for Computing Machinery (ACM)
- p. 210-218
Open AccessHierarchical Dependencies in Classroom Settings Influence Algorithmic Bias Metrics
Measuring algorithmic bias in machine learning has historically focused on statistical inequalities pertaining to specific groups. However, the most common metrics (i.e., those focused on individual- or group-conditioned error rates) are not currently well-suited to educational settings because they assume that each individual observation is independent from the others. This is not statistically appropriate when studying certain common educational outcomes, because such metrics cannot account for the relationship between students in classrooms or multiple observations per student across an academic year. In this paper, we present novel adaptations of algorithmic bias measurements for regression for both independent and nested data structures. Using hierarchical linear models, we rigorously measure algorithmic bias in a machine learning model of the relationship between student engagement in an intelligent tutoring system and year-end standardized test scores. We conclude that classroom-level influences had a small but significant effect on models. Examining significance with hierarchical linear models helps determine which inequalities in educational settings might be explained by small sample sizes rather than systematic differences.