About

Sergio Gago-Masague is an Associate Professor of Teaching and the Director of the MCS program at UC Irvine's Donald Bren School of Information & Computer Sciences. He conducts cross-disciplinary research in assistive and educational technologies, mentoring students in areas such as product and systems engineering, human-computer interaction, design and prototyping, data visualization, intelligent user interfaces, computer games, and medical informatics. He previously worked as a research scientist at UCI’s California Institute for Telecommunications and Information Technology and serves as the director of the Engaging Technology and Application Design Lab. Gago-Masague was an informatics lecturer at UCI before joining the computer science faculty as a professor of teaching in September 2018.

Selected publications

• Investigating Autograder Usage in the Post- Pandemic and LLM Era

  2025-02-18 · 1 citations

  articleSenior author

  This work investigates the impact of Large Language Models (LLMs) and the COVID-19 pandemic on student behavior with autograder systems in three programming-heavy courses. We examine whether the release of LLMs like ChatGPT and GitHub Copilot, along with post-pandemic effects, has modified student interactions with autograders. Using data from student submissions over five years, totalling over 4,500 students across over 420,000 submissions, we analyze trends in submission behaviors before and after these events. Our methodology involves tracking submission patterns, focusing on timing, frequency, and score.

  PublisherDOI

• Learning Fair Representations with Kolmogorov-Arnold Networks

  ArXiv.org · 2025-11-14

  preprintOpen accessSenior author

  Despite recent advances in fairness-aware machine learning, predictive models often exhibit discriminatory behavior towards marginalized groups. Such unfairness might arise from biased training data, model design, or representational disparities across groups, posing significant challenges in high-stakes decision-making domains such as college admissions. While existing fair learning models aim to mitigate bias, achieving an optimal trade-off between fairness and accuracy remains a challenge. Moreover, the reliance on black-box models hinders interpretability, limiting their applicability in socially sensitive domains. To circumvent these issues, we propose integrating Kolmogorov-Arnold Networks (KANs) within a fair adversarial learning framework. Leveraging the adversarial robustness and interpretability of KANs, our approach facilitates stable adversarial learning. We derive theoretical insights into the spline-based KAN architecture that ensure stability during adversarial optimization. Additionally, an adaptive fairness penalty update mechanism is proposed to strike a balance between fairness and accuracy. We back these findings with empirical evidence on two real-world admissions datasets, demonstrating the proposed framework's efficiency in achieving fairness across sensitive attributes while preserving predictive performance.

  PublisherOA PDFDOI

• Mobile Computing Framework for Machine Learning-Based Stress Detection

  2025-07-21 · 1 citations

  articleSenior author

  Stress is a pervasive issue in modern society, and a plethora of technological approaches have been developed for its detection. In this study, we propose an end-to-end framework for detecting chronic stress using only data collected from a smartwatch and a lightweight machine learning model that runs within a mobile application. Previous work achieved accurate results but required additional technology, such as cloud servers and custom sensors not readily available to the public. We tested lightweight models and found that a fine-tuned LightGBM achieved an $81.6 \% \mathrm{~F} 1$-score, while a hyperdimensional computing (HDC) model, optimizing efficiency with a slight accuracy tradeoff, reached 73%.

  PublisherDOI

• Predictors of postoperative recovery using self-reported HRQoL among children undergoing elective surgery

  Paediatrics & Child Health · 2025-09-06

  articleOpen access

  Abstract Objectives There has been a growing emphasis on holistic approaches to assessing postoperative recovery by using self-reported health-related quality of life (HRQoL). Identifying groups of children at higher risk of poor recovery has become important. The aim of the study is to identify predictors of paediatric postoperative recovery assessed by self-reported HRQoL. Methods One hundred forty-eight children ages 4 to 12 years completed the Child Health Rating Inventories (CHRIS2.0) to measure overall, physical and mental health, preoperative anxiety, and postoperative pain. Four linear regressions were used to identify predictors of overall, physical and mental health and postoperative pain. Predictors included child gender, race/ethnicity and language, surgical severity, child and caregiver preoperative anxiety, and caregiver distress. Results Child male gender (p = 0.03, 95% confidence interval [CI] [−10.15, −0.65]) and identifying as English-speaking Latinx (p = 0.03, 95% CI [0.58, 13.25]) predicted poorer postoperative overall health. Higher child preoperative anxiety (p < 0.001, 95% CI [0.39, 1.50]) and higher caregiver preoperative distress (p = 0.003, 95% CI [−1.09, 0.28]) predicted poorer postoperative overall health. Conclusions The results of this self-reported study validated previously established predictors of recovery (preoperative anxiety and caregiver distress). Novel predictors, including child male gender and race/ethnicity and language, were identified, providing new insights into factors influencing recovery outcomes.

  PublisherOA PDFDOI

• Enhanced Detection of Transdermal Alcohol Levels Using Hyperdimensional Computing on Embedded Devices

  arXiv (Cornell University) · 2024-03-18 · 1 citations

  preprintOpen accessSenior author

  Alcohol consumption has a significant impact on individuals' health, with even more pronounced consequences when consumption becomes excessive. One approach to promoting healthier drinking habits is implementing just-in-time interventions, where timely notifications indicating intoxication are sent during heavy drinking episodes. However, the complexity or invasiveness of an intervention mechanism may deter an individual from using them in practice. Previous research tackled this challenge using collected motion data and conventional Machine Learning (ML) algorithms to classify heavy drinking episodes, but with impractical accuracy and computational efficiency for mobile devices. Consequently, we have elected to use Hyperdimensional Computing (HDC) to design a just-in-time intervention approach that is practical for smartphones, smart wearables, and IoT deployment. HDC is a framework that has proven results in processing real-time sensor data efficiently. This approach offers several advantages, including low latency, minimal power consumption, and high parallelism. We explore various HDC encoding designs and combine them with various HDC learning models to create an optimal and feasible approach for mobile devices. Our findings indicate an accuracy rate of 89\%, which represents a substantial 12\% improvement over the current state-of-the-art.

  PublisherOA PDFDOI

• Enhanced Detection of Transdermal Alcohol Levels Using Hyperdimensional Computing on Embedded Devices

  2024-06-30 · 4 citations

  articleSenior author

  Alcohol consumption has a significant impact on individuals’ health, with even more pronounced consequences when consumption becomes excessive. One approach to promoting healthier drinking habits is implementing just-in-time interventions, where timely notifications indicating intoxication are sent during heavy drinking episodes. However, the complexity or invasiveness of an intervention mechanism may deter an individual from using it in practice. Previous research tackled this challenge using collected motion data and conventional Machine Learning (ML) algorithms to classify heavy drinking episodes, but with impractical accuracy and computational efficiency for mobile devices. Consequently, we have elected to use Hyperdimensional Computing (HDC) to design a just-in-time intervention approach that is practical for smartphones, smart wearables, and IoT deployment. HDC is a framework that has proven results in processing real-time sensor data efficiently. This approach offers several advantages, including low latency, minimal power consumption, and high parallelism. We explore various HDC encoding designs and combine them with various HDC learning models to create an optimal and feasible approach for mobile devices. Our findings indicate an accuracy rate of 89%, which represents a substantial 12% improvement over the current state-of-the-art.

  PublisherDOI

• Maximizing Individual Learning Goals Through Customized Student-Project Matching (SPM) in CS Capstone Projects

  2024-10-13

  article

  This full innovative practice paper describes a computational tool designed to optimally match students to industry-sponsored capstone projects in a software engineering capstone course for Computer Science undergraduates at an R1 University (R1U). In the context of these capstone courses, where students stand at the culmination of their academic journey, aligning students' personal learning goals and existing computing skills with team formations becomes critical. This paper presents the Student-Project Matching Tool (SPMT), created to help students find the best available industry-sponsored projects based on their desired learning outcomes, project requirements, and their interests in each project. To choose the learning outcomes they aim to achieve, students can select from a list of predefined software engineering categories and the skills needed to achieve proficiency in each category. The initial list of technical skills for each category was recorded from job postings on a variety of well-known job-search websites, and was further refined by the capstone program's industry partners. Allowing students to select the skills they will work on ensures that they have opportunities and exposure to the skill sets required for employment while still working on one of their most appealing projects. We have developed and piloted the SPMT, which utilizes student vectors to represent their interests and experiences across various software engineering skill sets. Similarly, this tool uses vectors to represent the skills required by each available project, aligning with the exact dimensions as those of the student vectors. The SPMT calculated Euclidean distances between the student interest and project requirement vectors. Next, the resulting Euclidean distances were multiplied with weights associated with students' level of interest in each industry-sponsored project. Subsequently, we framed the student-project matching process as a linear sum assignment problem, aiming to minimize the total sum of Euclidean distances between each student-project pair. The output of the SPMT process consistently matched students with teams that met their software engineering interests and project priorities. Our results reveal increased engagement and growth toward students' desired learning outcomes and computing skills. Specifically, after the first term of the capstone sequence, most students self-reported higher levels of proficiency growth in the skills within their desired software engineering category. This suggests that the SPMT effectively provides students with valuable learning experiences relevant to their career interests and representative of real-world settings.

  PublisherDOI

• Investigating the Role of Socioeconomic Factors on CS1 Performance

  2024-05-08 · 3 citations

  articleSenior author

  Computer Science (CS) students at the University of California, Irvine (UCI) have experienced academic probation rates higher than 50%. Particularly concerning, statistical analysis showed that students who self-identified as belonging to an underrepresented group (URG) experienced an even higher probation rate. Moreover, students who entered academic probation were twice as likely to leave the CS program. We designed and conducted a comprehensive survey involving 757 CS1 students at UCI to delve further into their past experiences, challenges, and perspectives to gain further insights into the factors contributing to these trends. Specifically, we studied (1) the role of socioeconomic factors such as mental health, academic preparedness, and computing participation in CS students' success, (2) to what extent these factors affect underrepresented group, first-generation, and female students, and (3) experiences that distinguish the most impacted minority groups. Our findings reveal significant correlations between underperformance in CS1 and socioeconomic factors, including satisfaction with course completion regardless of grade, mental health challenges, and insufficient pre-college math preparation. Many of these factors had strong associations with all minority groups. Moreover, our data shows that most URG students enter the program with weaker math preparation than their peers, often don't have prior programming experience, and once enrolled, they have limited interactions within the CS community. These insights highlight the urgency of redesigning academic support practices to support students with diverse backgrounds and experiences. There is a growing need to implement tailored interventions and support mechanisms for CS students, focusing on addressing the disparities in preparation, perspectives, and experiences. Our findings highlight the pressing need to reevaluate current academic support practices and provide a foundation for developing targeted support programs to guide struggling students toward greater success in CS.

  PublisherDOI

• Smartwatch-Based Prediction of Transdermal Alcohol Levels Using Hyperdimensional Computing

  2024-11-10

  articleSenior author

  Excessive alcohol consumption was responsible for 6% of global deaths in 2023. To encourage healthier drinking habits and enhance user awareness of their current condition, just-in-time interventions prove to be a suitable approach for informing users about their current state of intoxication. Current methods for determining blood alcohol content are intrusive and many also invasive, requiring users to use breathalizers or actively engage in urine or blood tests. In this study, we introduce an application utilizing Hyperdimensional Computing to predict if a user is under the influence of alcohol, achieving an accuracy of 93.5% on average. Furthermore, this application is designed to run on both smartphones and smartwatches, enabling full on device computation and online learning through a C implementation utilizing vectorial operations. The application has shown to be very efficient, having a training time per instance of 13.2 and 1.25ms on smartwatch and smartphone respectively and inference time of 6.8 and 1.1ms. Moreover the energy consumption of the running application is negligible compared to the energy usage of the idle device.

  PublisherDOI

• Measuring CS Student Attitudes Toward Large Language Models

  2024-03-14 · 2 citations

  article

  With the mainstream adoption of Large Language Models (LLMs), members of both academia and the media have raised concerns around their impact on student learning and pedagogy. Many students and educators wonder about the pedagogical fit of this emerging technology. We aim to measure the adoption of and attitudes toward LLMs among the CS student population at an R1 University to determine how students are using these new tools. To this end, we conducted a large survey study targeting two populations participating in computing courses at the university: intro-sequence students (ISS) and experienced students (ES).

  PublisherDOI

Awards & honors

• $537,000 NSF REU Site Grant for AIoT-Sys (2026)
• UCI Celebration of Teaching: ICS Honorees (2024)
• Faculty and Staff Honored at 2023 ICS Awards Celebration (20…