Undergraduate Research Fellowship
The SDS Undergraduate Research Fellowship offers a paid opportunity for students of all majors to engage in impactful research under the guidance of UTSA faculty. The application form includes short-answer questions and a faculty reference check.
Students should review the faculty research projects listed below and indicate their preferences. While we aim to match students with projects that align with their interests, placements depend on project needs and availability, and specific matches cannot be guaranteed.
Projects
Preliminary Study of AI-Based Personalized Music Intervention for Alleviating Anxiety and Depression
This project explores the feasibility of using artificial intelligence to generate personalized music interventions aimed at reducing mental distress, such as anxiety and depression. The objective is to develop and evaluate an AI-driven music generation model tailored to the psychological needs of individual subjects. We will collaborate with the UTSA Psychology Department to recruit participants and assess the effectiveness of personalized AI-generated music in reducing mental health symptoms. The key areas of focus include AI model development, music personalization algorithms, and psychological impact assessment.
Semi-Supervised Learning Approaches for Defect Detection in Nuclear Materials Fabrication
This project from the UTSA Vision & Artificial Intelligence Lab in Computer Science is part of a larger collaboration with the UTSA Extreme Environment Materials Lab in Physics, focused on the application of novel AI methods to materials fabrication. The project will explore unsupervised and semi-supervised learning approaches to detecting defects in images of fabricated nuclear materials. Analysis of these materials and defects will help to inform the fabrication process for our collaborators.
EfficientAI: Compressive Sensing and AI Integration for Low-Power, Energy-Harvesting IoT Systems in Data Science Applications
This project focuses on implementing compressed sensing and AI algorithms on battery-less IoT device efficiently to reduce the need of sampling and storing large amount of signals for health monitoring applications while addressing the intrinsic weakness of battery-less IoT devices. One of the potential data applications is to design and implement an AI/ML algorithm that can automatically discriminate life-threatening ventricular arrhythmias (VAs) (i.e., binary classification: VAs or non-VAs) from sparse (labeled one-channel intracardiac electrograms recordings while being able to be deployed and run on the given microcontroller platform.
Improve Mobility of Patients with Smart Wheelchairs
This project aims to develop a smart wheelchair to assist individuals with reduced mobility. The key area of research includes implement of camera to image acquiring, application of AI for object detection on Internet of Things, motion control and path planning, and status update along the path using RFID.
Intelligent Smart-Phone Based Assistive Driving Solution for Power Wheelchairs
Power wheelchairs (PWCs) have been widely utilized to improve the independence of people with disabilities. Although PWCs usually provide convenient joystick-type interfaces that can be easily operated with hands, for people with severe cognitive, motion, or sensory issues, it is still a difficult task to operate PWCs to their fullest extent for an extended period of time. To alleviate the operation difficulties and ease the burden and stress of disabled individuals, this project focuses on the design and development of a cost-effective, smartphone-based Intelligent Assistive Driving system for Power Wheelchair (PWC IA-Driving).
Optimizing Distributed Deep Neural Network (DNN) Inference for Low Latency and Energy Efficiency on Resource-Constrained Edge Devices
This research project aims to develop and evaluate novel methods for optimizing distributed deep neural network (DNN) inference to achieve low latency and energy efficiency on resource-constrained edge devices. Current approaches for distributed DNN inference typically involve splitting neural network layers or operations across multiple edge devices. However, these configurations are often pre-determined and fail to adapt to fluctuations in network latency and bandwidth, resulting in performance degradation and increased energy consumption.
Tracing Ecological Changes During the Early Cretaceous Using Thin Section Analysis
About 120 million years ago, marine ecosystems had to adapt to major environmental changes that resulted in oceanic anoxia. The objectives of this project are to (1) trace changes in the ecology of these ecosystems based on thin sections analysis, and (2) identify what environmental parameter forces the ecosystems to adapt based on the review of existing geochemical dataset. Students will have hands-on experience in the fields of microscopic analysis of rock samples, data analysis using the language R, and presentation of scientific results.
Training Large Language Models at the Speed of Light
As large language models (LLMs)—the technology behind chatbots, translation tools, and other AI applications—become increasingly complex, the amount of computation required to train them has skyrocketed. Traditional computer processors often struggle to handle this demand efficiently. This project aims to explore silicon photonics, a groundbreaking technology that uses light, not electricity, to process and transmit data. Light is much faster and more energy-efficient than electrical signals, making silicon photonics a potential game-changer for training large AI models.
