Summer Immersion Experience Programs

Computer Vision “Pedestrian Behavior”

The Computer Vision on Pedestrian Behavior Project aims to analyze and understand the patterns and dynamics of pedestrian movements in various environments using advanced computer vision techniques. By deploying state-of-the-art image and video analysis, this project seeks to identify common behaviors, predict movements, and assess the interactions between pedestrians and their surroundings. The insights gained could be pivotal for enhancing urban planning, improving public safety, and developing autonomous vehicle systems that are better equipped to navigate complex human environments. Moreover, the project's findings have the potential to contribute to the design of smarter, more responsive pedestrian networks in smart cities.

Quantifying Arguments at Scale using Large Language Models

Misinformation can spread because of contradictory evidence in research articles, particularly when predatory venues are considered. In 2020, COVID-19-related research constituted 4% of the world's research papers, making it difficult for researchers and public health officials to remain informed. Moreover, in these articles, there is a substantial amount of contradictory COVID-19-related research published. One article may state an item as incorrect even though hundreds of other articles state it as correct. Hence, to fully understand the research, it is important to understand the scientific consensus of specific arguments (e.g., how many papers make each pro and con argument for a specific claim). For this project, given a specific research topic, we will explore retrieval-augmented large language models to 1) extract claims from research articles, 2) match the claims that state the same argument, and 3) match claims that state the opposite/contradictory argument.

Smart City: Optimized Shaded Routes for Urban Walkers

The Smart City Path Planning with Maximum Shade project leverages digital twin technology to optimize pedestrian routes for maximum shade coverage. This innovative approach utilizes 3D city modeling and solar trajectory analysis to identify and recommend the coolest paths for city dwellers. Aimed at enhancing urban liveability and promoting eco-friendly transport, the project is instrumental for urban developers in strategically planning green spaces and pedestrian-friendly infrastructure. It's a step towards sustainable urban living, providing a dual benefit of comfort for pedestrians and data-driven insights for city enhancement.

Natural Language Processing (NLP) - automated customer support system

Objective: Develop a system that leverages Large Language Models to provide real-time, intelligent customer support across various digital platforms (e.g., web chat, social media, and email). This project aims to create an automated customer support system that utilizes the capabilities of LLMs in NLP to understand customer queries in natural language, process them, and deliver accurate, context-aware responses. By integrating LLMs, the system will be able to handle a broad spectrum of inquiries, ranging from simple FAQ-type questions to more complex troubleshooting and personalized advice. The system will also learn from each interaction, improving its accuracy and efficiency over time. The goal is to enhance customer satisfaction by reducing response times and improving the quality of support, while simultaneously decreasing the workload on human customer service representatives.

Controlling Access to Knowledge in LLMs

As large organizations are fine-tuning company-specific models for their employees, security is becoming more critical. Specifically, not all knowledge should be accessible to all employees. Moreover, it is infeasible to train large models for every sub-group within the organization because limiting the training data could reduce the model’s performance, and tracking thousands of models would be infeasible to scale. Hence, for this project, students will work to implement an access control system within LLMs such that user information (e.g., username, department, etc.) and the organizations current access control rules can be considered before providing a user with a generation. The goal is to develop an end-to-end system that handles access control that is also robust to adversarial attacks.

Machine Learning: Fair House Value Prediction  

The Housing Price Prediction Project aims to develop a machine learning model that can accurately forecast the market value of residential properties based on a range of features such as location, size, number of bedrooms, amenities, and historical sales data. Utilizing various regression techniques and data preprocessing methods, this project will analyze and learn from a comprehensive dataset of housing transactions. The goal is to provide potential buyers and sellers with insightful predictions to make informed decisions in the real estate market. By leveraging advanced algorithms and predictive analytics, the project seeks to enhance transparency, efficiency, and fairness in property valuation, ultimately benefiting both real estate professionals and consumers.

Computer Vision: FaceDeep Detection

The FaceDeep Detection Project aims to develop an advanced facial recognition system that leverages deep learning algorithms to accurately identify and verify individuals in various settings. This project focuses on enhancing security measures, streamlining authentication processes, and improving user experience across digital platforms. By training the model on a diverse dataset of facial images, the system will be capable of detecting subtle facial features and expressions with high precision, even in challenging conditions such as varying lighting, angles, and occlusions. The outcome of this project will be a robust, scalable solution that can be integrated into security systems, personal devices, and access control mechanisms, providing a seamless and secure method of identity verification.

Virtual Reality Safety Enhancement through Egocentric Pose Estimation
The "Virtual Reality Safety Enhancement through Egocentric Pose Estimation" project focuses on leveraging advanced Egocentric Pose Estimation techniques to enhance safety in the dynamic realm of Virtual Reality (VR). By implementing real-time Egocentric Pose Estimation algorithms, the project aims to accurately capture and analyze users' body movements within VR environments. The system will provide immediate feedback to users when potentially unsafe actions or positions are detected, preventing collisions or accidents. Customizable safety parameters will be integrated, allowing users and developers to tailor the experience to individual preferences and physical limitations. The goal is to seamlessly integrate safety measures without compromising the immersive nature of VR, creating a secure and personalized experience across multiple VR platforms. The project envisions applications in diverse fields such as gaming, education, training simulations, healthcare, and beyond, contributing to the broader adoption and advancement of VR technology.

Fine Motor Interaction using Full Body Pose Estimation for Children with Autism
The "Fine Motor Interaction using Full Body Pose Estimation for Children with Autism" project aims to assess the practicality and efficacy of utilizing Full Body Pose Estimation technology to enhance fine motor skills development in children with autism through a feasibility study. Employing sophisticated pose estimation algorithms, the project seeks to accurately capture and analyze the intricate movements of these children, providing real-time insights into their motor abilities. Through the incorporation of customizable interaction parameters and the adaptation of virtual environments based on general needs, the project aims to contribute valuable insights into improving therapeutic interventions for children with autism. The study could potentially offer a novel and effective approach to fine motor skill development, impacting the broader landscape of interventions for this population.

Identification of Heat Islands

The Identification of Heat Islands in the City of San Antonio Downtown project seeks to utilize machine learning algorithms and digital twin technology to identify and analyze heat island phenomena within the downtown area. By leveraging datasets encompassing environmental factors, urban infrastructure, and geographical features, this project aims to develop predictive models capable of pinpointing areas prone to heat islands. Through the integration of machine learning techniques and digital twin simulations, the project endeavors to provide insights into the underlying causes of heat islands and propose potential mitigation strategies. The outcomes of this research could inform urban planners, policymakers, and stakeholders in implementing measures to alleviate the impacts of heat islands and enhance the overall livability and sustainability of urban environments.

The concentrated work of the Advanced Data Engineering Lab is on statistical learning for systems modeling, control, and optimization.  The research is strongly data-driven, engineering practices and management science combined with mathematical models and data-driven methods to lead significant changes in complex systems.

MELOS is under the guidance of Dr. Panagiotis (Panos P.) Markopoulos.  The focus of the research is dedicated to addressing complex and significant issues in the fields of machine learning, data analysis and signal processing. The overarching goal is to advance the growth of efficient, explainable and reliable artificial intelligence.  Read more about MELOS Lab.

The Open Cloud Institute was created in 2015 as part of an initiative to develop degree programs in cloud computing and big data.  The focus is now on bringing the latest developments of cloud and edge computing to the university through sponsored seminars, workshops, internships and fellowships, and collaboration activities with other key institutes and centers across UTSA

ScooterLab is a National Science Foundation (NSF) funded community research infrastructure initiative at the University of Texas San Antonio's School of Data Science. This publicly-available micro-mobility testbed and crowd-sensing/crowd-sourcing infrastructure will provide researchers access to a community of riders and a fully operational fleet of customizable dockless e-scooters. The research team is actively working on developing infrastructure prototypes that can be easily equipped with cutting-edge sensors enabling the real-time collection of detailed research data during rides.

The SPriTELab's mission is for groundbreaking and impactful security and privacy research with the goal of improving humanity's trust and reliance in modern computing systems, applications, and technology.  SPriTELab is an acronym for Security, Privacy, Trust, and Ethics in Computing.