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The Texas Cybersecurity Clinic is a two-semester sequence that first equips students with the technical and business skills of an entry-level cybersecurity analyst (semester 1) and then partners them in (supervised) teams with a small local business, municipal government, nonprofit to render pro bono cybersecurity services (semester 2). During the first semester, students will learn key cybersecurity defense concepts and skills, such as vulnerability assessment, network configuration and security, access controls, authorization techniques, responding to a cyberattack, business planning, and penetration testing. Students will also learn how to form an effective cybersecurity operations team and communicate with organization and business leaders and employees about essential cybersecurity controls and functions. By the conclusion of this course, students will be prepared to work within their assigned teams to assess, design, and render a cybersecurity improvement project plan for their client organization next semester.

The Texas Cybersecurity Clinic is a two-semester sequence that first equips students with the technical and business skills of an entry-level cybersecurity analyst (semester 1) and then partners them in (supervised) teams with a Central Texas-based small business, municipal government, or nonprofit to render pro bono cybersecurity services (semester 2). During the first semester, students will learn key cybersecurity defense concepts and skills, such as vulnerability assessment, network configuration and security, access controls, authorization techniques, responding to a cyberattack, business planning, and penetration testing. Students will also learn how to form an effective cybersecurity operations team and communicate with organization leaders and employees about essential cybersecurity controls and functions. During the second semester, students work within their assigned teams to assess, design, and render a cybersecurity improvement project plan for their designated client organization, building cybersecurity capacity and bolstering the client organization’s ability to recover from a cyber incident long-term.

Explore common data collection, management, and sharing practices in information technology and emerging technologies, such as search engines and AI systems. Students will read papers and engage in discussions about the pros and cons of established data practices and learn about the three main components of responsible data management: 1) consent and ownership, 2) privacy and anonymity, and 3) broader impact. Students will also practice how to collect data, make data-driven decisions, and design data-driven products through group projects as UX designers, researchers, and data scientists. The course will bring in interdisciplinary perspectives with guest speakers from archive science, engineering, and respponsible AI, to provide a holistic view of broader data ecosystems and infrastructures.

Develop prompts for text and image generation through an iterative cycle, making the most of foundation models, including large language models and diffusion models. Overview of the field of prompt engineering, including historical development, ethical dilemmas, and the creation of chatbots.

Develop prompts for text and image generation through an iterative cycle, making the most of foundation models, including large language models and diffusion models. Overview of the field of prompt engineering, including historical development, ethical dilemmas, and the creation of chatbots.

The current Web has experienced tremendous changes to connect data, people, and knowledge. There are a couple of exciting efforts trying to bring the Web to its full potential. The Semantic Web is one of them which is heavily embedded in the Artificial Intelligence area with the long-term goal to enhance the human and machine interaction by representing data semantics, integrating data silos, and enabling intelligent search and discovery. This course aims to provide the basic overview of the Semantic Web in general, and data semantics in particular, and how they can be applied to enhance data integration and knowledge inference. Ontology is the backbone of the Semantic Web. It models the semantics of data and represents them in markup languages proposed by the World Wide Web Consortium (W3C). W3C plays a significant role in directing major efforts at specifying, developing, and deploying standards for sharing information. Semantically enriched data paves the crucial way to facilitate the Web functionality and interoperability. This course contains three parts: Semantic Web language, RDF graph database (i.e., RDF triple store), and its applications. The fundamental part of the course is the Semantic Web languages. It starts from XML and goes further to RDF and OWL. The RDF graph database part introduces different APIs of Jena and its reasoners. The application part showcases current trends on semantic applications. Prerequisites Basic knowledge of HTML and XML is desired. Course Objectives This course aims to develop a critical appreciation of semantic technologies as they are currently being developed. At the end of this course, students should be able to: • sketch the overall architecture of the Semantic Web. • identify the major technologies of the Semantic Web and explain their roles. • illustrate the design principles of the Semantic Web by applying the technologies. • understand certain limitations of the Semantic Web technologies, and be aware of the kinds of services it can and cannot deliver. Course aims are achieved through: • Lectures covers basic knowledge of the Semantic Web • Projects applying semantic technologies to concrete problems of information delivery and use • Assignments of practicing and utilizing key semantic technologies

Introduction to combining human and machine intelligence to benefit people and society. Explore cutting-edge research on a number of subjects related to human-AI interaction, including the psychological and societal impacts of AI as well as design guidelines and methods for human-centered AI.