Engineer the Future of Artificial Intelligence
This first-of-its-kind program combines the INI's strength in information security with the growing demand for skilled AI engineers.
Through the Master of Science in Artificial Intelligence Engineering - Information Security (MSAIE-IS) you develop a foundation in information security and learn to apply AI to the design and implementation of information security systems, including networks, software and services.
Launched in 2022 to answer the need for AI engineers who understand the cybersecurity implications of their work, this program heavily focuses on AI and security courses, including topics like systems and tool chains, deep learning, ethics and Machine Learning (ML). This program is part of a suite of AI engineering graduate degrees offered by 麻豆村's College of Engineering.
Outcomes
Internship
Students gain real-world experience by interning at government agencies, major companies and startups.
Top Companies: Amazon
Top Role: Machine Learning Intern; Software Engineer InternMedium Hourly Wage: $51
Career Outcomes
The MSAIE-IS program is our newest program and we currently have only a few data points to represent this program. If you would like to learn more about our career outcomes, explore our outcomes page.
Specialize Your Curriculum
Due to the interdisciplinary nature of this program, the curriculum is carefully paced and designed to prepare you with a strong basis in AI engineering principles and information security before you customize your degree through electives.
You begin by examining the offensive and defensive aspects of AI and its impact on cybersecurity ecosystems. You build on this by focusing on how machine learning (ML) and AI technologies can be incorporated to reach cybersecurity goals, as well as understanding the cyber risks and scalable threats of ML/AI technologies themselves.
You can specialize your security focus through several electives, with topics ranging from ethical penetration testing to engineering policy, usable security and cryptography.
Learning Outcomes
- Demonstrate knowledge of artificial intelligence methods, systems, tool chains and cross-cutting issues including security, privacy, and other ethical, societal and policy challenges
- Demonstrate knowledge and skills related to information security and privacy principles
- Apply state-of-the-art techniques for information security and privacy to artificial intelligence systems including algorithms and infrastructure
- Apply artificial intelligence concepts to the design and implementation of information security systems including networks, software, and services
- Evaluate trade-offs involving information security, policy, business, economic and management principles in artificial intelligence systems
- Create and demonstrate new specialized knowledge through advanced research or development in their chosen area of focus (Advanced study option)
Explore Select MSAIE-IS Courses
14-757: Introduction to Machine Learning with Adversaries in Mind Taught by Dr. David Varodayan
This course serves as an introduction to ML techniques with an additional focus on evasion, poisoning and exploratory attacks, and defenses against these attacks. The course will cover the following ML problems and methods: classification, regression, dimensionality reduction, clustering, expectation-maximization, Markov models and neural networks. Students will complete programming problems on implementation, attack and defense of spam filters, image classifiers, network anomaly detectors, human activity classifiers, real estate pricing models, search engines, and more.
14-789: AI in Business Modeling Taught by Dr. Mohamed Farag
Business models simplify the representation of business systems to improve their performance. AI brings Innovation to the creation and advancement of such business models throughout the business lifecycle. For example, AI can boost the efficiency of business ideation, growth and scaling to increase the opportunities for your project success. However, adopting Artificial Intelligence has much more to it than learning the technical aspects. In this course, we aim to cover two main themes: The innovation in using AI to build and scale businesses, Adopting Artificial Intelligence to an existing enterprise body with legacy processes This course is targeted toward graduate students with interest in learning business foundations for AI along with establishing and growing businesses using the state-of-art AI-focused techniques.
14-795: AI Applications in Information Security Taught by Dr. David Varodayan
Information security attacks can generate vast amounts of data in the form of files, logs, network packets, and more. In this course students will learn how AI systems leverage data to detect and attribute threats such as spam, malware, botnets and network intrusion. The course will examine each of the following stages in an AI workflow in the context of information security applications: data preparation and visualization; feature extraction and selection; model selection, training, tuning and evaluation. The course will also discuss issues of AI explainability and adversarial attacks against AI.