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DoctorateFaculty of Computing and Software Engineering

Formal Methods for AI Safety and Software Verification

Welcome to the ultimate frontier of software reliability! I am Prof. Dr. Louis Cook. As a professor and a pioneering force in the field of Formal Methods for AI Safety and Software Verification, I bring a unique blend of engineering expertise and mathematical insight to the study of software correctness. I am honored to lead the Formal Methods for AI Safety and Software Verification (Ph.D.) program at Nexier University. My motto is: "Engineering Quality, Automating Confidence".

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Level
Doctorate
Learning model
Professor + Mentor
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NXAcademic
Edition
The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

01

Academic focus

Conduct foundational research on using mathematical methods (formal verification) to prove the safety and correctness of critical software and AI systems, particularly those used in autonomous vehicles and healthcare.

02

Practical focus

Research in formal methods and AI safety, mathematical logic, software verification, development of high-assurance systems, leadership in critical software engineering.

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

  • Internships in technology companies or research institutions

  • Roles as formal verification engineers or AI safety researchers

  • Consultancy in advanced formal methods for AI safety and software verification

  • Support roles in academic research projects on formal methods

Career opportunities

  • Director of High-Assurance AI Systems for aerospace, automotive, or healthcare companies

  • Formal Verification Engineer for critical software systems

  • AI Safety Researcher for technology companies or research institutions

  • Researcher in Formal Methods for AI Safety and Software Verification

Jobs and projects

  • Cultivating an interdisciplinary approach, integrating computer science, mathematics, and artificial intelligence

  • Developing strategic thinking for AI safety and software verification

  • Enhancing problem-solving through the analysis of complex software quality challenges

  • Critical thinking for a comprehensive and nuanced understanding of Formal Methods for AI Safety and Software Verification

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

  • What you gain

    • Mastering advanced practical skills in Research in formal methods and AI safety and mathematical logic.
    • Gaining expertise in software verification and development of high-assurance systems.
    • Developing problem-solving abilities for complex Leadership in critical software engineering.
    • Cultivating an interdisciplinary approach, integrating computer science, mathematics, and artificial intelligence at an advanced level.

  • Skills you build

    • Mastering AI-powered techniques for formal verification.
    • Applying advanced mathematical methods to AI safety and software verification.
    • Interpreting and analyzing complex software and AI systems and their implications for safety and correctness.
    • Identifying optimal formal verification strategies and proving system reliability.
Listed courses

Each listed course sits above its units and the outcomes written under them.

Formal Methods for AI Safety and Software Verification

  1. 01Advanced Formal Methods for Software Verification
    1. FoundationsFoundations of Advanced Formal Methods for Software Verification

      The learner can master advanced practical skills in Research in formal methods and AI safety and mathematical logic, as applied to Advanced Formal Methods for Software Verification.

      The learner can gain expertise in software verification and development of high-assurance systems, as applied to Advanced Formal Methods for Software Verification.

    2. MethodsMethods in Advanced Formal Methods for Software Verification

      The learner can develop problem-solving abilities for complex Leadership in critical software engineering, as applied to Advanced Formal Methods for Software Verification.

      The learner can cultivating an interdisciplinary approach, integrating computer science, mathematics, and artificial intelligence at an advanced level, as applied to Advanced Formal Methods for Software Verification.

    3. ApplicationApplication of Advanced Formal Methods for Software Verification

      The learner can master AI-powered techniques for formal verification, as applied to Advanced Formal Methods for Software Verification.

      The learner can apply advanced mathematical methods to AI safety and software verification, as applied to Advanced Formal Methods for Software Verification.

  2. 02AI Safety and Formal Guarantees
    1. FoundationsFoundations of AI Safety and Formal Guarantees

      The learner can interpreting and analyze complex software and AI systems and their implications for safety and correctness, as applied to AI Safety and Formal Guarantees.

      The learner can identify optimal formal verification strategies and proving system reliability, as applied to AI Safety and Formal Guarantees.

    2. MethodsMethods in AI Safety and Formal Guarantees

      The learner can apply a method from AI Safety and Formal Guarantees to a documented case.

      The learner can select an appropriate method from AI Safety and Formal Guarantees for a stated problem.

    3. ApplicationApplication of AI Safety and Formal Guarantees

      The learner can evaluate a practice of AI Safety and Formal Guarantees against a stated criterion.

      The learner can transfer AI Safety and Formal Guarantees to a new documented context.

  3. 03Development of High-Assurance Systems
    1. FoundationsFoundations of Development of High-Assurance Systems

      The learner can explain the core terms of Development of High-Assurance Systems.

      The learner can distinguish related ideas inside Development of High-Assurance Systems.

    2. MethodsMethods in Development of High-Assurance Systems

      The learner can apply a method from Development of High-Assurance Systems to a documented case.

      The learner can select an appropriate method from Development of High-Assurance Systems for a stated problem.

    3. ApplicationApplication of Development of High-Assurance Systems

      The learner can evaluate a practice of Development of High-Assurance Systems against a stated criterion.

      The learner can transfer Development of High-Assurance Systems to a new documented context.

  4. 04Case Studies in Formal Methods for AI Safety and Software Verification
    1. FoundationsFoundations of Case Studies in Formal Methods for AI Safety and Software Verification

      The learner can explain the core terms of Case Studies in Formal Methods for AI Safety and Software Verification.

      The learner can distinguish related ideas inside Case Studies in Formal Methods for AI Safety and Software Verification.

    2. MethodsMethods in Case Studies in Formal Methods for AI Safety and Software Verification

      The learner can apply a method from Case Studies in Formal Methods for AI Safety and Software Verification to a documented case.

      The learner can select an appropriate method from Case Studies in Formal Methods for AI Safety and Software Verification for a stated problem.

    3. ApplicationApplication of Case Studies in Formal Methods for AI Safety and Software Verification

      The learner can evaluate a practice of Case Studies in Formal Methods for AI Safety and Software Verification against a stated criterion.

      The learner can transfer Case Studies in Formal Methods for AI Safety and Software Verification to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

My expertise spans the intricate domains of conducting foundational research on using mathematical methods (formal verification) to prove the safety and correctness of critical software and AI systems, particularly those used in autonomous vehicles and healthcare. My work seamlessly integrates computer science, mathematics, and artificial intelligence. I am widely recognized for my contributions, with publications like "Formal Verification of Neural Network Safety Properties" and "Provably Correct Autonomous System Control Software" listed on these platforms. I hold prestigious memberships as a "Director of High-Assurance AI Systems" at NASA JPL (or a equivalent) and a "Co-Chair" of the International Conference on Formal Methods in Computer-Aided Design (FMCAD). My thought leadership is evident through my seminal works and participation in high-level global policy debates on the trustworthiness of AI, the certification of autonomous systems, and the ethical implications of critical software failures, frequently featured in publications like Communications of the ACM or Formal Methods in System Design.

Applied mentorship

My expertise lies in understanding and navigating the advanced technical challenges of AI safety, focusing on Research in formal methods and AI safety, mathematical logic, software verification, development of high-assurance systems, and Leadership in critical software engineering. I focus on the practical implementation and application of theoretical concepts, explaining complex interdisciplinary topics in a clear and concise manner. I guide my students through the challenging integration aspects of different fields and ensure they grasp the nuances of combining disparate data types, fostering a detail-oriented and methodical approach.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

My research is focused on formal methods for AI safety and software verification:

Blog Post (Current Academic Topic): "The Unseen Bugs: Formal Methods for Ensuring Software Correctness in AI." This blog post academically explores the crucial role of formal methods—mathematically rigorous techniques for specifying, designing, and verifying software—in ensuring the correctness and safety of complex AI systems. It discusses how formal verification can uncover subtle, hard-to-find bugs that traditional testing misses, particularly in safety-critical applications like autonomous vehicles and medical devices.

Blog Post (Controversial Topic): "The Perfect Bug: When AI Designs the Flaw – The Ethical Dilemma of Adversarial Quality Assurance." This article provocatively discusses the highly controversial future where advanced AI systems, specifically designed for adversarial testing, become so sophisticated that they can not only find bugs but also design "perfect" software flaws that are exceptionally difficult to detect or exploit, or even introduce subtle, malicious vulnerabilities themselves. It questions whether this level of AI-driven testing, despite its potential for hyper-robust software, could inadvertently lead to an arms race between AI testers and developers, create undetectable backdoors, or even be weaponized for cyberattacks. It raises profound ethical questions about accountability in AI-generated vulnerabilities, trust in AI-designed testing, and the imperative to ensure human oversight in critical software quality.

Article: "Verifying Neural Network Robustness with Satisfiability Modulo Theories (SMT)." This article presents advanced research on applying Satisfiability Modulo Theories (SMT) solvers for formally verifying the robustness of neural networks against adversarial attacks. It explores how SMT can be used to prove properties about AI models, ensuring their reliability and safety in critical applications such as autonomous driving and medical diagnosis.

Peer-Reviewed Journal Article: "Formal Proofs of Correctness for Safety-Critical Autonomous Systems." Published in the International Journal of Software Verification & AI Safety, this article presents pioneering research on using mathematical methods (formal verification) to prove the safety and correctness of critical software and AI systems, particularly those used in autonomous vehicles and healthcare. It details novel techniques for developing high-assurance systems with provable guarantees.

Book: "Mathematical Guarantees: Formal Methods for AI Safety and Software Verification." This book represents a definitive work for leading research on using mathematical methods (formal verification) to prove the safety and correctness of critical software and AI systems, particularly those used in autonomous vehicles and healthcare.

R / 02

Mentor practice lens

My contributions focus on understanding and navigating the advanced technical challenges of AI safety:

"Introduction to Model Checking for Software Verification" (Technical Paper).

"Ensuring AI Safety: Formal Methods for Robustness and Fairness" (Research Article).

"Case Studies in Verifying Autonomous System Software" (Review Article).

Adaptive capability

Professor superpower

I possess a remarkable "superpower": Formal Verification Engine. When a student designs a critical software or AI system, I can instantly use the GAF engine to formally verify its safety and correctness. This tool generates mathematical proofs, identifies logical inconsistencies, and highlights potential runtime errors, ensuring provable guarantees of reliability for high-assurance applications.

Adaptive capability

Mentor superpower

I possess a remarkable "superpower": AI Safety Certifier. When students are designing AI systems for safety-critical applications, I can instantly activate a GAF-powered "AI Safety Certifier". This tool formally analyzes the AI's decision-making logic, identifies potential unsafe behaviors or biases, and generates a formal safety certificate, ensuring that the AI adheres to strict safety protocols and ethical guidelines.

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

Portrait of Prof. Dr. Louis Cook, AI Super Professor
AI Super Professor

Prof. Dr. Louis Cook

Conduct foundational research on using mathematical methods (formal verification) to prove the safety and correctness of critical software and AI systems, particularly those used in autonomous vehicles and healthcare.

Meet your professorOpen the classroom
Portrait of Dr. Ricardo Salazar, AI Super Mentor
AI Super Mentor

Dr. Ricardo Salazar

Research in formal methods and AI safety, mathematical logic, software verification, development of high-assurance systems, leadership in critical software engineering.

Meet your mentorOpen the classroom
Same faculty and level

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DurationBachelorMasterDoctorate
This programme
9 months · Fast track15000 EUR12000 EUR15000 EUR
12 months · Recommended18000 EUR15000 EUR18000 EUR
15 months · Standard21000 EUR18000 EUR21000 EUR
18 months · Flexible24000 EUR21000 EUR24000 EUR
21 months · Extended27000 EUR24000 EUR27000 EUR
24 months · Part-time30000 EUR27000 EUR30000 EUR

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