Where AI Safety Breakthroughs Begin
Advanced research center pushing the boundaries of AI safety, alignment, and governance. Where academic rigor meets real-world impact.
24 Active Projects
8 Research Labs
150+ Publications
Peer Reviewed
40+ Partners
Open Access
EU AI Act Compliance Deadline
Terranova MU is fully aligned with emerging AI governance frameworks
285
Days
14
Hours
42
Minutes
08
Seconds
Advanced Research Areas
Adversarial Robustness
Developing cutting-edge methods to ensure AI systems remain reliable under adversarial conditions and edge cases.
Alignment Testing
Rigorous testing frameworks to ensure AI systems behave according to intended objectives and human values.
Autonomous Systems Safety
Ensuring autonomous systems can safely operate in real-world environments with minimal human intervention.
Critical Infrastructure AI
Specialized research on AI applications in power grids, water systems, and other essential services.
Quantum-AI Security
Pioneering research on intersection of quantum computing and AI security paradigms.
Our Research Labs
Safety Lab
Lead: Dr. Sarah Chen
Focus: AI Safety & Robustness
Developed the Chen-2024 Adversarial Test Suite, now adopted by 30+ institutions
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Alignment Lab
Lead: Dr. Marcus Thompson
Focus: Value Alignment
Published 24 peer-reviewed papers on alignment verification methods
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Autonomy Lab
Lead: Dr. Priya Sharma
Focus: Autonomous Systems
Real-world testing framework for autonomous vehicle safety
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Infrastructure Lab
Lead: Dr. James Wilson
Focus: Critical Infrastructure
NIST partnership on power grid AI resilience
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Interpretability Lab
Lead: Dr. Elena Rodriguez
Focus: AI Explainability
XAI framework adopted by European regulators
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Governance Lab
Lead: Dr. Kai Zhang
Focus: Policy & Governance
Contributed to EU AI Act technical recommendations
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Ethics Lab
Lead: Dr. Amara Okafor
Focus: Ethical AI Systems
Ethics framework for algorithmic fairness
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Quantum Lab
Lead: Dr. Boris Petrov
Focus: Quantum Computing
Quantum-resistant AI cryptography breakthrough
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Our Research Methodology
01
Discovery
Identify critical safety gaps through literature review and stakeholder engagement.
02
Hypothesis
Develop testable hypotheses and theoretical frameworks for safety solutions.
03
Testing
Rigorous experimentation and validation in controlled laboratory environments.
04
Validation
Peer review and cross-validation with external research groups and institutions.
05
Publication
Share findings in top-tier journals and conferences to advance field knowledge.
06
Implementation
Support real-world adoption through tools, partnerships, and training programs.
Featured Publications
Certified Adversarial Robustness via Randomized Smoothing
We present a novel framework for achieving certified robustness guarantees against adversarial perturbations using randomized smoothing techniques on deep neural networks.
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156 Citations
Interpretability Without Sacrificing Accuracy: A Framework for XAI
We propose a unified framework that achieves high model accuracy while maintaining interpretability, addressing the long-standing accuracy-interpretability tradeoff.
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89 Citations
Value Alignment Through Iterative Preference Learning
Novel methodology for aligning AI systems with human values through iterative preference learning and multi-stakeholder feedback integration.
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124 Citations
Our Research Impact
150+
Peer-Reviewed Papers Published
2,400+
Total Citations
$12M+
Active Research Grants
40+
International Collaborators
Partnership Categories
Academic
Collaborations with top universities worldwide advancing fundamental research in AI safety.
Government
Partnerships with national agencies on policy development and regulatory frameworks.
Enterprise
Working with industry leaders to implement safety practices in production systems.
Defence
Strategic research partnerships on critical infrastructure and autonomous systems security.