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Research & Development
Artificial Intelligence and Machine Learning (AI/ML)
Integrating top-down, model-based understanding with bottom-up, data-driven insights, we design, develop, and deploy novel artificial intelligence (AI) and machine learning (ML) algorithms and inferential frameworks to achieve tangible impact in healthcare, cybersecurity, defense, and other critical domains.
We actively seek collaborators in the AI research community who are anti-bigotry, anti-eugenics, anti-misogyny, and anti-scientific racism. We care about AI risk, prioritizing research that mitigates actual and near-term risks over imaginary monster risks.
WHY GALOIS?
In the brave new world of generative AI, trust and reliability are key concerns. Galois has more than two decades of experience leveraging the mathematical rigor of formal methods to offer an unparalleled level of assurance in system correctness and security, uniquely suiting us to meet the challenges of trust in AI/ML
We don’t just consider the technical side of software development; we consider the needs and experience of the human beings who will use it. Harnessing insights from cognitive science, human factors, and psychometrics, we aim to build systems that users find helpful, reliable, and effortlessly intuitive.
Our team combines extensive formal methods experience with a mastery of AI/ML techniques and tools, human-machine teaming, multi-agent system design, cognitive science, and more. Our approach aims to address the challenge of trust-in-systems at multiple levels: technical, cognitive, and social.
AREAS OF EXPERTISE
Adversary Resistance
Researching and developing advanced adversary resistance strategies, ensuring that AI/ML models still function as intended and remain trustworthy even under the most challenging conditions or attacks.
AI for Formal Methods
Developing interactive tools that harness the potential of generative AI technologies to make formal methods more accessible to and usable by a wider base of people.
AI/ML Model Auditing and Assurance
Leveraging formal methods and XAI expertise to eliminate broad classes of errors and instill a high degree of trust in the correctness and safety of critical AI/ML systems.
Human Machine Teaming
Creating AI/ML systems that operate less like tools and more like collaborators.
Decision Support Systems
Researching AI/ML models designed to sift through large, ambiguous, or incomplete data sets, identifying patterns and generating insights to facilitate informed decision-making.
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