Cognitive neuroscientist and AI architect with 15+ years of experience designing, engineering, and deploying psychologically grounded AI solutions. PhD in Cognitive Neuroscience and Artificial Intelligence from the University of Western Australia; deep expertise in human behaviour modelling, psychometrics, causal inference, and Responsible AI governance and risk management. Architected and operated production-grade generative AI platforms in highly regulated industries, delivering human-centric AI that augments decision-making and drives measurable outcomes. Champion ethical AI, end-to-end model lifecycle management (MLOps and LLMOps), rigorous evaluation, and safety by design to realise human potential and build high-performing, positive organisational cultures.

• Oversaw a boutique artificial intelligence consultancy specialising in AI solutions for regulated sectors.
• Devised agentic AI systems modelled on human decision-making, applying cognitive science to enhance user engagement and interaction.
• Designed and launched Retrieval-Augmented Generation (RAG) pipelines grounded in psychological frameworks, ensuring AI outputs emulate human cognitive processes and communication standards.
• Established behavioural AI evaluation protocols to rigorously validate accuracy and psychological soundness, bolstering user trust and adoption.
• Delivered enterprise-level training initiatives on ethical AI deployment and advanced strategies for human–AI collaboration.

• Transformed life insurance underwriting by developing AI-driven platforms that enhanced underwriter expertise while maintaining empathy and fairness.
• Initiated and delivered a generative AI underwriting assistant utilising multi-agent LLM architecture with RAG, expediting processing by 4.4x and improving decision precision.
• Originated advanced prompt engineering strategies informed by psychological research, enabling AI to interpret nuanced human scenarios in insurance contexts.
• Rolled out conversational AI solutions skilled at gathering sensitive health information with empathy and cultural awareness.
• Formulated and upheld comprehensive AI governance frameworks in accordance with APRA regulatory requirements, guaranteeing fair and compliant results for all stakeholders.
• Managed a cross-functional product team of six, embedding AI capabilities seamlessly into core product workflows.

• Applied cognitive modelling techniques in geospatial intelligence to create AI systems that amplify human pattern recognition.
• Constructed AI-powered decision support platforms merging satellite imagery analytics with geological expertise.
• Coordinated collaborative AI development processes that integrated domain expert knowledge with sophisticated analytics.
• Supervised and nurtured a multidisciplinary team spanning earth sciences, data engineering, and cognitive systems.

• Initiated research at the convergence of human cognition and artificial intelligence, advancing foundational understanding in both fields.
• Guided studies into cognitive architectures for planning and reasoning, directly influencing contemporary LLM advancements.
• Composed and published peer-reviewed articles on human intelligence assessment employing neuroimaging and behavioural modelling methods.
• Devised psychometric models to quantitatively assess cognitive abilities and predict performance outcomes.
• Mentored and supported PhD candidates conducting research on neuroimaging of cognitive control, reasoning, attention, and consciousness.

• Executed pioneering experimental neuroimaging studies exploring the neural representation of verbal instructions in the frontoparietal cortex, yielding insights that shaped prompt engineering techniques and informed behavioural paradigms for large language models (LLMs).

• Acquired a well-rounded online MBA education with a strong focus on lean innovation, strategic product management, and exemplary organisational leadership practices.

• Gained in-depth proficiency in cognitive psychology, statistical methods, and neuroscience through a demanding double major Honours programme.

• Exhibited advanced proficiency in designing and operationalising models with PyTorch, TensorFlow, JAX, LangChain, Hugging Face, OpenAI APIs, and Anthropic APIs, addressing intricate machine learning problems.

• Constructed resilient data pipelines and conducted sophisticated analytics using Python, R, MATLAB, SQL, Spark, Databricks, and vector databases, underpinning evidence-based decision-making.

• Devised and automated scalable cloud infrastructures on AWS, Azure, and Google Cloud Platform; coordinated containerised deployments with Docker and Kubernetes; established CI/CD pipelines and MLflow to optimise machine learning workflows.

• Performed advanced EEG/fMRI data interpretation, formulated psychometric models, and applied behavioural statistics to support scientific and applied investigations.

• Fused cognitive psychology and neuroscience insights into AI system design for over 15 years, advancing model transparency and human-centric outcomes.
• Originated and verified psychometric models, executed latent trait analyses, and crafted behavioural prediction algorithms.
• Published peer-reviewed articles on human intelligence, cognitive regulation, and decision-making.
• Translated psychological theory into AI logic and algorithmic structures to address practical challenges.

• Oversaw comprehensive AI product delivery within interdisciplinary teams, guaranteeing synergy with organisational goals and technical rigour.
• Encouraged collaboration among product managers, engineers, designers, and data scientists to stimulate innovation.
• Adopted Agile methodologies, focusing on iterative experimentation and rapid prototyping.
• Converted research findings into impactful production features, generating tangible business outcomes.

• Formulated ethical AI frameworks for insurance solutions, securing full APRA compliance and regulatory adherence.
• Exhibited in-depth knowledge of GDPR, privacy-preserving machine learning strategies, and interpretable AI approaches.
• Devised and implemented adversarial testing and red-teaming procedures to reinforce LLM safety and resilience.
• Advocated for human-centric AI initiatives that enhance human judgement and foster responsible progress.

• Devised a comprehensive theoretical framework for LLM personalisation by applying cognitive behavioural therapy concepts, such as:
• Applied latent trait modelling to customise dynamic and adaptive user engagement approaches
• Directed reinforcement learning from human feedback to maintain consistency with validated psychological practices
• Formulated sophisticated conversational pathways inspired by therapeutic communication frameworks

• Established and executed sophisticated protocols for ethical AI implementation in human assessment, including:
• Introduced bias identification and reduction pipelines based on psychometric analysis
• Adopted explainable AI frameworks to deliver actionable intelligence to diverse professional audiences
• Developed and deployed privacy-enhancing solutions for the secure handling of confidential personal information

• neuroprompting.xyz: Compiled and integrated an extensive prompt collection that merges cognitive neuroscience principles with state-of-the-art artificial intelligence methodologies.

• Composed a detailed examination of utilising sleep-time compute cycles to enhance cost-effectiveness in AI infrastructure.

• Introduced pragmatic methodologies for refining resource distribution in extensive machine learning environments.

• Showcased industry examples evidencing tangible reductions in operational expenditure.

• Explored vulnerabilities in large language models (LLMs) by merging experimental psychology techniques.

• Uncovered cognitive biases shaping model responses and exposure to adversarial prompts.

• Outlined mitigation approaches rooted in empirical psychological findings.

• Examined system architectures that facilitate ongoing reasoning within artificial intelligence platforms.

• Originated conceptual frameworks for uninterrupted cognitive operations in neural networks.

• Assessed the influence of continuous inference cycles on model dependability and output quality.

• Questioned dominant perspectives on the scalability of artificial intelligence.

• Devised innovative strategies to surpass prevailing model scaling barriers.

• Demonstrated alternative routes for advancing AI capabilities through empirical support.

• Scrutinised underlying factors contributing to prompt injection vulnerabilities in LLMs.

• Engineered structured methodologies for identifying and countering injection attacks.

• Summarised best practices to fortify conversational AI against adversarial exploitation.

• Investigated hierarchical neural processing in both biological and artificial intelligence contexts.

• Contrasted multi-layered system architectures to clarify similarities in data handling.

• Advanced interdisciplinary insights into the emergence of intelligence.

• Chronicled the progression and ramifications of the DAN attack on AI safety standards.

• Explored protective measures and community responses within the AI sector.

• Drew key insights for future adversarial risk management.

• Performed extensive red-teaming evaluations on leading-edge LLMs.

• Appraised the effectiveness of diverse security assessment techniques.

• Suggested refinements to existing red-teaming protocols for heightened resilience.

• Established monitoring systems to identify misbehaviour in reasoning models.

• Reviewed unintended impacts of obfuscation strategies within AI frameworks.

• Formulated policy recommendations to harmonise transparency with security.

• Investigated psychological mechanisms underlying emergent behaviours in large language models.

• Detected concealed vulnerabilities stemming from cognitive blind spots in AI logic.

• Proposed remedial measures to address unpredictable system outputs.