Research

We present ACO-ToT, an algorithm that leverages fine-tuned LLM "ants" guided by ant colony optimization to efficiently uncover optimal reasoning paths for complex problems.

We present MINDSTORES, an experience-augmented planning framework that enables embodied agents to build and leverage mental models through natural interaction, improving zero-shot LLM planning for complex open-world tasks.

We introduce forward neural network models that integrate fin stiffness to predict UUV kinematics, thrust, and efficiency, improving performance in bio-inspired fin designs.

We introduce forward neural network models that integrate fin stiffness to predict UUV kinematics, thrust, and efficiency, improving performance in bio-inspired fin designs.

We aggregate corpora from UN General Debates and emerging media for conflict prediction.

We analyze changes in early voting and absentee voting rates in Virginia since the 2020 General Election, examining factors like voter enthusiasm, party initiatives, and political issues to understand post-COVID voting behavior.

Developed power-aware machine learning models for underewater vehicle control, improving efficiency while maintaining performance.

We analyze the UN General Debate Corpus to track the influence of African post-colonial states, using natural language processing to forecast shifts in global priorities and the efficacy of crisis resolution measures.

We develop a search-based inverse model that leverages a kinematics-to-thrust neural network to determine flapping fin kinematics achieving target thrust and smooth transitions, enabling real-time control adjustments for UUV propulsion.

We propose an opt-in system with multimodal similarity measures and metadata tagging for fair IP compensation and attribution, resolving copyright disputes over LLM-generated content.

We develop a non-dimensional figure of merit (FOM) to evaluate fin designs and kinematics for bio-inspired UUVs, using computational models to predict thrust, power, and efficiency, optimizing gait performance and material selection.

We identify ocular biomarkers and define data collection procedures for saccade and pursuit tasks to develop a gaze-based diagnostic tool for early neurodegenerative disease detection.

Development of Quantum Machine Learning Agents to Model Simple Economies

We propose a new approach to agent-based modeling that integrates quantum computing, creating adaptive learning agents to simulate decision-making in complex environments, offering performance benefits over traditional models.

We propose the establishment of a dedicated youth group on AI within global governance frameworks, ensuring youth representation in discussions on data privacy, ethical AI, and the digital divide to foster positive AI solutions.

This brief calls on the UK to invest in quantum literacy and youth advocacy initiatives alongside robust policy frameworks that bolster national security and international cooperation, securing its leadership in the emerging quantum revolution.

Analysis of how European university models influenced American higher education and innovation.