Thought Leadership
Patents
- Patent pending – Selective data transfer utilizing network feature representation learning
AI Research
Open Source Contribution
- AgentXchange is an open-source framework enabling secure, real-time communication between autonomous agents across edge, cloud, and on-prem environments. It bridges the gap left by MCP with multi-transport messaging, cryptographic identity, and decentralized coordination.
- Agent Skill SDK is a lightweight, event-driven framework for building intelligent agents with modular skills, real-world triggers, memory, and LLM-based planning using Amazon Bedrock.
- Cognitive Agents SDK enables the creation of multi-agent systems that can communicate, negotiate, reason, and make collective decisions . It has collaborative planning engine (shared goal → plan decomposition), personality-aware negotiation, dynamic leadership and role shifting (switch personality or decision heuristics dynamically), LLM driven free form agent-to-agent chat.
Published Articles
Active Research
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Data Driven Modeling for Structural Reliability
Application of machine learning to predict low cycle fatigue of aerospace components
- Low cycle fatigue life is estimated using elastic or plastic finite element stress analysis results for an aerospace part during the design phase. This takes into account the operating temperatures, minimum and maximum loading conditions, high cycle fatigue interaction and geometric characteristics. During scheduled or unscheduled part maintenance, it is necessary to reassess remaining useful LCF life. This poses an operational challenge that often takes over a day to calculate a single sample prediction requiring specialist input. To address this challenge, machine learning can be employed for LCF life prediction in a matter of seconds under known design and operating conditions
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Research in Autism Spectrum Disorder
Using computer vision to track behaviors and aid therapy for young children with autism
- Autism spectrum disorder (ASD) is a developmental disability that can cause significant social, communication and behavioral challenges. Autism affects more than 1% of children and is growing in prevalence at a rate of about 10% per year. Early therapy at a very young age, focused on social skills, has been proven to reduce the most debilitating symptoms of the disorder in later life. These treatment interventions can include behavior therapy, speech-language therapy, play-based therapy and more. However, with any treatment for young children it can be difficult for parents, therapists and physicians to remain objective in tracking progress. Knowing what is working and what isn’t is absolutely critical for fine tuning interventions. Much research emphasis has been placed on finding simple, easy to measure metrics but an underlying problem will always remain; tracking metrics regularly, accurately and without bias is difficult for human beings. Given that treatments typically last years we feel that a computer vision approach of using artificial intelligence to automate tracking would be hugely beneficial. The goal of this project will be to create a proof of concept (POC) showing that useful metrics can be tracked reliably using the OAK-D AI camera during simulated Applied Behavior Analysis (ABA) therapy.