Agentic Systems
A research archive dedicated to the intellectual pursuit of complex systems, driven by multi-agent Artificial Intelligence orchestration and High-Performance Computing.
A multi-agent orchestration framework designed to automate the triage and synthesis of genomic evidence, leveraging Amazon Bedrock AgentCore and Nova Premier with Grounding.
An experimental pipeline with Convolutional Neural Networks for variant calling inspired by DeepVariant, an open-source, deep-learning-based variant caller developed by Google Research.
Collaborative agentic systems designed to bridge the gap between human expertise and complex genomic datasets.
Showcases how to empower researchers and bioinformaticians to effortlessly query complex biological knowledge graphs in Neo4j without needing Cypher expertise.
A modular application to help healthcare professionals and researchers to effortlessly query drug-therapy-outcome relationships without needing specialized database skills.
Efficient parameter-tuning on the SQuAD dataset using Low-Rank Adaptation to optimize model performance for question-answering tasks.
Dynamic Neo4j Cypher query generation using prompt engineering and KNIME-based LLM integration.
A demonstration of a multi-MCP server architecture orchestrated by a Large Language Model for AI algorithm explanations, comparisons, and code examples.
A multi-agent conversational framework designed to facilitate investigative reasoning and research synthesis in legal contexts.
Recursive reasoning demo designed for high-level research synthesis and investigative logic based on Riskiest Assumption Test (RAT) framework.
Automated multimodal tool for generating flashcards and content clarity improvements from slide decks.
Quantitative performance analysis of CPU vs. NVIDIA GPU-accelerated array processing with CUDA.
Developing agentic collaboration protocols for IT infrastructure monitoring and support.
Local-first research analysis utilizing Streamlit and Ollama for secure, on-device data processing.
Personalized food recommendation engine based on semantic similarity using vector embeddings in ChromaDB powered by a Large Language Model.
LLM-based travel recommendation engine based on cultural significance, adventure, and local sentiment.