Hello, I'm Alex

Recently had the opportunity to demo Antidote Intelligence at Continue DX, showcasing our content-aware data poisoning detection system to industry professionals. The response was incredibly encouraging, with several attendees expressing interest in the practical applications for enterprise ML pipelines.

Current Outreach Efforts

I'm actively reaching out to vector embedding marketing vendors and MIT researchers to explore collaboration opportunities around data quality assessment. The core methodology we've developed—using AI agents for hypothesis generation and systematic validation—has broader applications beyond just poisoning detection.

Key Technical Innovations

• 6-agent pipeline for comprehensive content analysis
• Statistical validation using sample size significance algorithms
• Content-aware filtering that goes beyond metadata
• Real-time detection for high-stakes ML applications

Industry Impact Potential

The conversations at Continue DX reinforced something important: data quality is the silent crisis in ML. While everyone focuses on model architecture and training techniques, corrupted training data can undermine even the most sophisticated systems. This is especially critical in financial services, healthcare, and autonomous systems where the stakes are highest.

Looking forward to sharing more updates as these partnerships develop. If you're working on data quality challenges or RAG system validation, I'd love to connect and discuss potential applications.

Create a virus yourself and watch it spread. See live SIRD data between cities and learn how certain viruses spread more than others through our interactive realtime simulation.

Stochastic Markov Chain Algorithm

Using a Stochastic Markov Chain Algorithm written in NumPy and SciPy, we use distance between cities to determine how likely it is for the virus to spread. We then mix up the matrix and update the SIR accordingly.

Once the Python program compiles this information, we then visualize this simulation in Godot with a beautiful 3D globe interface.

Mathematical Framework

The total state of the global population is distributed between N cities with distinction between susceptible, infected, and recovered populations:

Technical Implementation

• Markov Matrix M: Probabilistically shuffles population between cities
• Global SIR Function Σ: Locally spreads diseases according to SIR model
• Cleaning Functions C₁, C₂: Maintain population constants using Hare-Neimeyer Method
• 3D Visualization: Real-time Godot globe showing spread patterns

Grant Recognition

This project received grant funding to incorporate Neural Networks for enhanced predictive modeling. The combination of stochastic modeling with machine learning creates a powerful tool for understanding pandemic dynamics.

The simulation allows users to experiment with different virus parameters and observe how mathematical models translate into real-world spread patterns, making complex epidemiological concepts accessible and interactive.