Upcoming
Upcoming Meetups
See what is on the calendar and RSVP to join us.
PyData St. Louis Kickoff Meetup: Quantum vs Classical Randomness
2026-02-02 · 6:00 PM
St Louis County Library Grand Glaize Branch
Randomness powers simulation, cryptography, statistics, and machine learning, but are all sources of randomness the same?
In this talk, we’ll explore the difference between classical randomness (dice, noise, pseudorandom number generators) and quantum randomness (measurement, superposition, and qubits). We’ll discuss what “random” really means, how random numbers are generated in practice, and whether quantum mechanics truly produces a deeper kind of randomness than anything classical physics allows.
The talk is beginner-friendly and requires no physics background.
The Hidden Power of Synthetic Data: Teaching Models What Real Data Can’t
2026-03-02 · 6:00 PM
St. Louis County Library Daniel Boone Branch
Real-world data can be limited, expensive, or restricted by privacy concerns.
Synthetic data offers a way to generate realistic datasets that help train and test models when real data isn’t enough.
Debunking the myths of Quantum AI
2026-04-06 · 5:30 PM
Spark Coworking St. Louis
Join us at PyData St. Louis for a talk and community discussion on debunking the myths of Quantum AI and understanding what the technology can realistically do today.
Quantum computing and AI are often discussed together, but many claims about “Quantum AI” are misunderstood or exaggerated.
This session will explore what quantum computing actually is, where it might intersect with machine learning, and what data scientists should realistically expect from the field.
Embracing Noise: How Data Corruption Can Make Models Smarter
2026-05-04 · 5:30 PM
Spark Coworking St. Louis
Machine learning is often built on the assumption of clean, high-quality data. In reality, data is messy, incomplete, and noisy.
This session explores a powerful idea: introducing controlled corruption during training can improve robustness, reduce overfitting, and help models perform better in real-world conditions.
Efficient Neural Networks Through Tensor Networks
2026-06-08 · 5:30 PM
Spark Coworking St. Louis
Modern AI systems continue to grow larger and more computationally expensive.
This session explores an alternative approach inspired by physics and scientific computing: tensor networks.
These techniques can compress neural network representations while preserving much of their performance, offering a possible path toward more efficient AI systems.
How Much Energy Do Pruned LLMs Actually Save?
2026-07-13 · 5:30 PM
Spark Coworking St. Louis
As AI models continue to grow in size and capability, concerns about their computational cost and energy consumption have become increasingly important.
One popular approach to reducing these costs is pruning, a technique that removes less important components of a neural network to create smaller and potentially more efficient models.
LLM-Driven Merge Conflict Resolution: From Frustration to Automation
2026-07-28 · 7:00 PM
Online
Merge conflicts are an unavoidable part of collaborative software development, often slowing teams down and creating friction during the development process. Recent advances in Large Language Models (LLMs) have opened new possibilities for understanding code context and assisting developers in resolving conflicts more efficiently.
In this talk, Advitya Gemawat, ML Engineer at Microsoft, will explore how LLMs can be applied to merge conflict resolution, the challenges involved in understanding code changes across branches, and the opportunities for building intelligent developer tools.
Drawing from his experience building machine learning and AI systems at Microsoft, Advitya will discuss practical approaches, current limitations, and the future of AI-assisted software engineering.
Whether you're a software engineer, data scientist, researcher, or AI enthusiast, this session will provide valuable insights into how modern AI is transforming the developer experience.
Processing Async Jobs in Django
2026-08-11 · 5:30 PM
Spark Coworking St. Louis
Modern Python applications often need to handle work that shouldn't block user requests, from sending emails and processing files to running long-running background jobs. Celery has become one of the most widely used tools for building scalable asynchronous systems, but designing these systems comes with important architectural decisions and trade-offs.
The AI Security Paradox: Asymmetric Threats and the Future of Defense
2026-08-31 · 5:00 PM
Online
Software engineering is undergoing its biggest transformation in decades, but application security is shifting in the opposite direction. While AI tools allow developers to write code faster than ever, foundation models trained on decades of public code are accelerating the volume of vulnerable software flowing into production. At the same time, AI grants attackers infinite patience and speed—enabling automated reconnaissance and rapid exploit generation against large, complex enterprise attack surfaces.
This creates a paradox: security hasn't improved by default, and organizations are effectively paying a 'token tax' to scan and fix the very vulnerabilities generated by AI.
In this session, Ammar Alim (Product Security Engineering Leader at Adobe) breaks down how developers and security practitioners across all experience levels can rebalance this equation. We will explore:
The 'Wounded Buffalo' Effect: Why legacy codebases and AI-driven attack vectors give adversaries a short-term advantage.
The Token Tax Fallacy: Why buying off-the-shelf security tokens won't solve the security crisis without deep architectural integration.
Building Next-Era Defense Systems: How to combine deep domain/business logic awareness with custom AI engineering (RAG, knowledge graphs, and autonomous self-healing agents) to turn reactive security into automated, context-aware pipelines.
New meetups are being scheduled. Check back soon.