Pittsburgh TechFest 2026

In the highly active tech landscape of the Pittsburgh region, where startups and established companies compete for success, innovation reigns supreme. As the markets evolve with new ideas toppling former dominants, the quest for smaller, faster, and better products intensifies. In this fast-paced environment, few have the luxury of exploring available resources and solutions via Master of Software Engineering program at Carnegie Mellon University.
This presentation will focus on the significance of establishing successful relationships with academia and harnessing such engagements for a competitive edge. Specifically, we will delve into the partnerships between the Master of Software Engineering program and industry. Through this case study, we will explore how companies can tap into academic expertise, research, and resources to thrive in the ever-evolving tech industry.

If your infrastructure disappeared tomorrow, would you understand how to rebuild it? Not just what needed to be restored or what configuration might need to be reapplied, but what depends on what, which teams need to be engaged, and that one command, run by that one person, that one time, that somehow became the thing standing between you and recovery.The truth is, creating infrastructure is often the easy part—especially today, when an answer is only a prompt away. The hard part is understanding the story behind it: why things exist, when they need to happen, and how the pieces depend on one another. That story is unique to every environment, and too often only exists in the memories of the people who built it.By utilizing Domain Storytelling, we can preemptively survive our infrastructure doomsday scenario. Through a collaborative visual exercise, we can identify actors, systems, decisions, and dependencies so that the entire organization can understand how an environment would need to be rebuilt. We’ll also explore how these stories can reveal a team’s gaps in understanding, uncover issues with circular dependencies, and use these conversations to drive changes in the in the architecture and operational practices themselves. Most importantly, these stories become living artifacts that evolve alongside our infrastructure, ensuring that our understanding grows as our environments change. Because when doomsday arrives, the difference between recovery and rebuilding from scratch is not whether you have a backup. It’s whether your organization knows the story that got you there.

Analysis, drafting, modeling, coordination — the work businesses are built out of — just got radically cheaper. That single change reprices two things at once, and most organizations are only doing the math on one of them. On the business side, the operating model assumed capacity costs headcount. That assumption is now wrong, and cost structure, org design, and margin all move with it. On the career side, the entry-level rung was never really about the work product. It was an apprenticeship subsidy: firms paid juniors above their immediate value because grinding through that work was how judgment got built. Companies are now cutting that rung to capture the savings, without noticing they are cutting their own leadership pipeline. This session works both sides of the same equation. It covers what actually got cheap and what did not, why AI savings show up as margin in year one and a broken bench in year five, and what stays scarce when generation is free: judgment, accountability, and orchestration. Attendees leave with one test they can apply from either side of the table — is this organization operationalizing AI, or demoing it?

Machine Learning Engineers: Put Perforated to the Test

Perforated is challenging a fundamental assumption in machine learning: that improving model performance means collecting more data or building bigger models. Its technology adds new learning signals during training to help models learn more efficiently, potentially achieving stronger performance with less training data and fewer parameters. It integrates directly into existing PyTorch workflows without requiring a complete model rebuild.

At TechFest 2026, we're putting that claim to the test.

We're inviting machine learning engineers, AI engineers, data scientists, and developers working with neural networks to participate in a live technology test. See the technology in action, examine the results, ask the hard technical questions, and bring your own perspective on whether the approach delivers.

NO PITCH-It's a chance to test the technology.

Come ready to challenge assumptions, scrutinize the results, and see what happens when Perforated's approach is put in front of people who actually build and optimize ML models.

If you work with PyTorch, model optimization, computer vision, neural networks, pruning, quantization, or data-efficient ML, we want you in the room.