Muntaser Syed
Lead Gen AI Engineer at Insight Global, former technical lead at Nvidia
Melbourne, Florida, United States
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Muntaser is a researcher in AI and ML. Formerly a technical AI lead at Nvidia, he currently works at Insight Global as a Lead GenAI Engineer
Area of Expertise
Topics
Securing MCP workflows for Agentic AI
Agentic AI and MCP have proliferated widely across the tech ecosystem in the last year. WIth this, comes a whole set of security concerns and issues. In this session we look at securing Agentic AI, MCP and the entire AI pipeline.
The Human in the Loop: How TDD Keeps You in the Driver's Seat of AI-Assisted Development
AI coding assistants are rewriting the developer experience. Code appears in seconds, entire modules materialize from a prompt, and the temptation to ship first and test later has never been stronger. But speed without discipline is just faster failure.
This session tells the story of what happened when a practitioner refused to let AI bypass the fundamentals. Over the course of shipping four open-source packages to PyPI, building multiple frameworks, and building enterprise-grade systems, a pattern emerged: Test-Driven Development doesn't just survive AI-assisted workflows; it becomes the single most important practice keeping the human in control.
Through live demonstrations and real project walkthroughs, you'll see what a disciplined AI-assisted TDD workflow actually looks like in practice: the red-green-refactor cycle with a non-deterministic collaborator, the failures that taught hard lessons, the guardrails that had to be formalized into repeatable process, and the pre-push checklists that saved production more than once. This isn't a talk about testing AI models. It's about what happens to software quality when the fastest code generator in the room has no concept of accountability, and how TDD gives that accountability back to the human.
You'll walk away with a concrete, battle-tested framework for maintaining engineering rigor in AI-assisted development without sacrificing the speed advantage that makes these tools worth using in the first place.
JSON-LD Is Great (and Here's How We're Improving It): Building jsonld-ex
JSON-LD is everywhere: Google Search, Verifiable Credentials, schema.org, knowledge graphs. It's a W3C standard used on billions of web pages. And it has serious gaps: no built-in validation, security vulnerabilities in context resolution, no streaming support for large datasets, and zero affordances for the AI/ML workloads that increasingly consume it.
I conducted a deep gap analysis of JSON-LD and identified 26 specific improvements across security, performance, validation, developer experience, and AI integration. Then I started building jsonld-ex - an extended JSON-LD library for Python that implements these fixes.
In this session, I'll walk through the problems with live examples (yes, I'll break JSON-LD on stage), then show how jsonld-ex addresses them:
What you'll see live:
Context injection attacks against standard JSON-LD processors: and how jsonld-ex prevents them
SHACL/ShEx shape validation integrated directly into the processing pipeline
Confidence scores and provenance tracking baked into JSON-LD nodes: critical for AI systems consuming linked data
A working MCP (Model Context Protocol) server that lets AI agents query and validate knowledge graphs through jsonld-ex
What you'll walk away with:
Understanding of JSON-LD's real-world limitations beyond the spec
Practical techniques for securing JSON-LD in production APIs
How linked data and AI agents intersect through MCP
A Python library you can extend or contribute to
If you work with APIs, knowledge graphs, semantic web, or AI systems that consume structured data: this talk shows you what's broken under the hood and how to fix it.
Hands-On XAI Evaluation: How to Know If Your Model's Explanations Are Trustworthy
Explainability methods like SHAP, LIME, and Integrated Gradients are widely adopted in production ML systems; but run three different methods on the same model and input, and you'll often get three different answers. How do you know which explanation to trust? This hands-on workshop introduces quantitative evaluation of explanations, a rapidly growing area of XAI research that gives practitioners objective tools to measure explanation quality instead of relying on intuition or visual inspection. Working through three progressive Google Colab notebooks, attendees will first generate explanations from multiple methods on a real dataset and see exactly how and why they disagree. Next, they'll apply evaluation metrics across three dimensions: faithfulness (does the explanation reflect what the model actually uses?), robustness (is the explanation stable under small input changes?), and complexity (is the explanation simple enough to act on?). Finally, attendees will use a full evaluation pipeline to build comparison tables and learn a practical decision framework for choosing the right explainer for different deployment scenarios: regulatory audits, customer-facing products, and stakeholder communication. No prior XAI experience is required; attendees need only intermediate Python, familiarity with scikit-learn or PyTorch, and a Google account for Colab. Everyone leaves with working notebooks and the ability to integrate explanation quality checks into their ML pipelines immediately.
Association for Software Testing - CAST 2026 Sessionize Event
Orlando Code Camp 2026 Sessionize Event
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