David vonThenen
AI/ML Engineer | Keynote Speaker | Building Scalable ML Solutions & AI Architectures | Python, Go, C++
Long Beach, California, United States
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David is a Senior AI/ML Engineer within the Office of the CTO at NetApp, where he’s dedicated to empowering developers to build, scale, and deploy AI/ML solutions in production environments. He brings deep expertise in building and training models for applications like NLP, vision, real-time analytics, and even models to classify diseases in a medical setting. His mission is to help users build, train, and deploy AI models efficiently, making advanced machine learning accessible to users of all levels.
Before NetApp, he was heavily involved in the AI/ML community, specifically in conversational AI solutions and driving AI platform growth in a DevRel and pre-sales role. David frequently shares his insights at industry conferences and events, offering hands-on guidance for implementing AI/ML in cloud environments. David's prior experience includes contributing to the Kubernetes and CNCF ecosystems, working hands-on with VMware virtualization, implementing backup/recovery solutions, and developing hardware storage adapter firmware and drivers.
Area of Expertise
Topics
Sarcastically Speaking: Unlocking Multi-modal Sentiment Analysis with NLP and Facial Expressions
Sentiment analysis is easy—until sarcasm enters the chat. Traditional natural language processing models often stumble when trying to decode sarcastic nuances, missing crucial contextual cues and delivering misleading results. To tackle this, we will explore a multi-modal approach that integrates facial expression analysis with textual inputs, dramatically improving accuracy in sentiment detection, particularly for sarcasm. By combining transformer-based NLP models and facial landmark detection, we create a richer, context-aware understanding of sentiment.
In this session, you'll explore how facial movements—like subtle eye rolls or eyebrow raises—can be quantified, combined with language embeddings, and processed to uncover hidden sarcastic sentiment. We'll walk through real-world datasets, demonstrate model training and evaluation, and share insights on deploying these models effectively in production. Attendees will leave with practical strategies and code examples, ready to integrate facial and textual analysis to tackle sarcasm head-on in their own NLP applications. As always, we will have live demos plenty.
API Security for the AI Era: Detecting and Preventing Adversarial Manipulation
In a digital landscape dominated by APIs and AI, security threats from adversarial manipulation have become critical risks. This session explores the intersection of APIs, AI security, and adversarial attacks. We'll dissect how adversaries manipulate APIs feeding data to machine learning models—by injecting noise, crafting misleading inputs, and exploiting data obfuscation techniques—to compromise model integrity and security. Attendees will gain insights into real-world adversarial scenarios, learn practical defensive techniques, and understand the implications for privacy, model fairness, and data reliability.
The session will provide practical examples and live demonstrations showcasing how adversarial strategies can exploit API vulnerabilities to undermine AI models. We'll examine defensive frameworks and best practices for securing APIs against adversarial attacks, ensuring data integrity, maintaining privacy compliance, and reinforcing ethical AI usage. By the end, attendees will be equipped with strategies for hardening their AI-driven APIs, proactively identifying vulnerabilities, and deploying robust security measures to mitigate adversarial threats.
Title Rethinking RAG: How MCP and Multi-Agents Will Transform the Future of Intelligent Search
Retrieval-Augmented Generation (RAG) is a cornerstone of modern AI solutions, powering search, observability, and analytics. But what if agents could dynamically ingest new data sources and consult with each other seamlessly? Enter Model Context Protocol (MCP) and Multi-Agent Protocols; two groundbreaking technologies poised to redefine how agentic systems function. MCP enables AI models to flexibly integrate external, contextual data, while Multi-Agent protocols opens new possibilities for inter-agent cooperation and smarter, collective decision-making.
In this session, you'll discover how MCP and Multi-Agent protocols fundamentally reshape RAG systems, paving the way toward truly adaptive, conversational agents that continuously evolve and collaborate. Through a series of engaging, live demos, you'll see firsthand how these technologies can dramatically improve search relevance, data observability, and overall system intelligence. Join us to understand and prepare for this transformative shift because the next wave of RAG and Agentic AI isn't just coming, it's already here. Are you ready?
Devoxx Morocco Upcoming
Rethinking RAG: How MCP and Multi-Agents Will Transform the Future of Intelligent Search
KubeCon + CloudNativeCon North America 2025 Sessionize Event Upcoming
Open Data Science Conference (ODSC) West Upcoming
Rethinking RAG: How MCP and Agent2Agent Will Transform the Future of Intelligent Search
All Things Open Upcoming
TinyML Meets PyTorch: Deploying AI at the Edge with Python Using ExecuTorch
API World 2025 Sessionize Event Upcoming
AI_dev: Open Source GenAI & ML Summit Europe 2025 Sessionize Event
WeAreDevelopers World Congress 2025 Sessionize Event
DevBcn 2025 Sessionize Event
Render (RenderATL) 2025 Sessionize Event
Open Data Science Conference
Workshop: Adaptive RAG Systems with Knowledge Graphs: Building Reinforcement-Learning-Driven AI Applications
Devoxx UK
The Rise of Agentic AI: Harnessing Open Source for Dynamic Decision-Making
Devoxx France
Explaining the Unexplainable: Python Tools for AI Transparency using Captum
All Things Open AI
Leveraging Knowledge Graphs for RAG: A Smarter Approach to Contextual AI Applications
NVIDIA GTC 2025
Crack the AI Black Box: Practical Techniques for Explainable AI
Southern California Linux Expo (SCaLE) 22x
Training Multi-Modal ML Classification Models for Real-Time Detection of Debilitating Disease
Southern California Linux Expo (SCaLE) 22x
Demystifying Building Natural Language Processing ML Models and How to Leverage Them By Example
Developer Week 2025
KEYNOTE: The Sound of Innovation: Why Voice Cloning Will Redefine Human-Computer Interaction
Developer Week 2025
Navigating the Edge-Cloud Bridge: Building Resource-Optimized IoT/Edge Assistants with LLMs
Open Data Science Conference 2024
Workshop: Building Multiple Natural Language Processing Models to Work In Concert Together
Real Time Communications Conference & Expo 2024
KeyNote: Training Machine Learning Classification Models for Creating Real-Time Data Points of Medical Conditions
Video: https://youtu.be/YgeinCCUBCk
Real Time Communications Conference & Expo 2024
Session: Building Multiple Natural Language Processing Models to Work In Concert Together
Video: https://youtu.be/0DHHS17mn_o
AI DevSummit 2024 Sessionize Event
SCaLE 21x (2024)
Title: Voice-Activated AI Collaborators: A Hands-On Guide Using LLMs in IoT & Edge Devices
Video: https://youtu.be/9Nj4hKy70yQ
IEEE RTC Conference
Title: Enhancing Real-Time WebRTC Conversation Understanding Using ChatGPT
Video: https://youtu.be/u-Q2TdzS7d8
IEEE RTC Conference
Title: Edge Devices as Interactive Personal Assistants: Unleashing the Power of Generative AI Agents
Video: https://youtu.be/ctyWBG-x9y8
Nexus x TPF GenAI Rush 2023
Title: Streamlining Communication Workflows
Video: https://www.youtube.com/watch?v=8gfWnN_hwGk
API World 2022
Title: Enabling Untapped Use Cases in ML/AI : Edge, Memory-Constrained, and Server-side Use Cases
Video: https://youtu.be/XYQPIHazMK8
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