Name: April 22 - Munich AI, ML and Computer Vision Meetup
Start: 2026-04-22T15:30:00.000Z
Location: Impact Hub Munich GmbH

Join our virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Feb 5, 2026
9 - 11 AM Pacific
Online. **[Register for the Zoom!](https://voxel51.com/events/ai-ml-and-computer-vision-meetup-february-5-2026)**

**Unlocking Visual Anomaly Detection: Navigating Challenges and Pioneering with Vision-Language Models**

Visual anomaly detection (VAD) is pivotal for ensuring quality in manufacturing, medical imaging, and safety inspections, yet it continues to face challenges such as data scarcity, domain shifts, and the need for precise localization and reasoning. This seminar explores VAD fundamentals, core challenges, and recent advancements leveraging vision-language models and multimodal large language models (MLLMs). We contrast CLIP-based methods for efficient zero/few-shot detection with MLLM-driven reasoning for explainable, threshold-free outcomes. Drawing from recent studies, we highlight emerging trends, benchmarks, and future directions toward building adaptable, real-world VAD systems. This talk is designed for researchers and practitioners interested in AI-driven inspection and next-generation multimodal approaches.

*About the Speaker*

[Hossein Kashiani](https://www.linkedin.com/in/hossein-kashiani/) is a fourth-year Ph.D. student at Clemson University. His research focuses on developing generalizable and trustworthy AI systems, with publications in top venues such as CVPR, WACV, ICIP, IJCB, and TBIOM. His work spans diverse applications, including anomaly detection, media forensics, biometrics, healthcare, and visual perception.

**Data-Centric Lessons To Improve Speech-Language Pretraining**

Spoken Question-Answering (SQA) is a core capability for useful and interactive artificial intelligence systems. Recently, several speech-language models (SpeechLMs) have been released with a specific focus on improving their SQA performance. However, a lack of controlled ablations of pretraining data processing and curation makes it challenging to understand what factors account for performance, despite substantial gains from similar studies in other data modalities. In this work, we address this gap by conducting a data-centric exploration for pretraining SpeechLMs.

We focus on three research questions fundamental to speech-language pretraining data:

* How to process raw web-crawled audio content for speech-text pretraining;
* How to construct synthetic pretraining datasets to augment web-crawled data;
* How to interleave (text, audio) segments into training sequences.

We apply the insights from our controlled data-centric ablations to pretrain a 3.8B-parameter SpeechLM, called SpeLangy, that outperforms models that are up to 3x larger by 10.2% absolute performance. We hope our findings highlight the impact of effective data curation for speech-language pretraining and guide future data-centric exploration in SpeechLMs.

*About the Speaker*

[Vishaal Udandarao](https://www.linkedin.com/in/vishaal-udandarao) is a third year ELLIS PhD student, jointly working with Matthias Bethge at The University of Tuebingen and Samuel Albanie at The University of Cambridge/Google Deepmind. He is also a part of the International Max Planck Research School for Intelligent Systems. He is mainly interested in understanding the generalisation properties of foundation models, both vision-language models (VLMs) and large multi-modal models (LMMs), through the lens of their pre-training and test data distributions. His research is funded by a Google PhD Fellowship in Machine Intelligence.

**A Practical Pipeline for Synthetic Data with Nano Banana Pro + FiftyOne**

Most computer-vision failures come from the rare cases, the dark corners, odd combinations, and edge conditions we never capture enough in real datasets. In this session, we walk through a practical end-to-end pipeline for generating targeted synthetic data using Google’s Nano Banana Pro and managing it with FiftyOne. We’ll explore how to translate dataset gaps into generation prompts, create thousands of high-quality synthetic images, automatically enrich them with metadata, and bring everything into FiftyOne for inspection, filtering, and validation. By the end, you’ll understand how to build a repeatable synthetic-first workflow that closes real vision gaps and improves model performance on the scenarios that matter most.

*About the Speaker*

[Adonai Vera](https://www.linkedin.com/in/adonai-vera/) \- Machine Learning Engineer & DevRel at Voxel51\. With over 7 years of experience building computer vision and machine learning models using TensorFlow\, Docker\, and OpenCV\. I started as a software developer\, moved into AI\, led teams\, and served as CTO\. Today\, I connect code and community to build open\, production\-ready AI\, making technology simple\, accessible\, and reliable\.

**Making Computer Vision Models Faster: An Introduction to TensorRT Optimization**

Modern computer vision applications demand real-time performance, yet many deep learning models struggle with high latency during deployment. This talk introduces how TensorRT can significantly accelerate inference by applying optimizations such as layer fusion, precision calibration, and efficient memory management. Attendees will learn the core concepts behind TensorRT, how it integrates into existing CV pipelines, and how to measure and benchmark improvements. Through practical examples and performance comparisons, the session will demonstrate how substantial speedups can be achieved with minimal model-accuracy loss. By the end, participants will understand when and how to apply TensorRT to make their CV models production-ready.

*About the Speaker*

[Tushar Gadhiya](https://www.linkedin.com/in/tushar-gadhiya/) is a Technical Lead at Infocusp Innovations, specialising in deep learning, computer vision, graph learning, and agentic AI. My experience spans academic research as a PhD holder and industry work, where I have contributed to multiple patents.

Bogotá AI, Machine Learning and Computer Vision Meetup

Jimmy Guerrero

Feb 5 - AI, ML and Computer Vision Meetup

Online event

Join our virtual Meetup to hear talks from experts on cutting-edge topics at the intersection of Visual AI and video use cases.

**Time and Location**

Feb 11, 2026
9 - 11 AM Pacific
Online. **[Register for the Zoom!](https://voxel51.com/events/visual-ai-for-video-use-cases-february-11-2026)**

**VIDEOP2R: Video Understanding from Perception to Reasoning**

Reinforcement fine-tuning (RFT), a two-stage framework consisting of supervised fine-tuning (SFT) and reinforcement learning (RL) has shown promising results on improving reasoning ability of large language models (LLMs). Yet extending RFT to large video language models (LVLMs) remains challenging. We propose VideoP2R, a novel process-aware video RFT framework that enhances video reasoning by modeling perception and reasoning as distinct processes. In the SFT stage, we develop a three-step pipeline to generate VideoP2R-CoT-162K, a high-quality, process-aware chain-of-thought (CoT) dataset for perception and reasoning.

In the RL stage, we introduce a novel process-aware group relative policy optimization (PA-GRPO) algorithm that supplies separate rewards for perception and reasoning. Extensive experiments show that VideoP2R achieves state-of-the-art (SotA) performance on six out of seven video reasoning and understanding benchmarks. Ablation studies further confirm the effectiveness of our process-aware modeling and PA-GRPO and demonstrate that model's perception output is information-sufficient for downstream reasoning.

*About the Speaker*

[Yifan Jiang](https://www.linkedin.com/in/yifanj921/) is a third-year Ph.D. student in the Information Science Institute at the University of Southern California (USC-ISI), advised by Dr. Jay Pujara, focusing on natural language processing, commonsense reasoning and multimodality large language models.

**Layer-Aware Video Composition via Split-then-Merge**

Split-then-Merge (StM) is a novel generative framework that overcomes data scarcity in video composition by splitting unlabeled videos into separate foreground and background layers for self-supervised learning. By utilizing a transformation-aware training pipeline with multi-layer fusion, the model learns to realistically compose dynamic subjects into diverse scenes without relying on expensive annotated datasets. This presentation will cover the problem of video composition and the details of StM, an approach looking at this problem from a generative AI perspective. We will conclude by demonstrating how StM is working, and outperforming state-of-the-art methods in both quantitative benchmarks and qualitative evaluations.

*About the Speaker*

[Ozgur Kara](https://www.linkedin.com/in/ozgurr-kara/) is a 4th year Computer Science PhD student at the University of Illinois Urbana-Champaign (UIUC), advised by Founder Professor James M. Rehg. His research builds the next generation of video AI by tackling three core challenges: efficiency, controllability, and safety.

**Video-native VLMs and control**

We show how image-native vision–language models can be extended to support native video understanding, structured reasoning, tool use, and robotics. Our approach focuses on designing data, modeling, and training recipes to optimize for multimodality input and interaction patterns - treating vision and perception as a first class citizens. We discuss lessons learned from scaling these methods in an open-source model family and their implications for building flexible multimodal systems.

*About the Speaker*

[Akshat Shrivastava](https://www.linkedin.com/in/akshatsh/) is the CTO and co-founder of Perceptron, previously leading AR On-Device at Meta and conducting research at UW.

**Video Intelligence Is Going Agentic**

Video content has become ubiquitous in our digital world, yet the tools for working with video have remained largely unchanged for decades. This talk explores how the convergence of foundation models and agent architectures is fundamentally transforming video interaction and creation. We'll examine how video-native foundation models, multimodal interfaces, and agent transparency are reshaping enterprise media workflows through a deep dive into Jockey, a pioneering video agent system.

*About the Speaker*

[James Le ](https://www.linkedin.com/in/khanhnamle94)currently leads the developer experience function at TwelveLabs - a startup building foundation models for video understanding. He previously operated in the MLOps space and ran a blog/podcast on the Data & AI infrastructure ecosystem.

Andres Ernesto Rodriguez Gonzale

Feb 11 - Visual AI for Video Use Cases

AI/ML/CV Meetup for practitioners; attendees will gain actionable takeaways from talks on anomaly detection with uncertainty, world models, and durable AI pipelines.

Join us to hear talks from experts on cutting-edge topics across AI, ML, and computer vision!

**[Pre-registration is mandatory.](https://voxel51.com/events/seattle-ai-ml-and-computer-vision-meetup-february-12-2026)**

**Time and Location**

Feb 12, 2026
5:30 - 8:30 PM

[Union AI Offices](https://maps.app.goo.gl/gvQofNAZdBKQXmTJ9)
400 112th Ave NE #115
Bellevue, WA 98004

**ALARM: Automated MLLM-Based Anomaly Detection in Complex-EnviRonment Monitoring with Uncertainty Quantification**

In the complex environments, the anomalies are sometimes highly contextual and also ambiguous, and thereby, uncertainty quantification (UQ) is a crucial capacity for a multi-modal LLM (MLLM)-based video anomaly detection (VAD) system to succeed. In this talk, I will introduce our UQ-supported MLLM-based VAD framework called ALARM. ALARM integrates UQ with quality-assurance techniques like reasoning chain, self-reflection, and MLLM ensemble for robust and accurate performance and is designed based on a rigorous probabilistic inference pipeline and computational process.

*About the Speaker*

[Congjing Zhang](https://www.linkedin.com/in/congjing-zhang-3b03b0227/) is a third-year Ph.D. student in the Department of Industrial and Systems Engineering at the University of Washington, advised by Prof. Shuai Huang. She is a recipient of the 2025-2027 Amazon AI Ph.D. Fellowship. Her research interests center on large language models (LLMs) and machine learning, with a focus on uncertainty quantification, anomaly detection and synthetic data generation.

**The World of World Models: How the New Generation of AI Is Reshaping Robotics and Autonomous Vehicles**

World Models are emerging as the defining paradigm for the next decade of robotics and autonomous systems. Instead of depending on handcrafted perception stacks or rigid planning pipelines, modern world models learn a unified representation of an environment—geometry, dynamics, semantics, and agent behavior—and use that understanding to predict, plan, and act. This talk will break down why the field is shifting toward these holistic models, what new capabilities they unlock, and how they are already transforming AV and robotics research.

We then connect these advances to the Physical AI Workbench, a practical foundation for teams who want to build, validate, and iterate on world-model-driven pipelines. The Workbench standardizes data quality, reconstruction, and enrichment workflows so that teams can trust their sensor data, generate high-fidelity world representations, and feed consistent inputs into next-generation predictive and generative models. Together, world models and the Physical AI Workbench represent a new, more scalable path forward—one where robots and AVs can learn, simulate, and reason about the world through shared, high-quality physical context.

*About the Speaker*

[Daniel Gural](https://www.linkedin.com/in/daniel-gural/) leads technical partnerships at Voxel51, where he’s building the Physical AI Workbench, a platform that connects real-world sensor data with realistic simulation to help engineers better understand, validate, and improve their perception systems.

**Modern Orchestration for Durable AI Pipelines and Agents - Flyte 2.0**

In this talk we’ll discuss how the orchestration space is evolving with the current AI landscape, and provide a peak at Flyte 2.0, which makes truly dynamic, compute aware, and durable AI orchestration easy for any type of AI application, from computer vision, agents, and more!

Flyte, the open source orchestration platform, is already being used by thousands of teams to build their AI pipelines. In-fact it’s extremely likely you’ve interacted with AI models trained on Flyte, while on social media, listening to music on using self driving technologies.

*About the Speaker*

[Sage Elliott](https://www.linkedin.com/in/sageelliott/) is an AI Engineer at Union.ai (core maintainers of Flyte).

**Context Engineering for Video Intelligence: Beyond Model Scale to Real-World Impact**

Video streams combine vision, audio, time-series and semantics at a scale and complexity unlike text alone. At TwelveLabs, we’ve found that tackling this challenge doesn’t start with ever-bigger models — it starts with engineering the right context. In this session, we’ll walk engineers and infrastructure leads through how to build production-grade video AI by systematically designing what information the model receives, how it's selected, compressed, and isolated. You’ll learn our four pillars of video context engineering (Write, Select, Compress, Isolate), see how our foundation models (Marengo & Pegasus) and agent product (Jockey) use them, and review real-world outcomes in media, public-safety and advertising pipelines.

We’ll also dive into how you measure context effectiveness — tokens per minute, retrieval hit rates, versioned context pipelines — and how this insight drives cost, latency and trust improvements. If you’re deploying AI video solutions in the wild, you’ll leave with a blueprint for turning raw video into deployable insight — not by model size alone, but by targeted context engineering.

*About the Speaker*

[James Le](https://www.linkedin.com/in/khanhnamle94) currently leads the developer experience function at TwelveLabs - a startup building foundation models for video understanding. He previously operated in the MLOps space and ran a blog/podcast on the Data & AI infrastructure ecosystem.

Build Reliable AI apps with Observability, Validations and Evaluations

As generative AI moves from experimentation to enterprise deployment, reliability becomes critical. This session outlines a strategic approach to building robust AI apps using Monocle for observability and the VS Code Extension for diagnostics, and bug fixing. Discover how to create AI systems that are not only innovative but also predictable and trustworthy.

*About the Speaker*

[Hoc Phan](https://www.linkedin.com/in/hocphan/) has 20+ years of experience driving innovation at Microsoft, Amazon, Dell, and startups. In 2025, he joined Okahu to lead product and pre-sales, focusing on AI observability and LLM performance. Previously, he helped shape Microsoft Purview via the BlueTalon acquisition and led R&D in cybersecurity and data governance. Hoc is a frequent speaker and author of three books on mobile development and IoT.

Seattle AI, Machine Learning and Computer Vision Meetup

Sage Elliott

Frank

Jason Moore

Feb 12 - Seattle AI, ML and Computer Vision Meetup

Join the Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

**[Pre-registration is mandatory to clear security.](https://voxel51.com/events/boston-ai-ml-and-computer-vision-meetup-february-18-2026)**

**Date and Location**

Feb 18, 2026
5:30 - 8:30 PM EST
[Microsoft Research Lab – New England (NERD)](https://maps.app.goo.gl/8sa45qw6jWftQ6Rg8) at MIT
Deborah Sampson Conference Room
One Memorial Drive, Cambridge, MA 02142

**SNAP: Towards Segmenting Anything in Any Point Cloud**

Segmenting objects in 3D point clouds is a core problem in 3D scene understanding and scalable data annotation. In this talk, I will present SNAP: Segmenting Anything in Any Point Cloud, a unified framework for interactive point cloud segmentation that supports both point-based and text-based prompts across indoor, outdoor, and aerial domains. SNAP is trained jointly on multiple heterogeneous datasets and achieves strong cross-domain generalization through domain-adaptive normalization. The model enables both spatially prompted instance segmentation and text-prompted panoptic and open-vocabulary segmentation directly on point clouds. Extensive experiments demonstrate that SNAP matches or outperforms domain-specific methods on a wide range of zero-shot benchmarks.

*About the Speaker*

[Hanhui Wang](https://www.linkedin.com/in/hanhuiwang/) is a first-year Ph.D. student at the Visual Intelligence Lab at Northeastern University. His research centers on 3D scene understanding, with recent work on point cloud segmentation and structured representations, and broader interests in generation and reasoning for multimodal 3D/4D perception.

**Culturally Adaptive AI**

AI can now generate videos, images, speech, and text that are almost indistinguishable from human-created content. As generative AI systems become more sophisticated, we end up questioning our feeds' credibility and whether they're even real. There is a need, now more than ever, to develop models that help humans distinguish between real and AI-generated content. How can we shape the next generation of AI models to be more explainable, safe, and creative? How can we make these models teach humans about different cultures, bridging the gap between human and AI collaboration?

This talk highlights emerging techniques and the future of AI that will improve trust in generative AI systems by integrating insights from multimodality, reasoning, and factuality. Tomorrow's AI won't just process data and generate content; rather, we imagine it will amplify our creativity, extend our compassion, and help us rediscover what makes us fundamentally human.

*About the Speaker*

[Anku Rani](https://www.linkedin.com/in/anku-rani/) is a doctoral researcher at the Massachusetts Institute of Technology, investigating machine learning models for video generation along with projects at the intersection of natural language processing and human-computer interaction. Her research spans multimodality, mathematical reasoning, attribution, and fact verification, with work published in leading AI conferences.

**Data Foundations for Vision-Language-Action Models**

Model architectures get the papers, but data decides whether robots actually work. This talk introduces VLAs from a data-centric perspective: what makes robot datasets fundamentally different from image classification or video understanding, how the field is organizing its data (Open X-Embodiment, LeRobot, RLDS), and what evaluation benchmarks actually measure. We'll examine the unique challenges such as temporal structure, proprioceptive signals, and heterogeneity in embodiment, and discuss why addressing them matters more than the next architectural innovation.

*About the Speaker*

[Harpreet Sahota](https://www.linkedin.com/in/harpreetsahota204/) is a hacker-in-residence and machine learning engineer with a passion for deep learning and generative AI. He’s got a deep interest in RAG, Agents, and Multimodal AI.

**Better Together: Leveraging Unpaired Multimodal Data for Stronger Unimodal Models**

Traditional multimodal learners find unified representations for tasks like visual question answering, but rely heavily on paired datasets. However, an overlooked yet potentially powerful question is: can one leverage auxiliary unpaired multimodal data to directly enhance representation learning in a target modality? We introduce UML: Unpaired Multimodal Learner, a modality-agnostic training paradigm in which a single model alternately processes inputs from different modalities while sharing parameters across them. This design exploits the assumption that different modalities are projections of a shared underlying reality, allowing the model to benefit from cross-modal structure without requiring explicit pairs. Theoretically, under linear data-generating assumptions, we show that unpaired auxiliary data can yield representations strictly more informative about the data-generating process than unimodal training. Empirically, we show that using unpaired data from auxiliary modalities---such as text, audio, or images---consistently improves downstream performance across diverse unimodal targets such as image and audio. Our project page is [here.](https://unpaired-multimodal.github.io/)

*About the Speaker*

[Sharut Gupta](https://www.linkedin.com/in/sharut-gupta/) is a fourth-year Ph.D student at MIT CSAIL, advised by Prof. Phillip Isola and Prof. Stefanie Jegelka. Prior to this, she completed her undergraduate studies in Mathematics and Computing at the Indian Institute of Technology, Delhi (IIT Delhi), during which she worked with Prof. Yoshua Bengio on her thesis. She has also spent time at Meta SuperIntelligence Labs (Meta AI), and Google DeepMind.

**Neural Radiance Fields for Image Verification**

We propose an image verification method that embeds physical refraction as an authenticity signature. To verify an image, we compare it to a pixel-aligned reconstruction derived from the refraction and flag inconsistencies. Manipulations are detectable because maintaining geometric consistency with the refractive object is difficult without knowing its refractive properties. Unlike prior work that relies on simple analytic refractions and slow per-scene NeRF optimization, we train a compact, scene-agnostic neural refraction field that models complex geometries and enables instant, high-fidelity reconstruction for detection and localization.

*About the Speaker*

[Sage Simhon](https://www.linkedin.com/in/sage-simhon-10b159198/) completed her BSc in Electrical Engineering and Computer Science at MIT and her MEng in Computer Science at MIT. She is interested in the intersection of physics, AI, and computer vision. She currently works on AI for physics simulation at Pasteur Labs.

Boston AI, Machine Learning and Computer Vision Meetup

Chad Vawter

Alex Kyle

haoc.du

Rachel Kuznetsov 

Bill Ko/ or Ying Ko

Jason Smith

Jonathan Kwan

James Ojoawo

kushal123478

Ashish Singh

Andrew Trias

Ansh Soni

Georgiana Lungu

Jon Pearce

sam wu

chengjia feng

Yen Diep

ananya hegde

Feb 18 - Boston AI, ML and Computer Vision Meetup

Microsoft  NERD New England Research & Development Center

Join the Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

**[Register to reserve your spot](https://voxel51.com/events/munich-ai-ml-and-computer-vision-meetup-april-22-2026)**

**Date, Time and Location**

Apr 22, 2026
5:30 - 8:30 PM

[Impact Hub Munich](https://munich.impacthub.net/)
Gotzinger Str. 8
München, Germany

**Learning Disentangled Motion Representations for Open-World Motion Transfer**

Recent progress in image- and text-to-video generation has made it possible to synthesize visually compelling videos, yet these models typically lack an explicit, reusable notion of motion. In this talk, I will present recent work on learning high-level, content-independent motion representations directly from open-world video data, with a focus on our NeurIPS spotlight paper introducing DisMo.

By disentangling motion semantics from appearance and object identity, such representations enable open-world motion transfer across semantically unrelated entities and provide a flexible interface for adapting and fine-tuning modern video generation models. Beyond generation, I will discuss how abstract motion representations support downstream motion understanding tasks and why they offer a promising direction for more controllable, general, and future-proof video models. The talk will conclude with a broader perspective on the opportunities and challenges of motion-centric representations in computer vision and video learning.

*About the Speaker*

[Thomas Ressler-Antal](https://www.linkedin.com/in/thomas-ressler-494758133/) is a PhD student at the Computer Vision & Learning Lab at LMU Munich, advised by Björn Ommer. My research focuses on representation learning from large-scale, open-world video data, with an emphasis on disentangling motion from appearance. I am particularly interested in motion understanding, video generation, and transferable representations that enable controllable and general-purpose video models. My work has been published at NeurIPS as a spotlight paper on learning abstract motion representations from raw video.

**Towards Generating Fully Navigable 3D Scenes**

3D world generation is a longstanding goal of computer vision with applications in VR/gaming/movies, robotics, and digital twins. Recent progress in generative models, in particular image and video diffusion models, enables automatic generation of photorealistic 3D environments. This talk describes a simple yet effective framework to exploit these models for 3D scene genration. Namely, we'll briefly talk about early approaches (Text2Room, ViewDiff) and dive deep into our recent state-of-the-art approach WorldExplorer.

*About the Speaker*

[Lukas Höllein](https://lukashoel.github.io/images/me.JPG) is a a PhD student at the Visual Computing & Artificial Intelligence Lab at the Technical University of Munich, supervised by Prof. Dr. Matthias Nießner. My research lies at the intersection of computer vision/graphics and machine learning, concerning mostly 3D reconstruction and generation. I'm especially interested in the creation of fully navigable 3D worlds with the help of generative AI.

**Finding Motion in Commotion: Estimating and Anticipating Motion in Everyday Visual Scenes**

Motion is an intrinsic property of video data. How do we harness motion from the abundance of videos to advance vision foundation models? This talk will examine key challenges and emerging opportunities in motion estimation and motion-aware representation learning at scale. Drawing on our latest results from NeurIPS and ICCV, the talk will show how motion-centric learning can enable more versatile and generalisable vision foundation models.

*About the Speaker*

[Nikita Araslanov](https://www.linkedin.com/in/nikita-araslanov/) is a postdoctoral researcher in the Computer Vision Group at TU Munich. His research focuses on semantic and 3D visual inference from video data, with the goal of bridging visual perception and reasoning about complex phenomena. He earned his PhD in Computer Science from TU Darmstadt (2022) and was a visiting researcher at Google (2024–2025).

**Small Models, Big Intelligence: How vLLM Semantic Router Uses Sub-2B Language Models for Production-Scale Routing**

The vLLM Semantic Router introduces a groundbreaking approach to intelligent LLM request routing through its MoM (Mixture of Models) family, a collection of specialized small language models that make split-second routing decisions for production systems. This system operates between users and models, capturing signals from requests, responses, and context to make intelligent routing decisions, including model selection, safety filtering (jailbreak, PII), semantic caching, and hallucination detection.

In this talk, we'll explore how the router leverages tiny but powerful models like ModernBERT (encoder-based) and Qwen3 (0.6B-1.7B parameter decoder models) to achieve sub-10ms latency classification at over 10,000 queries per second. We'll dive into the technical architecture showing how these small models handle domain classification, jailbreak detection, PII protection, and hallucination detection, proving that for routing intelligence, size isn't everything.

*About the Speaker*

[Peter Bouda](https://www.linkedin.com/in/peter-bouda/) is an AI Engineer and tech leader with over 20 years of experience building cutting-edge solutions across AI/ML, NLP, and full-stack development. Currently architecting AI platforms at EY, he previously led the AI Lab at Apiax where he built sophisticated NLP stacks for regulatory compliance in the financial services industry, successfully raised over €1.5 million in EU R&D funding, and deployed production-grade microservices on Azure Kubernetes. The rest is biking: He regulary embarks on long-distance cycling trips and discovers new roads and trails in Portugal and Spain.

**Data Foundations for Vision-Language-Action Models**

Model architectures get the papers, but data decides whether robots actually work. This talk introduces VLAs from a data-centric perspective: what makes robot datasets fundamentally different from image classification or video understanding, how the field is organizing its data (Open X-Embodiment, LeRobot, RLDS), and what evaluation benchmarks actually measure. We'll examine the unique challenges such as temporal structure, proprioceptive signals, and heterogeneity in embodiment, and discuss why addressing them matters more than the next architectural innovation.

*About the Speaker*

[Harpreet Sahota](https://www.linkedin.com/in/harpreetsahota204/) is a hacker-in-residence and machine learning engineer with a passion for deep learning and generative AI. He’s got a deep interest in VLMs, Visual Agents, Document AI, and Physical AI.

München AI, Machine Learning and Computer Vision Meetup

Karlheinz Kleinhenz

Oskar Ahlgren

MT Nguyen

Maanav

fauzi sholichin

Shashank Shekhawat

cedricsillaber

Markus Böbel

Vitalii Andre

Vladimir Orlinski

Julian

Yana Kytniukh

Frank Merwerth 

JohannesS

Ran A. Azulay

Ekaterina M.

Maria Salfer

dario.sitnik

Roozbeh Fx

April 22 - Munich AI, ML and Computer Vision Meetup

Impact Hub Munich GmbH

AI, Machine Learning and Computer Vision Meetup Network

Grupos

{PRO_NETWORK_NAME} está no Meetup Pro com mais de {PRO_MEMBER_COUNT} membros em {PRO_GROUP_COUNT} Meetups em todo o mundo. Meetup Pro é a ferramenta profissional para organizar e comunicar uma rede de usuários, parceiros, colaboradores e membros.

This virtual group is for data scientists, machine learning engineers, and open source enthusiasts.

Every month we’ll bring you two diverse speakers working at the cutting edge of data science, machine learning, AI and computer vision.

This Meetup is sponsored by Voxel51, the lead maintainers of the open source FiftyOne computer vision toolset.

AI, Machine Learning and Computer Vision Meetup Network

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