Amsterdam AI Machine Learning and Computer Vision Meetup

Join us for an interactive workshop where we'll build production-ready computer vision pipelines using vibe coded FiftyOne plugins.

Register for the Zoom

Plugins enable you to customize the open-source FiftyOne computer vision app to match your exact workflow by easily incorporating data annotation, curation, model evaluation and inference.

We'll demonstrate how FiftyOne Skills and the MCP Server can streamline the journey from prototype to production-ready pipelines, keeping your development flow intact.

Perfect for open-source contributors, researchers, and enterprise teams seeking hands-on experience. All participants receive slides, notebooks, and access to GitHub repositories and videos from the workshop.

Mar 19, 2026
9 - 11 AM Pacific
Online. Register for Zoom!

Towards Reliable Clinical AI: Evaluating Factuality, Robustness, and Real-World Performance of Large Language Models

Large language models are increasingly deployed in clinical settings, but their reliability remains uncertain—they hallucinate facts, behave inconsistently across instruction phrasings, and struggle with evolving medical terminology. In my talk, I address methods to systematically evaluate clinical LLM reliability across four dimensions aligned with how healthcare professionals actually work: verifying concrete facts (FactEHR), ensuring stable guidance across instruction variations (instruction sensitivity study showing up to 0.6 AUROC variation), integrating up-to-date knowledge (BEACON improving biomedical NER by 15%), and assessing real patient conversations (PATIENT-EVAL revealing models abandon safety warnings when patients seek reassurance). These contributions establish evaluation standards spanning factuality, robustness, knowledge integration, and patient-centered communication, charting a path toward clinical AI that is safer, more equitable, and more trustworthy.

About the Speaker

Monica Munnangi is a doctoral student at the Khoury College of Computer Sciences at Northeastern University, advised by Saiph Savage. Her doctoral research, which she began in 2021 and expects to complete in 2026, focuses on multi-modal machine learning for healthcare. After being introduced to artificial intelligence and machine learning during her undergraduate studies, Munnangi earned her master’s degree from UMass Amherst.

Neural BRDFs: Learning Compact Representations for Material Appearance

Accurately modeling how light interacts with real-world materials remains a central challenge in rendering. Bidirectional Reflectance Distribution Functions (BRDFs) describe how materials reflect light as a function of viewing and lighting directions. Creating realistic digital materials has traditionally required choosing between fast parametric models that can't capture real-world complexity, or massive measured BRDFs that are expensive to acquire and store. Neural BRDFs address this challenge by learning continuous reflectance functions from data, exploiting directional correlations and symmetry to achieve significant compression while maintaining rendering quality. In this talk, we examine how neural networks can encode complex material behavior compactly, why this matters for rendering and material capture, and how neural BRDFs fit into the broader evolution toward data-driven graphics.

About the Speaker

Manushree Gangwar is a Machine Learning Engineer at Voxel51 working on data-centric visual AI. She holds an MS in Computer Science from Columbia University and has previously worked in robotics, autonomous driving, and AR/VR, with a focus on scene understanding and 3D reconstruction.

Supercharging AI agents with evaluations

Reliable deployment of AI agents depends on rigorous evaluation, which must shift from a nice-to-have QA step to a core engineering discipline. Robust evaluation is essential for safety, predictability, misuse resistance, and sustained user trust. To meet this bar, Evals must be deeply integrated into the agent development lifecycle. This talk will outline how simulation-based testing—using high-fidelity, controllable environments—provides the next generation of evaluation infrastructure for production-ready AI agents.

About the Speaker

Priya Venkat, PhD, is a Senior AI Manager at Intuit, where she leads teams that build and scale ML and Agentic AI systems for finance. Her work integrates cutting-edge agentic workflows and robust evaluation systems to drive business impact while ensuring AI safety and reliability. Priya is a strong advocate of responsible AI, and actively mentors the next generation of AI scientists and engineers.

Language Diffusion Models

Autoregressive models (ARMs) are widely regarded as the cornerstone of large language models (LLMs). Challenge this notion by introducing LLaDA, a diffusion model trained from scratch under the pre-training and supervised fine-tuning (SFT) paradigm. LLaDA models distributions through a forward data masking process and a reverse process, parameterized by a vanilla Transformer to predict masked tokens. Optimizing a likelihood bound provides a principled generative approach for probabilistic inference. Across extensive benchmarks, LLaDA demonstrates strong scalability, outperforming self-constructed ARM baselines. Remarkably, LLaDA 8B is competitive with strong LLMs like LLaMA3 8B in in-context learning and, after SFT, exhibits impressive instruction-following abilities in case studies such as multi-turn dialogue.

About the Speaker

Jayita Bhattacharyya is a AI ML Nerd with a blend of technical speaking & hackathon wizardry! Applying tech to solve real-world problems. The work focus these days is on generative AI. Helping software teams incorporate AI into transforming software engineering.

March 26, 2026
9 - 11 AM Pacific
Online. Register for the Zoom!

Scalable and Efficient Deep Learning: From Understanding to Generation

In an era where model complexity and deployment constraints increasingly collide, achieving both scalability and efficiency in deep learning has become essential. Scalable and efficient deep learning ensures that powerful models can be trained, deployed, and adapted under limited computational and data resources, enabling broader accessibility and practical application. From understanding to generation, this talk unifies methods that cut costs while preserving capability.

About the Speaker

Yitian Zhang is a fifth-year PhD student at Northeastern University, advised by Prof. Yun Raymond Fu. His research interests center around Efficient and Scalable AI, spanning Generative Models, Multimodal Large Language Models, and Foundation Models.

Grounding Visual AI Models in Real-World Physics

Generative video models have made rapid progress in visual realism, yet they frequently violate basic physical laws, producing implausible motion and incorrect cause-effect relationships. This talk presents MoReGen, a physics-grounded, agentic text-to-video generation framework that integrates Newtonian physics directly into the generation process via executable physics-engine code.

By coupling vision–language models with trajectory-based physical evaluation and iterative feedback, MoReGen produces videos that are both visually coherent and physically consistent. We further introduce MoRe Metrics and MoReSet, a benchmark and dataset designed to evaluate physics fidelity beyond appearance-based metrics such as FID and FVD. Together, this work demonstrates a path toward visual AI systems that reason about motion, interaction, and causality in the real world rather than hallucinating them.

About the Speakers

Professor Sarah Ostadabbas is an Associate Professor of Electrical and Computer Engineering at Northeastern University, where she directs the Augmented Cognition Lab (ACLab) and serves as Director of Women in Engineering. Her research focuses on computer vision and machine learning, with an emphasis on motion-centric representation learning, small-data AI, and applications in healthcare, defense, and behavior understanding under privacy and data constraints. She has authored over 130 peer-reviewed publications and received numerous honors, including the NSF CAREER Award, Sony Faculty Innovation Award, and the Cade Prize for Inventivity, along with multiple industry and federal research awards.

Xiangyu Bai is a third-year PhD student in the ACLab and leads the lab's work on physics-aware visual intelligence, with several publications in top-tier computer vision and robotics conferences.

WorldFormer: Diffusion Transformer World Models with Mixture-of-Experts for Embodied Physical Intelligence

World models have emerged as a foundational paradigm for enabling agents to simulate, predict, and reason about complex environments. Recent advances driven by diffusion transformer (DiT) architectures have dramatically expanded the fidelity, scalability, and physical plausibility of learned world models. In this work, we present a world model framework built upon the diffusion transformer paradigm, following the design philosophy of state-of-the-art systems such as NVIDIA Cosmos. Our approach comprises three core components: (1) a spatiotemporal variational autoencoder (VAE) that compresses high-resolution video into a compact continuous latent space with strong temporal causality, enabling efficient encoding and decoding of long-horizon video sequences; (2) a transformer-based diffusion backbone that operates on 3D-patchified latent tokens, leveraging self-attention and cross-attention with text embeddings to iteratively denoise Gaussian noise into physically coherent future video states using a flow matching objective; and (3) a scalable pre-training and post-training pipeline that first trains a generalist world foundation model on large-scale, diverse video data and then specializes it to target physical AI domains — such as robotic manipulation, autonomous driving, or embodied navigation — through task-specific fine-tuning.

Our model supports both text-to-world and video-to-world generation, enabling action-conditioned future state prediction for downstream planning and policy learning. We discuss implications for synthetic data generation, sim-to-real transfer, and the integration of world models into vision-language-action (VLA) pipelines for physical AI.

About the Speaker

Yanzhi Wang joined the Electrical & Computer Engineering department in August 2018 as an Assistant Professor. He earned his PhD at University of Southern California. His research interests include energy-efficient and high-performance implementations of deep learning and artificial intelligence systems; neuromorphic computing and non-von Neumann computing paradigms; cyber-security in deep learning systems; emerging deep learning algorithms/systems such as Bayesian neural networks, generative adversarial networks (GANs) and deep reinforcement learning.

Physical AI Research (PAIR) Center: Foundational Pairing of Digital Intelligence & Physical World Deployment at Northeastern University and Beyond

The Physical AI Research (PAIR) initiative advances the next frontier of artificial intelligence: enabling systems that can perceive, reason, and act reliably in the physical world. By uniting expertise across engineering, computer science, health sciences, and the social sciences, PAIR develops safe, transparent, and human-aligned AI that bridges digital models with real-world dynamics. The initiative is organized around three intellectual pillars: Learning and Modeling the World, through physics-informed multimodal learning, realistic simulations, and digital twins; Reasoning in the World, by integrating multimodal evidence to support grounded decision-making under uncertainty; and Acting in the World, by ensuring AI systems are verifiable, explainable, energy-efficient, and trustworthy. Together, these efforts position Physical AI as a foundational science driving innovation in health, sustainability, and security.

About the Speaker

Edmund Yeh is the Department Chair of Electrical and Computer Engineering at Northeastern University.

Apr 2, 2026
9 - 11 AM Pacific
Online. Register for the Zoom!

Async Agents in Production: Failure Modes and Fixes

As models improve, we are starting to build long-running, asynchronous agents such as deep research agents and browser agents that can execute multi-step workflows autonomously. These systems unlock new use cases, but they fail in ways that short-lived agents do not.

The longer an agent runs, the more early mistakes compound, and the more token usage grows through extended reasoning, retries, and tool calls. Patterns that work for request-response agents often break down, leading to unreliable behaviour and unpredictable costs.

This talk is aimed at use case developers, with secondary relevance for platform engineers. It covers the most common failure modes in async agents and practical design patterns for reducing error compounding and keeping token costs bounded in production.

About the Speaker

Meryem Arik is the co-founder and CEO of Doubleword, where she works on large-scale LLM inference and production AI systems. She studied theoretical physics and philosophy at the University of Oxford. Meryem is a frequent conference speaker, including a TEDx speaker and a four-time highly rated speaker at QCon conferences. She was named to the Forbes 30 Under 30 list for her work in AI infrastructure.

Visual AI at the Edge: Beyond the Model

Edge-based visual AI promises low latency, privacy, and real-time decision-making, but many projects struggle to move beyond successful demos. This talk explores what deploying visual AI at the edge really involves, shifting the focus from models to complete, operational systems. We will discuss common pitfalls teams encounter when moving from lab to field. Attendees will leave with a practical mental model for approaching edge vision projects more effectively.

About the Speaker

David Moser is an AI/Computer Vision expert and Founding Engineer with a strong track record of building and deploying safety-critical visual AI systems in real-world environments. As Co-Founder of Orella Vision, he is building Visual AI for Autonomy on the Edge - going from data and models to production-grade edge deployments.

Sanitizing Evaluation Datasets: From Detection to Correction

We generally accept that gold standard evaluation sets contain label noise, yet we rarely fix them because the engineering friction is too high. This talk presents a workflow to operationalize ground-truth auditing. We will demonstrate how to bridge the gap between algorithmic error detection and manual rectification. Specifically, we will show how to inspect discordant ground truth labels and correct them directly in-situ. The goal is to move to a fully trusted end-to-end evaluation pipeline.

About the Speaker

Nick Lotz is an engineer on the Voxel51 community team. With a background in open source infrastructure and a passion for developer enablement, Nick focuses on helping teams understand their tools and how to use them to ship faster.

Building enterprise agentic systems that scale

Building AI agents that work in demos is easy, building true assistants that make people genuinely productive takes a different set of patterns. This talk shares lessons from a multi-agent system at Cisco used by 2,000+ sellers daily, where we moved past "chat with your data" to encoding business workflows into true agentic systems people actually rely on to get work done.

We'll cover multi-agent orchestration patterns for complex workflows, the personalization and productivity features that drive adoption, and the enterprise foundations that helped us earn user trust at scale. You'll leave with an architecture and set of patterns that have been battle tested at enterprise scale.

About the Speaker

Aman Sardana is a Senior Engineering Architect at Cisco, I lead the design and deployment of enterprise AI systems that blend LLMs, data infrastructure, and customer experience to solve high‑stakes, real-world problems at scale. I’m also an open-source contributor and active mentor in the AI community, helping teams move from AI experimentation to reliable agentic applications in production.