Computer Vision Israel 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.

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.

May 21, 2026
9 - 11 AM pacific
Online. Register for the Zoom!

Beyond Models: LLM-Guided Reinforcement Learning for Real-World Wireless Systems

Reinforcement learning agents often perform well in simulation but break down when deployed in real, non-stationary, constraint-driven environments such as wireless systems. This work explores using large language models not as annotators or reward hacks, but as a reasoning layer that guides RL decision-making with domain logic, scenario interpretation, and adaptive constraints.

We present an architecture where the LLM provides structured, high-level advisory signals while the RL policy remains the final action authority to avoid hallucination-driven failures. Early experiments show that this hybrid setup improves robustness under distribution shifts and complex constraint scenarios where standard RL collapses. The goal is not to replace RL with LLMs, but to combine learning and reasoning into a more deployable control-intelligence framework.

About the Speaker

Fatemeh Lotfi is a Ph.D. researcher focused on integrating large language models and reinforcement learning for adaptive wireless control systems. Her work targets the limitations of classical RL under real-world uncertainty by introducing reasoning-driven guidance mechanisms using LLMs. She has contributed to multiple AI-for-infrastructure projects, including advanced O-RAN automation.

Responsible and Ethical AI in Healthcare: Building Trustworthy and Inclusive Intelligent Systems

In this session, I will discuss how Responsible AI principles: including fairness, transparency, accountability, and reliability can be practically embedded into healthcare AI systems. Key discussion points will include:

• Addressing bias and equity challenges in healthcare datasets and model training.
• Building explainable and interpretable AI to strengthen clinician trust and adoption.
• Ensuring ethical deployment of generative AI models within regulated healthcare environments.
• Establishing governance frameworks for data privacy, model monitoring, and regulatory compliance.

About the Speaker

Jahnavi Kachhia is the Global Product Owner, AI & ML at Abbott, leading large-scale AI initiatives for the FreeStyle Libre platform to enhance clinical decision-making and patient outcomes. Previously at Meta’s Reality Labs, she advanced AR/VR innovation and LLM-based intelligent systems. An active contributor to the AI research community, she serves on the IJCAI 2025 Program Committee and reviews for AAAI, IJCNN, and IEEE conferences.

AI Applications in Drug Repurposing

Drug repurposing is increasingly important because it offers a faster, lower-cost path to therapeutic discovery compared to de novo drug development, especially in oncology where many cancers still lack effective targeted options. In under-studied cancers such as endometrial cancer, the challenge is often a lack of large, high-quality clinical or response datasets, making purely data-dependent approaches difficult to scale reliably. This motivates combining data-independent strategies (e.g., pathway- and mechanism-driven modeling) with data-dependent learning when interaction evidence is available. A practical and scalable direction is drug–target interaction (DTI) prediction, where AI models can leverage molecular and protein representations to prioritize mechanistically plausible drug candidates for repurposing.

About the Speaker

Madhurima Mondal's academic journey has been shaped by strong foundations in mathematical and scientific problem-solving, including multiple national-level achievements such as Regional Mathematics Olympiad (RMO), NTSE, and the KVPY fellowship. She completed my B.Tech and M.Tech in Electronics & Electrical Communication Engineering from IIT Kharagpur, and I am currently a PhD candidate in Electrical & Computer Engineering at Texas A&M University,