Rome AI, Machine Learning and Computer Vision Meetup

Date, Time and Location

Jul 29, 2026

Online. Register for the Zoom!

The Agent Control Plane: Turning Coding Agents into Reliable Engineering Workflows

AI coding agents are powerful but often unreliable — they hallucinate, lose context, and produce inconsistent results across runs. In this talk, Alex introduces Atomic, an open-source control plane that adds persistent memory, deterministic workflow phases (Research → Specify → Implement → Ship), and human-in-the-loop gates around coding agents like Claude Code and GitHub Copilot. The result: repeatable, auditable engineering workflows that teams can actually trust in production.

About the Speaker

Alex Lavaee is an Applied AI engineer at Microsoft Research and the creator of Atomic, an open-source SDK that wraps deterministic, research-to-execution workflows around AI coding agents. He previously conducted AI research at Harvard Medical School and Boston University, and has worked as an MLE and data scientist at companies including Boeing and Themis AI, an MIT CSAIL spinoff.

UISurf: Toward Universal UI Automation with Cross-Environment Agents

In this talk, we introduce UISurf, an open-source multimodal agentic UI automation platform in which agents can perceive, reason, and collaborate across browser and desktop environments to complete end-to-end tasks that require interaction with multiple user interfaces.

UISurf comprises three main components: uisurf-agent, the runtime for UI automation agents; uisurf-admin, the session orchestration and management service; and uisurf-app, the full-stack user application. Its multi-agent architecture includes a planningagent that transforms natural-language requests into structured execution plans, specialized Browser and Desktop Agents for environment-specific interaction, an automationagent that coordinates execution and inter-agent handoff through Agent-to-Agent (A2A) communication, and a summarization_agent that produces the final task summary for the user. UISurf supports both autonomous execution and human-in-the-loop supervision, offering a practical and extensible framework for studying and deploying cross-environment UI automation.

About the Speaker

Dr. Henry Ruiz is a Research Scientist at Texas A&M University @ AgriLife Research, specializing in Artificial Intelligence (AI) and Remote Sensing. His work focuses on the development of advanced software systems and computational algorithms for analyzing multi-source remote sensing data, including satellite imagery, UAVs (Unmanned Aerial Vehicles), LiDAR (Light Detection and Ranging), and Ground Penetrating Radar (GPR).

From Manual Workflows to AI-Assisted Skills: Building Reliable Internal Automation

In this session, I will discuss how teams can turn repetitive manual workflows into reliable AI-assisted and automation-driven “skills.” I will share practical lessons from building internal tools for CAD and engineering workflows, including how automation can reduce manual effort, improve consistency, and support better process control. The talk will also cover why many AI/agent experiments fail when they are not connected to real team workflows, standards, and validation steps. Attendees will walk away with a practical framework for identifying repeatable workflows, designing useful internal tools, and adopting AI assistance without losing accuracy or trust.

About the Speaker

Janvi Vijaykumar Saddi \- Janvi Saddi is a Computer Science graduate and CAD/Data Automation professional with experience in data center design workflows\, AutoCAD automation\, process improvement\, and data analytics\. She currently works as a CAD Tech 2 at Astreya\, supporting Google data center design workflows by building internal tools that reduce manual effort\, improve accuracy\, and streamline engineering processes\. Her background also includes SQL\, Power BI\, market research analytics\, and AI\-assisted development\.

Building Safe Agent Sandboxes: Let Agents Act Without Breaking Production

AI agents become truly useful when they can take action, not just generate text. But giving agents access to code, data, and systems raises an important question: how do you let them explore, execute, fail, and improve without putting production at risk?

In this talk, we'll explore the sandbox pattern for agent systems and how to equip agents with tools to read, write, execute, and iterate within controlled environments while using permissions, human approval, and safety guardrails to keep them reliable. We'll cover practical architectures and lessons learned for building agents that can safely evolve from experimentation to production..

About the Speaker

Adonai Vera \- 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\.

Hacklab Cormano

Rome AI, Machine Learning and Computer Vision Meetup

Date, Time and Location

Aug 04, 2026

Online. Register for the Zoom!

Enabling Multimodal Agents on the Edge

The next generation of AI agents is moving beyond cloud-based text-only models and will interact with the physical multimodal world in real-time. For example in the vision domain, AI agents rely on Vision-Language Models (VLMs) in their backbone. However, deploying massive VLMs with billions of parameters on the edge devices remains a significant engineering hurdle.

Drawing on our recent ICML and CVPR research papers, this session explores advancements in agentic model optimizations, specifically how distillation and pruning transform 'heavyweight' models into lean, edge-ready engines. Lastly, I present our UI agent running on the actual phone that is being developed by our lab's team.

About the Speaker

Denis Gudovskiy is a Distinguished AI Engineer at Panasonic North America where he conducts R&D activities of various core AI methods, including multimodal and hardware-efficient agents, supervised and RL training pipelines, and robustness to out-of-distribution scenarios.

When the Camera Can’t Be Trusted: Health-Aware Visual AI for Reliable Near-Miss Detection

Near-miss detection systems are often evaluated as though every camera frame is equally trustworthy, even though blur, poor exposure, occlusion, contamination, and changing lighting can silently degrade the visual evidence used to make safety decisions. This talk presents an online camera-health framework that estimates visual reliability before downstream perception performance significantly deteriorates.

I will discuss how camera-health signals can support condition-aware evaluation, prioritize human review, reduce unreliable alerts, and trigger appropriate fallback behavior. Drawing from research in safety-critical visual perception, the talk will demonstrate how these principles can be adapted to industrial video systems operating across different cameras, shifts, layouts, and environmental conditions.

The presentation will also connect camera-health monitoring with rare-event discovery and failure-driven dataset improvement for more trustworthy near-miss detection.

About the Speaker

Shiva Aher is a computer vision researcher with a graduate background in computer science from the Georgia Institute of Technology, specializing in artificial intelligence.

Agentic VLM applications in manufacturing

Vision Language Models (VLMs) introduce net-new functionality to vision workloads in manufacturing that traditional computer vision models simply do not offer (e.g., open-vocabulary detection, in-context-learning). Even so, fine-tuned models like YOLO offer a level of precision and recall that today's VLMs struggle to match out-of-the-box.

Through agentic harnesses that coordinate calls to VLMs, we can start to deliver similar reliability on manufacturing-relevant tasks (e.g., many-class, many-instance detection), while also supporting the net new functionalities (e.g., multimodal search) that make VLMs distinct. In this talk, we walk through the design of these harnesses, how you serve them efficiently, and how they deliver value in manufacturing.

About the Speaker

Subraiz Ahmed is a member of the Technical Staff at Perceptron AI. He builds the infrastructure to serve frontier vision models. He previously founded a series of startups.

Rome AI, Machine Learning and Computer Vision Meetup

Date, Time and Location

Aug 06, 2026

Online. Register for the Zoom!

Do Speech Models Actually Understand Speech? Evaluating Speech LLMs Under Realistic Spoken Instruction Conditions

Speech Large Language Models (SLLMs) are increasingly capable; but are we evaluating them the right way? Most benchmarks rely on text prompts, yet real users interact with these systems through speech, a modality that introduces noise, disfluencies, and stylistic variation that text simply doesn't capture.

About the Speaker

Maike Züfle is a PhD student at the Karlsruhe Institute of Technology (KIT), working in Prof. Jan Niehues's group on interactive speech systems for more natural human–machine communication. Her research focuses on instruction-following speech models with speech as both input and output, with a recent emphasis on full-duplex systems. Beyond her research, she co-organises the instruction-following and speech translation metrics shared tasks at IWSLT. She is a 2026 Apple Scholar in AI/ML.

AI based Audio Forensics

In this presentation, attendees will discover several modules developed by Gradiant for the detection and analysis of synthetically generated or manipulated audio. The session will be delivered by one of the developers involved in the design and implementation of these technologies, providing first-hand insight into their capabilities and underlying methodology.

The presentation will cover the traceability module, which helps identify the origin of AI-generated content. It will also cover the segment detection tool, designed to locate manipulated regions within an audio recording, as well as the complete audio detection tool, which assesses whether an entire recording has been synthetically generated.

About the Speaker

Daniel Paniagua Ares is a research engineer at Gradiant. Graduated in computer engineering from the FIC and with a master's degree in AI from the VIU.

Curating, Searching, and Evaluating Audio Datasets in FiftyOne

In this talk, we'll start with the ESC-50 environmental-sound dataset to show how FiftyOne represents audio: browsing clips in the tabular view, rendering spectrograms directly in the sample grid with a custom renderer, and turning sounds into searchable vectors with CLAP embeddings. Then we'll demo a similarity-search panel that lets you query an entire audio collection by example clip or a natural-language prompt to quickly find matching sounds.

We'll conclude with a live research problem: Audio Moment Retrieval from the DCASE 2026 Challenge, where the goal is to localize the exact moment in a long recording that matches a text query. We'll frame this as temporal detection, evaluate predictions, and visualize ground-truth vs. predicted moments on an interactive timeline to intuitively expose model failure modes.

Attendees will leave with a concrete blueprint and open code for applying visual data-centric AI practices to their own audio and multimodal datasets.

About the Speaker

John Duncan is a Machine Learning Engineer, Customer Success at Voxel51. His research interests include vision, LiDAR, and audio perception for robots and intelligent systems.

Real-Time ASR at 4x on Consumer Hardware: The Meetily Architecture

This talk covers the engineering behind Meetily, an open-source meeting assistant that runs Whisper and NVIDIA Parakeet transcription entirely on-device. We'll walk through how we got Parakeet to roughly 4x real-time on consumer hardware, and the specific points where it still falls over.

We'll also get into the honest trade-offs between local and cloud inference: latency, accuracy, cost, and what you actually give up by choosing one over the other. Wrapping ML inference in a Rust/Tauri desktop app came with its own costs, which we'll unpack as well.

Finally, we'll look at what "fully local" really means at an architecture level, where that boundary sits, and how easily it leaks once you add model downloads, integrations, or a pluggable LLM backend.

About the Speaker

Sandeep Zachariah is the Founder and CEO of Zackriya Solutions and the leads the team behind Meetily, an open-source, privacy-first meeting assistant that runs Whisper and NVIDIA Parakeet transcription entirely on-device. He brings a rare full-stack perspective on audio AI — from low-level embedded systems and hardware acceleration up through real-time ASR and local LLM summarization — with deep experience deploying speech and ML models across servers, GPUs and consumer hardware.

Rome AI, Machine Learning and Computer Vision Meetup

Date, Time and Location

Aug 06, 2026

Online. Register for the Zoom

Do Speech Models Actually Understand Speech? Evaluating Speech LLMs Under Realistic Spoken Instruction Conditions

Speech Large Language Models (SLLMs) are increasingly capable; but are we evaluating them the right way? Most benchmarks rely on text prompts, yet real users interact with these systems through speech, a modality that introduces noise, disfluencies, and stylistic variation that text simply doesn't capture.

About the Speaker

Maike Züfle is a PhD student at the Karlsruhe Institute of Technology (KIT), working in Prof. Jan Niehues's group on interactive speech systems for more natural human–machine communication.

AI based Audio Forensics

In this presentation, attendees will discover several modules developed by Gradiant for the detection and analysis of synthetically generated or manipulated audio. The session will be delivered by one of the developers involved in the design and implementation of these technologies, providing first-hand insight into their capabilities and underlying methodology.

The presentation will cover the traceability module, which helps identify the origin of AI-generated content. It will also cover the segment detection tool, designed to locate manipulated regions within an audio recording, as well as the complete audio detection tool, which assesses whether an entire recording has been synthetically generated.

About the Speaker

Daniel Paniagua Ares is a research engineer at Gradiant. Graduated in computer engineering from the FIC and with a master's degree in AI from the VIU.

Curating, Searching, and Evaluating Audio Datasets in FiftyOne

In this talk, we'll start with the ESC-50 environmental-sound dataset to show how FiftyOne represents audio: browsing clips in the tabular view, rendering spectrograms directly in the sample grid with a custom renderer, and turning sounds into searchable vectors with CLAP embeddings. Then we'll demo a similarity-search panel that lets you query an entire audio collection by example clip or a natural-language prompt to quickly find matching sounds.

We'll conclude with a live research problem: Audio Moment Retrieval from the DCASE 2026 Challenge, where the goal is to localize the exact moment in a long recording that matches a text query. We'll frame this as temporal detection, evaluate predictions, and visualize ground-truth vs. predicted moments on an interactive timeline to intuitively expose model failure modes.

Attendees will leave with a concrete blueprint and open code for applying visual data-centric AI practices to their own audio and multimodal datasets.

About the Speaker

John Duncan is a Machine Learning Engineer, Customer Success at Voxel51. His research interests include vision, LiDAR, and audio perception for robots and intelligent systems.

Rome AI, Machine Learning and Computer Vision Meetup

Date, Time and Location

Aug 11, 2026

Online. Register for the Zoom!

As robotics and autonomous vehicle teams move from traditional perception models to end-to-end Physical AI systems, understanding model behavior is becoming harder than ever. These models ingest synchronized inputs from cameras, sensors, and other data streams, but their decisions can be difficult to explain, reproduce, and improve.

You’ll learn how to use multimodal data to investigate questions like: when did the model swerve, miss an object, misinterpret a scene, or behave unexpectedly — and how can you find every similar moment across your dataset?

Designed for robotics, AV, and machine learning teams, this session will show how FiftyOne helps turn multimodal data into a scalable workflow for model evaluation, debugging, and improvement.

Rome AI, Machine Learning and Computer Vision Meetup

Date, Time and Location

Aug 13, 2026

Online. Register for the Zoom!

We’ll walk through how modern agentic systems move beyond simple prompts—leveraging structured tools like dataset operations, embeddings, evaluation pipelines, and model execution to take real action. You’ll see how these agents can tag data, run inference, evaluate performance, and surface insights automatically, all within a unified workflow.

By combining natural language interfaces with programmable building blocks, teams can dramatically reduce manual effort, accelerate experimentation, and unlock faster decision-making across the ML lifecycle.

Whether you're building data-centric AI systems, managing large-scale vision datasets, or exploring agentic workflows for the first time, this session will give you a practical blueprint for getting started.

About the Speaker

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\.

Python Pescara

Ci mancava troppo organizzare dei talk, ma fa anche troppo caldo. Quindi, come risolviamo? Semplice: portiamo il talk direttamente in spiaggia!

Il mood sarà: piedi nella sabbia, bevanda in mano e orecchie rivolte alla nostra speaker, Lucia Coronel, e al suo talk:

Lucia ci parlerà di come utilizzare il deep learning per accelerare la scoperta di nuovi farmaci.

📍Dove: Zara Spiaggia Bar, Lungomare G. Matteotti, 90, 65122 Pescara.

🇺🇸

The mood: feet in the sand, a drink in hand, and all ears on our speaker, Lucia Coronel, and her talk:

Lucia will explain how deep learning can be used to accelerate drug discovery.

📍Where: Zara Spiaggia Bar, Lungomare G. Matteotti, 90, 65122 Pescara.

Rome AI, Machine Learning and Computer Vision Meetup

Time, Date and Location

Aug 20, 2026

Online. Register for the Zoom!

What You'll Walk Away With

• A working FiftyOne pipeline for finding rare classes in any visual dataset you own
• A repeatable four-stage funnel — compress, mine, confirm, prioritize — with a clear objective at each stage
• A fine-tuned detector that demonstrably outperforms one trained on the same number of randomly sampled images
• The mental model that data curation — not architecture or hyperparameters — is the highest-leverage thing you can do to improve a rare-class detector

Rome AI, Machine Learning and Computer Vision Meetup

Date, Time and Location

Aug 25, 2026

Online. Register for the Zoom!

Using Computer Vision to Advance the Sciences

I'll present some of our ongoing work on using computer vision to create impact in the sciences. These target a two areas, solar physics and evolutionary biology, that deal with objects of radically different sizes but are unified by a need for high quality, trustworthy data.

I'll show off our efforts, done in collaboration with domain experts, that aim to produce the best possible maps of the Sun's powerful magnetic field and have created some of the world's largest repositories of data about bird morphology.

About the Speaker

David Fouhey is an Associate Professor at New York University and a research scientist at Polymathic AI. Before joining NYU, he received a PhD in robotics from Carnegie Mellon, was a postdoc at UC Berkeley, and was a professor at University of Michigan.

Solaris: Building a Multiplayer Video World Model in Minecraft

This talk will introduce Solaris: a multiplayer video world model in Minecraft. I will first present SolarisEngine, the software platform we built to simulate realistic multiplayer gameplay between bots at scale, enabling us to collect a large training dataset of aligned multiplayer actions and frames.

I will then discuss our staged training pipeline, starting with single-player pre-training before converting the model into a long-horizon multiplayer generator through bidirectional training, followed by causal training, and concluding with Self Forcing. I will also cover our memory-efficient implementation of Self Forcing, called Checkpointed Self Forcing.

Finally, I will showcase generated videos illustrating how Solaris maintains coherent long-horizon multiplayer interactions.

About the Speaker

Oscar Michel is a PhD student at NYU advised by Prof. Saining Xie. His research studies world models: generative models of agents interacting in an environment.

Closing the human to robot gap for dexterous hands

Collecting task-specific robot data for multi-fingered hands is challenging due to the many difficulties that arise in teleoperation. That is why recently there has been a major focus on learning robot policies directly from human demonstrations. However, human demonstrations are difficult to work with; there is a major morphological and visual gap between human and robot hands, as well as between the environments they operate in.

In this talk, I'd like to discuss my efforts on closing this gap.

About the Speaker

Irmak Guzey I'm Irmak (she/her), a rising 3rd year PhD student at New York University, currently advised by Lerrel Pinto. My research focuses on robot learning for dexterous manipulation. I have been awarded a Fulbright scholarship and NYU's Best Master's Thesis Award in the past.

PyData Venice

We’ll meet #InPresence on Thursday, August 27 at 7:00 PM.

👥 We’ll have two interesting sessions, in Italian 🇮🇹 or in English 🇬🇧 or one and one:

And afterwards\, for those who want\, we’ll stay there for \( 🍸 \| 🍺 \) & \( 🍕 \| 🍔 \) and #networking until late ‼️

📽 And if you’d like to share ideas, projects, topics, or propose yourself as the next speaker, here’s the usual Google form or Sessionize !

Rome AI, Machine Learning and Computer Vision Meetup

Date, Time, and Location

Aug 27, 2026

Online. Register for the Zoom!

Robust Concept Protection against Diffusion-Based Image Editing and Personalization

Diffusion-based image editing and personalization models have made it increasingly easy to manipulate and replicate visual concepts from only a few reference images. However, existing protection methods often overfit to a single attack model and fail to generalize across diverse editing pipelines.

In this presentation, I will discuss recent advances in concept protection for generative AI systems, focusing on targeted perturbation strategies and style-sensitive diffusion representations. I will also present experimental findings across multiple editing and fine-tuning scenarios, highlighting the challenges of robustness, transferability, and imperceptibility in practical protection settings. Finally, I will discuss open problems and future directions toward trustworthy generative content ownership.

About the Speaker

Qiuyu Tang is a Ph.D. student in Computer Science and Engineering at Lehigh University. Her research focuses on trustworthy AI, media forensics, and robust protection methods against diffusion-based image editing and personalization systems. Her recent work explores concept protection, style safeguarding, semantic image manipulation, and generative AI robustness. She has contributed to multiple publications in computer vision and AI safety, including research on diffusion model protection and manipulation detection, and has also served as a conference workshop organizer.

From Pixels to the Planet: Building Scalable and Grounded AI for Science

AI has demonstrated a lot of new possibilities, from drafting emails to image editing and generation. The efficacy of AI models is largely built upon a standard machine learning pipeline, where data is fed into models to get representations and predictions, and the performance is evaluated with controlled benchmarks and metrics. However, the mismatch arises when we try to transit this pipeline to the interaction with the real world and use AI for scientific discovery. Beyond close-set decisions, scientists want to discover new categories and propose new hypotheses. In this talk, I will share how I address the challenges of AI for science from the perspectives of data-centric methods and interpretability approaches.

About the Speaker

Jianyang Gu is a postdoctoral scholar at The Ohio State University. His research focuses on using data-centric methods to build scalable and interpretable foundation models for science.

Beyond the Barn: Non-Invasive Acidosis Detection in Dairy Cattle Through Multimodal Gas Emission Intelligence

Rumen acidosis silently costs the global dairy industry billions annually and compromises animal welfare, yet current detection methods remain invasive, delayed, and impractical at scale. Our lab has pioneered a fundamentally new approach: capturing and analyzing exhaled CO₂ and CH₄ gas emission patterns through synchronized RGB-thermal imaging, turning every breath into a diagnostic signal. We developed DualGasNet, a dual-stream deep learning architecture with cross-attention fusion that detects acidosis non-invasively and in real time, achieving state-of-the-art accuracy on a first-of-its-kind livestock gas emission dataset we constructed from scratch.

To push toward explainable, farm-ready AI, we integrate vision-language models — CLIP and LLaVA-1.5 — enabling zero-shot diagnostic reasoning that bridges the gap between deep learning predictions and actionable veterinary insight. This talk will walk through the full pipeline from custom dataset creation to multimodal fusion to VLM-powered interpretation, offering the audience a compelling case study in how computer vision can solve high-impact, real-world problems outside traditional benchmarks.

About the Speaker

Taminul Islam is a Doctoral Research Fellow and PhD candidate at Southern Illinois University Carbondale with 40+ publications, 740+ citations, and an h-index of 16 — with publications in CVPR 2026, WACV 2026 (Oral), ICCV 2025, and Nature Scientific Reports, including a Highly Cited Paper for 2024–25.

Building Real-World Computer Vision Systems with Voxel51

This talk will explore practical workflows for building, evaluating, and improving modern computer vision systems. We’ll dive into real-world approaches to dataset curation, model analysis, multimodal AI workflows, and production-ready vision pipelines using open-source technologies.

The session is designed for engineers, researchers, and AI practitioners looking to better understand how teams are developing and scaling computer vision applications today. Expect practical demos, technical insights, and discussions around the evolving AI tooling ecosystem.

About the Speaker

Daniel Gural is an expert in Physical AI and has been working in the field for over 8 years. Working across healthcare he has experience in both operating use case as well as using Visual AI as an aid in psychology applications as well.

Rome AI, Machine Learning and Computer Vision Meetup

Time, Date and Location

Sep 02, 2026

Online. Register for the Zoom!

What You'll Walk Away With

• A working FiftyOne pipeline for any document collection you own
• A repeatable curation query that combines evaluation + embedding signals
• A fine-tuned LightOnOCR checkpoint that demonstrably outperforms the base model on your invoices
• The mental model that data curation — not architecture or hyperparameters — is the highest-leverage thing you can do to improve a document AI system

Rust Language Milan

Ticket Prices:

\- Individual / Student / Nonprofit: $425 early bird / $495 standard

Online Attendance: Yes! A virtual ticket is available with Pay What You Want pricing, starting at $0 USD. You can join online for free!

More info and tickets: https://rustconf.com/

In questo talk parleremo del linguaggio d programmazione Julia, un linguaggio semplice come Python e performante grazie alla compilazione just-in-time.

Questo appuntamento è stato spostato da luglio a settembre.

PNLUG

LUGMan

Esploreremo non solo le tecnologie della vita quotidiana, come PC, smartphone e Internet, ma anche il loro utilizzo e la loro manutenzione, con l'obiettivo di rendere l'utente capace di fruire di questi mezzi, di guidarlo e renderlo consapevole del mondo digitale attuale.

Maggiori info: wiki.golem.linux.it/CorsoALPHABIT2026

PyVenice

Ci troviamo in #presenza giovedì 17 settembre alle ore 19:00.

🗣 La scaletta non è ancora stata definita.

E dopo\, per chi vuole\, resteremo lì per \( 🍸 \| 🍺 \) e \( 🍕 \| 🍔 \)

📽 E se avete piacere di condividere idee, progetti, argomenti, o proporvi come prossimi speaker, lascio un form Google e la pagina Sessionize !

LUGMan

LUGMan

Da Confermare