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.

R-Ladies Rome

Today, Typst offers a modern alternative that combines beautiful typography with a simpler syntax and remarkably fast compilation, while integrating seamlessly with Quarto.

In this seminar, we will talk about how Typst can be a successor for LaTeX-based workflows for your typesetting needs. We will explore tools for layouts, diagrams, and other design elements through the Typst Universe. This presentation will include live demonstrations to introduce you to the wonderful world of Typst!

Whether you're preparing research papers, teaching material, technical reports, or books, this session will introduce a modern typesetting workflow that combines the flexibility of Quarto with the design capabilities of Typst. No previous experience with Typst or LaTeX is required—just curiosity about creating beautiful documents with open-source tools.

Speaker:

Derek Sollberger is a lecturer at Princeton University, where he teaches Data Science and Machine Learning. His work focuses on data communication, statistical computing in R, and conversations around artificial intelligence in education. He has developed engaging learning experiences that help students connect technical methods with clear, effective communication. Through his teaching and broader contributions to educator and R communities, Derek has built a reputation for making complex analytical ideas accessible and relevant.

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 !

Rome AI, Machine Learning and Computer Vision Meetup

Date, Time and Location

Sep 24, 2026

Online. Register for the Zoom!

How Do Mercedes-Benz AI Principles Drive our Innovation?

At Mercedes-Benz, our AI Principles guide every step of innovation, emphasizing responsible use, safety and reliability, explainability, and the protection of privacy. These principles go beyond statements and actively shape how we design, test, and deploy AI systems in real-world automotive and enterprise settings. In this talk, I will present how these principles inspired our recent research on when reusing LoRA (Low-Rank Adaptation) is effective. By combining theoretical analysis with synthetic data as a proxy for enterprise scenarios, we uncovered the strengths and limitations of modular AI components under constrained data access. Our findings provide practical guidance on when reused LoRAs could deliver high-quality results.

About the Speaker

Mei-Yen Chen is a Senior Data Scientist at Mercedes-Benz Tech Innovation GmbH in Germany with 10 years of industry experience in AI and data solutions. She leads early-stage AI projects across business functions and collaborates with research institutions on machine learning and responsible AI.

Region Tokens as the Visual Primitive: From Recognition to World Modeling

Patch-based tokenization has become the default interface between vision encoders and downstream models, yet patches carry no semantic structure and scale poorly with resolution and temporal extent. This talk presents a research program centered on replacing patch tokens with region-level representations — semantically dense tokens grounded in visual entities rather than arbitrary grid crops.

I will describe RELOCATE, REN, and T-REN, a progression of methods that produce region tokens via pooling, train them with region-level objectives, and extend them to video with temporal coherence. I will then present ongoing work integrating region tokens into VLMs to directly expand visual context capacity, and preliminary results on future region trajectory prediction as a foundation for world modeling.

The broader thesis is that region-level tokens are a more natural unit of visual computation than patches, and their advantage compounds as task complexity, resolution, and temporal horizon increase.

About the Speaker

Savya Khosla is a second-year Ph.D. student at the University of Illinois Urbana-Champaign, advised by Prof. Derek Hoiem and Prof. Alex Schwing.

Leveraging Text-To-Image Diffusion Models for Consistent Set-to-Set Generation

Image collections are humans' primary way of capturing the world, yet advances in generative editing remain largely inapplicable to this modality. We address this gap by introducing Match-and-Fuse - a zero-shot, training-free method for consistent set-to-set generation from image collections that share a common visual element but differ in viewpoint, capture time, and surrounding content.

About the Speaker

Kate Feingold is a PhD student in Computer Vision at the Weizmann Institute of Science. Her research sits at the intersection of generative models, 3D/4D perception, and multimodal learning, focusing on problems where vision meets other modalities or paradigms in creative tasks.

Yield Estimation of a Coffee in a dense environment

This presentation provides a detailed workflow related to coffee yield estimation in a dense environment. With photos of pre-harvest coffee plants from a couple of coffee estates, details related to pre-processing, annotation to detect regions of interest (ROI), object detection training and inferencing results with various Yolo models and finally segmentation with SAM2 and Yolo\*-seg with training and inference results to determine the count of raw, pre-mature, mature and over-mature coffee berries and finally the yield of the entire estate. All this is based on real world data captured on iPhone and android phones.

About the Speaker

Raghu M. Rao is a consultant working on applications of computer vision AI models. He was previously with AMD and Xilinx. He has a Ph.D. in Wireless Communications from UCLA and is a Senior Member, IEEE. His current interests are in applications of AI for agriculture, health care and wireless communications.

LUGMan

LUGMan

Rome AI, Machine Learning and Computer Vision Meetup

Date, Time and Location

Oct 08, 2026

Online. Register for the Zoom!

Designing Multi‑Agent Systems: Sequential, Parallel, and Beyond with ADK

Multi‑agent systems are powerful but choosing the wrong interaction pattern can quickly lead to fragile, slow, or expensive AI systems.

You’ll learn:

• When sequential agents are the right choice and when they become a bottleneck
• How parallel agents improve speed and coverage (and the trade‑offs they introduce)
• Common failure modes in poorly designed agent interactions
• Practical criteria for choosing the right pattern based on task, latency, and reliability

By the end of the session, you’ll have a clear mental model for designing multi‑agent systems that are intentional, scalable, and production‑ready.

About the Speaker

Dr Roushanak Rahmat is an Enterprise AI Architect, Google Developer Expert (AI & Cloud), and recognized among the Top 100 Women in Tech (2025). With a PhD in Artificial Intelligence and over 15 years of experience, she specializes in designing and delivering enterprise-scale Generative AI, Agentic AI, and Deep Learning solutions that transform industries including healthcare, finance, energy, and public services.

Privacy by Deployment: Architecting Agent-Driven Localization Workflows for Regulated Environments

Most enterprise AI today is private by promise - a DPA, a SOC 2 report, or a contract clause that says, "we won't train on your data". For a regulated buyer, these are remedies after a breach, not controls that prevent or contain one. For organizations in healthcare, finance, defense, and government, privacy often requires stronger guarantees: data residency, customer-controlled execution, and, in some cases, operation within air-gapped environments.

This session demonstrates how agentic AI can automate a localization workflow while operating within these constraints. Using a real-world localization pipeline as an example, we will show how agentic systems can coordinate translation, review, quality assurance, and content preparation tasks while incorporating human checkpoints for approval and oversight.

We will also walk through the architectural patterns that enable these workflows to run inside customer-controlled and air-gapped environments without transferring sensitive content outside the customer boundary. The session includes a live product demonstration.

Key Takeaways

• Architectural patterns for deploying agentic AI in air-gapped and customer-controlled environments
• How agentic systems can automate localization workflows while preserving critical human review and approval processes
• Practical considerations for operating agentic workflows in regulated environments with auditability and governance requirements

About the Speaker

Shruti Joshi is building an AI powered secure localization stack for regulated industries such as healthcare, legal, finance that cannot send their content to a typical hosted SaaS. She brings 12+ years of engineering and architecture experience to the question this talk addresses: how do you make an agentic AI system deployable inside a regulated perimeter.

MCP Is the Interface; Skills Are the Operating Discipline

This talk shows how MCP and Agent Skills work together in practical agent systems. MCP gives agents a standard interface to tools, data, and workflows; skills encode the operating discipline that makes those connections reliable. Using a sanitized field-operations ledger as the case study, the talk walks through source intake, normalized state, uncertainty labels, role prompts, QA gates, and share-safe status drafting.

About the Speaker

Chuck Hernandez is an AI engineering and client-delivery leader with 10+ years across software, data platforms, and enterprise implementation, including 3+ years shipping production GenAI systems.

Agentic engineering is about good guidance.

Garbage Inn. Is garbage out? This is true. For many input and output processes. In biological life and in computer systems, and equally true when working with LLM’s. The better the prompt, the better the context, the better the focus, And the better the contextual awareness, the better the quality of the output the LLM’s generates.

About the Speaker

Dimitri Geelen builds things that don't need him once they're done. Frameworks, transitions, agentic systems — the measure of success is always the same: does it hold up when he leaves the room? He understands not just how to deploy, but what it takes for a new service to survive and scale inside a complex enterprise.

Da Confermare