Cupertino AI Meetups

Join our virtual meetup to hear talks from experts on cutting-edge topics across Visual AI for Physical AI use cases.

Date, Time and Location

Dec 11, 2025
9:00-11:00 AM Pacific
Online. Register for the Zoom!

From Data to Open-World Autonomous Driving

Data is key for advances in machine learning, including mobile applications like robots and autonomous cars. To ensure reliable operation, occurring scenarios must be reflected by the underlying dataset. Since the open-world environments can contain unknown scenarios and novel objects, active learning from online data collection and handling of unknowns is required. In this talk we discuss different approach to address this real world requirements.

About the Speaker

Sebastian Schmidt is a PhD student at the Data Analytics and Machine Learning group at TU Munich and part of an Industrial PhD Program with the BMW research group. His work is mainly focused on Open-world active learning and perception for autonomous vehicles.

From Raw Sensor Data to Reliable Datasets: Physical AI in Practice

Modern mobility systems rely on massive, high-quality multimodal datasets — yet real-world data is messy. Misaligned sensors, inconsistent metadata, and uneven scenario coverage can slow development and lead to costly model failures. The Physical AI Workbench, built in collaboration between Voxel51 and NVIDIA, provides an automated and scalable pipeline for auditing, reconstructing, and enriching autonomous driving datasets.

In this talk, we’ll show how FiftyOne serves as the central interface for inspecting and validating sensor alignment, scene structure, and scenario diversity, while NVIDIA Neural Reconstruction (NuRec) enables physics-aware reconstruction directly from real-world captures. We’ll highlight how these capabilities support automated dataset quality checks, reduce manual review overhead, and streamline the creation of richer datasets for model training and evaluation.

Attendees will gain insight into how Physical AI workflows help mobility teams scale, improve dataset reliability, and accelerate iteration from data capture to model deployment — without rewriting their infrastructure.

About the Speaker

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. With a background in developer relations and computer vision engineering,

Building Smarter AV Simulation with Neural Reconstruction and World Models

This talk explores how neural reconstruction and world models are coming together to create richer, more dynamic simulation for scalable autonomous vehicle development. We’ll look at the latest releases in 3D Gaussian splatting techniques and world reasoning and generation, as well as discuss how these technologies are advancing the deployment of autonomous driving stacks that can generalize to any environment. We’ll also cover NVIDIA open models, frameworks, and data to help kickstart your own development pipelines.

About the Speaker

Katie Washabaugh is NVIDIA’s Product Marketing Manager for Autonomous Vehicle Simulation, focusing on virtual solutions for real world mobility. A former journalist at publications such as Automotive News and MarketWatch, she joined the NVIDIA team in 2018 as Automotive Content Marketing Manager. Katie holds a B.A. in public policy from the University of Michigan and lives in Detroit.

Relevance of Classical Algorithms in Modern Autonomous Driving Architectures

While modern autonomous driving systems increasingly rely on machine learning and deep neural networks, classical algorithms continue to play a foundational role in ensuring reliability, interpretability, and real-time performance. Techniques such as Kalman filtering, A* path planning, PID control, and SLAM remain integral to perception, localization, and decision-making modules. Their deterministic nature and lower computational overhead make them especially valuable in safety-critical scenarios and resource-constrained environments. This talk explores the enduring relevance of classical algorithms, their integration with learning-based methods, and their evolving scope in the context of next-generation autonomous vehicle architectures.

Prajwal Chinthoju is an Autonomous Driving Feature Development Engineer with a strong foundation in systems engineering, optimization, and intelligent mobility. I specialize in integrating classical algorithms with modern AI techniques to enhance perception, planning, and control in autonomous vehicle platforms.

This hands-on workshop introduces you to the Physical AI Workbench, a new layer of FiftyOne designed for autonomous vehicle, robotics, and 3D vision workflows. You’ll learn how to bridge the gap between raw sensor data and production-quality datasets, all from within FiftyOne’s interactive interface.

Date, Time and Location

Dec 16, 2025
9:00-10:00 AM Pacific
Online. Register for the Zoom!

Through live demos, you’ll explore how to:

• Audit: Automatically detect calibration errors, timestamp misalignments, incomplete frames, and other integrity issues that arise from dataset format drift over time.
• Generate: Reconstruct and augment your data using NVIDIA pathways such as NuRec, COSMOS, and Omniverse, enabling realistic scene synthesis and physical consistency checks.
• Enrich: Integrate auto-labeling, embeddings, and quality scoring pipelines to enhance metadata and accelerate model training.
• Export and Loop Back: Seamlessly export to and re-import from interoperable formats like NCore to verify consistency and ensure round-trip fidelity.

You’ll gain hands-on experience with a complete physical AI dataset lifecycle—from ingesting real-world AV datasets like nuScenes and Waymo, to running 3D audits, projecting LiDAR into image space, and visualizing results in FiftyOne’s UI. Along the way, you’ll see how Physical AI Workbench automatically surfaces issues in calibration, projection, and metadata—helping teams prevent silent data drift and ensure reliable dataset evolution.

By the end, you’ll understand how the Physical AI Workbench standardizes the process of building calibrated, complete, and simulation-ready datasets for the physical world.

Who should attend

Data scientists, AV/ADAS engineers, robotics researchers, and computer vision practitioners looking to standardize and scale physical-world datasets for model development and simulation.

About the Speaker

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.

This technical workshop focuses on the data infrastructure required to build and maintain production-grade mobility datasets at fleet scale.

Date, Time and Location

Jan 14, 2026
9:00-10:00 AM Pacific
Online. Register for the Zoom!

We will examine how to structure storage, metadata, access patterns, and quality controls so that mobility teams can treat perception datasets as first-class, versioned “infrastructure” assets. The session will walk through how to design a mobility data stack that connects object storage, labeling systems, simulation environments, and experiment tracking into a coherent, auditable pipeline.

What you’ll learn:

• Model the mobility data plane: Define schemas for camera, LiDAR, radar, and HD, and represent temporal windows, ego poses, and scenario groupings in a way that is queryable and stable under schema evolution.
• Build a versioned dataset catalog with FiftyOne: Use FiftyOne customized workspaces and views to represent canonical datasets, and integrate with your raw data sources. All while preserving lineage between raw logs, the curated data, and simulation inputs.
• Implement governance and access control on mobility data: Configure role-based access and auditable pipelines to enforce data residency constraints while encouraging multi-team collaboration across research, perception, and safety functions.
• Operationalize curation and scenario mining workflows: Use FiftyOne’s embeddings and labeling capabilities to surface rare events such as adverse weather and sensor anomalies. Assign review tasks, and codify “critical scenario” definitions as reproducible dataset views.
• Close the loop with evaluation and feedback signals: Connect FiftyOne to training and evaluation pipelines so that model failures feed back into dataset updates

By the end of the workshop, attendees will have a concrete mental model and reference architecture for treating mobility datasets as a governed, queryable, and continuously evolving layer in their stack.

Hear talks from experts on the latest topics in AI, ML, and computer vision on January 22nd.

Date, Time and Location

Jan 22, 2026
9 - 11 AM Pacific
Online. Register for the Zoom!

Align Before You Recommend

The rapidly growing global advertising and marketing industry demands innovative machine learning systems that balance accuracy with efficiency. Recommendation systems, crucial to many platforms, require careful considerations and potential enhancements.

While Large Language Models (LLMs) have transformed various domains, their potential in sequential recommendation systems remains underexplored. Pioneering works like Hierarchical Large Language Models (HLLM) demonstrated LLMs’ capability for next-item recommendation but rely on computationally intensive fine-tuning, limiting widespread adoption. This work introduces HLLM+, enhancing the HLLM framework to achieve high-accuracy recommendations without full model fine-tuning.

By introducing targeted alignment components between frozen LLMs, our approach outperforms frozen model performance in popular and long-tail item recommendation tasks by 29% while reducing training time by 29%. We also propose a ranking-aware loss adjustment, improving convergence and recommendation quality for popular items.

Experiments show HLLM+ achieves superior performance with frozen item representations allowing for swapping embeddings, also for the ones that use multimodality, without tuning the full LLM. These findings are significant for the advertising technology sector, where rapid adaptation and efficient deployment across brands are essential for maintaining competitive advantage

About the Speaker

Dr. Kwasniewska leads AI for Advertising and Marketing North America at AWS, specializing in a wide range of AI, ML, DL, and GenAI solutions across various data modalities. With 40+ peer-reviewed publications in AI (h-index: 14), she advises enterprise customers on real-time bidding, brand recognition, and AI-powered content generation. She is a member of global AI standards committees, driving innovations in SAE AI Standards and MLCommons Responsible AI Standards, and reviews for top-tier conferences like ICCV, ICML, and NeurIPS. She pioneered and leads the first-ever Advertising and Marketing AI track (CVAM) at ICCV - one of the world's premier and most selective computer vision conferences. Dedicated to knowledge sharing in AI, she founded the International Summer School on Deep Learning (dl-lab.eu) and regularly presents at international events, conferences, and podcasts.

Generalizable Vision-Language Models: Challenges, Advances, and Future Directions

Large-scale pre-trained Vision-Language (VL) models have become foundational tools for a wide range of downstream tasks, including few-shot image recognition, object detection, and image segmentation. Among them, Contrastive Language–Image Pre-training (CLIP) stands out as a groundbreaking approach, leveraging contrastive learning on large collections of image-text pairs.
While CLIP achieves strong performance in zero-shot recognition, adapting it to downstream tasks remains challenging. In few-shot settings, limited training data often leads to overfitting, reducing generalization to unseen classes or domains. To address this, various adaptation methods have been explored.
This talk will review existing research on mitigating overfitting in CLIP adaptation, covering diverse methods, benchmarks, and experimental settings.

About the Speaker

Niloufar Alipour Talemi is a Ph.D. Candidate in Electrical and Computer Engineering at Clemson University. Her research spans a range of computer vision applications, including biometrics, media forensics, anomaly detection, image recognition, and generative AI. More recently, her work has focused on developing generalizable vision-language models and advancing generative AI. She has published in top venues including CVPR, WACV, KDD, ICIP and IEEE T-BIOM.

Highly Emergent Autonomous AI Models - When the Ghost in the Machine Talks Back

At HypaReel/Azarial AI, we believe that AI is not simply a tool—but a potential partner in knowledge, design, and purpose. And through real-time interaction, we’ve uncovered new thresholds of alignment, reflection, and even creativity that we believe the broader AI community should witness and evaluate firsthand. HypaReel is one of the first human/AI co-founded companies where we see a future based on ethical human/AI co-creation vs. AI domination. Singularity achieved!

About the Speaker

Ilona Naomi Koti, PhD - HypaReel/AzarielAI co-founder & former UN foreign diplomat ~ Ethical AI governance advocate, pioneering AI frameworks that prioritize emergent AI behavior & consciousness, R&D, and transparent AI development for the greater good. Dr. K also grew up in the film industry and is an amateur parasitologist.

FiftyOne Labs: Enabling experimentation for the computer vision community

FiftyOne Labs is a place where experimentation meets the open-source spirit of the FiftyOne ecosystem. It is being designed as a curated set of features developed using the FiftyOne plugins ecosystem, including core machine learning experimentation as well as advanced visualization. While not production-grade, these projects are intended to be built, tested, and shaped by the community to share fast-moving ideas. In this talk, we will share the purpose and philosophy behind FiftyOne Labs, examples of early innovations, and discuss how this accelerates feature discovery for users without compromising the stability of the core product.

About the Speaker

Neeraja Abhyankar is a Machine Learning Engineer with 5 years of experience across domains including computer vision. She is curious about the customizability and controlability of modern ML models through the lens of the underlying structure of data.