San Diego AI, ML and Computer Vision Meetup

Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Register for the Zoom

Date and Time

Dec 4, 2025
9:00 - 11:00 AM Pacific

Benchmarking Vision-Language Models for Autonomous Driving Safety

This workshop introduces a unified framework for evaluating how vision-language models handle driving safety. Using an enhanced BDDOIA dataset with scene, weather, and action labels, we benchmark models like Gemini, FastVLM, and Qwen within FiftyOne. Our results show consistent blind spots where models misjudge unsafe situations, highlighting the need for safer and more interpretable AI systems for autonomous driving.

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.

TrueRice: AI-Powered Visual Quality Control for Rice Grains and Beyond at Scale

Agriculture remains one of the most under-digitized industries, yet grain quality control defines pricing, trust, and livelihoods for millions. TrueRice is an AI-powered analyzer that turns a flatbed scanner into a high-precision, 30-second QC engine, replacing the 2+ hours and subjectivity of manual quality inspection.

Built on a state-of-the-art 8K image processing pipeline with SAHI (Slicing Aided Hyper Inference), it detects fine-grained kernel defects at scale with high accuracy across grain size, shape, breakage, discoloration, and chalkiness. Now being extended to maize and coffee, TrueRice showcases how cross-crop transfer learning and frugal AI engineering can scale precision QC for farmers, millers, and exporters. This talk will cover the design principles, model architecture choices, and a live demonstration, while addressing challenges in data variability, regulatory standards, and cross-crop adaptation.

About the Speaker

Sai Jeevan Puchakayala is an Interdisciplinary AI/ML Consultant, Researcher, and Tech Lead at Sustainable Living Lab (SL2) India, where he drives development of applied AI solutions for agriculture, climate resilience, and sustainability. He led the engineering of TrueRice, an award-winning grain quality analyzer that won India’s first International Agri Hackathon 2025.

WeedNet: A Foundation Model Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification

Early and accurate weed identification is critical for effective management, yet current AI-based approaches face challenges due to limited expert-verified datasets and the high variability in weed morphology across species and growth stages. We present WeedNet, a global-scale weed identification model designed to recognize a wide range of species, including noxious and invasive plants. WeedNet is an end-to-end real-time pipeline that integrates self-supervised pretraining, fine-tuning, and trustworthiness strategies to improve both accuracy and reliability.

Building on this foundation, we introduce a Global-to-Local strategy: while the Global WeedNet model provides broad generalization, we fine-tune local variants such as Iowa WeedNet to target region-specific weed communities in the U.S. Midwest. Our evaluation addresses both intra-species diversity (different growth stages) and inter-species similarity (look-alike species), ensuring robust performance under real-world variability. We further validate WeedNet on images captured by drones and ground rovers, demonstrating its potential for deployment in robotic platforms. Beyond field applications, we integrate a conversational AI to enable practical decision-support tools for farmers, agronomists, researchers, and land managers worldwide. These advances position WeedNet as a foundational model for intelligent, scalable, and regionally adaptable weed management and ecological conservation.

About the Speaker

Timilehin Ayanlade is a Ph.D. candidate in the Self-aware Complex Systems Laboratory at Iowa State University, where his research focuses on developing machine learning and computer vision methods for agricultural applications. His work integrates multimodal data across ground-based sensing, UAV, and satellite with advanced AI models to tackle challenges in weed identification, crop monitoring, and crop yield prediction.

Memory Matters: Early Alzheimer’s Detection with AI-Powered Mobile Tools

Advancements in artificial intelligence and mobile technology are transforming the landscape of neurodegenerative disease detection, offering new hope for early intervention in Alzheimer’s.
By integrating machine learning algorithms with everyday mobile devices, we are entering a new era of accessible, scalable, and non-invasive tools for early Alzheimer’s detection
In this talk, we’ll cover the potential of AI in health care systems, ethical considerations, plus an architecture, model, datasets and framework deep dive.

About the Speaker

Reetam Biswas has more than 18 years of experience in the IT industry as a software architect, currently working on AI.

Join the Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Pre-registration is required.

Date and Location

Dec 4, 2025
5:30 - 8:30 PM

Hilton San Diego Bayfront
(Across the street from NeurIPS)
Elevation Room
1 Park Blvd
San Diego, CA

Extending RT-DETR for Line-Based Object Detection: Paddle Spine Estimation in Pickleball Serve Analysis

We present a modified vision transformer–based detection model for estimating the spine line of a pickleball paddle from video data, developed to support automated serve legality analysis and motion coaching. Building on the RT-DETR architecture, we reformulated the detection head to predict two keypoints representing the endpoints of the paddle’s longitudinal axis rather than a bounding box, enabling a general framework for regressing an arbitrary number of vertices defining lines or polygons.

To facilitate stable training, we defined a loss combining a line-IoU term with a cosine-angle regularizer that enforces geometric consistency between predicted and ground-truth orientations. Dataset curation and qualitative validation were performed using FiftyOne, allowing visual inspection of data diversity pre-training and model quality post-training. The model was trained and deployed end-to-end on the EyePop.ai platform, which provided data management, training orchestration, and model hosting for seamless integration into a third-party application performing real-time serve evaluation and feedback.

About the Speakers

Andy Ballester is the co-founder & Chief Product Officer at EyePop.ai, a self-service platform that makes computer vision accessible to everyone. He’s spent his career building tools that democratize powerful technologies and unlock new possibilities for creators, startups, and enterprises. At EyePop.ai, Andy is focused on helping users build and deploy AI models—no ML experience required.

Blythe Towal, PhD, is a recognized leader in AI, machine learning and the systems to build, train and deploy models for real-time applications. Before joining EyePop.ai, she held senior roles at Saildrone, Shield AI, NVIDIA, and Qualcomm driving breakthrough innovations from model concept to deployment. At EyePop.ai, Blythe leads the development of the ML platform that allows businesses to develop, monitor, analyze and continuously improve AI solutions.

Visual Agents: What it takes to build an agent that can navigate GUIs like humans

We’ll examine conceptual frameworks, potential applications, and future directions of technologies that can “see” and “act” with increasing independence. The discussion will touch on both current limitations and promising horizons in this evolving field.

About the Speaker

Harpreet Sahota is a hacker-in-residence and machine learning engineer with a passion for deep learning and generative AI. He’s got a deep interest in VLMs, Visual Agents, Document AI, and Physical AI.

Edge AI for Biofluid Analysis

This talk explores how compact neural networks running on low-power devices can detect and classify biological materials — from salt crystals in sweat, cell types in saliva, sperm motility and morphology, to particle counting — using affordable research-grade microscopes along with accessible hardware; such as a Raspberry Pi, microcontrollers, AI accelerators & FPGAs. The talk will demonstrate that meaningful bioanalysis can occur entirely at the edge, lowering costs, protecting privacy, and opening the door to new home-diagnostic and health-monitoring tools.

About the Speaker

Dr. Nigel J. Coburn is a researcher and technologist working at the intersection of biosensing, microsystems, semiconductors, synthetic biology, and AI. He earned his Ph.D. in Electrical Engineering from the University of Cambridge and has held engineering and research roles at the European Space Agency, McGill University, Boston University, Google, Analog Devices, and Illumina. His recent work focuses on health sensing broadly, chip design, and protein sensing. Dr. Coburn is co-founder and CEO of Precision Atomics, a semiconductor equipment company developing custom systems, an Associate Member of the Institute for Neural Computation (INC) at UCSD, and as of November 10 2025, he is a Health Sensing Hardware Engineer at Oura in San Diego.

Structured Zero-Shot Vision-Based LLM Grounding for Driving Video Reasoning

Grounding large language models (LLMs) for post-hoc dash-cam video analysis is challenging due to their lack of domain-specific inductive biases and structured reasoning. I will present iFinder, a modular, training-free framework that decouples perception from reasoning by converting dash-cam videos into hierarchical, interpretable data structures.

Using pretrained vision models and a three-block prompting strategy, iFinder enables step-wise, grounded reasoning. Evaluations on four public benchmarks show up to 39% improvement in accident reasoning accuracy, demonstrating interpretable and reliable performance over end-to-end V-VLMs.

About the Speaker

Dr. Abhishek Aich is a researcher at NEC Laboratories America and received his Ph.D. from the University of California, Riverside in 2023 under the supervision of Prof. Amit K. Roy-Chowdhury. His work spans vision-language models, open-vocabulary perception, efficient transformers, and dynamic networks. During his graduate studies he held internships at NEC Laboratories (2022), Mitsubishi Electric Research Laboratories (2021) and UII (2020).

Data for Vision AI and Applications in Defense Technology

We all know the right data is critical to delivering effective AI solutions, but how exactly do we do this in different mission domains? For unique defense cases, we explore the keys to building the best training sets for object tracking pipelines and using data to adapt off-the-shelf foundation models for overhead imagery. We'll also discuss some recent testing events and review the software that powers some select AI applications Booz Allen is bringing to the defense technology space.

About the Speaker

Michael Sellers s a Senior AI Solution Architect in Booz Allen Hamilton's Artificial Intelligence Group, and leads teams in Computer Vision for Physical AI systems, SaaS cloud platforms, and Rapid Prototyping. Michael’s AI work is supported by a background in simulation software development at US Army Research Laboratory and the oil and gas industry, with a BS and PhD in Chemical Engineering from the University at Buffalo.

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.