Name: Dec 4 - San Diego AI, ML, Computer Vision Meetup
Start: 2025-12-04T17:30:00-08:00
End: 2025-12-04T20:30:00-08:00
Location: Hilton Bayfront Hotel

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

**[Pre-registration is required.](https://voxel51.com/events/san-diego-ai-ml-and-computer-vision-meetup-december-4-2025)**

**Date and Location**

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

[Hilton San Diego Bayfront](https://www.hilton.com/en/hotels/sancchh-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](https://www.linkedin.com/in/andrewballester/) 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](https://www.linkedin.com/in/blythetowal/), 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](https://www.linkedin.com/in/harpreetsahota204/) 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](https://www.linkedin.com/in/nigelcoburn/) 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](https://www.linkedin.com/in/abhishekaich27/) 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](https://www.linkedin.com/in/sellersmichael/) 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.

Jimmy Guerrero

San Diego AI, ML and Computer Vision Meetup

AI, Machine Learning and Computer Vision Meetup Network

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Dec 4 - San Diego AI, ML, Computer Vision Meetup

Hilton Bayfront Hotel

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San Diego AI, ML and Computer Vision Meetup

Dec 4 - San Diego AI, ML, Computer Vision Meetup

San Diego AI, ML and Computer Vision Meetup

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