Silicon Valley AI, Machine Learning & Computer Vision Meetup

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Part 1: Navigating the GUI Agent Landscape

Understanding the Foundation Before Building

The GUI agent field is evolving rapidly, but success requires an understanding of what came before. In this opening session, we'll map the terrain of GUI agent research—from the early days of MiniWoB's simplified environments to today's complex, multimodal systems tackling real-world applications. You'll discover why standard vision models fail catastrophically on GUI tasks, explore the annotation bottlenecks that make GUI datasets so expensive to create, and understand the platform fragmentation that makes "click a button" mean twenty different things across datasets.

We'll dissect the most influential datasets (Mind2Web, AITW, Rico) and models that have shaped the field, examining their strengths, limitations, and the research gaps they reveal. By the end, you'll have a clear picture of where GUI agents excel, where they struggle, and, most importantly, where the opportunities lie for your own contributions.

About the Instructor

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 RAG, Agents, and Multimodal AI.

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Quiero ser parte del mundo de AI, como lo logro?

En esta charla, compartiré mi trayectoria personal hacia el mundo de la inteligencia artificial (IA), comenzando con mi formación como ingeniero electrónico y mi doctorado en neuroinformática. Destacaré cómo mi tesis laureada sobre modelos volumétricos realistas para la localización precisa de fuentes EEG abrió puertas a oportunidades en procesamiento digital y visión 3D. Con experiencia docente en la Universidad Nacional de Colombia y certificaciones en machine learning y deep learning, discutiré cómo estos hitos me llevaron a desempeñarme como desarrollador de currículo para DeepLearning.AI, ofreciendo valiosas lecciones para quienes deseen seguir un camino similar.

Presentador

Ernesto Cuartas es un ingeniero electrónico y PhD en neuroinformática. Tesis PhD laureada “Forward volumetric modeling framework for realistic head models towards accurate EEG source localization”. Profesor asociado Universidad Nacional de Colombia. Experto en implementación y desarrollo de proyectos en procesamiento digital de señales, procesamiento digital de imágenes, visión 3D, computación gráfica, geometría computacional, fotogrametría e inteligencia artificial. Con certificaciones profesionales en machine learning, deep learning y data engineering. Actualmente trabajo como curriculum developer/engineer para DeepLearning.AI.

Domina tus Datos Médicos: De la Curación al Impacto Clínico

Los datos de alta calidad son la base de un aprendizaje automático efectivo en el ámbito de la salud. Esta charla presenta estrategias prácticas y técnicas emergentes para gestionar datasets de imágenes médicas, desde la generación de datos sintéticos y la curación, hasta la evaluación y el despliegue.

Comenzaremos con casos de estudio reales de investigadores y profesionales que están transformando sus flujos de trabajo en imágenes médicas mediante prácticas centradas en los datos. Luego pasaremos a un tutorial práctico utilizando FiftyOne, la plataforma open-source para la inspección visual de datasets y la evaluación de modelos. Los asistentes aprenderán a cargar, visualizar, curar y evaluar datasets médicos en distintos tipos de imágenes.

Ya seas investigador, clínico o ingeniero de ML, esta charla te brindará herramientas e ideas prácticas para mejorar la calidad de tus datos, la fiabilidad de tus modelos y su impacto clínico.

Presentadora

Paula Ramos tiene un doctorado en Visión Artificial y Aprendizaje Automático, con más de 20 años de experiencia en el campo tecnológico. Desde principios de la década del 2000 en Colombia, ha desarrollado novedosas tecnologías integradas de ingeniería, principalmente en Visión Artificial, robótica y Aprendizaje Automático aplicados a la agricultura.

Agentes AI Multi-Fuente y Embebidos

Demostraré cómo construir agentes de IA contextualmente conscientes, capaz de responder y tomar acciones entre multiples sistemas privados y la implementación de RAG semántico a través de fuentes de datos dispares, embebidos en sistemas existentes, todo esto sin necesidad de una infraestructura compleja de MLOps.

Presentador

Kevin Blanco es un Senior DevRel Advocate, Charlista Internacional con más de 15 años en liderazgo tecnológico. Ha diseñado estrategias de IA en IBM Watson y desarrollado soluciones para Google, Microsoft y Nintendo.

Más allá del modelo: Metodología y buenas prácticas para liderar proyectos exitosos de IA con CPMAI

El éxito de los proyectos de IA no depende solo del modelo o de los datos, sino de cómo se gestionan desde el inicio. En esta charla exploraremos la metodología CPMAI (Cognitive Project Management for AI) avalada por el Project Management Institute - PMI, un marco estructurado que permite a los equipos de IA alinear sus iniciativas con objetivos de negocio, gestionar riesgos éticos y mejorar los resultados. Compartiremos buenas prácticas que pueden ser adaptadas por profesionales técnicos para mejorar la entrega de valor en cada fase del proyecto e implementar soluciones de IA éticas y responsables.

Presentadora

Ivonne Mejía B. es especialista en gestión de proyectos tecnológicos, con más de 20 años de experiencia internacional en el sector privado y académico en México, Canadá y Estados Unidos. Está certificada en CPMAI™, PMP®, Prosci®, y cuenta con un diplomado en Liderazgo Tecnológico por UC Berkeley. Disfruta colaborar, aprender en comunidad y compartir su experiencia para ayudar a las organizaciones a definir estrategias de transformación con IA y liderar soluciones éticas y responsables.

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Part 2: From Pixels to Predictions - Building Your GUI Dataset

Hands-On Dataset Creation and Curation with FiftyOne

The best GUI models are only as good as their training data, and the best datasets are built by understanding what makes GUI interactions fundamentally different from natural images. In this practical session, you'll build a complete GUI dataset from scratch, learning to capture the precise annotations that GUI agents need.

Using FiftyOne as your data management backbone, you'll import diverse GUI screenshots, explore annotation strategies that go beyond bounding boxes, and implement efficient labeling workflows. We'll tackle the real challenges: handling platform differences, managing annotation quality, and creating datasets that transfer to new domains. You'll also learn advanced techniques like synthetic data generation and automated prelabeling to scale your annotation efforts.

Walk away with a production-ready dataset and the skills to build more—because in GUI agents, data quality determines everything.

By the end, you'll have both a dataset and the methodology to build the next generation of GUI training data.

About the Instructor

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 RAG, Agents, and Multimodal AI.

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Exploiting Vulnerabilities In CV Models Through Adversarial Attacks

As AI and computer vision models are leveraged more broadly in society, we should be better prepared for adversarial attacks by bad actors. In this talk, we'll cover some of the common methods for performing adversarial attacks on CV models. Adversarial attacks are deliberate attempts to deceive neural networks into generating incorrect predictions by making subtle alterations to the input data.

About the Speaker

Elisa Chen is a data scientist at Meta on the Ads AI Infra team with 5+ years of experience in the industry.

EffiDec3D: An Optimized Decoder for High-Performance and Efficient 3D Medical Image Segmentation

Recent 3D deep networks such as SwinUNETR, SwinUNETRv2, and 3D UX-Net have shown promising performance by leveraging self-attention and large-kernel convolutions to capture the volumetric context. However, their substantial computational requirements limit their use in real-time and resource-constrained environments.

In this paper, we propose EffiDec3D, an optimized 3D decoder that employs a channel reduction strategy across all decoder stages and removes the high-resolution layers when their contribution to segmentation quality is minimal. Our optimized EffiDec3D decoder achieves a 96.4% reduction in #Params and a 93.0% reduction in #FLOPs compared to the decoder of original 3D UX-Net. Our extensive experiments on 12 different medical imaging tasks confirm that EffiDec3D not only significantly reduces the computational demands, but also maintains a performance level comparable to original models, thus establishing a new standard for efficient 3D medical image segmentation.

About the Speaker

Md Mostafijur Rahman is a final-year Ph.D. candidate in Electrical and Computer Engineering at The University of Texas at Austin, advised by Dr. Radu Marculescu, where he builds efficient AI methods for biomedical imaging tasks such as segmentation, synthesis, and diagnosis. By uniting efficient architectures with data-efficient training, his work delivers robust and efficient clinically deployable imaging solutions.

What Makes a Good AV Dataset? Lessons from the Front Lines of Sensor Calibration and Projection

Getting autonomous vehicle data ready for real use, whether for training, simulation, or evaluation, isn’t just about collecting LIDAR and camera frames. It’s about making sure every point lands where it should, in the right frame, at the right time.

In this talk, we’ll break down what it actually takes to go from raw logs to a clean, usable AV dataset. We’ll walk through the practical process of validating transformations, aligning coordinate systems, checking intrinsics and extrinsics, and making sure your projected points actually show up on camera images. Along the way, we’ll share a checklist of common failure points and hard-won debugging tips.

Finally, we’ll show how doing this right unlocks downstream tools like Omniverse Nurec and Cosmos—enabling powerful workflows like digital reconstruction, simulation, and large-scale synthetic data generation

About the Speaker

Daniel Gural is a seasoned Machine Learning Engineer at Voxel51 with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data.