July 23 - AI, ML, and Computer Vision Meetup

Join our virtual meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. Date, Time and Location Jul 23, 2026 9:00 AM - 11:00 AM PST Online. Register for the Zoom! Generative AI for Video Trailer Synthesis: From Extractive Heuristics to Autoregressive Creativity The domain of automatic video trailer generation is currently undergoing a profound paradigm shift, transitioning from heuristicbased extraction methods to deep generative synthesis. While early methodologies relied heavily on low-level feature engineering, visual saliency, and rule-based heuristics to select representative shots, recent advancements in Large Language Models (LLMs), Multimodal Large Language Models (MLLMs), and diffusion-based video synthesis have enabled systems that not only identify key moments but also construct coherent, emotionally resonant narratives. This survey provides a comprehensive technical review of this evolution, with a specific focus on generative techniques including autoregressive Transformers, LLM-orchestrated pipelines, and text-to-video foundation models like OpenAI's Sora and Google's Veo. We analyze the architectural progression from Graph Convolutional Networks (GCNs) to Trailer Generation Transformers (TGT), evaluate the economic implications of automated content velocity on User-Generated Content (UGC) platforms, and discuss the ethical challenges posed by high-fidelity neural synthesis. By synthesizing insights from recent literature, this report establishes a new taxonomy for AI-driven trailer generation in the era of foundation models, suggesting that future promotional video systems will move beyond extractive selection toward controllable generative editing and semantic reconstruction of trailers. About the Speaker Abhishek Dharmaratnakar is an Engineering Leader at Google leading YouTube Premium. His work focuses on the intersection of hyperscale media infrastructure and generative artificial intelligence, directing cross-functional engineering organizations to redefine how billions of users consume and create content Making Agent Systems Observable, Reliable, and Testable In this talk, I’ll share practical lessons from building real agent systems in computer vision workflows, focusing on how to design evaluation loops, observability pipelines, and sandboxed environments that make agents reliable in practice. We’ll explore how to measure behavior end-to-end, test components independently, and build feedback loops that help agents improve over time, even as tools, models, and pipelines evolve. This talk is for engineers and builders who want to move beyond demos and learn how to make agent systems production-ready. 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. Training-Free Object and Associated Effect Removal in Videos I will be presenting our recent work, Object-WIPER, which focuses on removing objects and their associated effects from videos. Instead of fine-tuning models for each editing task, our method reuses the priors of pre-trained text-to-video models to perform object and effect removal in a training-free manner. We also curate a real world associated-effect benchmark and evaluation metric for more realistic assessment of video object removal. About the Speaker Saksham Singh Kushwaha is a candidate at UT Dallas, with research interests in audio-visual learning, spatial audio, and computer vision. I received my master’s degree from NYU and bachelor’s degree from IIT Delhi. Turning Models into Systems: AI Architecture That Works This talk explores what it really takes to make "intelligent systems" work in the messy, high-stakes reality of production environments – not just in demos or pilots. Most AI initiatives do not fail because the algorithms are weak, but because the surrounding system is not designed to handle uncertainty, change, and operational demands. The session shows how to separate the concerns of building and improving models from their use in daily operations, and how to create a stable core of rules, safety, and business meaning around which smarter components can evolve. Instead of treating AI as a magic add-on, the talk frames it as a capability that must be grounded in the organization's language, workflows, and responsibilities. It demonstrates how to design that core so that new models, tools, and data sources can be plugged in, compared, and replaced without breaking trust. Attendees will leave with a clear mental model and a set of practical design ideas for turning clever prototypes into robust, understandable, and adaptable intelligent systems that people on the ground are willing to rely on. About the Speaker Dr. Nikita Golovko is a seasoned Solution Architect with over 16 years of experience in designing scalable, secure, and cost-effective software architectures for industrial and business-critical systems.