AI, Machine Learning and Computer Vision Meetup
Jan 30, 2025 at 10 AM Pacific
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Swimming Upstream: Using Machine Vision to Create Sustainable Practices in Fisheries of the Future
Orvis Evans
AI.Fish
Fishing vessels are on track to generate 10 million hours of video footage annually, creating a massive machine learning operations challenge. At AI.Fish, we are building an end-to-end system enabling non-technical users to harness AI for catch monitoring and classification both on-board and in the cloud. This talk explores our journey in building these approachable systems and working toward answering an old question: How many fish are in the ocean?
About the Speaker
Orvis Evans is a Software Engineer at AI.Fish, where he co-architects ML-Ops pipelines and develops intuitive interfaces that make machine vision accessible to non-technical users. Drawing from his background in building interactive systems, he builds front-end applications and APIs that enable fisheries to process thousands of hours of footage without machine learning expertise.
Scaling Semantic Segmentation with Blender
Vincent Vandenbussche
Prose
Generating datasets for semantic segmentation can be time-intensive. Learn how to use Blender’s Python API to create diverse and realistic synthetic data with automated labels, saving time and improving model performance. Preview the topics to be discussed in this Medium post.
About the Speaker
Vincent Vandenbussche has a PhD in Physics, is an author, and Machine Learning Engineer with 10 years of experience in software engineering and machine learning.
WACV 2025 - Elderly Action Recognition Challenge
Paula Ramos, PhD
Voxel51
Join us for a quick update on the Elderly Action Recognition (EAR) Challenge, part of the Computer Vision for Smalls (CV4Smalls) Workshop at the WACV 2025 conference!
This challenge focuses on advancing research in Activity of Daily Living (ADL) recognition, particularly within the elderly population, a domain with profound societal implications. Participants will employ transfer learning techniques with any architecture or model they want to use. For example, starting with a general human action recognition benchmark and fine-tuning models on a subset of data tailored to elderly-specific activities.
Sign up for the EAR challenge and learn more.
About the Speaker
Paula Ramos, PhD is a Senior DevRel and Applied AI Research Advocate at Voxel51.
Transforming Programming Ed: An AI-Powered Teaching Assistant for Scalable and Adaptive Learning
Nittin Murthi Dhekshinamoorthy
University of Illinois Urbana-Champaign
The future of education lies in personalized and scalable solutions, especially in fields like computer engineering where complex concepts often challenge students. This talk introduces Lumina (AI Teaching Assistant), a cutting-edge agentic system designed to revolutionize programming education through its innovative architecture and teaching strategies. Built using OpenAI API, LangChain, RAG, and ChromaDB, Lumina employs an agentic, multi-modal framework that dynamically integrates course materials, technical documentation, and pedagogical strategies into an adaptive knowledge-driven system. Its unique “Knowledge Components” approach decomposes programming concepts into interconnected teachable units, enabling proficiency-based learning and dynamic problem-solving guidance. Attendees will discover how Lumina’s agentic architecture enhances engagement, fosters critical thinking, and improves concept mastery, paving the way for scalable AI-driven educational solutions.
About the Speaker
Nittin Murthi Dhekshinamoorthy is a computer engineering student and researcher at the University of Illinois Urbana-Champaign with a strong focus on advancing artificial intelligence to solve real-world challenges in education and technology. He is the creator of an AI agent-based Teaching Assistant, leveraging cutting-edge frameworks to provide scalable, adaptive learning solutions, and has contributed to diverse, impactful projects, including natural language-to-SQL systems and deep learning models for clinical image segmentation.
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