Visual AI Hackathon - New York City
Oct 27, 2024 at 11:30 AM to 4:30 PM Eastern
Register for the Hackathon
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Visual AI Hackathon - New York City
Join us for an exciting hackathon in NYC, where ML enthusiasts and college students alike will come together to tackle real-world challenges in the field. With cash prizes, intimate space, refreshments, and swag, participants can expect an immersive experience filled with learning, networking, and the opportunity to showcase their skills. Whether you’re a beginner eager to explore foundational concepts or an intermediate looking to add flair to your projects, this event offers something for everyone.
Attend workshops during the hackathon to learn some of the best practices in Computer Vision and ML. Judges, including industry experts, will evaluate submissions across various levels, with prizes awarded to the most innovative solutions. Don’t miss out on this chance to collaborate, learn, and contribute to the vibrant AI community.
Come build projects, engage with fellow enthusiasts, and be part of the future of machine learning!
What you can expect
🚀 Tech Talks: Deep dive tech talks on Computer Vision and Data-Centric AI.
🔧 Hands-on Workshops: Learn how to build AI applications through code examples
🏅 Engaging Challenges: Tackle real-world problems with AI solutions.
🤝 Networking Opportunities: Connect with fellow developers, builders, and industry experts.
🎁 Exciting Prizes: Demo your projects and compete for cash prizes.
Join one of our three tracks
🧭 Level 0 – Beginner: For those who are just getting started on their ML Journey!
🛞 Level 1: – Intermediate: For those familiar with some Data Science or ML practices, but looking to level up!
🔥 Level 2 – Expert: For those who are the frontier of ML or Computer Vision today looking to make a positive impact!
When and Where
11:30 AM – 4:30 PM Eastern
LOFT393 in Tribeca
393 Broadway, 2nd Floor
New York, NY
Snacks and refreshments will be available!
Hosts / Instructors
Dan Gural
Machine Learning Engineer and Developer Evangelist
Daniel Gural is a seasoned Machine Learning Evangelist with a strong passion for empowering Data Scientists and ML Engineers to unlock the full potential of their data. Currently serving as a valuable member of Voxel51, he takes a leading role in efforts to bridge the gap between practitioners and the necessary tools, enabling them to achieve exceptional outcomes. Daniel’s extensive experience in teaching and developing within the ML field has fueled his commitment to democratizing high-quality AI workflows for a wider audience.
Beyond his professional role at Voxel51, Daniel actively contributes to the growth and development of young entrepreneurs and STEM students at the high school and college levels, serving as a mentor. His dedication to fostering the next generation of innovators showcases his belief in the importance of sharing knowledge and inspiring others to embrace the world of AI.
Jacob Marks, PhD
Machine Learning Engineer and Researcher
Jacob Marks is a Machine Learning Engineer and Developer Evangelist at Voxel51, where he helps bring transparency and clarity to the world’s data. He is passionate about education, and brings years of experience teaching, TAing, and tutoring to this workshop. Prior to joining Voxel51, Jacob founded a startup to help emerging musicians connect and share creative content with fans. Before that, he worked at Google X, Samsung Research, and Wolfram Research. In a past life, Jacob was a theoretical physicist. In 2022, he completed his Ph.D. at Stanford, where he investigated quantum phases of matter.
Allen Lee
Machine Learning Engineer
Allen Lee is a Customer Success Machine Learning Engineer at Voxel51, where he is helping build the world’s leading visual AI platform. He has worked in computing for many years and is passionate about helping teams to collaboratively build practical, effective AI/ML solutions. Prior to joining Voxel51 he spent over ten years as co-founder of a thriving scientific software consultancy. Allen lives in the lovely Garden State of New Jersey with his family.