Genevieve Patterson is the chief scientist at TRASH, a startup that is developing computational filmmaking tools for mobile iphono-graphers. Before that, she was a Postdoctoral Researcher at Microsoft Research New England. Her work is about creating dialog between AI and people. Her interests include video understanding, visual attribute discovery, human-in-the-loop systems, fine-grained object recognition, medical image understanding, and active learning. Genevieve received her Ph.D. from Brown University in 2016 under the direction of James Hays.
Deep Narain Singh is Data Scientist with specialization in machine learning and deep learning. He has extensive work experience in building NLP/Computer Vision products using AI/ML/DL. He has spent 12 years in industry working with startups and large scale companies. He holds a Master’s degree in Data Science from the University Of New Haven/Galvanize and completed his undergraduate in Civil Engineering from NIT Jaipur.
Computer Vision is one of the hottest research fields within Deep Learning at the moment. It sits at the intersection of many academic subjects, such as Computer Science (Graphics, Algorithms, Theory, Systems, Architecture), Mathematics (Information Retrieval, Machine Learning), Engineering (Robotics, Speech, NLP, Image Processing), Physics (Optics), Biology (Neuroscience), and Psychology (Cognitive Science).
In those moments of boredom when you're playing with Snapchat's filters - sticking your tongue out, ghoulifying your features, and working out how to get the flower crown to fit exactly on your head - surely you've had a moment where you've wondered what's going on, on a technical level - how Snapchat manages to match your face to the animations?