A+E Networks seeks to partner with graduate students to create compelling visualizations of historical narratives and information, and/or premium story presentations featuring innovative interaction design components and multimedia elements. Mobile-friendly, shareable and interactive, these visualizations or experiences should bring to life ideas, personalities and major moments in the history of nearly any topic in digital prototypes. They should push the boundaries of what is possible with mobile devices- from augmented reality to device-to-device communication. Selected projects will be distributed via A+E’s social media and digital channels on completion of the project.
NYC Media Lab collaborated with Viacom’s New Experience Team (NEXT) for the Viacom Virtual Reality Fellowship that took place in summer 2016. Viacom NEXT, led by Chaki Ng, with Rob Ruffler and David Liu, is focusing intensely on VR. The Fellowship was a way to foster a sharing of techniques with young talent from local NYC universities where there is tremendous interest and enthusiasm for rapid prototyping in VR.
In the fall of 2016 and spring of 2017, NYC Media Lab and MLBAM will explore the future of sports media in a series of meetups culminating in a hackathon for designers, developers and engineers in New York City. With an emphasis on virtual and augmented reality, data visualization and experiments in new multimedia experiences, this program will seek to foster connections across New York City’s universities and drive prototyping activity.
In partnership with Verizon Open Innovation, NYC Media Lab executed the Verizon Connect Futures Research & Prototyping Challenge which propelled the development of new media and technology projects from universities across New York City. The Challenge linked students and faculty with industry experts.
Vonage and NYC Media Lab convened a team from NYU’s Tandon School of Engineering to examine IoT products to develop a perspective on what’s possible with machine learning platforms and algorithms, and to determine which algorithms would be best suited for the various types of learning that would be required in the IoT field.