EE Student and Faculty Groups Win NYC Media Lab Grand Prize Overall and Enabling Technology Awards

　　NYC Media Lab ’19 featured a Demo Expo and Startup Pavilion — a showcase of 100 emerging media and technology prototypes created by faculty and students across the City. Demo participants are presenting their startups, research, and prototypes to a crowd of more than 1,000 attendees, including thought leaders and fellow technologists from leading digital media, technology, and communications companies. 

　　纽约市媒体实验室19日举办了一个演示博览会和创业馆，展示了全市师生创作的100个新兴媒体和技术原型演示参与者将向1000多名参与者展示他们的初创企业、研究成果和原型，其中包括思想领 袖和来自领先数字媒体、技术和通信公司的同行技术专家。

　　EE PhD students Hassan Akbari, Bahar Khalighinejad and EE Professor Nima Mesgarani won the Grand Prize Overall for their project: Reconstructing Intelligible Speech from the Human Brain, which focuses on brain signals of subjects listening to speech with invasive electrodes, and shows intelligible reconstruction of speech from the neural responses. 

　　EE博士生Hassan Akbari、Bahar Khalighinejad和EE教授Nima Mesgarani因他们的项目获得了大奖：从人脑中重建可理解的语音，该项目专注于使用侵入性电极听语音的受试者的脑信号，并显示从神经反应中可理解的语音重建。

　　EE PhD student Craig Gutterman, Engineering undergraduate students Trey Gilliland and Sarthak Arora, Katherine Guo, Xiaoyang Wang, and Les Wu of Bell Labs, and EE Professors Ethan Katz-Bassett and Gil Zussman received the top award in Enabling Technology for their project “Requet: Real-Time QoE Detection for Encrypted YouTube Traffic”. The project focuses on the fact that as video traffic dominates the Internet, operators need to detect video Quality of Experience (QoE) to ensure video traffic support. However, with deployment of end-to-end encryption, network packet-based detection is becoming ineffective. To resolve this issue, Requet enables real-time QoE metric detection for encrypted video traffic using machine learning. The system was presented in ACM MMSyS’19 and the Media Lab demonstration included very recent results by Trey and Craig. 

　　EE博士生Craig Gutterman，贝尔实验室的工程本科生Trey Gilliland和Sarthak Arora，Katherine Guo，Xiaoyang Wang和Les Wu，以及EE教授Ethan Katz Bassett和Gil Zussman因他们的项目“Requet: Real-Time QoE Detection for Encrypted YouTube Traffic”获得了最佳技术支持奖。该项目的重点是，由于视频流量在互联网上占主导地位，运营商需要检测视频体验质量（QoE），以确保视频流量支持。然而，随着端到端加密技术的应用，基于网络包的检测变得越来越无效为了解决这个问题，requet使用机器学习实现了对加密视频流量的实时qoe度量检测。该系统在ACM MMSyS’19中展示，媒体实验室演示包括Trey和Craig的最新结果。

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