Toward Automatically Evaluating Security Risks and Providing Cyber Threat Intelligence
Thu, Mar 4, 2021, 4:30 pm to 5:30 pm
Seminars
Photonic Chip based Frequency Combs
Tue, Apr 13, 2021, 10:00 am to 11:00 amProtecting User Security and Privacy in Emerging Platforms
Tue, Feb 16, 2021, 4:30 pm to 5:30 pmProbing Biology in the Native State: From Long-Term Brain Electrophysiology to Subcellular Dynamic Imaging
Tue, Feb 23, 2021, 4:30 pm to 5:30 pmMulti-Functional Devices and Systems: Applications and Emphasis on Global Health
Mon, Feb 15, 2021, 4:30 pm to 5:30 pmComputational Optics for Control and Readout of Neural Activity
Wed, Feb 17, 2021, 4:30 pm to 5:30 pmPreconditioning Helps: Faster Convergence in Statistical and Reinforcement Learning
Mon, Apr 19, 2021, 4:30 pm to 6:00 pmAbstract:
Deep Networks from First Principles
Fri, Jan 15, 2021, 4:30 pm to 5:30 pmElectrical Engineering & Center for Statistics and Machine Learning Seminar
Please register using this link
Abstract:
High-Speed CMOS Serial Transmitters for 56-112Gb/s Electrical Interconnects
Mon, Nov 23, 2020, 4:30 pm to 5:30 pmAbstract: Data rates in high-speed wireline communication links continue to increase, fueled by demands in data center and high-performance computing applications. In recent years, serial link data rates have increased from 28Gb/s to 56Gb/s, with 112Gb/s rapidly approaching. To achieve these higher data rates across high-loss electrical channels, standards are switching from NRZ to PAM4 signaling.
Deep Convolutional Neural Networks for Device Identification
Mon, Nov 9, 2020, 4:30 pm to 5:30 pmAbstract: Network densification is poised to enable the massive throughout jump expected in the era of 5G and beyond. In the first part of the talk, we identify the challenges of verifying identify of a particular emitter in a large pool of similar devices based on unique distortions in the signal, or ‘RF fingerprints’, as it passes through a given transmitter chain. We show how deep convolutional neural networks can uniquely identify a radio in a large signal dataset composed of over a hundred WiFi radios with accuracy close to 99%.
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