5G cellular networks will use high-frequency millimeter-wave (mmWave) communication, which promises high data rates and ample spectrum availability. Students working on this project will help conduct a mmWave wireless channel measurement campaign around the COSMOS testbed (www.cosmos-lab.org), a wireless networking testbed located at Columbia stretching between 120th and 136th St. In collaboration with Bell Labs students will be able to use unique, state-of-the-art mmWave equipment to conduct these measurements (see pre-pandemic example in https://wimnet.ee.columbia.edu/wp-content/uploads/2019/08/mmNets2019_COSMOS_28GHz.pdf). The measurements will play an important role in the development of network-level control algorithms, which is the other, more analytical side of this research project.

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This study is the first step in exploring an emerging and previously understudied data stream - verbal communication between healthcare providers and patients. In partnership between Columbia Engineering, School of Nursing, Amazon, and the largest home healthcare agency in the US, the study will investigate how to use audio-recorded routine communications between patients and nurses to help identify patients at risk of hospitalization or emergency department visits. The study will combine speech recognition, machine learning and natural language processing to achieve its goals.

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Future wireless networks will use high-frequency millimeter-wave (mmWave) links for transmitting and receiving information with high throughput. A key difference between mmWave links and conventional sub-6GHz links is that mmWave links are severely affected by weather conditions. Students working on this project will use a state-of-the-art mmWave radar to assess the impact of wind speed, temperature, humidity, and other factors on the high-frequency link. The end goal of the project is to develop a classifier that can infer weather conditions based on the signal received from the mmWave radar. In this project, students are expected to learn how the mmWave radar works, design experiments to obtain labeled data, perform measurements, and develop the classifier.

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Orienting to a novel event is a rapid shift in attention to a change in one’s surroundings that appears to be a fundamental biological mechanism for survival and essentially functions as a “what is it” detector. Orienting appears to play a central role in human learning and development, as it facilitates adaptation to an ever-changing environment. Thus, orienting can be viewed as an allocational mechanism in which attention sifts through the complex multi-sensory world and selects relevant stimuli for further processing. The selection of stimuli for further processing has implications for what will be encoded into memories and how strong those memory traces will be. The ability to differentiate between relevant and irrelevant input, to inhibit the processing of irrelevant stimuli, and to sustain attention requires control, and inhibitory processes that improve with age.

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We are constantly exposed to inputs from the outside world, but we do not perceive everything we are exposed to. Some inputs are rather weak: we might perceive them at one point in time, but not at another. The state of our brains right before we receive such sensory inputs influences whether or not we perceive them. Brain oscillations are proposed to play a key role in setting these brain states; however, how exactly these brain rhythms influence our perception remains a topic of active research.

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Microelectrode array recordings from patients undergoing surgical evaluation have captured typical clinical seizures. Because of the extreme pathological conditions at these times, identifying single units from extracellular data is a particular challenge. Our group has developed techniques for tracking neurons through the ictal transition. We are applying them to newly acquired data and addressing fundamental questions about the activity of different cell classes at seizure initiation.

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