Backer Profile: Ming Zeng, Doctoral Student at Carnegie Mellon University

Ming Zeng   Ming Zeng is a Ph.D. student at Carnegie Mellon University. His research interests include machine learning, deep learning, human behavior modeling, and natural language processing. His work is primarily focused on deep neural networks (DNNs) for human activity recognition. The deep learning models used in his work mainly include convolutional neural networks (CNNs) and Long Short Term Memory (LSTM) with different architectures according to different applications. In addition, instead of treating the deep neural network as a black box, he is trying to interpret the features extracted from DNNs.


Ming will explore the data in the human activity recognition related areas. Deep neural networks can take advantage of the large dataset to train a good model. In addition, because the dataset involves a number of sensors, it is worth trying neural networks with a sensor fusion approach.

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Homepage: Homepage at CMU
Connect with Ming on LinkedIn

Backer Profile: Jonathan Rubin, Research Scientist at PARC

Jonathan Rubin Jonathan is a Research Scientist at PARC, where his research focuses on the use of machine learning and deep learning in biomedical and physiological data. He is interested in the analysis of data from mobile and wearable devices for pervasive and ubiquitous health management purposes. He has investigated how mobile and wearable technology can be used within health condition management systems for disorders such as panic disorder, post-traumatic stress disorder and depressive disorder. He is particularly interested in the analysis of physiological data in order to infer information about psychological state. His research interests include affective computing, ubiquitous computing, artificial intelligence, machine learning, deep learning, and physiological data analysis. Jonathan holds a Ph.D. from the University of Auckland where his research focused on the use of artificial intelligence for strategy generation in computer games.

Jonathan will use the data that provides to improve and broaden the types of activity recognition that currently exist. In addition, he will use physiological data from wearable devices for affective computing purposes.

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On GitHub: