Institute Professor, Electrical and Computer Engineering
Director of Graduate Programs
Elza Erkip is an Institute Professor in the Electrical and Computer Engineering Department at New York University Tandon School of Engineering. She received the B.S. degree in Electrical and Electronics Engineering from Middle East Technical University, Ankara, Turkey, and the M.S. and Ph.D. degrees in Electrical Engineering from Stanford University, Stanford, CA, USA. Her research interests are in information theory, communication theory, and wireless communications.
Dr. Erkip is a member of the Science Academy of Turkey and is a Fellow of the IEEE. She received the NSF CAREER award in 2001, the IEEE Communications Society WICE Outstanding Achievement Award in 2016, and the IEEE Communications Society Communication Theory Technical Committee (CTTC) Technical Achievement Award in 2018. Her paper awards include the IEEE Communications Society Stephen O. Rice Paper Prize in 2004, the IEEE Communications Society Award for Advances in Communication in 2013 and the IEEE Communications Society Best Tutorial Paper Award in 2019. She was a member of the Board of Governors of the IEEE Information Theory Society 2012-2020, where she was the President in 2018. She was a Distinguished Lecturer of the IEEE Information Theory Society from 2013 to 2014.
Dr. Erkip has had many editorial and conference organization responsibilities. Some recent ones include IEEE International Conference on Communications, Communications Theory Symposium Technical Co-Chair in 2021; IEEE Journal on Selected Areas in Information Theory Guest Editor in 2021; Asilomar Conference on Signals, Systems and Computers, MIMO Communications and Signal Processing Track Chair in 2017; IEEE Wireless Communications and Networking Conference Technical Co-Chair in 2017; IEEE Journal on Selected Areas in Communications Guest Editor in 2015; and IEEE International Symposium of Information Theory General Co-Chair in 2013.
Middle East Technical University, Turkey 1990
Bachelor of Science, Electrical and Electronics Engineering
Stanford University, 1993
Master of Science, Electrical Engineering
Stanford University, 1996
Doctor of Philosophy, Electrical Engineering
- IEEE Communications Society Best Tutorial Paper Award, 2019.
- IEEE Information Theory Society, President, 2018.
- IEEE Communications Society Communication Theory Technical Committee (CTTC) Technical Achievement Award, 2018.
- IEEE Communications Society WICE Outstanding Achievement Award, 2016.
- Clarivate Highly Cited Researcher, 2014, 2015.
- IEEE Communications Society Award for Advances in Communication, 2013.
- Science Academy of Turkey, 2013.
- IEEE Fellow, 2011.
- IEEE Communications Society Stephen O. Rice Paper Prize in the Field of Communication Theory, 2004.
- National Science Foundation CAREER Award, 2001.
The National Science Foundation grant for this research was obtained by Siddharth Garg and Elza Erkip, professors of electrical and computer engineering, and Yao Wang, professor of computer science and engineering and biomedical engineering. Wang and Erkip are also members of the NYU WIRELESS research center.
Increasing amounts of data are being collected on mobile and internet-of-things (IoT) devices. Users are interested in analyzing this data to extract actionable information for such purposes as identifying objects of interest from high-resolution mobile phone pictures. The state-of-the-art technique for such data analysis employs deep learning, which makes use of sophisticated software algorithms modeled on the functioning of the human brain. Deep learning algorithms are, however, too complex to run on small, battery constrained mobile devices. The alternative, i.e., transmitting data to the mobile base station where the deep learning algorithm can be executed on a powerful server, consumes too much bandwidth.
This project that this NSF funding will support seeks to devise new methods to compress data before transmission, thus reducing bandwidth costs while still allowing for the data to be analyzed at the base station. Departing from existing data compression methods optimized for reproducing the original images, the team will develop a means of using deep learning itself to compress the data in a fashion that only keeps the critical parts of data necessary for subsequent analysis. The resulting deep learning based compression algorithms will be simple enough to run on mobile devices while drastically reducing the amount of data that needs to be transmitted to mobile base stations for analysis, without significantly compromising the analysis performance.
The proposed research will provide greater capability and functionality to mobile device users, enable extended battery lifetimes and more efficient sharing of the wireless spectrum for analytics tasks. The project also envisions a multi-pronged effort aimed at outreach to communities of interest, educating and training the next generation of machine learning and wireless professionals at the K-12, undergraduate and graduate levels, and broadening participation of under-represented minority groups.
The project seeks to learn "analytics-aware" compression schemes from data, by training low-complexity deep neural networks (DNNs) for data compression that execute on mobile devices and achieve a range of transmission rate and analytics accuracy targets. As a first step, efficient DNN pruning techniques will be developed to minimize the DNN complexity, while maintaining the rate-accuracy efficiency for one or a collection of analytics tasks.
Next, to efficiently adapt to varying wireless channel conditions, the project will seek to design adaptive DNN architectures that can operate at variable transmission rates and computational complexities. For instance, when the wireless channel quality drops, the proposed compression scheme will be able to quickly reduce transmission rate in response while ensuring the same analytics accuracy, but at the cost of greater computational power on the mobile device.
Further, wireless channel allocation and scheduling policies that leverage the proposed adaptive DNN architectures will be developed to optimize the overall analytics accuracy at the server. The benefits of the proposed approach in terms of total battery life savings for the mobile device will be demonstrated using detailed simulation studies of various wireless protocols including those used for LTE (Long Term Evolution) and mmWave channels.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
- Siddharth Garg,
- Elza Erkip,
- Yao Wang