Joshua Yang

Contact

Prof. Joshua Yang

Department of Electrical and Computer Engineering
University of Southern California
3740 McClintock Avenue, EEB 340
Los Angeles, CA 90089-2562, USA

Email: joshua.yang@usc.edu

Research Field and Activities

Neuromorphic Computing
Emerging Memory Technologies
Memristive Devices and Materials
In-Memory and Near-Sensor Computing
Analog and Parallel Computing
AI Hardware Acceleration

Short Biography

Professor J. Joshua Yang is the Arthur B. Freeman Chair Professor in the Department of Electrical and Computer Engineering at the University of Southern California (USC). A pioneer in neuromorphic computing and emerging memory technologies, he is best known for his groundbreaking work on resistive switching devices and memristive systems.

He earned his Ph.D. from the University of Wisconsin–Madison, following a Bachelor’s from Southeast University. Prior to joining USC, he was a lead researcher at HP Labs and later a professor at the University of Massachusetts Amherst. His work has resulted in over 120 granted patents and numerous high-impact publications in top journals such as Nature, Science, and IEEE.

Professor Yang currently directs the Center of Excellence on Neuromorphic Computing, co-directs USC’s Institute for the Future of Computing, and is the Scientific Advisory Board Chairman of TetraMem Inc., a startup developing AI hardware.

References / Publications

J.J. Yang, D.B. Strukov, and D.R. Stewart, “Memristive devices for computing,” Nature Nanotechnology, 2013. DOI: 10.1038/nnano.2012.240.
J.J. Yang, M.D. Pickett, X. Li, D.A.A. Ohlberg, D.R. Stewart, and R.S. Williams, “Memristive switching mechanism for metal/oxide/metal nanodevices,” Nature Nanotechnology, 2008. DOI: 10.1038/nnano.2008.160.
Z. Wang, S. Joshi, S.E. Savel’ev, H. Jiang, R. Midya, P. Lin, M. Hu, N. Ge, J.P. Strachan, Z. Li, Q. Wu, M. Barnell, G.-L. Li, H. Xin, R.S. Williams, J.J. Yang, and Q. Xia, “Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing,” Nature Materials, 2017. DOI: 10.1038/nmat4756.
J. Borghetti, G.S. Snider, P.J. Kuekes, J.J. Yang, D.R. Stewart, and R.S. Williams, “‘Memristive’ switches enable ‘stateful’ logic operations via material implication,” Nature, 2010. DOI: 10.1038/nature08940.
C. Li, M. Hu, Y. Li, H. Jiang, N. Ge, E. Montgomery, J. Zhang, W. Song, P. Lin, Z. Wang, N. Davila, C.E. Graves, Z. Li, J.P. Strachan, M. Barnell, Q. Wu, R.S. Williams, J.J. Yang, and Q. Xia, “Analogue signal and image processing with large memristor crossbars,” Nature Electronics, 2018. DOI: 10.1038/s41928-017-0002-z.
C. Li, D. Belkin, Y. Li, P. Yan, M. Hu, N. Ge, H. Jiang, E. Montgomery, P. Lin, Z. Wang, W. Song, N. Davila, C.E. Graves, Z. Li, J.P. Strachan, M. Barnell, Q. Wu, R.S. Williams, J.J. Yang, and Q. Xia, “Efficient and self-adaptive in-situ learning in multilayer memristor neural networks,” Nature Communications, 2018. DOI: 10.1038/s41467-018-04484-2.
Z. Wang, S. Joshi, S. Savel’ev, W. Song, R. Midya, Y. Li, M. Rao, P. Yan, J.P. Strachan, M. Barnell, Q. Wu, M. Hu, H. Jiang, N. Ge, R.S. Williams, Q. Xia, and J.J. Yang, “Fully memristive neural networks for pattern classification with unsupervised learning,” Nature Electronics, 2018. DOI: 10.1038/s41928-018-0059-6.
Z. Wang, H. Wu, G.W. Burr, C.S. Hwang, K.L. Wang, Q. Xia, and J.J. Yang, “Resistive switching materials for information processing,” Nature Reviews Materials, 2020. DOI: 10.1038/s41578-019-0148-z.
M. Hu, C.E. Graves, C. Li, Y. Li, N. Ge, E. Montgomery, N. Davila, H. Jiang, P. Lin, C. Wu, J. Zhang, Z. Wang, Z. Li, J.P. Strachan, M. Barnell, Q. Wu, R.S. Williams, J.J. Yang, and Q. Xia, “Memristor-based analog computation and neural network classification with a dot product engine,” Advanced Materials, 2018. DOI: 10.1002/adma.201705914.