Jeremy Liu

Machine learning and deep neural networks
CV
Resume
Dissertation

Publications
Boltzmann machime modeling of layered MoS2 synthesis on a quantum annealer
Computational Materials Science 173 (2020): 109429.
Adiabatic Quantum Computation Applied to Deep Learning Networks
Entropy. 2018, 20(5), 380; https://doi.org/10.3390/e20050380
A Study of Complex Deep Learning Networks on High-Performance, Neuromorphic, and Quantum Computers
ACM Journal on Emerging Technologies in Computing Systems (JETC) 14.2 (2018): 19.
New Technologies to Enhance Computer Generated Interactive Virtual Humans
The Proceedings of the SISO Fall Simulation Innovation Workshop, Orlando, Florida: SISO. 2018.
Characterizing Molecular-Dynamics Simulations Using Non-Local Spatial-Temporal Metrics
International Conference on Scientific Computing, 2017.
Autoencoder-derived Features as Inputs to Classification Algorithms for Predicting Well Failures
SPE Western Regional Meeting. Society of Petroleum Engineers, 2015

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Code Snippet
Boltzmann machine with intra-layer connectivity implemented on a D-Wave adiabatic quantum annealer.
Trained using constrastive divergence but uses the annealer as a sampler to estimate hidden layer distribution.
Mapping of Boltzmann machine units to hardware quantum bits determined through reduction of entropy.
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E-mail:
jeremyjl@usc.edu
jeremyjingliu@gmail.com