Pay attention to this space for research opportunities. I will post them here as they arise.
Postdocs: I may have openings for postdocs in 2021. Occasional funding for teaching postdocs may be available through the department. Contact me if interested.
Grad students: I currently have openings for MS and PhD students in the space of HPC for deep learning. Current VT MS and PhD students who are interested in working with me should contact me by e-mail to setup a meeting with me. Prospective graduate students should also contact me to alert me to their application to VT.
Undergrads: I occasionally have space for undergrad researchers. I encourage CMDA students who want to do research with me to do well in CMDA 3634, with me (preferred) or with another instructor.
2021/05/27 – Russell J. Hewett was awarded Early Career Research Award from Department of Energy, Office of Science, Advanced Scientific Computing Research for Domain-Decomposition Induced Parallelism for Scientific Deep Learning at Extreme Scale.
2021/04/26 – Russell J. Hewett, Ali Habibnia (VT economics), and Dawson Miller received an award from the Virginia Tech College of Science’s Lay Nam Chang Dean’s Discovery Fund for Deep Learning for Non-linear Common-factor Modeling and Forecasting in Economics.
2020/12/04 – DistDL v0.3.1 released on GitHub. [Link].
Parallel Primitives for Domain Decomposition in Neural Networks
L-Sweeps: A scalable, parallel preconditioner for the high-frequency Helmholtz equation
computational machine learning & inverse problems group
group members
Russell J. Hewett
principle investigator
Dawson Miller
graduate student
Joe Weissman
graduate student
Daniel Hajialigol
undergraduate student
Xuanjie Chen
undergraduate student
group alumni
ThaoVy Nguyen
Data Scientist @ L3-Harris
Jacob Merizian
Entrepeneur @ QuickTech Medical
Thomas Grady
PhD Student @ Georgia Tech
Distributed Deep Learning
DistDL, Model parallelism, software, and more
High-frequency Helmholtz
3D sweeping pre-conditioners, parallelism, and more
Solar Data Analysis
Solar Data Analysis, often with Python and SunPy
Solar Tomography
Numerics and 3D reconstructions of the solar atmosphere
Subsurface Imaging
PySIT, wave propagation, FWI, and more
Parallel Primitives for Domain Decomposition in Neural Networks
Physical characteristics of the stream – lake transitional zone affect littoral lake metabolism
L-Sweeps: A scalable, parallel preconditioner for the high-frequency Helmholtz equation
DistDL: Distributed Deep Learning for PyTorch
A Linear Algebraic Approach to Model Parallelism in Deep Learning
Modeling in Subsurface Geophysics: Mathematics, Computation, and Machine Learning
A Linear Algebraic Framework for Distributed Deep Learning
A Linear-algebraic Approach to Distributed Deep Learning
Evolution of a Scalable 3D Helmholtz Solver with Geophysical Applications
