Dr. Laura Brown
Assistant Professor, Computer Science
Dr. Brown’s
research is centered broadly on the application and design of
methods in artificial intelligence and machine learning. This
work spans from the theoretical design of algorithms for
feature selection and learning Bayesian networks, to the
application of methods across domains including clinical
healthcare, biomedicine, power distribution networks, electric
microgrids, and computer systems research.
Dr. Brown's group is investigating parallel machine learning
algorithms on wide variety of datasets. This project seeks to
better characterize and understand the algorithms. The
evaluation looks beyond the standard machine learning metrics
of model quality, such as, accuracy, AUC, precision, recall,
etc. and incorporates metrics parallel computing community,
such as, speed-up.
Recently, Dr. Brown and
Dr. Zhenlin Wang
have been examining the use of machine learning methods in
computer systems research. Specifically, the work looks to
better understand, model, and predict performance of
applications in heterogeneous (different computer architectures
and hardware configurations) data centers. Preliminary work has
verified that machine learning methods can be used to create a
decision model to select memory virtualization approaches. The
current research aims to model co-run interference, when two
applications are run on a single chip, and predict this
interference for new hardware settings.
For more information, please visit Dr. Brown's
website.
Recent publications
| 01 |
Selective Switching Mechanism in Virtual Machines with Support Vector Machines and Transfer Learning W. Kuang, L. E. Brown, Z. Wang Machine Learning (2014) |
| 02 |
Accurate Preterm Labor Diagnosis Using a CD55-TLR4 Combination Biomarker Model S. Pratap, L. E. Brown, M. G. Izban, S. Nowicki, B. J. Nowicki Journal of Biomedical Science and Engineering, vol. 6, p. 253 (2013) |
| 03 |
Impact of Interior Permanent Magnet Rotor Design on Signal Injection Based Sensorless Control and Power Conversion I. P. Brown, G. Y. Sizov, L. E. Brown IEEE Energy Conversion Congress and Exposition, Denver, CO. September 2013 |
| 04 |
Detection of Coherent Groups of Generators and the Need for System Separation using Synchrophasor Data M. Ali, B. A. Mork, L. J. Bohmann, L. E. Brown IEEE 7th International Power Engineering and Optimization Conference, Langkawi, Malaysia. June 2013 |
| 05 |
To Feature Space and Back: Identifying Top Weighted Features in Polynomial Support Vector Machines L. E. Brown, I. Tsamardinos, D. Hardin Intelligent Data Analysis, vol. 16, p. 551 (2012) |
