Suely Oliveira, Ph.D.
My research is interdisciplinary, I focus on the computational and theoretical aspects of algorithms in Scientific Computing and Machine Learning for Engineering, Biology (Genetics) and Medicine. My projects have included clustering algorithms for protein-protein networks, neural networks for antibiotic resistance, and interpretable neural networks for test assessments. In these areas the use of machine learning, optimization and high-performance computing is essential. I have co-authored over 80 refereed papers on these topics and also two books: Building Proofs: A Practical Guide, World Scientific and Writing Scientific Software, Cambridge University Press. A more comprehensive list of publications, including collaborators and former PhD students can be found on my personal web page. As a sample, below are 14 selected published papers. For more complete information, see my personal web-page: (homepage.divms.uiowa.edu/~oliveira/).
Research Interests (Keywords)
Machine Learning, Scientific Computing, Optimization, High Performance Computing, Clustering Algorithms, Neural Networks, Genetics, Data Science
- Cancer Cells are Highly Susceptible to Accumulation of Templete Insertions linked to MMBIR,
Beth Osia, Thamer Alsulaiman, Tyler Jackson, Juraj Kramara, Suely Oliveira, and Anna Malkova,
Nucleic Acids Research, 2021, gkab685. https://doi.org/10.1093/nar/gkab685
- Estimation of Multidimensional Item Response Theory Models with Correlated Latent Variables using variational autoencoders,
Geoffrey Converse, Mariana Curi, Suely Oliveira, Jonathan Templin,
Machine Learning 110, 1463–1480 (2021). https://doi.org/10.1007/s10994-021-06005-7
- Distributed Evolution of Deep Autoencoders,
Hajewski J., Oliveira S., Xing X. (2022) . In: Arai K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 283. Springer, Cham. Computing Conference, London, UK, July 2021
https://doi.org/10.1007/978-3-030-80119-9_6. See also https://arxiv.org/pdf/2004.07607.pdf
- Incorporating item response theory into knowledge tracing,
Geoffrey Converse, Suely Oliveira, and Shi Pu
In Ido Roll, Danielle McNamara, Sergey Sosnovsky, Rose Luckin, and Vania Dimitrova, editors, Artificial Intelligence in Education, pages 114–118. Springer International Publishing, 2021.
- Efficient Evolution of Variational Autoenconders,
Jeff Hajewski and Suely Oliveira,
11th Annual Computing and Communication Workshop and Conference, IEEE CCWC 2021 - Virtual, USA, January 2021, pp. 1541-1550.
- Year, Location and Species Information In Predicting MIC Values with Beta-Lacatamase Genes,
Cory Kramer-Edwards, Mariana Castanheira, Suely Oliveira,
Data mining from genomic variants and its application to genome-wide analysis,
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) workshop, Dec. 2020, pp. 1383-1390.
- Identifying Beta-Lactamase Resistance with Neural Network,
Cory Kromer-Edwards, Jace Neubaum, Suely Oliveira, Caitlin Smith, Evan Walser-Kuntz, and Andrew West,
IEEE International Conference on Bioinformatics and Biomedicine Proceedings 2019, San Diego, USA. (published in February 2020
- Autoencoders for Educational Assessment,
Geoffrey Converse, Mariana Curi, and Suely Oliveira,
AIED 2019 Proceedings-- The 20th International Conference on Artificial Intelligence in Education 2019, Chicago, USA.
- Interpretable Variational Autoenconders for Cognitive Models,
Mariana Curi, Geoffrey Converse, Jeff Hajewski and Suely Oliveira,
IJCNN International Joint Conference on Neural Networks 2019, Budapest, Hungary.
- Distributed SmSVM Ensemble Learning,
Jeff Hajewski and Suely Oliveira,
INNS International Neural Network Society, Big Data and Deep Learning,
Springer-Verlag Proceedings, 2019, Sest Levanti, Italy.
- Community Detection Algorithm for Big Social Networks using Hybrid Architecture,
Rahil Sharma and Suely Oliveira, Big Data Research, vol 10, pp. 44-52, 2017. (pdf)
- Identification and Prediction of Functional Protein Modules using Community Detection Algorithms,
S. Oliveira and Rahil Sharma,
International Journal of Bioinformatics Research and Applications, Vol. 12, No. 2, pp. 129 - 148, 2016
- Network Algorithms for Protein Interactions,
Algorithmic and AI Methods for Protein Bioinformatics, ed. Yio Pan, Jianxin Wang and Min Li,
Wiley, book chapter pp. 357-376 ISBN: 978-1-118-34578-8, 2013.
- Clustering for Bioinformatics via Matrix Optimization,
S. Oliveira and D. E. Stewart,
2nd ACM-BCB'11 Conference on Bioinformatics, Computational Biology and Biomedicine,
Chicago, IL, USA, 2011, pp. 559-563.
- Algorithmic Foundations
- Health- and Human-Centric Computing
- Verifiable, Dependable, and High-Performance Systems