Machine Learning

Uncovering New Data Science, Solutions

菠菜网lol正规平台 faculty work in machine learning includes deep learning, big data, new technologies, and tools for integrating multidisciplinary knowledge in practical analytics projects. This includes mining and management, modeling, algorithms, presenting results, and assessing impact.

Our research applications meet a broad range of pressing societal needs, from drug discovery to climate science; agriculture to smart cities; and from the stock market to space travel. Below is a rotating selection of our standout investigators in machine learning.

   ○ Recent News and Highlights
   ○ Related Strength: Artificial Intelligence
   ○ Related Strength: Ethical Technology

• Selected Publications

  • Damodaran, A., Di Troia, F., Visaggio, C. A., Austin, T. H., & Stamp, M. (2017). A comparison of static, dynamic, and hybrid analysis for malware detection. Journal of Computer Virology and Hacking Techniques, 13.

    Eirinaki, M., et al (2022). Real-time recommendations for energy-efficient appliance usage in households. Frontiers in Big Data, 5.

    Huang, Y., Srivastava, R., Ngo, C., Gao, J., Wu, J., & Chiao, S. (2023). Data-driven soil analysis and evaluation for smart farming using machine learning approaches. Agriculture, 13(9). 

    Liu, G., et al. (2022). Novel Robust Indoor Device-Free Moving-Object Localization and Tracking Using Machine Learning With Kalman Filter and Smoother. IEEE Systems Journal, 16(4).

    Patil, P., Wu, C. S., Potika, K., & Orang, M. (2020). Stock market prediction using ensemble of graph theory, machine learning and deep learning models. Proceedings of the 3rd International Conference on Software Engineering and Information Management.

    Wu, C. S., Bhandary, U. (2020). Detection of Hate Speech in Videos Using Machine Learning, 2020 International Conference on Computational Science and Computational Intelligence.

• Award Highlights

  • Guzun, “Scalable and Adaptable Sparsity-driven Methods for more Efficient AI Systems” — NSF CAREER, 2023

    Park, Di Troia, “REU Site: Undergraduate Research Experience for Women in Machine Learning-based Cybersecurity” — NSF, 2023

    Ramasubramanian, Lee, S.J., Lee, W., "Thrombosis in Microgravity" — NASA, 2020

• Innovations

  • Neural Network In-Memory Computing [pdf]
    A solution to slow training speeds and other issues associated with synapses in neural network in-memory computing.

• Affiliates

  • Agricultural Research Institute (ARI)

    CSU STEM-​NET

Featured Faculty

Fabio Di Troia
Assistant Professor of Computer Science
Machine Learning, Cybersecurity, Malware Detection, Network Analysis, Biometrics, Sentiment Analysis, Spam Detection
ORCID: 0000-0003-2355-7146

Magdalini Eirinaki
Professor of Computer Engineering
Recommender Systems, Machine Learning, Graph Mining, Deep Learning
ORCID: 0000-0002-4711-3366

Jerry Gao
Professor of Computer Engineering
Software Engineering, IT Development Applications, Mobile Computing, Cloud Computing, Testing

Gheorghi Guzun
Assistant Professor of Computer Engineering
Big Data, Machine Learning, Energy Efficient AI, Compression, Quantization, Scalable Algorithms and Data Structures
ORCID: 0000-0001-9162-7981

Wendy Lee
Assistant Professor of Computer Science
Sequencing Artifacts Detection, Machine Learning, Microbiome, Bisphenol-A, RNA-Seq
ORCID: 0000-0002-8421-0536

Guannan Liu
Assistant Professor of Applied Data Science
Machine-Learning Techniques, Artificial-Intelligence Systems, Big-Data Analytics, Wireless Signal Processing
ORCID: 0000-0001-5548-9040

Katerina Potika
Associate Professor of Computer Science
Algorithmic Design, Network Analysis, Machine Learning, Deep Learning, Intrusion Detection and Malware, Blockchain, Distributed Algorithms, Game Theory, Graphing
ORCID: 0000-0003-0332-1347

Mike Wu
Assistant Professor of Computer Science
Big Data, Machine Learning, Software Engineering
ORCID: 0000-0001-7382-499X

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