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The Machine Learning Optimization and Signal Processing Laboratory (MILOS LAB) was founded in 2015 by Professor Panos P. Markopoulos at the Rochester Institute of Technology. Our research is on theory and algorithms for machine learning, data science, and adaptive signal processing and it is supported by multiple federal agencies and industry partners.

Our Mission

Conduct fundamental research in the areas of machine learning and signal processing.

Deliver practical solutions to technological challenges that are relevant to today’s society and of interest to the nation.

Provide high-quality and inclusive education and mentor the next generation of innovators and technology leaders.

Research Expertise

Our expertise is in the areas of machine learning, data analysis, and adaptive signal processing, with an aim to advance efficient, explainable, and trustworthy artificial intelligence. In these areas, we focus on both theoretical (computational and statistical) foundations and on practical algorithmic solutions, evaluated in a wide range of real-world applications.

Current research topics include:

  • Dynamic data subspace learning based on Lp-norm projections.
  • Robust learning with limited, faulty, and adversarially corrupted data.
  • Tensor methods for multi-way data analysis and processing.
  • Tensor factorization methods for modeling efficient and explainable neural networks.
  • Learning from multimodal data and deep learning fusion (recent topic).
  • Continual learning with increasing parameter tensor ranks (recent topic).

Application areas include:

  • Computer vision.
  • Remote sensing.
  • Communication systems.
  • Healthcare technology.


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