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Engineering | SENSIP

Kristen Jaskie, PhD

Student Name: Kristen Jaskie, PhD 

Email: kristen.jaskie@asu.edu

 
Biography

I’m a Ph.D. student in the Electrical Engineering department at ASU, specializing in Machine Learning and Signal Processing. I received my B.S and M.S. in Computer Science with an emphasis in AI and Machine Learning at the University of Washington and the University of California San Diego respectively. I am currently working on two different research projects with the SenSIP lab. I am developing and testing a state-of-the-art algorithm to solve the Positive Unlabeled (PU) machine learning problem and using it to identify objects and events from image and video data, along with other signal processing applications.
Additionally, I am working on a research project with SenSIP and Prime Solutions Group (PSG) to use machine learning and signal processing to design new solutions for adaptive energy load customer segmentation and forecasting for energy signals obtained from smart grid technology.

Research Picture:

 
List of Publications

  1. K. Jaskie, A. Spanias, “Positive and Unlabeled Learning Algorithms and Applications: A Survey,” IEEE IISA, Patras, Greece, July 2019.
  2. K. Jaskie, C. Elkan, A. Spanias, “A Modified Logistic Regression for Positive and Unlabeled Learning”, IEEE Asilomar Conference on Signals, Systems, and Computers, Monterey, California, November 2019.
  3. J. Andrade, S. Katoch, P. Turaga, A. Spanias, C. Tepedelenlioglu & K. Jaskie, “Formation-aware cloud segmentation of ground-based images,” IEEE IISA, Patras, Greece, July 2019.
  4. E. Pedersen, S. Rao, S. Katoch, K. Jaskie, A. Spanias, C. Tepedelenlioglu, and E. Kyriakides, “PV Array Fault Detection using Radial Basis Networks”, IEEE IISA, Patras, Greece, July 2019.
  5. S. Ranganath, J. Thiagarajan, D. Rajan, M. Banavar, A. Spanias, J. Fan, K. Jaskie, C. Tepedelenlioglu, “Interactive Signal Processing Education Applications for the Android Platform,” ASEE Computers in Education Journal, July, 2019.

Acknowledgements: 

I acknowledge the SenSIP Center of Electrical Engineering for the excellent advisory and support.