Kristen Jaskie
Kristen Jaskie is a third generation Arizonan who loves the desert but hates the heat. Travel, reading, machine learning, and teaching are her favorite hobbies. She received her Ph.D. in Signal Processing and Machine Learning through the Electrical Engineering department and the SenSIP center at Arizona State University in Tempe, Arizona in 2021 and her B.S. and M.S. degrees in computer science with an emphasis in Machine Learning from the University of Washington in Seattle, Washington and the University of California San Diego in San Diego, California, respectively. Kristen has worked as a machine learning research scientist with Prime Solutions Group since 2018 and is the owner and senior scientist of Data Analytics Consulting LLC. Between 2011 and 2018, she was a full-time faculty member of Computer Science at Glendale Community College in Glendale, Arizona, and the department chair between 2016 and 2018 before she resigned to pursue her Ph.D. Prior to that, Kristen worked at both the San Diego Supercomputing center and for BRE Systems performing systems and machine learning research.
Kristen’s research interests include machine learning and deep learning algorithm development and application, with a focus on semi-supervised learning and the positive and unlabeled learning problem. She is the author of multiple papers including “Positive and Unlabeled Learning Algorithms and Applications: a Survey”, “A Modified Logistic Regression for Positive and Unlabeled Learning”, and “PV Fault Detection Using Positive Unlabeled Learning”. She has written a book entitled “Positive Unlabeled Learning” that is currently in editing. Her PhD dissertation was titled “Positive Unlabeled Learning - Optimization and Evaluation”.
Kristen Jaskie received her Ph.D. in Signal Processing and Machine Learning at ASU in 2021 and her B.S. and M.S. degrees in Computer Science with an emphasis in Machine Learning and Mathematics from the University of Washington in Seattle, WA and UC San Diego in San Diego, California respectively.
Dr. Jaskie's main research interests lie in Machine Learning, most specificially:
- Semi-Supervised ML
- The Positive Unlabeled learning problem
- Complex-Valued Neural Networks
- Reinforcement Learning
Positive Unlabeled Learning
- K. Jaskie and A. Spanias, Positive Unlabeled Learning. Book Submission to Morgan and Claypool Publishers, Synthesis Lectures on Artificial Intelligence and Machine Learning (In Review), 107 pages, Submitted May 2021.
- K. Jaskie, J. Martin, and A. Spanias, “Photovoltaic Fault Detection using Positive Unlabeled Learning,” Appl. Sci., no. Intelligent Fault Diagnosis of Power Systems, 2021.
- K. Jaskie, C. Elkan, and A. Spanias, “A Modified Logistic Regression for Positive and Unlabeled Learning,” in IEEE Asilomar, Pacific Grove, California, pp. 0–5, Nov. 2019.
- K. Jaskie and A. Spanias, “Positive and Unlabeled Learning Algorithms and Applications: a Survey,” in IEEE IISA, Patras, Greece, pp. 1–8, Jul. 2019.
Solar Energy Forecasting and Fault Detection
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J. Martin, K. Jaskie, Y. Tofis, and A. Spanias, “PV Array Soiling Detection using Machine Learning,” in IEEE IISA, Greece, Jul. 2021.
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K. Jaskie, D. Smith, and A. Spanias, “Deep Learning Networks for Vectorized Energy Load Forecasting,” in IEEE IISA, Piraeus, Greece, 2020.
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D. Smith, K. Jaskie, J. Cadigan, J. Marvin, and A. Spanias, “Machine Learning for Fast Short-Term Energy Load Forecasting,” in IEEE ICPS, Tampere, Finland, 2020.
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E. Pedersen, S. Rao, S. Katoch, K. Jaskie, A. Spanias, C. Tepedelenlioglu, E. Kyriakides, “PV Array Fault Detection using Radial Basis Networks,” in IEEE IISA, Patras, Greece, Jul. 2019.
Education Papers
- K. Jaskie, J. Larson, M. Johnson, K. Turner, M. O’Donnell, J. Blain Christen, S. Rao, A. Spanias, “Research Experiences for Teachers in Machine Learning,” in FIE 2021: Frontiers in Education, Lincoln, NE, USA, (Accepted) Oct. 2021.
- J. S. Larson, M. O’Donnell, K. Eustice, C. Nichol, K. Jaskie, A. Spanias, K. Farnsworth, J. Blain Christen, M. Lee, “Lessons Learned from Evaluating Three Virtual Research Experiences for Teachers (RET) Programs Using Common Instruments and Protocols (Evaluation),” in ASEE, Washington, D.C., Jul. 2021.
- K. Jaskie, J. Martin, S. Rao, W. Barnard, P. Spanias, E. Kyriakides, Y. Tofis, L. Hadjidemetriou, M. Michael, T. Theocharides, S. Hadjistassou, A. Spanias, “IRES Program in Sensors and Machine Learning for Energy Systems,” 11th Int. Conf. Information, Intell. Syst. Appl. IISA 2020, 2020. (Student Paper Award)
Invited Presentations and Honors
- K. Jaskie, “Positive Unlabeled Learning for Tiny ML”, invited TinyML Presentation with world-wide participation. Available at https://www.youtube.com/watch?v=uk6SlTzfbUY March 9, 2021
- ASU University Graduate Fellowship (UGF) for Summer 2021
- K. Jaskie, Session Chair: The Sensors and Machine Learning Workshop (SensMACH) 2020, Tempe, Arizona, Oct. 14, 2020.
- Fast Pitch Award for best research and presentation:
- J. Martin, K. Jaskie, Y. Tofis, and A. Spanias, “Machine Learning for Solar Array Soilage Identification,” presented at the 9th annual Arizona Student Energy Conference (AZSEC 2020), Phoenix, Arizona, Oct. 2020.
- K. Jaskie, A. Spanias. Machine Learning and the Positive Unlabeled Learning Problem. A tutorial presented at IEEE IISA 19, Patras, Greece, July 16, 2019. (Invited Speaker)
- K. Jaskie, Session Chair: IEEE IISA 19, Patras, Greece, July 16, 2019.
Courses
2022 Spring
| Course Number | Course Title |
|---|---|
| EEE 480 | Feedback Systems |
| EEE 591 | Seminar |
| EEE 591 | Seminar |
| EEE 480 | Feedback Systems |
| EEE 480 | Feedback Systems |
| EEE 480 | Feedback Systems |
| EEE 591 | Seminar |
| EEE 591 | Seminar |
2021 Fall
| Course Number | Course Title |
|---|---|
| EEE 480 | Feedback Systems |
| EEE 480 | Feedback Systems |
| EEE 480 | Feedback Systems |
| EEE 480 | Feedback Systems |
| EEE 480 | Feedback Systems |
| EEE 591 | Seminar |
| EEE 591 | Seminar |
Dr. Jaskie taught Computer Science and Mathematics for 10 years at Glendale Community College from 2011-2021:
- Discrete Mathematics (MAT 227 at GCC / MAT 243 at ASU)
- Introduction to Computer Science (CSC 110)
- Data Structures (CSC 205)
Fall 2021: Feedback Systems EEE 480/591 at ASU
IEEE
Board of Directors, McDowell Sonoran Land Trust
Board member of a nonprofit dedicated to the preservation and conservation of the McDowell Mountain Range in Scottsdale, AZ from 1993-1997 beginning at the age of 13 as the only board member under the age of 35. Gave talks and lectures to the Scottsdale City Council and groups of up to 2,000 teachers and businesspeople. Successfully campaigned to have Scottsdale citizens purchase the mountain range and form what is now the McDowell Sonoran Preserve.
Prime Solutions Group (2018 - present)
Machine Learning Research
Principal Machine Learning Research Engineer 2021 - present
Leadership role in performing cutting edge research in ML and ML applications. PI on several government contracts.
Data Scientist 2018 - 2020
Worked on ML projects including creating GANs models for SAR radar, energy modeling, and other projects.
Data Analytics Consulting, LLC. – Owner/Chief Scientist (2015 - 2020)
Machine Learning Applications
Proposed a DoD SBIR to use Machine Learning to perform threat detection over multiple domains. Performed multiple small contracts with private companies to perform analysis and model creation using ML techniques.
San Diego Supercomputing Center (2010)
Fraud Detection
Designed strategies and algorithms to detect Medicare insurance fraud.
BRE Systems, Systems and Machine Learning Research (2000-2005)
Biometric Voiceprint Identification
Did proprietary research on a Phase I and Phase II SBIR for the Air Force Research Lab. Researcher and implementer for a Secure Satellite Radio system using voiceprint authorization and encryption algorithms. Designed and implemented program infrastructure and protocols.