Stacy L. Tantum

Electrical and Computer Engineering

Associate Professor of the Practice in the Department of Electrical and Computer Engineering

Stacy L. Tantum Profile Photo
Stacy L. Tantum Profile Photo

Research Interests

Physics-based statistical signal processing • Context-aware machine learning • Domain-informed data science
Explainable/interpretable machine learning • Privacy-aware machine learning • Bias in machine learning
Neuroscience-informed teaching • Metacognition • Cognitive load theory • Mindset-aware learning

Bio

Stacy Tantum received the B.S.E.E. degree in Electrical Engineering and Economics from Tufts University in 1994, and the M.S. and Ph.D. degrees in Electrical Engineering, both from Duke University in 1996 and 1998, respectively.  During the summer of 1996 she was a Summer Research Assistant at SACLANT Undersea Research Center in La Spezia, Italy, where she collaborated on her PhD research on passive sonar with an international team of research scientists. Dr. Tantum is currently an Associate Professor of the Practice in Electrical and Computer Engineering at Duke University, where she is also the Faculty Director of the 360 Coaching Program for first-year advising in the Pratt School of Engineering.

Her current research interests are in the areas of domain-informed data science and context-aware machine learning, in which notions of privacy, bias, and making data science and machine learning decisions understandable and interpretable are at the forefront. She translates these interests to her efforts to improve student educational experiences by incorporating mindset-aware learning and neuroscience-informed teaching practices, such as metacognition and cognitive load, into the learning experiences she designs.

Education

  • B.S.E.E. Tufts University, 1994
  • M.S. Duke University, 1996
  • Ph.D. Duke University, 1998

Positions

  • Associate Professor of the Practice in the Department of Electrical and Computer Engineering

Courses Taught

  • ECE 110L9: Fundamentals of Electrical and Computer Engineering - Lab
  • ECE 110L: Fundamentals of Electrical and Computer Engineering
  • ECE 392: Projects in Electrical and Computer Engineering
  • ECE 480: Applied Probability for Statistical Learning
  • ECE 487: System Design for Machine Learning and Signal Processing
  • ECE 580: Introduction to Machine Learning
  • EGR 101L: Engineering Design and Communication