ANNA HAENSCH

Anna is a senior data scientist in the Tufts University Data Intensive Studies Center with a secondary appointment in the Department of Mathematics. She earned her Ph.D. in mathematics from Wesleyan University in 2013, after which she was a visiting Scientist at the Max Planck Institute for Mathematics. The roots of her research are in computational number theory, specifically in using modern computational tools and capabilities to answer longstanding, previously intractable, open problems. Until 2021, she was on the faculty of the Duquesne University Department of Mathematics & Computer Science, where she was granted tenure and promotion to Associate Professor in 2020.

On a leave from academia, she spent 15 months working as a research data scientist at a start-up in the Boston area called Tagup. In this position, her interest in algorithmic development led her to explore tools in data science and machine learning. Specifically, the ways we can use these tools to make a safer and more equitable world, from understanding and mitigating the impacts of climate change to resource allocation on a local and global scale. In this position, Anna began to consider the ways that machine learning can be applied to the domain of offshore wind to optimize the life of wind turbines and the safety of turbine operators.

Since joining Tufts she's developed several transdisciplinary collaborations. She works closely with the Tufts Univeristy Art Gallery to apply the princples of data science to develop tools in improve equity in public art. She continues her work on turbines with faculty in enginneering, policy, and public health, and is Co-PI on an NSF grant to develop policy-aware digital twins of offshore wind turbines.

In 2013 she was awarded the AAAS-AMS Mass Media Fellowship during which she spent 10 weeks working on the Science Desk at National Public Radio. In addition to the technical applications of data science, she's interested in the ways that data and numerical literacy more generally shape the way we produce and consume media. She is committed to using the tools of data science to promote social justice, which she does as co-director of the QSIDE Small Town Police Accountability (SToPA) Research Lab.

In her free time she likes to make art.

Education

• Doctor of Philosophy, Wesleyan Univ, USA, 2013
• Bachelor of Science, State University of New York at New Paltz, USA, 2007

Academic Positions

• Associate Professor
  Duquesne University, Department of Mathematics, 1 Aug 2020 - 15 May 2021
• Assistant Professor
  Duquesne University, Pittsburgh, United States 1 Aug 2013 - 1 Aug 2020

Teaching

Data Science

• Fletcher DHP-D258: Python for Global Applications
• Tufts DISC Workshop: Introduction to Python and the Principles of Data Visualization
• Tufts DISC Workshop: Retrieving & Analyzing Twitter Data with Python
• Tufts DISC Workshop: How to Build a Twitter Bot
• Invited Workshop: Teaching Data Science & Statistics for Social Justice

Mathematics

• Foundations of Mathematical Proof Writing
• An Inquiry Based Learning Course in Number Theory

Selected Publications

Haensch, A. (2022). Data Feminism. AMERICAN MATHEMATICAL MONTHLY, 129(5), 496-500. doi:10.1080/00029890.2022.2044214

Dutour Sikirić, M., Haensch, A., Voight, J., & van Woerden, W. P. J. (n.d.). A canonical form for positive definite matrices. Open Book Series, 4(1), 179-195. doi:10.2140/obs.2020.4.179

Haensch, A., & Knudson, K. (2022). Python for Global Applications: teaching scientific Python in context to law and diplomacy students. In Proceedings of the Python in Science Conference. SciPy. doi:10.25080/majora-212e5952-00b

Haensch, A. (2022). Reflections on hyperbolic space. Snapshots of modern mathematics from Oberwolfach, 2021(07), 1-10. doi:10.14760/SNAP-2021-007-EN