Email: anna.haensch at tufts dot edu
Github: @annahaensch
LinkedIn: annahaensch
Twitter: @extremefriday
Pronouns: she/her
My first name is pronounced "Ah-Nah" and my last name rhymes with "bench."
Anna Haensch holds a PhD in mathematics and is currently a senior data scientist at the Tufts Institute for Artificial Intelligence (formerly the Data Intensive Studies Center) at Tufts University with secondary faculty appointments in the Department of Mathematics and the Fletcher Graduate School for International Affairs. Her research interests are in science and technology policy, especially the use of artificial intelligence in the public sector and critical infrastructure. Her recent work spans applications of artificial intelligence and statistical machine learning across social and complex systems, energy infrastructure, and law enforcement, with the goal of fairness, safety, and equity.
Anna Haensch holds a PhD in mathematics and is currently a senior data scientist at the Tufts Institute for Artificial Intelligence (formerly the Data Intensive Studies Center) at Tufts University with secondary faculty appointments in the Department of Mathematics and the Fletcher Graduate School for International Affairs. Her research deals with applications of AI and statistical machine learning to problems at the intersection of technology and society. She has led NSF-funded work alongside engineers in renewable energy, applying machine learning models to offshore wind farm safety and reliability. She is also director emeritus of the Data Science, Police Accountability, and Community Engagement (formerly SToPA) Research Lab, where she works alongside an interdisciplinary team to develop statistical tools and neural networks to make data on policing more accessible and useful. In 2013 she was awarded a AAAS Mass Media Fellowship which she spent working on the science desk at National Public Radio in Washington, D.C. In addition to her academic research, Anna is interested in the ways that numerical literacy shape democracy and how that is reflected in the media. Prior to her present position, Anna also held a postdoctoral position at the Max Planck Institute for Mathematics in Germany, and was a tenured faculty member in the Department of Mathematics and Computer Science at Duquesne University.
Anna Haensch is a senior data scientist in the Tufts Institute for Artificial Intelligence (formerly the Data Intensive Studies Center) at Tufts University with a secondary appointment in the Department of Mathematics and the Fletcher Graduate School for Global Affairs. 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 brief 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 University Art Gallery to apply the principles of data science to develop tools to improve equity in public art. She continues her work on turbines with faculty in engineering, policy, and public health, and is Co-PI on an NSF grant to develop policy-aware digital twins of offshore wind turbines. She also does work related to the connection between public opinion and public policy and is especially interested in policing. She is director emeritus of the Data Science, Police Accountability, and Community Engagement (formerly SToPA) Research Lab
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. Consequently, she is committed to practicing data science in a way that connects the technical to the social.
In her free time she likes to make art.