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Now you see me - Leila Taghizadeh
1. Can you remember the moment you realised you liked mathematics and decided that you wanted to become a mathematician?
Yes! When I was at school, I enjoyed mathematics classes. While most of my classmates were usually complaining about mathematics, I liked to do long calculations and then sometimes to look for a mistake in my computations. In the first school years I was enjoying math and any math-related classes like physics. I did not know that I am going to be a mathematician or I am going to decide so at some point. Later in the beginning of high school, when all the students should choose their major interest to continue, I was thinking between two directions to continue: either mathematics and physics or biology and chemistry. I enthusiastically chose mathematics and physics, and I am still happy with my decision. Later at the end of the high school I decided again to continue mathematics in university and to become a mathematician.
2. Can you briefly explain your research to a lay audience?
My research field in mathematics is uncertainty quantification (UQ), which is a bridge between mathematics, and statistics and probability. UQ is a multidisciplinary field and has a close connection to scientific machine learning and data science with a wide range of applications in computational science and engineering including medicine and biology. In real-world problems there are always some uncertainties and variability and it is of great importance to quantify them so that you can get a more precise quantitative understanding of the physical or biological phenomena. UQ addresses these uncertainties in two directions: forward and inverse problems. The goal of forward UQ is to analyze how uncertainties propagate through a mathematical model and effects the model solution, while inverse UQ aims to estimate the uncertainties and model unknowns by formulating them as inverse problems and solving them from statistical and Bayesian perspective. My research focus is currently infinite-dimensional Bayesian inverse problems and machine learning for UQ.
3. Which aspects of the job do you enjoy the most? And which do you like the least?
I like the fact in an academic career that you always encounter new challenges and you should be ready for them. I also like the fact that you always meet and work with new people and build a research network in the community. You are also free to put your thoughts and remedies on a mathematical problem into a paper by designing mathematical algorithms to solve the problem and overcome the mathematical challenges.These are all some lovely aspects of an academic job.
4. Have you had people who have encouraged you to pursue your career? How important were they?
Yes, there is a person who always encourages me to come out of my comfort zone and to start and face new challenges, motivates me to continue and not to give up, and supports me in failure and success moments. This person is my husband, who has been always very supportive and had an important role in up and downs throughout my academic career. On top of that, I always receives kind support from professors and experienced colleagues along my academic journey who are very kind and mindful to help me in different aspects of my academic career, and I am always thankful and appreciate it.
5. Being a mathematician means dealing with many frustrating situations, such as getting stuck on a problem for a while. How do you cope with that?
Yes, that is true. These frustrating situations are unavoidable in an academic career and one needs to get along with it. When I get stuck on a problem, first I do my best to resolve it, and if it does not work, and if it is not an urgent issue, I usually put the problem away for a while and give it some time so that I can refresh myself, and in the meanwhile I work on another problem while I have an eye open to the first problem. After some time when I get back to it, I usually have a better and more rigorous solution for it.
6. In your opinion, which are the main challenges for women in science? And how can these challenges be overcome?
Academic research always come with challenges for both men and women. But some challenges in science could be more pronounced for women. I think female scientists should be more self-confident when facing new challenges; especially younger women should believe in themselves. They should volunteer for academic tasks, apply for jobs, etc. In this case, I would listen carefully to senior colleagues and get advice from them; in the fem*MA network we are happy to help female mathematicians and guiding them to mentoring programs at TU Wien .
7. What advice would you give to students who want to work in academia?
Do your best and be the perfect version of yourself in any moment and part of your career. The moment and the chance will not happen again; you are now making and shaping your future career. You should be proud of yourself when you look back to this moment in the future. Therefore work hard and do not lose any time.
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Further Links to interviews with Leila Taghizadeh
Her own webpage:
https://www.tuwien.at/mg/asc/taghizadeh
An interview by TU Wien about women in science:
https://www.tuwien.at/alle-news/news/woman-in-science-leila-taghizadeh-mathematikerin
Projektass.in(FWF) Dr.in techn.Leila TaghizadehMSc
Who is Leila Taghizadeh?
Leila Taghizadeh is the Head of the Research Group for Uncertainty Quantification and an FWF Elise Richter Fellow at the Institute of Analysis and Scientific Computing at TU Wien. She currently serves as the principal investigator of the FWF Elise Richter Project V1000. In addition, she is a member of the Vienna Center for PDEs as well as the Society for Industrial and Applied Mathematics (SIAM).
Her research focuses on developing mathematical and computational methods for uncertainty quantification (UQ) and statistical inverse problems in complex systems arising in computational science and engineering. To this end, she draws on a broad spectrum of mathematical and statistical tools, including numerical methods for partial differential equations, probability theory, statistics, inverse problems, and optimization.
Projektass.in(FWF) Dr.in techn.Leila TaghizadehMSc
Who is Leila Taghizadeh?
Leila Taghizadeh is the Head of the Research Group for Uncertainty Quantification and an FWF Elise Richter Fellow at the Institute of Analysis and Scientific Computing at TU Wien. She currently serves as the principal investigator of the FWF Elise Richter Project V1000. In addition, she is a member of the Vienna Center for PDEs as well as the Society for Industrial and Applied Mathematics (SIAM).
Her research focuses on developing mathematical and computational methods for uncertainty quantification (UQ) and statistical inverse problems in complex systems arising in computational science and engineering. To this end, she draws on a broad spectrum of mathematical and statistical tools, including numerical methods for partial differential equations, probability theory, statistics, inverse problems, and optimization.