Sunčica Čanić

Sunčica Čanić was the only woman to hold a prestigious Cullen Distinguished Professorship position at the University of Houston. Čanić moved to UC (University of California) Berkeley’s Mathematics Department in the Fall of 2018, and is currently serving as a Full Professor there. Čanić’s current research interests include analysis and numerical simulation of partial differential equations with application to problems in medicine and active materials. She was recently elected SIAM Fellow, Class 2014, for her ‘contributions to the modelling and analysis of partial differential equations motivated by applications in the life sciences.’

 

 

We repost here an extract from the interview to Prof. Sunčica Čanić, published in the Mathesia Outlook on Data Science: a report exploring how Data Science and Applied Mathematics will shape our future.

 

What do you reckon as the most interesting problem you have faced within the broad field of data science?

Being an applied mathematician with research interests in biomedical applications and real-life problems, data science is an important tool to address issues related to the modelling of reallife phenomena and the parameter dependence of solutions.

 

In what application field would you say data science will truly make a difference in the coming 4 – 5 years?

As my research is related to the field of health, I think one of the major impacts that data science will have in the near future is its impact on health care.

Consumer devices are already available that can track our heart rate, sleep patterns, exercise and nutrition. As we move one step further, monitoring of patients in terms of ECG, glucose levels etc is also at the brink of widespread use.

Using this large base of data to understand how different life style choices affect our health and making recommendations based on this data will be a significant step forward in comparison with the current situation, which is based on self-reported epidemiological data.

Data science is constantly evolving and it is important to build new knowledge on case studies and examples that would provide a testing base, and to use and develop mathematical techniques that would guide and justify the new approaches.

 

What do you expect will be the research challenges, critical issues and risks data science will have to cope with in the near future?

One of the challenges that still needs to be overcome is to develop closer ties between industry and applied mathematics, so that highly trained mathematicians with the cutting-edge knowledge in mathematical methods for data analysis can gain experience and skills from the specific industrial sector. On the other hand, encouraging open research in industry, where research results are published together with the data, would help attract professional mathematicians and increase the knowledge base for further developments in this area.