Evercita Eugenio


1.  What interested you in the doctoral program in Applied and Computational Mathematics and Statistics at Notre Dame?

The doctoral program in Applied and Computational Mathematics and Statistics at the University of Notre Dame interested me because of its applied coursework and diverse faculty. Each faculty also had collaborations with people outside of the department and so the inter-disciplinary nature of the department was appealing.

2.  What was the best part of the program?

The best part of the program was the opportunities it provided. There was opportunity to expand research with collaborators from within the department and outside. There were opportunities to work with other graduate students, so I did not feel extremely isolated in my work. There were opportunities to also attend conferences and workshops, which allowed for amazing networking opportunities.

3.  Tell us about your doctoral thesis.

My doctoral thesis was focused on data privacy. More specifically it focused on differential privacy, where balancing between protecting the privacy of individuals who contribute to datasets and releasing data sets of good utility is of extreme importance. Even with datasets anonymized, there is a still a possibility that an intruder may identify a subject in a released data set. Many of the existing methods for data privacy and confidentiality do not quantify the amount of privacy that the data set may leak. Differential privacy provides a conceptual approach to bring strong mathematical guarantee for privacy protection and quantifies the amount of privacy the data set leaks when it is released for public use. My dissertation explores the recently developed differentially private data synthesis (DIPS) methods for incorporating differential privacy when generating synthetic data to be publicly released. One algorithm I developed involved constructing differentially privacy microdata from low dimensional histograms by solving linear equations with Tikhonov regularization. Another algorithm used an exponential random graph model to incorporate differential privacy into social network data

4.  What are you working on now?

Now I am working at Sandia National Laboratories as a statistician within the Cyber Security & Data Science group.

5.  How did the program prepare you for your career?

The program prepared me for my career because it allowed me to develop skill sets that can be applied to a wide range of topics. Particularly my dissertation was in data privacy, so that has prepared me well for the transition into a cyber security domain.

6.  What are your career plans for the near future?

My career plans for the near future are to develop as a scientist and researcher at Sandia National Laboratories. There is a lot of growth possible, and the opportunities to lead projects and tasks.

7.  What advice would you give to students considering the program?
The advice I would give to students considering the program is to really think about the work that they want to do and what interests them because it is five years of your life you'll be dedicating to a PhD program. It is important to be happy with the research you are doing, otherwise it will make it very hard to make progress. Also, find an advisor that you really connect with from a research perspective and also from a personal work relationship. You'll be spending a lot of time together and it is important to make sure that you can work together and have clear goals in mind.