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Shant Mahserejian


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

Two things: a) The opportunity to apply mathematical approaches to solve problems on interdisciplinary projects with collaborators from other departments, where cutting edge science occurs; b) to further develop and utilize statistical skills simultaneously in the same department.

2.  What was the best part of the program?

Seeing the ACMS department grow from its infancy into a recognizable name on campus, and helping develop that reputation.

3.  Tell us about your doctoral thesis.

My dissertation dealt with a sub-cellular bio-polymer found in the cytoskeleton called the "microtubule". Much of the detailed makeup of the microtubule structure is not observable in modern laboratory settings, and not much is known about the mechanisms that dictate its peculiar behavior, called dynamic instability. Computational models help simulate these structural details, as well as the sporadic switches between growth and shortening phases observed in dynamic instability. I used these simulations to develop a statistical tool for analyzing microtubule data, which enabled me to identify a new third phase called "stutters", previously overlooked phases that are intermediate to the classically observed growth and shortening phases. Additionally, I developed predictive models to study which micro-level structural features drive the possible macro-level changes between different phases.

4.  What are you working on now?

I'm working on using the statistical tool that I developed to compare simulated and experimentally derived data, and to show that the stutter phases are not only present in real world observations, but they also play an important transitional role at the onset of catastrophic rapid shortening phases.

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

The ACMS PhD program that I followed helped to develop the statistical skill set that was missing from my applied math background. More specifically, I learned how to interpret data using regression techniques, and to uncover less visible connections using machine learning approaches. By obtaining this knowledge, I feel that I am now prepared to study different real world scenarios that require data analysis, to gain a deeper understanding through building computational models and generating simulated data representing those scenarios, and to properly compare data sets to tell a complete story of what drives the scenario in question.

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

I have currently accepted a position as an Applied Statistics Research Scientist at the Pacific Northwest National Laboratory, where I will be using my applied mathematics and statistics skills to tackle interdisciplinary problems in a collaborative environment in order to find scientific solutions that benefit national security, energy, environmental, and other concerns.

7.  What advice would you give to students considering the program?
Don't be afraid of being challenged and uncomfortable while taking advantage of all the resources provided at Notre Dame to help you learn as much as you can. It's the last chance you'll have to do so, before you'll have to do it on your own!