Austin Allen is in his second year of study in the Master of Biostatistics program. His hometown is Grantsville, Utah. Austin earned his undergraduate degree in Biostats/Data Science from Brigham Young University-Idaho.
What first sparked your interest in biostatistics?
- I was working as a research assistant in a wet lab. One day, I asked my advisor, “If you could go back and study something other than cell biology, what would you do?” He listed about 10 different career paths that he would seriously consider. After that conversation, I started exploring paths like bioinformatics and statistics, and that led me to change the trajectory of my undergraduate experience.
What attracted you to the Master of Biostatistics program at Duke?
- Research opportunities. Duke is a leading research institution with opportunities for students to join projects in virtually any area of interest. As a bonus, Duke is in one of the most beautiful areas of the country, which provides quick access to beautiful hikes and mountain biking trails, loads of incredible food, and a quick two-hour drive to the beach.
What do you enjoy most about this program?
- This program is designed to train students to think like biostatisticians. That means we’re often “untrained” from unhelpful mindsets and expectations we carry from our undergraduate experience. For example, this program is designed to be collaborative, exactly what you would expect as a professional. Rather than looking at each other as competitors, we learn to treat each other as colleagues, often asking for validation on complex problems or tasks.
What are some of your favorite classes?
- Here’s a list of my personal favorite classes:
- BIOSTAT 705: Applied Biostatistical Methods Part 2 – This course is taught in the second semester of the program. It ties a lot of concepts together from previous classes in a very practical and applicable way. It’s an incredibly useful class for every student in the program.
- BIOSTAT 821: Software Tools for Data Science – This is a data science class taught in Python. It can be challenging for students without Python experience, but it pays dividends if you’re willing to put in the work. The primary professor for the course has an immense amount of experience in a professional data science setting, so you have a chance to really engage with tools used in the industry.
- BIOSTAT 719: Generalized Linear Models – This is one of the most useful classes for every student in the program, regardless of whether they plan to be a biostatistician or a data scientist. Students will discover that concepts start to really “click” in this course, concepts that are essential for both statistical analyses and machine learning methods.
What skills have you gained from the program?
- I came into the program with a background in statistics and data science. That being said, my skillset has dramatically improved in these and other important areas. Here are just a few:
- Study design: Learning how to design a study to properly answer the research question is a major component of this program. Related to this is being able critically read research literature and evaluate the study’s design.
- Statistical methods: I have learned how, when, and when not to implement various statistical methods ranging from non-parametric tests to Poisson regression.
- Data science programming: My general programming skills have been honed and sharpened, and I have learned a variety of tools and applications used in data science.
What does the program do well?
- There are many things that I could list, but here are some of my favorite observations:
- The program does a great job at preparing students for real-world work. We almost always use data that come from the “wild,” and we often are required to locate our own data sources and design our own research questions.
- The program provides an amazing number of resources for students to succeed. We have excellent TAs. We have student mentors that are always a text away. One of the most amazing resources is our own in-house career advisor. Students are required to meet with her every week for the first year of the program to prepare for Ph.D. programs or industry, depending on the student’s needs. After the first year, she encourages us to practice interviews with her, form job search groups, and assist each other with resume design.
- My last observation is that the program is designed in a particular way that students learn to trust each other, rely on each other, and work closely together.
What's the most challenging aspect of the program?
- This program is very flexible. Students are offered a selection of second-year courses from which to choose, based on their plans for working in clinical research, data science, or continuing to a Ph.D. program. This is a wonderful thing! However, many students are intimidated by the options because they fear choosing the “wrong” class or missing out on an opportunity. To mitigate this concern, the program directors, Greg Samsa and Jesse Troy, provide course counseling. Ultimately, students shouldn’t be afraid, even if they’re not sure where they want to end up. You really can’t go wrong! And it’s been my experience that talking with Greg, Jesse and other students has helped me choose excellent courses for my second year.
Did you participate in a summer internship?
- Yes, I did.
What type of work did you do for your internship?
- I worked as an intern in Duke’s BERD Core Internship Training Program (BCTIP). My assigned area was the Duke Cancer Institute. During the internship, I designed studies, built models, made a ton of mistakes, and learned how to approach abstract problems and structure analyses to address specific study aims.
What advice do you have for incoming students?
- I can’t stress this enough: USE YOUR RESOURCES! There are a) students around you, b) there are spectacular TAs assigned to every class, and c) there are professors who ACTUALLY CARE ABOUT YOU and want you to succeed. In addition to these study helps, there are the department superstars. These include names such as Kendall, Laura, Michelle, Greg, and Jesse. You have a plethora of opportunities to succeed in every way you hope to.
What type of work do you see yourself doing in the future?
- I see myself working in health data science, though the specific type of data I'll focus on is still evolving. I enjoy the challenge of working with messy EHR data and am fascinated by large-scale 'omics data. I could also see myself exploring data from wearable devices or medical imaging. But regardless of the type of data, my plan is to build models that help investigators tackle complex health-related problems.
What do you like to do outside of work?
- I have a two-year-old girl and a newborn baby boy. I love taking them for mini adventures, letting them experience things like the local farmer’s market, hikes in the forest, and running amok at the many community playgrounds in the area.