Published: Sept. 25, 2018

Hometown: Littleton, CO
Laboratory: Theodore Randolph Lab

My path to engineering

Austin DanielsI grew up in Littleton, Colorado—a medium-sized town about an hour south of Boulder. Science and engineering were always interests of mine growing up. I enjoyed learning about different STEM-related fields both during and outside of my normal coursework, and by the time I had to apply to different colleges, engineering was the clear choice for my studies.

While many researchers are interested in working on problems in a specific area, I was always more interested in finding challenging problems that would use the diverse knowledge and experience I had accumulated earlier in my academic career. I have always been interested in pursuing knowledge in a variety of different areas—the variety of fields chemical engineers are knowledgeable in was one of the main reasons I chose to study chemical engineering over other engineering disciplines.

When I was exploring different career paths at the end of my undergraduate studies a comment from another student stuck with me: graduate school was meant for people who wanted to “build a better mousetrap” as opposed to just working on “existing mousetraps” as engineers in industry often end up doing. This summed up why graduate school was the path for me.

My research focus

My research currently involves using machine learning approaches to better characterize protein aggregates present in protein-based drugs and therapeutics. These aggregates can dramatically impact the safety of the drug, and it is critical for people developing, producing and using these products to at the very least be able to monitor these aggregates to minimize the risk of the drug triggering adverse effects.

While there are several techniques for monitoring the properties of these aggregates in development and manufacture, it is often difficult to process the measurements taken even from a single technique to make any meaningful predictions of a product’s safety. We have found that machine learning approaches greatly simplify this analysis, making it much easier to compare aggregates between different samples. The inherent multidisciplinary nature of this project is what I fell in love with during my studies to date.

Why CU Boulder?

For me, CU offered the most exciting research opportunities relative to the other schools I was considering, and the success of the research rarely seemed as if it were at the expense of the sanity of the graduate students. Most of the graduate students I spoke with were happy with their research, being productive but still finding a good work-life balance.

I cannot emphasize enough how nice of an area Boulder, Colorado, and the surrounding Denver Metro area is. Colorado offered a lot to see and do when I am not working in both Denver and the mountains and was ultimately the place I was going to be happiest in for the duration of my graduate studies.

Another element that I have fallen in love with since joining the school is the overall environment of the college. It is incredibly easy to get to know different people in different labs and departments. This naturally fosters a sense of community between different labs and graduate students even after they join a particular lab. While graduate studies are certainly never easy, I constantly feel that I have fellow graduate students and professors both within and outside my lab I can fall back on for support whenever I need it.