Building Tools for a Successful Scientific Career

Picture this: You’re looking at a blurry photograph of a group of people. They appear squished, stretched, or rotated.

As a result of the distortion, it’s difficult to correctly identify the people in the photo.

Now meet August George. He’s a PhD intern at the Environmental Molecular Sciences Laboratory (EMSL), a Department of Energy, Office of Science user facility. He’s found a solution to this problem.

“I developed a software tool that is able to accurately and efficiently identify objects in these three-dimensional images with distortions,” explains George, a PhD candidate at Oregon Health and Science University.

He created object-detection and pattern recognition algorithms for 3D image datasets that contain noise and distortions. The method that George developed can be used across several of EMSL’s imaging capabilities, including X-ray computed tomography, atom probe tomography, and electron cryotomography.

George, EMSL biological physicist and computational scientist Margaret Cheung, and several other EMSL staff recently published research in Protein Science on how this work was applied to identifying protein structures in tomograms.

For the last 18 months, George has worked to create the computational tool under Cheung’s mentorship.

“August is an outstanding PhD intern whom I first recruited when I joined EMSL,” says Cheung. “He is a highly motivated individual with a broad set of quantitative skills in solving complex problems using computational approaches.”

Jay Bardhan, EMSL’s Computing, Analytics, and Modeling (CAM) science area leader, sees the emerging high-performance computing area as a unique opportunity and responsibility to mentor while supporting diversity, equity, and inclusion.

“Every single one of us got to where we are through the encouragement, support, and feedback from our mentors, and it's a privilege to be part of an organization where so many staff show a real dedication to mentorship,” says Bardhan. “EMSL's unique interdisciplinary mission necessitates a strong mentoring culture and development mindset, especially in the new Computing, Analytics, and Modeling science area, where there are so many rapidly evolving fields—artificial intelligence just to name one.”

In recognition of National Mentoring Month, we asked George to share about his experience being mentored by Cheung and working in the CAM science area.

What have you learned from your mentor?

I have learned how to better organize a project that involves multiple people as well as manage my time better. Before joining EMSL, I hadn’t done much research with a team before, so Margaret helped me learn how to better communicate effectively with team members from different scientific disciplines and levels of experience. Also, Margaret helped me develop a better system for organizing my notes on a daily and weekly basis, which ended up saving me a lot of time when I needed to review my work and write a manuscript.

Why is mentorship important to early-career scientists?

I think mentorship is important because it provides an alternative perspective that comes from experience, as well as connections that can help grow your career. More specifically, a good mentor can assist with grant proposals and also serve as a collaborator to help develop your scientific career. Also, a good mentor will have connections in industry, government, and/or academia, which can help an early-career scientist switch careers or get a new job.

What’s your favorite part of working at EMSL?

I enjoy the collaboration between scientists from different disciplines and also the wide range of projects at EMSL. Here, there are people working on large-scale environmental science problems all the way down to nanoscale molecular biology problems, with access to state-of-the-art laboratories and computational resources. As a computational scientist, this type of research environment is very intellectually stimulating.

What excites you about your science?

I enjoy building high-performance computational tools that accelerate scientific discovery and utilizing those tools to help solve key challenges related to human health and the environment. Better computational tools are needed to build models of large complex systems as well as make predictions and inferences based on the available data. I am excited to develop these tools and play a part in how technology changes our understanding of the world.