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Bobby Bridges: Tools for success

ORNL’s Bobby Bridges uses mathematics and machine learning to develop security and privacy solutions. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

As a research scientist in the Cyber Resilience and Intelligence Division at the Department of Energy’s Oak Ridge National Laboratory, Robert (Bobby) Bridges uses mathematics and machine learning to develop security and privacy solutions. His previous research includes mathematical methods for anomaly detection on streaming data, in-vehicle network security and reverse engineering, and evaluation of enterprise network security tools. Currently, Bridges is developing a novel method for differentially private optimization, which enables data scientists to train machine learning algorithms on private datasets, such as healthcare or census data, with provable privacy guarantees. He also develops data science approaches to reduce waste, fraud, and abuse in the nation’s Medicare system.

As an undergraduate and graduate student, Bridges’ passion was for pure mathematics. His dissertation focused on solutions to Schröder’s equation in several variables, a functional equation arising in complex analysis that naturally fits into the subject of composition operator theory. Before joining the Lab, he volunteered as a consultant for Ogre Industries, where he developed an algorithm to support the production of jet engines by flagging defective parts. His path to national security sciences was made possible, he says, by dedicated mentors who helped him along the way.

Read below how mentoring shaped Bridges’ career path and the advice he offers to prospective mentees.

I enjoy mentorship because I love helping people who are working to help themselves and who are on their own paths toward success. When I meet students who are passionate and hardworking, I’m excited to be a part of their journey and give them tools that will help them succeed.

As an undergraduate, I was lucky to be accepted as an intern at California Polytechnic State University, San Luis Obispo, through a National Science Foundation Research Education for Undergraduates opportunity. Aside from the many lessons in mathematics and research, the most important lesson of the experience was that having a career in research was possible. This realization changed my trajectory. One of the most valuable things an internship opportunity provides is an avenue to discover potential career paths and to learn about the steps necessary for achieving your goals. These opportunities can offer important insights for the next-generation workforce and inform early-career decisions that shape future successes.

Many people seem to learn best by example, through their own experiences or from the experiences of those they respect. The mentor-protégé relationship is a natural environment for a student to encounter new examples and experiences, and often the mentor and teammates benefit as well. As an intern, I learned not just about what jobs were available to me, but I learned that those who are farther along in their careers are people, too. My mentors also struggled with learning new concepts and experienced imposter phenomena, but on the other end they had mastered concepts and practices I found remarkable and worthy of emulation. In short, there are lessons beyond science in mentoring that are valuable to students.

I am grateful that I am now in the position to be a mentor. Not only is helping others rewarding, but it is also a continual learning experience for me. My mentees have been impressive and are often better problem solvers than I was at their level. Observing the much smarter solutions provided by interns is humbling, educational, and exciting.

For students seeking the support of a mentor, my advice is to choose carefully. Identify what you want from a mentor. Is it someone who is the best in their area? Is it someone with whom you feel comfortable working and interacting? Is it someone who can help position you for a next job? Is it someone whose skills complement your own? For example, as a mathematician, I can gain depth from working with other mathematicians; on the other hand, I gain breadth when working with a domain expert in, for example, automotive networks or healthcare data who provides data and relevant science problems. Students should also recognize that the best mentors are not necessarily the best researchers and vice versa.