Building the Societal Engineer

By Mike Seele

Bringing various expertise together is not just for today’s research. It also is effective training for engineering students, who can learn to use it to solve societal problems today and throughout their careers. The College of Engineering is embedding this approach in its educational mission at the undergraduate and graduate levels.

For example, the undergraduate curriculum now includes a required course in data science for students in all majors. Data science has emerged from a discreet discipline just a few years ago to one that is ubiquitous throughout engineering and elsewhere.

“The era of the single-discipline engineer is over,” says Dean Kenneth R. Lutchen. “Most innovation now requires multiple engineering disciplines interacting with large data sets. Making sure our students are literate in data analysis is fully in keeping with our mission to create Societal Engineers. I have heard from leaders in industry that data analysis is becoming a key attribute they are looking for when hiring engineers, and one not often found. Having this knowledge, Boston University engineers will have the tools to improve society for many years to come.”

“We are one of the first engineering schools nationally that has designed a curriculum for which students in every major will take an interdisciplinary, data-driven approach,” Lutchen adds. “We recognize it as essential and we are aware that in the future every engineering discipline will intersect with data science.”

For students who want to take a deeper dive, machine learning—one of four optional, interdisciplinary concentrations—is available. The three-course sequence is designed to equip students with the skills and credential to pursue careers or graduate school in this area. Completion of the 12-credit sequence is noted on the students’ transcripts, and includes an experiential component. That could be a senior design project, laboratory research, internships, directed study or another machine-learning-related experience.

“Machine learning is so cross-cutting, all engineers can benefit from learning it,” said Senior Associate Dean for Academic Program Solomon Eisenberg (BME, ECE). “Today’s problems require many fewer silos and much more cross-disciplinary exposure and integration.”

Exposure to multiple disciplines is not limited to undergraduates. Master’s degree students in all disciplines have access to specializations in data analytics, robotics and cybersecurity. And, each year, several incoming doctoral students are identified for their potential to work in the six research themes and are offered fellowships they didn’t know existed.

 “PhD students are at the heart of making discoveries,” said Associate Dean for Research & Faculty Development Elise Morgan (ME, MSE, BME). “We identify top candidates who have the skills in these convergent areas and nominate them for convergent fellowships.”

Although most students are not familiar with the idea of convergent research themes, their eyes are opened when they learn about them. “When they visit, they see a good opportunity,” Morgan says. “They are often very excited.”