Professor Rebecca W. Doerge is the Glen de Vries Dean’s Chair in the Mellon College of Science at Carnegie Mellon University (CMU). She holds faculty appointments in both the Department of Statistics and the Department of Biological Sciences. In the past, she served as the Head of the Department of Statistics at Purdue University where she was the Trent and Judith Anderson Distinguished Professor of Statistics and Professor of Agronomy. At Purdue, she served as the President’s Fellow on Big Data and Simulation and the Director of the Statistical Bioinformatics Center. She has numerous accolades for her scholarship and service such as elected fellow of the American Statistical Association (ASA), American Association for the Advancement of Science (AAAS), and fellow of the Committee on Institutional Cooperation (CIC).
Your research area is in statistical genetics is by its very nature is applied. How do you feel the perception of statistics as a discipline by other disciplines has changed over your career?
Professor Doerge argues that it has two directions: how statistics is viewed by other disciplines and the reverse. Many of the applied disciplines are very “appreciative and respectful that statistics is a discipline in itself, and are very willing to work with a statistician.” She notes that many non-specialists appreciate the time that applied statisticians have invested to learn their disciplinary challenges and vocabulary. Specifically, in the biology and genetics world “statisticians who have invested the time and effort are considered as equals.”
“Now if you flip it on its ear, and discuss how are [applied disciplines] that use statistics perceived by statisticians. The answer, in my opinion, is not well.” One reason is the perception that those in applied disciplines lack theoretical depth and rigor. She does believe that is changing. She looks to bioinformatics as an example where the very best statisticians learned the biology, and then proceeded to learn the computing along with the statistics.
“It is not an equal two-sided arrow.” One remedy she suggests is that card-carrying statisticians come to accept there is a considerable number of non-statisticians who know a lot (about statistics), and who can still contribute to the field of statistics, specifically in their own discipline (e.g. ecology, forensics, genetics, etc.) Statisticians need to get beyond the view that either “you are a statistician or you are not,” and begin to embrace collaborative science.
Commenting on her own research background as a statistical geneticist, she feels that her own work is viewed as a contribution to the genetics community, but not to the statistics community. Even so, the fact she is a statistician working in genetics is a contribution unto its own.
What have you learned in your time serving as a department head at Purdue and now as a dean at CMU?
First of all, she confesses serving in administrative roles was never her plan. She claims her strength is mentoring graduate students, and attributes her success in that capacity to “treating each independently” and knowing each student as a person. She developed this skillset starting as an assistant professor. When she was the department head at Purdue, she learned that faculty, especially junior faculty, were much like graduate students. Specifically, you must foster respect and establish a repertoire; build relationships. The surprising, and disappointing, part about becoming a department head and then Dean is the negative view that faculty have about administrators. This is an issue that she wishes to speak openly about as she feels it is a message and attitude that must change.
Professor Doerge does hope to demonstrate that one can be successful as both successful as an administrator and a researcher. One of her personal goals is to change the attitude about administrators. She strives to be a “cool administrator who does research” and fosters a teamwork atmosphere.
Professor Doerge feels mentoring graduate students prepared her to become a department head. Further, she feels being a head of a department, especially at one of the largest statistics departments in the country, is great preparation for serving as a dean. Having been a head of department, I am a more supportive and sympathetic dean. “It’s all about communication, respect, knowing people, not reacting, consider the person sitting in front of you, and transparency.”
Drawing from her experience, she believes that successful leadership is about people. “Everyone wants to be valued, respected, and informed.”
At Purdue, you held a joint appointment in Agronomy. At CMU, you hold a joint appointment in Biological Sciences. What benefits and challenges do you see for joint appointments for statisticians?
Professor Doerge believes her Purdue joint appointment between the departments of Statistics and Agronomy in the Colleges of Science and Agronomy, respectively, enabled her to become a visible leader on campus.
“There are faculty and administrators that do not like joint faculty appointments, but I like them a lot. For interdisciplinary statisticians, you have the benefit of the statistics community and the community of the application. This doubles the exposure. You serve as a connector for undergraduates, graduate students, and faculty. At a university, it allows those in joint appointments to become a leader in transdisciplinary collaboration. I see it as a huge positive all the way around.
The ongoing issues with joint appointments typically occur in preparation for promotion. Specifically, the promotion process for junior faculty may become far more complicated when different departments and perhaps colleges are involved. In Professor Doerge’s opinion joint appointments work well when it there is a major and minor appointment. At Purdue 75% and 25% faculty appointments are common, yet at Carnegie Mellon University it is quite common to have 50-50% appointments that require independent votes from both departments and colleges.
Carnegie Mellon is one of the very few universities to have its own department dedicated to machine learning? How does this influence the academic culture for a statistician?
Professor Doerge believes that having a CMU Department of Machine Learning enables/supports interdisciplinary collaborations between machine learning folks and specific disciplines such as physicists and biologists; disciplines that often do not always have the opportunity to interact in the statistics world. The fact that CMU has a department of machine learning fits the culture of CMU. We are “notorious for solving real world problems with computing.”
What do you think of the emerging field of data science?
“My first reaction is: I do not understand what [data science] is because I am a statistician. My second reaction is data science is more understandable as a discipline descriptor than statistics simply because “data” is in the name. It brings a thoughtfulness about data, and what data can do for us … to a society/culture level. Data science as a group of words is modernizing the discipline of statistics.” She brings up that statistics departments should include data science in their names as was the case at Yale University and recently at Carnegie Mellon University.
Do tenure processes fulfill the world’s current need for scholars in statistics?
“No, it produces scholars of the past. Current day scientists/academics are becoming increasing collaborative; the tenure process should take this into account. The majority of the tenure track positions in the U.S. today are not delivering faculty who work in teams. Interdisciplinary research, teamwork, working with students, faculty, and staff is the path to solving real problems. The driving question we must ask ourselves every day is, are we solving real world problems in the best manner possible? Are we putting the power of diversity of thought, training, and culture to work?”
Are we pushing the boundaries of statistics at the same rate that has been done before?
“I think so… yes, and probably faster. Big data are pushing statistics both theoretically and in application. We are living in a reality where the magnitude of data available may be beyond asymptotics. No one ever thought we would collect this much data. Are we working beyond infinity simply because we are turning to machine learning, deep learning, and active learning, instead of asymptotic theory?”
Do you think statistics communication pose difficulties that other science communication fields do not have? If yes, why?
“I hate to generalize, but historically I think statisticians are not terribly gregarious and communicative.” She cautions her generalization with stating “There are always exceptions.” She adds “There needs to be an appreciation within the statistics community that communication is important. As with technical skills like computing and mathematics, we need to value communication: Written, verbal, presentation, and professionalism.”
What are ways statisticians can improve their communication skills?
Her answer is to “do it.” “We all shy away from things that make us uncomfortable. For graduate students, the best thing to do is teach. There are two different levels of communicating in this setting: lecturing and office hours. Lecturing allows for organizing thoughts, presenting it to the audience, see if they are paying attention, etc. Office hours allow for one-on-one communication.
Give talks, posters, and volunteer within your department, university, and community. Gaining experience with communication skills does not have to be limited to statistics. For written communication, you just need to write. … you learn about yourself when you write. Do it every day. It does not have to be restricted to statistics.”
As an example, in her own research group, graduate students write a weekly summary of their research progress. It is not about necessarily what they accomplished every week. Instead it is about organizing thoughts and putting those thoughts/results/ideas into writing. The best way to gain confidence with both written and spoken communication is to practice, practice, practice everyday.”
— Interview by Will Eagan–