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Women in Statistics and Data Science Is Headed to the West Coast

1 June 2017 477 views No Comment

Building on the success of last year’s conference, which welcomed more than 300 attendees and 25 sponsors and exhibitors, the 2017 Women in Statistics and Data Science Conference will be held in sunny La Jolla, California—its first time on the West Coast—October 19–21.

In store are short courses, an opening mixer, concurrent sessions, poster sessions, and a celebratory dinner. Leaders from academia, industry, and government will come together to present a world-class experience for attendees, from those just starting out in the profession to those who are seasoned professionals.

Also planned is a hackathon service project. Attendees will put their expertise to work developing a project that contributes to the UN COMMIT initiative. Teams will work to plan, design, and build an application or to analyze complex data sets and develop a visualization. The overarching goal for the projects is to advance the COMMIT vision of “a life free of violence for women and girls.” Each team will make a five-minute presentation about their project on the Saturday of the conference.

Let’s Chat: Top WSDS Speakers Provide Insights Into Their Careers

This year’s WSDS keynote speaker is Donna Brogan, and the plenaries will be from Jeri Mulrow, Bonnie Ray, and Susmita Datta. Here, they reflect on their long careers, share advice for the future, and discuss the topics they plan to talk about.

Donna Brogan

During the Women in Statistics Conference last year, you presented a paper titled “Challenging Sex Discrimination: Reflections Over Seven Decades” in which you talked about how the courses you took at Gettysburg College inspired your interest in statistics. However, you also mentioned several obstacles you experienced after you graduated that prevented you from getting a job right away. Can you talk about the career options available to you in the ’60s, along with some of the challenges you faced?

As a new graduate of Gettysburg College in 1960 with a BA in mathematics and induction into Phi Beta Kappa, I searched for a job. I was not qualified for public school (K–12) teaching, a common career option for females at the time, because I did not take the required education courses in college. Newspapers in 1960 published available positions under the headings of “Help Wanted Male” or “Help Wanted Female.” Female jobs typically were secretarial, clerical, nursing, K–12 teaching, child care, food server, or light manufacturing, none of which I thought would use my interest and talent in mathematics. Some of the male jobs were technical and math related and of interest to me, but I assumed I would not be considered for employment if I applied.

Job interviews on Gettysburg College campus with employers in 1960 confirmed my assessment of a dismal job market for females who wanted to work in a male-dominated occupation or career. For example, an interviewer for IBM told me my score on the IBM mathematics aptitude test was the highest he had ever seen. Then, he offered me a position as a secretary or sales associate. When I countered that I was interested in the IBM advertised positions that required a bachelor’s degree in mathematics (e.g., computer programmer), he simply said IBM hired only males for technical positions.

My first failed job search in 1960 motivated me to attend graduate school, and I earned a master’s degree in statistics from Purdue University in 1962. Unfortunately, I encountered the same job market situation in 1962 as I had two years earlier, even though I had a master’s degree now. Furthermore, I had limited geographic mobility for employment because I had recently married and my husband had accepted a faculty position at Iowa State University to begin in the fall of 1962.

My second failed job search in 1962, along with ISU having one of the best statistics departments in the country, motivated me to apply for the PhD program in statistics at ISU. I was accepted and received a one-year university-wide fellowship with an attractive stipend. My subsequent ISU years were funded by an NIH biometry traineeship that sparked my interest in biostatistics and provided an attractive stipend.

I earned my statistics PhD from ISU in 1967. I applied for two tenure-track academic positions and received an offer from each: Duke University School of Medicine and UNC-Chapel Hill School of Public Health. I accepted the UNC offer. Although I did manage to finally acquire a position in my chosen field of interest in 1967, there were continuing instances of sex discrimination over the next several decades. However, I very much enjoyed my career in biostatistics.

Throughout your career, you have had to advocate for women’s education and employment. How did you develop the confidence to be an advocate? What is the most important lesson you’ve learned from these experiences?

My many earlier experiences of advocacy are what I now would call “personal advocacy.” This early period covers high school in the mid-1950s up to my first professional position at UNC-Chapel Hill in the late 1960s. The scenario was this: I wanted to achieve some goal for myself, I incurred obstacles to meeting that goal, and I proactively attempted to overcome the to reach my goal. Many of these earlier experiences involved what I later realized was societal sex discrimination. Sometimes I was successful in my personal advocacy and sometimes not.

One example of personal advocacy occurred when I was a statistics doctoral student at ISU. My husband resigned his ISU faculty position in 1965 and enrolled as a creative writing graduate student at the University of Iowa. We needed to find less expensive housing in Ames for our family, which now included two young children. I applied to rent ISU graduate student housing for families, but was denied because only male graduate students were eligible. I appealed this decision to the housing office and was denied again. I wrote to and/or personally contacted the next level of supervisors up the line as far as I could go to protest the decision. I don’t remember if I reached the ISU president’s office. Eventually, ISU allowed my family and me to live in the graduate student family housing, but did not change its policy that only male graduate students were eligible for this housing.

So where did I get the confidence or grit to appeal the official housing ruling, present my case to supervisors up the line, and expect that I might be successful? I’m not sure. I have been a goal-oriented person since my early days, deciding in first grade that I wanted to be a straight-A student. This perseverance and drive toward a chosen goal for myself is not something I learned directly from my family. In fact, I think it is something I learned as a reaction to my family background, which was a chaotic and unstable situation because of my mother’s mental illness. I realized at a young age, around nine years old, that my mother was not a capable adult and I had the opportunity and necessity to take on some adult responsibilities at a much younger age than most children.

In the late 1960s, while a faculty member at UNC-Chapel Hill, I pivoted from “personal advocacy” to “advocacy for women,” primarily because of joining a women’s liberation group. I read The Feminine Mystique by Betty Friedan, as well as radical feminist literature by different authors, and underwent a profound “consciousness raising” experience regarding societal sex discrimination in the United States. After this pivot, I often used my response to personal experiences of sex discrimination to address the discrimination policy against all women. I organized a group of women or used legal support to obtain changes for all women, not just for myself. Two examples of sex discrimination practices in Atlanta that were changed by these efforts are the voter registration system for women and men in DeKalb County, Georgia, and the fringe benefit of free undergraduate tuition at Emory University for children of Emory faculty and staff.

Two important lessons I learned from these experiences are the following:

  • It takes a lot of time, effort, anxiety, and sometimes money to combat sex discrimination, whether one is doing personal advocacy or advocating for women in general. Typically, this is time and effort taken from other parts of one’s life (e.g., family, career, self-care, hobbies, or personal interest).
  • Organizing a group effort or a legal effort generally seems to be more effective than combatting sex discrimination as a single person.
What advice would you offer a female undergraduate statistics major?

I don’t have undergraduate teaching experience or enough current knowledge of the job market to give specific advice. But, here is some general advice regarding a statistics career that seems to me to be timeless, whether one is embarking on a career after earning an undergraduate or a graduate statistics degree.

First, in addition to your statistics major or emphasis, complementary courses in other mathematical sciences are likely to be useful (e.g., computer science, mathematics, data management, etc.)

Second, it may be useful to have a concentration or above average knowledge in at least one area of application of statistics. One of the fascinating features of statistics is the application area can be almost anything. So, choose one or more areas that are of high interest to you.

Third, choose a statistics job or a statistical specialty for further education that is of interest to you or, even better, a passion for you. I think there is nothing deadlier than spending a lot of time on work or education that is not interesting to you.

Fourth, be willing and able to work as a team member on projects. This requires interpersonal skills beyond your technical skills, and you may not learn these skills in your statistics courses.

Fifth, be able to communicate, both verbally and written, your research design ideas, your chosen statistical methods, and your statistical analysis results to your project team as well as to additional technical and nontechnical audiences. You may not explicitly learn these communication skills in your statistics courses.

Sixth, most people in their early career will benefit from a mentor. Perhaps you will be fortunate and a more senior supervisor or professor or colleague will volunteer to serve in this role for you. If not, be proactive and identify a mentor who you think will be helpful to you and willing to do so.

Seventh, network with other professionals in your field. Nowadays, a lot of networking appears to be done on social media, although personal contact is still useful.

Eighth, anticipate that continuing education, in both technical and nontechnical areas, will always be part of your career.

Ninth, recognize that your female gender may subject you to some unexpected, unpleasant, and unprofessional experiences from others. Hopefully such experiences will not happen to you, but be prepared to deal with them if they do occur.

Tenth, make time in your life for a passion that is different from your career. During such activity, you likely will find that your mind has blocked out any job-related concerns or stress that you may have. Another advantage is that your passion may put you into a social group that is different from your career network group. My passion was square dancing, especially challenge-level square dancing.

You retired from Emory in 2004, but have remained active in the world of statistics. Are there any projects you are working on that you are excited about?

The statistical projects I work on nowadays must be of strong interest to me and short term. Here are some recent examples. Occasionally, I teach continuing education workshops—one to three days long—about how to analyze complex survey data by using SUDAAN, SAS, or STATA. Frequently, I reply via email to survey data analysis questions from participants in my previous continuing education workshops. Periodically, attorneys ask me to review statistical analyses that have been offered as evidence in a court case, the most recent case (this year) being suspected physician overprescribing of controlled drugs. Sometimes, faculty in my previous department at Emory (biostatistics and bioinformatics) request my opinion on sampling problems. In a few months, I will give a seminar to the Emory University Emeritus College (EUEC) on why the 2016 U.S. presidential polls were misleading and the implications for future polling. A fun seminar that I presented for EUEC a few years ago was titled, “The Mathematics of Challenge-Level Square Dancing.”

You are returning to WSDS. Why is this conference important to you, and what is the focus of your talk going to be?

I did not attend the first conference of this group in 2014, but accepted an invitation to present a paper at the second WSDS conference in 2016 in Charlotte, North Carolina. I had a fantastic time in Charlotte, including my first experience of attending a statistics conference with a predominantly female audience and my observation of the enthusiasm of women of all ages for the diverse field of statistics. The conference clearly is a great setting for networking. In the early days of my career in the mid-1960s, there were few female faculty members in university statistics or biostatistics departments and few women who attended the annual Joint Statistical Meetings.

Another Charlotte highlight for me was that my daughter, Rebecca, a third-grade teacher in Atlanta, accompanied me to the conference to hear my presentation and, as it turned out, to operate the PowerPoint presentation for my talk. In earlier months, she had helped me choose which instances of sex discrimination to include in my talk and converted several old photos and newspaper clippings into PowerPoint slides for me. It was a great experience for me to collaborate with her on what seemed to be received by the audience as an interesting and inspiring talk.

I was pleased to be invited to give the keynote banquet talk at the 2017 WSDS conference in La Jolla. My talk title is “Lessons and Strategies Distilled from Life Events: My Professional Story.” I will review several life events that taught me lessons or strategies I was able to use and benefit from in my professional and personal life. Some events are sex discrimination experiences and mentoring experiences I mentioned in my 2016 WSDS talk, but additional events include working/lower class family background with parental mental illness, working consistently since age 15, moving up to middle class via education, and living as a breast cancer survivor. Some of my lessons and strategies may be useful for others. My daughter already is helping me put together this talk.

Jeri Mulrow

What/Who inspired you to be a statistician?

As an undergraduate mathematics major at Montana State University, in my junior year, my adviser said I needed to take some applied classes and suggested I try out statistics, computer science, accounting, or finance classes. I signed up for the accounting class, but dropped it before the deadline. I took a couple of computer programming classes and thought they were useful, but not something I wanted to do just for the sake of doing. I took the first-quarter statistics course and promptly sold back the book thinking all statistics was a bunch of formulas and not that interesting.

When I talked to my adviser again, he—fairly strongly—suggested I take the second follow-up statistics course, which was a series of short mini-courses including regression, analysis of variance, and experimental design. As I learned more about statistics in those mini-courses, I began to realize statistics could be applied in just about any discipline and I would not need to “pick” one subject area, but could work in a variety of subject areas. I was hooked.

I decided to apply for graduate school in statistics. I ended up going to Colorado State University. So, I guess the statistics faculty at Montana State University in 1980 inspired, or maybe pushed, me toward a career in statistics, which, by the way, has turned out very well for me.

Reflecting on your career, what is the most important lesson you’ve learned?

It’s hard to pick the most important lesson. Today, when I reflect on this question, I think being open to new ideas and ways of doing things is one of the most important lessons I’ve learned. This may seem intuitive to a student, but as one continues in their career, it is important to remain a “life-long learner.” Technology is a good example because it continues to rapidly change, which affords those of us in statistics with a continually expanding array of statistical methods and computational abilities. Another aspect of this is to be open to taking on new assignments or trying new things that require you to learn new subject areas. Keep a fresh perspective.

Looking to the future, what are you most excited about?

There are many exciting changes occurring in the realm of statistics and data. I think there is a need for sound statistical problem solving now more than ever. If we aren’t there now, we are on the cusp of being able to more easily handle and link larger and more disparate data sets, which I expect will allow us to better understand some of the more complex issues facing our society. I see this occurring in several areas, but it is definitely true in my current area of trying to report on and understand crime and victimization and their correlates. For example, we can now think about linking wage and earning data with police bookings with transportation data to better understand the relationship among access to public transportation, job potential, and criminal arrests. These are exciting times as the data landscape and our abilities to analyze expand.

How many female colleagues do you have, and what is your relationship with them like?

This is an interesting question. One of the reasons I joined and stayed in the federal statistical system was that, when I was first looking for employment, there were several women in leadership roles, including the commissioner of the Bureau of Labor Statistics, Janet Norwood, and the chief statistician of the United States, Katherine Wallman. In addition, there was and remains a large proportion of women in statistics. That said, I find that working in groups that are more evenly comprised of men and women usually are more productive and inclusive than groups that aren’t.

Currently, about 60% of my staff are women, but only four of 13 (31%) statistical agency heads are women. I have had three excellent bosses in my career; two were men and one was a woman. All three were great mentors and all three helped me in my career. Throughout my career, I don’t feel my relationship with female colleagues was necessarily any different from those with my male colleagues. I try to work collegially with all my colleagues. However, as I get older, I think we should all be more supportive of each other. I see it is important to have both role models and mentors.

What advice would you offer a female undergraduate statistics major?

I would say, “Stick with it!” There are great opportunities to work in a variety of subject areas. I would also say to look for employment in different sectors—including private industry, nonprofits, or government—in addition to academia. There are many interesting places to work and many challenging problems to help solve. Your statistical skills should provide you with a good foundation in whatever future endeavors you undertake.

What will be the focus of your talk?

I plan to talk a little bit about my current position as acting director of the Bureau of Justice Statistics, including our role as a federal statistical agency and the types of data we collect. I thought I would talk a bit about my career path. I am looking forward to the conference and to meeting many of the attendees.

Bonnie Ray

What/Who inspired you to be a statistician?

I always enjoyed mathematics growing up. It was so orderly and logical. My father encouraged me to consider becoming an engineer, but I had this idea that engineering required building things. Since I’ve never been great at working with my hands, I resisted engineering and instead majored in math. But my desire to “put things in order” to solve practical problems led me to study statistics in graduate school, after being introduced to the field through a basic statistics and a linear models class as an undergraduate.

Reflecting on your career, what is the most important lesson you’ve learned?

One important lesson I’ve learned, or rather continue to learn and remind myself of every day, is that I need to pursue opportunities that are right for me and my family, not necessarily opportunities that the external community will view as career advancement.

Looking to the future, what are you most excited about?

There are so many opportunities today for statisticians and data scientists to make an impact on critical issues facing our world, whether that’s analyzing sensor data to enable better management of energy needs, identifying optimal care pathways based on EMRs and/or insurance claims to reduce health care costs and improve patient outcomes, or understanding patterns of learning to provide educational instruction personalized for an individual’s learning style. However, one area that particularly excites me is using data science to improve mental health and well-being. Having a child who struggles with emotional and psychiatric issues and seeing what constitutes the “state-of-the-art” in terms of diagnosis and treatment has made me excited to see how new data sources and methods of statistical analysis can serve to push the boundaries with respect to identification and management of mental health.

How many female colleagues do you have, and what is your relationship with them like?

I’ve had a number of female colleagues, collaborators, and mentors over the course of my career who have been instrumental to my growth as a statistician and a leader. My working relationships with my female colleagues tend to develop into close and supportive friendships more often than not, although I will admit to a couple of instances in which some competitive behavior came into play. I attribute that more to a continuing under-representation of women in technical fields and a perception of limited opportunities for advancement, rather than any inherent female competitiveness. However, we need to remember that collaboration and team work strengthen all participants, rather than diminish the efforts of some individuals.

What advice would you offer a female undergraduate statistics major?

Make sure you take enough computer science courses to enable you to hold your own in a technical environment (which is almost every environment these days). While you don’t need to be a full stack developer, you do want to know enough not to feel intimidated when asked to collaborate with your (typically male) software engineering colleagues to deploy your great statistical modeling work in a production environment.

What will be the focus of your talk?

My talk will focus on how to evaluate a job opportunity thoroughly to make sure it’s right for you, rather than spending all of your energy trying to ensure an employer believes you are right for them. 

Susmita Datta

What/Who inspired you to be a statistician?

I really like this question. I was a physics major in my undergraduate. As you know, physics is a subject that deals with deterministic ways of understanding the laws of nature. However, in many of our undergraduate laboratory experiments, although the same experimental setup was used by all the students, the ultimate results never came out to be exactly the same. In those days, we tried to back calculate and somehow tried to make the result as close to what is already known to be the exact answer.

Later in my life, as a master’s student at Michigan State University, I took a graduate-level course in probability and was also introduced to the concept of a probability distribution. That concept of distribution was really a fascinating concept for me. I started thinking that all the results from those frustrating undergraduate lab classes actually came from a distribution. They really portrayed the subject-to-subject variability. To make the long story short, that class taught by a well-known professor of Indian origin really influenced me to consider statistics and probability as a potential subject for furthering my education. So, when there was a need for me to change the institution due to family consideration, I went for an MS in statistics at the University of Georgia.

However, my mind was already shaped somewhat in experimental sciences. As a result, during my PhD, I chose an adviser who was both in statistics and genetics. My adviser gave me a problem in theoretical population biology involving experiments on D. melanogaster, one of the most-studied organisms in biological research, particularly in genetics and developmental biology. This problem remained unsolved by none other than R. L. Fisher. It involved discrete time stochastic processes. I feel very lucky to have been exposed to such a fascinating research area of statistical genetics and genomics. I am also happy that I understood the scientific usefulness for the most annoying species—the fruit fly (D. melanogaster). This used to create havoc in mango-eating season in India in our childhood. 

Reflecting on your career, what is the most important lesson you’ve learned?

Unlike many other statisticians, I had the opportunities and interests to learn many interdisciplinary fields such as genomics, proteomics, metabolomics, and lipidomics. However, every such data platform has their uniqueness. Statistical modeling to uncover the useful information from the data, such as biomarker discovery aiding precision medicine and individualized medicine could be a daunting task. It not only requires one’s persistence, frustration tolerance, and risk-taking capacity, but also demands the desire to learn something new very quickly. I have trained myself to learn all these -omics platforms very fast, and it made me a fast learner overall.

Tenure-track academicians like me are involved in multi-track activities such as teaching, research, and service. To be successful, one needs to excel in all of them. Time management and prioritizing your efforts cannot be taken lightly. It is an art, and it is ever changing.

A good conducive academic environment is also needed for one to be successful. In adverse environments, it takes double the effort for an individual to be successful in his/her profession. Additionally, one cannot deny the fact that women researchers face additional biases in their work environment. Unfair things do happen, and one needs to pick their battles wisely.

One of the most important lessons I have learned to deal with such adversities is that one needs to appreciate her/his own self. Secondly, one needs to stay focused on his/her own goal. Third, one needs to understand the nature of every opportunity he/she gets in life. Once the opportunities match with our own goal, then one needs to really work hard to get the full benefit of that. However, if it doesn’t match, then one needs to learn “how to say no.” The last point is difficult to practice, but it is absolutely needed for long-term sustenance in one’s career.

Looking to the future, what are you most excited about?

I am dedicated to statistical research related to -omics data. There are multiple platforms of -omics data such as microarray, NGS, mass spectrometry, and NMR. They each have their own complex data structures. Developing novel statistical techniques to analyze and integrate such high-dimensional, voluminous biomedical Big Data; interpret them, and merge them with other clinical parameters for disease prediction, prevention, and treatment is extremely challenging, but fulfilling. The storage and computer memory demands for playing with such data demands additional skills. I am excited to keep learning those skills. I want to help others learn this fascinating area of -omics research. I have recently published a book, Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry. I hope to publish many such books in the near future.

Our data analysis laboratory, “Dattalab,” including our students, postdocs, and faculty collaborators is now fully functional at the University of Florida. The lab members, with our guidance, are capable of analyzing genomics, proteomics, metabolomics, and lipidomics data using a high-performance cluster computer called HyPerGator. HyPerGator is one of the best in the nation. We are extremely hopeful that, in the near future, we will develop much useful statistical software that will be efficient enough to use in the translational setup. I am also satisfied to attract female PhD students who are joining the workforce with much-needed research skills in today’s world. My earlier female master’s and PhD students are all happily employed and maintaining their family lives, and that makes me hopeful for an even better future.

How many female colleagues do you have, and what is your relationship with them like?

We have three more female full professors besides me and three associate professors. One female assistant professor will be joining in the fall of 2017. We get along very well. We always crave for each other’s company. However, all of us are extremely busy with our own professional activities and family demands. We do not get much time to socialize outside the workplace. However, we went to the March of Science in Gainesville together.

What advice would you offer a female undergraduate statistics major?

Some of the advice I provide is really applicable to both male and female students in the statistics undergraduate program. I believe this is a particularly fascinating time for undergraduate students choosing statistics as their major or minor. Statistics is applicable in each and every scientific discipline, especially the use of statistics in -omics research and the fields aligned with the Big Data initiative. Naturally, the scope of finding a job is much higher than before. I would strongly suggest that undergraduate students be hopeful and confident about their future. As the undergraduate degree is the earliest building block of an advanced-level statistics degree or participation in the workforce, it is crucial to take it seriously. Although getting a good grade is important, it is more important to have a good understanding of probability and statistical inference and, above all, develop computational skills dealing with Big Data.

Working on perfecting your calculus and computational skills goes a long way. Additionally, it is helpful to identify another area of interest to which you can apply statistics. Making yourself conversant with the basics of that field will definitely empower you.

It is imperative that female students recognize gender biases still exist in the workplace and address those in a candid manner. If you suffer from such experiences, you must talk to the college counselor, your peers, and your mentors and deal with it. However, you must not let those biases keep you from pursuing your goals. You can still navigate through with your focus on the career goal. Please remember that you not only have the responsibility of making yourself successful, but you serve as a role model for many female students. Prepare yourself to be academically strong and that will make you a confident person. Additionally, going for an advanced degree of at least an MS will help you draw a much higher salary and make you more competitive in the profession.

What will be the focus of your talk?

Although I have not made up my mind 100% regarding what I will talk about at the conference, I think I will focus on my personal career as an example of how I have navigated through the complexities of professional life being an immigrant woman with a two-body problem and a family life. I wrote an article in the April issue of CHANCE magazine titled “Advancing Omics Data Analysis: A Call for Participation by a Statistician in the Field.” I will talk about my contribution to -omics research and how it helped shape my career. I will also urge professional women to be kinder to their own and walk the walk opposed to talking the talk. I believe we can make a difference in the lives of other women if we try hard in our everyday lives. A small move goes a long way.

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