Blogpost: Parsa Bakhtary

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It is humbling to address future and current mathematicians, but as a former algebraic geometer myself, I will do my best to share with you my story. I work as a data scientist, which the Harvard Business Review in 2012 dubbed “the sexiest job of the 21st century,” at Facebook, which has been ranked by Glassdoor as one of the best companies for which to work. The path that led me from an eager math student who despised applications to where I am today has been a strange one, but the lessons I learned in my undergraduate and graduate math classes have had a profound impact on my ability to analyze concrete problems in industry.

After earning a B.S. in mathematics at UC Davis, I took a year off in which I decided to pursue a graduate education in the same subject. Seven years later, I finally received my doctorate from Purdue University, having written a thesis in the subject of algebraic geometry, and I was eager to take the path which would lead me towards a professorship somewhere. Unfortunately, I was unable to find a post doc in my home country of the US, so I took a position in Saudi Arabia at King Fahd University of Petroleum & Minerals, teaching calculus to aspiring petroleum engineers and occasionally publishing a paper. After three years there, I missed California and returned unemployed in the summer of 2012.

I quickly realized the job market for math professors wasn’t promising at the time, so I started looking for industry positions that would be suitable for someone with my background. After extensive Googling, I realized “data scientist” sounded like something I could do. I taught myself some Python and SQL, practiced analyzing and visualizing publicly available data sets in R and Excel, then started applying. After six months of unemployment, I caught a break and was offered a position at a startup in Chicago. The rest, as they say, is history.

My job at Facebook is unique in its flexibility and often quite challenging, though perhaps not in the same way as algebraic geometry. I have worked on game ranking, platform ecosystem health, comment ranking, celebrity usage patterns on Instagram, and discussion of TV show content on Facebook. I was lucky to be the first data scientist on Facebook Live when it launched, and our team helped grow it into one of the biggest live-streaming platforms in the world. The problems I work to solve can either be very technical, involving complex modeling and simulation, or it can be investigatory, requiring me to search for an explanation of an unusual phenomenon, or it can even be exploratory, such as trying to answer vague questions like “What makes a mobile game fun?”

The analytical training that we mathematicians receive put us at a unique advantage in the field of data science. The rigor we’re accustomed to help us break down a general question into concrete analytical pieces which we can answer with data. It is easy for us to spot errors in thinking, or situations where the evidence doesn’t actually answer the question. After learning some basic statistics and the familiarity with an analytical data manipulation environment (e.g. R or Excel), any mathematician can rapidly become a data scientist. The field of data science is also vast, as one can focus on subfields such as product analytics, visualization, or machine learning.

The biggest misconception people have about data science is that they think we all know how to program and have spent many years writing code. While some familiarity with SQL and analytical software is often desired, we are not programmers. We are, if anything, the voice of evidence at a company. We are there to help shape our colleagues’ understanding and intuition based on the data that we see, and to give actionable recommendations that will improve existing products and help define the appropriate strategies. It’s a fun job, and a great option for all mathematicians interested in industry.

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Opportunity: 33rd Annual Mathematical Problems in Industry (MPI) Workshop

Registration is now open for the 33rd Annual Mathematical Problems in Industry (MPI) Workshop, to be held June 19-23, 2017 at New Jersey Institute of Technology in Newark, NJ. The Department of Mathematical Sciences at NJIT is hosting the meeting, with Linda Cummings and Richard Moore acting as local organizers. Funding is provided by our industrial participants and the National Science Foundation.

The format of MPI 2017 will be familiar to those of you who have attended MPI or a similar week-long study group in the past. On Monday, several industrial participants present their research problems to an assembled group of professors, postdocs and graduate students working in the field of applied mathematics. These presentations are followed by break-out sessions, where teams form to work on the problems throughout the week. The week culminates in presentations delivered Friday to the assembled group of industrial
participants and applied mathematicians. A follow-up report is delivered to each industrial participant in the weeks following MPI. These reports are often modified and submitted for publication in peer-reviewed journals, and many past MPI workshops have produced fruitful long-term collaborations.

To learn more about MPI 2017 and prior workshops, please visit the workshop website:

http://web.njit.edu/~rmoore/MPI2017/

A link on the left menubar will direct you to the online registration form. Spaces and funding are limited, so please register as early as possible. Young researchers and those with prior experience at MPI or the GSMMC (see below) are especially encouraged to apply, as are members of groups traditionally underrepresented in applied mathematics.

Graduate students who have not already done so in a previous year are strongly encouraged to participate in the Graduate Student Mathematical Modeling Camp (GSMMC), held at Rensselaer Polytechnic Institute the week immediately preceding MPI. You will automatically be registered for MPI as a Camp attendee. Please follow the following link to register for the GSMMC:

http://homepages.rpi.edu/~schwed/Workshop/GSMMCamp2017/home.html

Although some of the industrial problems have already been selected, we are still
accepting applications to participate as problem-presenters. Please forward this email to industrial contacts who might be interested in exposing their research problems to a large body of creative problem-solvers with broad expertise in industrial applied math.

Looking forward to seeing you at MPI 2017!

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Blogpost: What are the obstacles to Math students entering BIG careers?

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by Dr. William D. Stone, Dean of Arts & Sciences and Professor of Mathematics, New Mexico Tech Mathematics Department

A strong Mathematical background is an excellent preparation for many exciting careers in business, industry, and government. So why don’t more of our students think in terms of these careers? I see two reasons.

The first reason I see, is that many faculty feel uncomfortable advising students into these paths, since they don’t have much experience with industry. Most of us went from college, to graduate school, to a faculty position. We don’t know that much about what a Mathematician does in a BIG career.

This is not an insurmountable problem. Do you have former students who have gone to industry jobs? Invite them back to talk to your Math Club. Or contact a BIG-SIGMAA member in your section and invite them to talk about what they do. Some of your students might get excited. Some students who may not have considered a math major, since they didn’t see career paths other than teaching, may now think about joining your department.

Another obstacle can be faculty attitude. If we think of it as a failure when one of our graduate students goes into a non-academic career, that attitude is conveyed even if we don’t say it directly.

To me, this attitude is short-sighted. Many students want to work on real, applied problems. We should be welcoming them into Mathematics, and helping them on their path. The more that scientists and engineers see the value of a mathematician on their research teams, the better for our profession. When we have students out in industry, we may find ourselves being drawn into some very interesting problems, with genuine consequences. It’s a win-win all around!

5 Things I Learned About Working at a National Lab

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After completing his Ph.D. in mathematics from the University of Florida, William Severa moved to the southwest to join Sandia National Laboratories as a full time researcher in the data-driven and neural computing department.

Pictured above: Sandia’s Z machine is the world’s most powerful and efficient laboratory radiation source.

1. It isn’t all cloak and dagger.

Yes, Sandia National Laboratories certainly works with sensitive and classified information—though what I learned is there’s a sizeable chunk of national labs’ work that is entirely unclassified and in-the-open. As a researcher, I continue to publish my work, and I can still discuss my research at conferences or meetings. More than that, there’s plenty of internal support to determine just what is sensitive and what isn’t, so you always know what you can or cannot say.

2. I still get to research cool ideas.

One of my worries about leaving academics was potentially losing research freedom. However, it turns out I’m afforded quite a bit of flexibility here. We are encouraged to pursue grants from a number of external funding agencies, and the Department of Energy has its own congressionally-authorized internal research funding called Lab Directed Research and Development (LDRD). These projects range in duration and scale, and the process provides a great mechanism to propose my own research ideas. LDRD projects are focused on high-risk, high-reward research, so they’re always up for the next great idea.

3. It’s an engaging interdisciplinary effort.

Every day I go to work with an incredibly diverse team. Since departments are centered on topics rather than degrees, we have a truly interdisciplinary effort. My co-workers’ backgrounds range from psychology and neuroscience to climate engineering and computer science (and mathematics!). Together we each use our expertise to contribute to a unified solution.

4. ‘Go ahead; Stretch out and try new things.’

I’m the type of person who is always excited to learn new things or apply what I know to new problems. However, as a pure mathematician leaving graduate school, I found it difficult to expand from my core expertise. At a national lab, I am constantly encouraged to approach new challenges. Some are close to my expertise, and some are a little farther. Either way this freedom lets me push my work into different and exciting directions.

5. They give us our breathing room.

Project timelines are on the order of years, not months. As such, we have the time to do basic research, not just push out a product. The exact schedule is, of course, dependent on the program. In my experience, the schedules have always been accommodating.

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Sandia National Laboratories is a multi-mission laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under Contract DE-AC04-94AL85000.

Opportunity: Machine Learning Workshop

Fundamentals of Machine Learning Workshop at Stanford University

March 31, 2017


Discover the basics behind the application of modern machine learning algorithms. The workshop instructors will discuss a framework for reasoning about when to apply various machine learning techniques, emphasizing questions of over-­fitting/under-­fitting, regularization, interpretability, supervised/unsupervised methods, and handling of missing data.  

For more information, visit the Stanford ICME website: https://icme.stanford.edu/events/fundamentals-machine-learning-workshop

Attendees should have undergraduate-­level knowledge of linear algebra and statistics, and basic programming experience (R/Matlab/Python). Please note that this is not a Stanford for-credit course.

Space is limited, so register today.


 

Thanks to Judy Logan from the Institute for Computational and Mathematical Engineering (ICME) at Stanford University and the Women in Data Science (WiDS) Conference for this post.

Opportunity: Roundtable on data science post-secondary education

Webcast on March 20: Meeting #2 of the Roundtable on Data Science Post-Secondary Education

The National Academies of Sciences, Engineering, and Medicine invite you to attend a one-day webcast on March 20 from 9am-4pm PST on data science post-secondary education. This meeting will bring together data scientists and educators to discuss how to define and strengthen existing data science programs and how to best engage and retain data science students. For more information, visit the event website or download the preliminary program.

During the event, we encourage webcast participants to send questions for the speakers to Ben Wender at bwender@nas.edu, who will read them out if time permits.