NSF-IPAM Workshop – 2017 UPDATE

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SUMMARY RECOMMENDATIONS
NSF-IPAM Mathematical Sciences Internship Workshop

Full report available here.

 (UPDATES IN BLUE ITALICS)

The report above reflects discussions and recommendation from the September 1-2, 2015 NSF-IPAM Mathematical Sciences Internship Workshop held at the Institute for Pure and Applied Mathematics (IPAM) at UCLA. The workshop was organized by Russel Caflisch, Mathematics, UCLA; Alan Lee, VP of Engineering, Advanced Micro Devices (AMD); Rachel Levy, Mathematics, Harvey Mudd College (facilitator) and James L Rosenberger, Statistics, Penn State. The diverse group of participants brought perspectives from academic (college/university, public/private), business (large/small) and governmental institutions as well as many areas of the mathematical sciences.

The goal of the two-day workshop was to discuss recommendations for infrastructure and programs.

The BIG Math Network has adopted the goals of the workshop to:

  • increase the number of internships targeting mathematical sciences students
  • open the internship pipeline to a diverse group of students
  • provide assistance with timing and logistics for undergraduates, graduate students and postdocs in pure and applied mathematics
  • provide training to prepare mathematical sciences students for internships
  • develop viable models of how internships best work for mathematical sciences students, postdocs and faculty and for industry/government

During the workshop participants spent two sessions in one of the following working groups: support, training, logistics, recruiting, culture. They also rotated to two other groups, participated in a charrette to respond to general questions, and provided comments in several all-group sessions. With the intentional overlap between topics and exchange between members of different groups, many ideas arose which resonated across the groups. This report represents central ideas that had strong support, as well as questions and considerations raised by the participants.

The following recommendations resonated across the working groups on support, training, logistics, recruiting, and culture. A target of 1000 graduate internships per year was suggested to meet the demand for internships arising from the strong production of Mathematics PhDs, and the large numbers of students pursuing BIG (Business, Industry, Government) careers after the undergraduate and Master’s levels. The recommendations are related as a distributed network, with different goals at each level.

Distributed Network Internship Initiative

National level: Create a national network to increase internship information exchange, data collection, access and opportunities

  • Design and implement a data-gathering project to inform a picture of the mathematical sciences internship landscape and provide baseline data for new initiatives.  UPDATE:  Efforts in this area are underway by the CBMS Research Advisory Group and AMS.
  • Provide communication and coordination of best practices, training materials and opportunities, models for local programs, and media to aid regional and local outreach efforts.  UPDATE:  The BIG Math Network is serving this purpose.
  • Build a national network of individuals, companies, government labs, academic institutions, math societies and mathematical sciences institutes to exchange information and work together to increase and advertise internship opportunities.  UPDATE:  The BIG Math Network is serving this purpose.
  • Develop funding mechanisms and pursue funding for mathematical sciences internship stipends (seed money), internship training and internship development.  UPDATE:  The NSF has started an internship program in collaboration with the national labs.  Other funding pathways are primarily through regular hiring mechanisms or departmental arrangements.

Regional level: Establish regional internship centers to build internship contacts and organize training opportunities  UPDATE: BIG Math Network is seeking funding for a new Northeastern US hub based on existing efforts at the University of Illinois, Urbana-Champaign.

  • Build internship contacts and opportunities in the region
  • Offer centralized training (that could be replicated locally), such as short courses in programming, soft skills and data.
  • Hire internship development staff to serve as liaisons between local institutions and potential internship sites and to promote mathematical sciences internships in BIG by communicating how mathematical sciences students make contributions.

Local academic level: Encourage and enable student participation in internships in mathematical sciences departments. UPDATE:  BIG Math Network is running workshops that help departmental leaders identify and prioritize new initiatives.  First workshop was held at a Spring 2017 TPSE Chair+1 Meeting at UMBC.  Second workshop will be a Minitutorial at the 2017 SIAM Annual Meeting.  Contact us for assistance running a workshop in your department or region.

  • Encourage students to pursue training and internships.
  • Disseminate information from national and regional organizations.
  • Identify the department chair, director of graduate study, or an interested faculty member to build local institutional mechanisms for internships.

 

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Threefold advice: making the jump from geometric group theorist to computer vision specialist

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by Lucas Sabalka

I began my mathematical career as a research mathematician, but now I work in industry even though my degree is not in an applied area. With so few academic jobs available recently, transitioning to industry is becoming more common for mathematics PhDs. So to help any mathematicians thinking about that transition, let me tell you how I got where I am.

I had always planned on being a professor as I pursued my PhD. That’s what I became: after two postdocs and a decent rate of publication, I got a tenure-track position at a research university. A career in academia has significant pluses, including the promise of tenure and thinking about interesting problems all day. However, through the course of these positions, I gradually realized the impact of two important minuses of a career in academia. One is that, with academic jobs so few and far between, you typically do not get to choose where you live. My wife and I are from Nebraska, and wanted to end up close to family and friends. The second is that research is driven by self-motivation. That’s good for someone like me who is highly self-motivated, but it can also add undue stress: I was easily on-track for tenure, but found myself pushing hard to make a name for myself with little recognition.

The experience that changed my career path from academia to industry was a consultantship. A co-author and good friend of mine, Dr. Josh Brown-Kramer, was working as an applied mathematician at a start-up tech company in my home town called Ocuvera. I have an undergraduate degree in math, computer science, and history, and together with Josh, I had competed in and won a few programming contests back in the day. I had done very little programming in the intervening years, but I had enough knowledge to pick up coding quickly. Josh put in a good word for me, and got me a full-time consulting position one summer. That position turned out to be a good opportunity for the company to see that I was a good fit culturally and could contribute positively to their products, as well as a good opportunity for me to see what working in industry was like. A few months after my consultantship ended, the company extended me a full-time offer. It was a difficult decision to make, but the draw of moving back home and (what was for me) the lower stress of working in industry led my decision. I took the plunge and switched careers: from “mathematician” to “applied mathematician”.

That transition was anxiety-inducing. I had prepared for many years to be in academia. It had the promise of tenure, and it was familiar. Industry was scary: what if my company folded? How would I handle the different stresses? In retrospect, I should have had more confidence in myself. I now trust that I will be able to find another job if my current job were to disappear. The stressors are different, but overall my stress levels have decreased. I have more time for hobbies, including advocacy and volunteerism (I speak with elected officials and thought leaders about climate change and the transition to a clean energy economy).

My job is Computer Vision Specialist. I develop algorithms for computers, equipped with 3-dimensional cameras, to automatically monitor patients in hospital settings. If the algorithms detect risky behavior from the patient that could increase their risk of falling, they automatically alert hospital personnel to determine an appropriate course of action. Falls cost hospitals and patients billions of dollars per year and can result in death. Helping reduce fall risk and introducing automated monitoring should reduce health care costs as well as improve patient outcomes and save lives. It is rewarding to feel like this project could help improve people’s lives.

My dissertation was in geometric group theory, a topic at the intersection of algebra and topology. While my job does not call for geometric group theory or really any graduate-level mathematics, I do use undergraduate-level mathematics concepts extensively, including statistics, probability, calculus, Euclidean geometry, various computer science algorithms, and linear algebra. We use machine-learned algorithms and we also write computer vision algorithms by hand. Consider, for example, taking an array of points in 3-space representing a single camera frame from a video stream of a hospital room, and trying to identify exactly those points that represent a bed. What properties of a bed are important, and how do you quantify that in a way a computer could evaluate? Once you know where the bed is, which points in 3-space represent the patient, and which the nurse? How will you deal with noisy or missing data? I may not be using the tools of my specialization, but I am using the problem-solving skills that I developed while pursuing my degree. My degree is not applied, but having a PhD in mathematics in any subject shows that you’re good at problem solving.

My advice to mathematics PhD students considering industry for work is threefold. First, remember that your degree will mean you are a very good problem solver, and have confidence that there are companies that value your skills. Second, it’s a good idea to get some classes under your belt that could help you in your desired fields: computer programming, statistics, probability, finance, or any classes that could apply in industry. These classes aren’t necessary, but can distinguish you from other candidates and help prepare you for the transition. Third, if possible, I recommend finding an internship in the field you’re looking at. This will give you valuable experience, help you know what to expect, show you whether you’d like that industry job, and will help you on the job market. Even if you don’t take other classes or have an internship, companies provide new employees training for their new roles.

If you are faced with a career change and decide to leave academia, remember: a PhD shows you are a good learner and you have the problem-solving skills necessary to succeed in industry!

Contact: sabalka@gmail.com

Academia trained me for a BIG career

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by Peter D. Horn

I am honored to share some career advice with the young and mathematically-inclined. When I fit that description, I felt a lack of diversity in the opinions and advice I was hearing from my mentors. This wasn’t their fault, but mine. Classic case of selection bias, as I only sought advice from my professors.  My first recommendation is to connect with many math folks who have walked a variety of paths to get a sense of what is out there (reading the posts on this blog is a great first step!).

When I was finishing up my math major, I felt there was more math for me to learn, and I went on to get a PhD in low-dimensional topology. As a grad student, I was encouraged to pursue a postdoc. By the time I was deep into my postdoc, I had a tenure-track job in my sights. It wasn’t until my third year into a tenure-track position that I evaluated my career choice and realized I would be happier doing something else.

I reached out to a few friends from grad school who went into government and industry, as well as a couple former academics who transferred to tech and finance jobs.  I did a little research to see what was out there, and found “data science” to be a broad enough field to entertain my intellectual curiosities (e.g. machine learning algorithms) while providing plenty of job security (i.e. strong business demand).  Currently, I am a data scientist at the MITRE Corporation, a non-profit company that does R&D for many federal agencies.  I love working at MITRE because I get to define what type of data scientist I want to be.  In my first year, I worked on research projects involving machine learning and agent-based models to drive policy analysis, and I prototyped a web-based simulation tool to explore workforce strategies for the VA.  It’s great to be at a company where the work is challenging and impactful.

While in the transition to industry, I realized that much of my academic training and some of my hobbies positioned me to be an attractive candidate.  As a math major/PhD candidate/professor, I had accrued a ton of experience teaching myself complex, abstract concepts. Employers seek out job candidates who can demonstrate the ability to pick up new things quickly.  Working in help centers/recitations/lectures, I had accrued a ton of experience explaining deep, technical material to non-technical audiences.  Employers like to hire teachers because they can put you in front of customers or use you to mentor young staff.  As a mathematician, you have surely gained similar experience.  Find a way to brag about your superpowers!

You’re going to need programming skills.  In my journey, I was lucky to have learned to code.  In college, I learned a bit of Java in CS 101.  In grad school, the math department hired me by the hour to maintain their website.  I chose to write up my homework in LaTeX.  Frequently, I would need to do some computations in Mathematica, Maple, Matlab, or Sage.  As a postdoc, I got bored one summer and wrote a couple of card games in Objective-C.  For a research paper, I needed to diagonalize some matrices over a non-commutative base ring, and I wrote the code to do this from scratch in Python.  Before I had even heard of data science, I had ten programming/markup languages under my belt, and I put all of them on my resumé to show employers that I am comfortable writing code.  If you don’t have experience programming, I recommend you pick up Python. It’s a good general purpose language.  Pick a project and use Python to attack it (e.g. implement matrix multiplication from scratch).

The last piece of advice I have is to acquire domain knowledge and to network. The biggest hurdle I had in my journey was learning to communicate with potential employers.  I decided to take online courses in data analytics and machine learning, and these courses taught me what people in industry care about, how they talk, and what tools they use.  I also participated in some coding and data science competitions online.  Since I had a noticable lack of business experience, these competitions were something I could point to as proof that I could do data science.  I would also recommend attending meetups in your area. In my experience, meetup people are very friendly and helpful.

Transitioning out of academia was scary, but it has been one of my best decisions.  At first I was worried I wouldn’t be what employers were looking for, but I learned that many employers want to build companies with people from diverse backgrounds. Don’t worry about trying to fit the mold.  Reach out to friends, former classmates, and friends of friends, and you will find all the support you need.

Lost in translation: Academic work beyond academia

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In the airport again, in an #awkwardboardingselfie for #jmm2017

 

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Listening to interdisciplinary conversations as part of IUSE grant SUMMIT-P
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QUBESHub.org

 

Dr. Carrie Diaz Eaton, Unity College

I have a pretty unusual set of grants. The skill set for my grants is the same: working with a variety of people from a variety of different backgrounds and disciplines to advance quantitative skills. For one of these grants, QUBES (Quantitative Undergraduate Biology Education and Synthesis, qubeshub.org), I am the QUBES Consortium liaison. My job is to reach out to all sorts of partner organizations, institutions, professional societies and faculty members interested in improving the quantitative skills of all students in life science. This means that I help people make connections across disciplinary silos, travel to conferences, hold leaderships positions in interdisciplinary undergraduate mathematics education, help write collaborative grants, manage budgets, manage communications, and assist in forming strategic partnership agreements. It turns out that my dissertation research in systems theory paid off quite well, since it turns out that social change theory and systems theory are more related than one would think.

That seems like a pretty academic outreach job description, right? But you can get a lot of the same skills through leadership positions at your own university. This isn’t my first experience working across disciplines. I was a President of the Spanish Language Club in college, on the executive board of my Service Sorority, had interdisciplinary course training in biology (including ecology, wildlife, and marine science) and mathematics (including computing and statistics). In grad school, I participated in interdisciplinary university-wide teaching training and book discussions. As faculty at a small liberal arts school, I formed a college-wide teaching discussion group, advised and employed students from a variety of majors, and collaborated with faculty in different departments to improve writing and applications in my math courses. I have also served on several college-wide committees including the general education committee and an accreditation committee, which also has forced me to collaborate regularly with a diverse set of stakeholders.

So how do these academic skills translate beyond academia? Here are some keywords:

  • Non-profit development and partnerships,
  • Working with a diverse set of colleagues across the world,
  • Grant and report writing,
  • Statistics and big data trends,
  • Careers in environmental biology,
  • Mathematical modeling education, undergraduate biology education research (and pretty much everything about the guiding document in biology, Vision and Change),
  • Systems thinking for social movement, systems change theory,
  • Project evaluation,
  • Grant and project management, organizational planning and workflow, team leadership,
  • Social media marketing,
  • 101 tips for travel to anywhere from Bangor, Maine (okay, maybe this is less relevant for most jobs, but I’m a fountain of information about direct flight options from the airports in my state),
  • and more…. *Whew*.

Best learning on the job ever, but on the other hand when people wonder what I do on grant time….