The importance and excitement of team science — and how optimization research fits in almost everywhere

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Juliane Mueller
© 2010 The Regents of the University of California,
through the Lawrence Berkeley National Laboratory

I am currently a research scientist in the Computational Research Division at Lawrence Berkeley National Lab. I got promoted into this position very recently. Before, I was one of the Luis W. Alvarez Fellows in Computing Sciences at Berkeley Lab. 

My education (MSc and PhD) was in applied mathematics. Early on during my Master’s education, I got interested in optimization and I enjoyed learning about different mathematical algorithms that were developed for solving application problems to optimality. The applicability and in particular the usefulness of mathematical algorithms for improving life and solving real life problems intrigued me. After my PhD, I went to Cornell University as a postdoc where I was introduced to optimization applications related to climate model development and the environment. Through my advisor, I learned about the different National Labs and their named postdoctoral fellowships which basically allow a recipient to develop their own research agenda. I applied for and accepted the Alvarez Fellowship at Berkeley Lab. In contrast to other National Labs, Berkeley Lab did not have an optimization research group. Some might consider this as a disadvantage because there is no senior person to give me guidance and feedback on my work. However, the non-existence of an optimization group also poses an opportunity. It means that there are many domain scientists who may have difficult unsolved optimization problems that they may not know how to tackle.  This means a lot of collaboration possibilities, and eventually, with some momentum and a lot of effort, even the possibility to establish an optimization group at Berkeley Lab. 

In contrast to the more academic setting that I had so far been exposed to during my education, Berkeley Lab offered a completely new setting of interdisciplinary work and opportunities to collaborate with domain scientists from all science areas. I reached out to many scientists to discuss about their work and to see if they run into optimization problems that they do not know how to address efficiently. Sure enough, I found many takers. Collaborating with domain scientists and the breadth of application problems I get to work on are the most interesting part of my work. I constantly get to learn about new science areas, I learn new terminology, and I encounter new classes of unsolved optimization problems. Throughout the three years I have spent at Berkeley Lab so far, I have worked together with scientists in climate research, combustion, cosmology, spectroscopy, light source lattice design, and plasma accelerator design. 

I mostly collaborate with scientists who develop simulation models to study physical phenomena. Although the scientists understand the physics extremely well and are able to model the physics with high accuracy, most simulation models have parameters that must be adjusted. This is often done based on the scientists’ knowledge and experience, which is a valid approach for some science areas. But in other areas, an efficient approach for adjusting the simulation model parameters is needed and sophisticated optimization algorithms (and thus my work) enter the game. The challenge lies often first in learning enough about the domain scientists’ work to understand the goals of the research. The next challenge is in formulating a sound optimization problem, and then finally developing new solution methods. The most rewarding part of my work is the excitement of the domain scientists as they are able to use a new tool to solve their problems more efficiently (I make my codes publicly available), as they see completely unexpected solutions that they would have otherwise never expected (which often uncovers characteristics of the problem that were not expected), and as their simulations now make better predictions and allow scientific results to be found more efficiently. 

The reason why I decided to stay at Berkeley Lab beyond my postdoc are the exciting and relevant problems I can collaborate on with other scientists. I constantly learn new things and most importantly, other scientists are completely open to explore new ideas, and they welcome the opportunities to learn about new methods. I have not met a single scientist who wouldn’t agree on a meeting to talk about collaboration possibilities. Obviously, being willing to reach out to and collaborate with other domain scientists is a necessity at any National Lab. Research is not done all by yourself, at least not if your goal is to do more than just publishing. You have to be willing to sometimes go out of your way (meaning your comfort zone of research topics) to explore new ideas. You have to be willing to invest a bit of your time in running some initial optimization trials to see if there is actually any hope at all for the domain scientist’s problem. But this also means that you can use these preliminary results as a starting point for a grant application. 

The soft funding situation (all of your salary comes from projects with finite duration) may make some people anxious at times. Somewhere in the back of your head, there will always be this question of whether or not you will have money next year. That’s a thought one has to be willing to live with, but then again, in today’s world, nothing is certain and I don’t feel too stressed about this (yet). But as mentioned, you need to be willing to approach other scientists, especially if you are a bit junior and not that well known around the lab. I started doing this early on during my postdoc already. Even if not every discussion leads to a project, at least people will know what you are working on and they will come back to you in the future if they encounter a problem they know you can help with. For this, I find it extremely important to be able to talk about my work in layman’s terms since not everyone has a thorough mathematics education. 

My advice for anyone who is looking for a successful career at Berkeley Lab is that you have to be able to work independently as much as collaboratively. You have to be able to come up with novel ideas to solve problems. At the same time, the concept of team science as introduced by E.O. Lawrence in the 1930s remains an integral part of the Lab’s efforts today, and the success of collaborating teams with mixed skills and diverse backgrounds has proven to be the best way to tackle the most complex science problems. Therefore, it is important that you stay curious, that you are open to new ideas, that you are willing to step out of your research comfort zone to learn new things and explore new science areas. The lab setting gives you the opportunity to grow. Take the chance! You don’t have to know all the details of everyone’s research, but keep on learning, keep asking questions in meetings — people are more than happy to explain their research to you. If you are still in college, attend some introductory lectures on topics that are outside your area — engineering, economics, programming. I found that the classes I took on intercultural communication were extremely valuable. National labs attract researchers from all over the world and you will end up in a very diverse setting. Having some kind of an idea how to navigate this setting effectively is extremely helpful. Lastly, be open to talk to people, volunteer to help at outreach and other lab events. It takes some effort, especially if you are more on the shy side, but it pays off, I promise!

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My BIG Math Experience: A Java Web Development Boot Camp

Joyce-YangJoyce C. Yang

This summer, Codework Academy at Montgomery College had its first Java Web Development boot camp.  The program was in Gaithersburg, MD and taught students how to write and deploy web applications in eight weeks.  I participated in the boot camp, which was full-time, 9 am to 5 pm every day.  Starting from object-oriented programming fundamentals, I learned how to think like a programmer. The main things I gained from the camp were the programming skills and the professional network.

bootcamp

One of the model-view-controller (MVC) projects that our team worked on: a boot camp finder that that enabled users to search for boot camps from a database, apply to a camp as a student, and accept applicants as an administrator. Top: a preliminary version of the code for the boot camp model.  Bottom: the final version on the live site

Programming Skills

Until the boot camp, I did not have experience in Java or C.  While looking for employment opportunities, I examined software engineering job listings and they generally required those languages.  Since I had had experience in Python, R, Matlab, and Visual Basic, I was familiar with programming fundamentals.   The Java boot camp was a good way to learn new programming concepts that were relevant and apply them immediately.

Some of the skills I learned

  • Using relational database management systems—we used MySQL and PostgreSQL
  • Using the concept of encapsulation for data hiding
  • Making “Input–Processing- Output” (IPO) diagrams
  • Developing the model, view, and controller of an application
  • Using an application framework (Spring) to streamline the development process
  • Deploying applications to a cloud service (Heroku)

 

Building networks

I learned a lot outside the classroom by talking to others, and I expanded my professional network.  One graduate student had switched majors from chemical engineering to computer science, and they helped me decide to learn more about careers in web development.  Another student was considering applying to a four-year college, and in my capacity as a college graduate, I offered some advice.  The Montgomery College web development boot camp was supported by a grant.  As a result, it was completely free, and people who were underemployed and unemployed could attend! Students were constantly talking about new ways of solving problems, and the environment was collaborative.

The boot camp was quite challenging, and students needed to meet strict requirements.  The program’s aim was to make assignments as close to “real life” as possible.  Each day at camp consisted of testing code, determining new issues to fix, and fixing them.  One of the main differences between web development and math is that web development does not usually have well-posed problems.  There can be times when the problem is not clear.  I was prepared for the boot camp, but I wish that, before I started, I had learned a bit more object-oriented programming.   Overall, I gained software skills and a great professional network from this Java web development boot camp.

 

About the author: Joyce C. Yang graduated from Harvey Mudd College in December 2016 with a Bachelor’s degree in Mathematics. An experienced K-12 teacher, she has also worked on research problems in graph theory, statistics, and abstract algebra.  Currently living in the DC area, she is looking for employment opportunities. Joyce can be reached at jcyang@hmc.edu

New Career Brochure from the Society for Industrial and Applied Mathematics (SIAM)

The 2017 edition of SIAM’s careers brochure, Careers in Applied Mathematics: Options for STEM Majors , is now available and is a great resource for anyone wondering what they can do with math.

Available in both print and PDF format, this full-color, 32-page publication contains personal stories and advice—as well as salary and job skills information—from 27 women and men working in a variety of capacities at companies ranging from industry giants to small start-ups, research labs, and non-profits.

View, download, and order print copies of the brochure here.

This brochure is a long standing and important resource for the SIAM community—present and future—and SIAM is proud to make it available to the broad public. If you would like to give feedback on the SIAM careers brochure, please send your thoughts to leblanc@siam.org.

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.

 

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

Lucas_Sabalka

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

Free AAAS Career Webinar

AAAS Career Development Center logo

Webcast: Transitioning into a Non-academic Career
Tuesday, June 2012:00-1:00 p.m. EDTRegister now

This workshop explores the skills and best practices for transitioning from an academic environment to one of many non-academic career paths. It introduces strategies for career planning, emphasizing an ongoing process for professional development throughout your career.

Join us for this FREE webcast!


Presenter: Josh Henkin, PhD – Founder, STEM Career Services, LLC

Josh Henkin
Josh is the founder of STEM Career Services, a career coaching company aimed at helping STEM graduates launch and sustain careers outside of academia. He conducts workshops at conferences, universities and institutes across the country and provides career coaching to STEM graduates at all levels of their careers. Josh sits on the National Postdoctoral Association Board of Directors. He is also an AAAS Science and Technology Policy Fellow Alum, AAAS member, and is an AAAS Career Development Center subject matter expert.

Agenda:

  • Being strategic in your career planning
  • What skills you need for non-academic jobs and how to acquire these skills while still in the lab
  • Networking as a part of life
  • Crafting your “elevator pitch”
  • How to create a master resume (inclusive of all your skills)
  • Creating position-specific resumes
  • Ample time will be provided for Q&A with Josh

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.