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


NSF-IPAM Workshop – 2017 UPDATE


NSF-IPAM Mathematical Sciences Internship Workshop

Full report available here.


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


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!


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.


  • 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

Videos available from the IPAM National Meeting of Women in Financial Math

You can find the Videos and PDFs on the IPAM website.

Thursday, April 27, 2017

9:35 – 10:00
Tanya Beder (SBCC Group)

The Here-to-Stay Roles of Big Data and Machine Learning
PDF Presentation

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10:05 – 10:55
Karyn Williams (Two Sigma Investments)

Panel Discussion on Predictions for FinTech & Asset Management with Darcy Pauken and Anjun Zhou

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11:05 – 11:55
Monique Miller (Wilshire Funds Management)

Panel Discussion on Predictions for Portfolios and the Role of Robo Advisors with Cleo Chang, Tina Singh, and Jia Ye

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2:00 – 2:50
Rosemary Macedo (QS Investors)

Panel Discussion on The Outlook for Quantitative Investing with Cristina Polizu, Gita Rao, and Elizabeth Smith

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3:00 – 3:50
Tanya Beder (SBCC Group)

Panel Discussion on New Directions in Financial Mathematics –Risk/Algorithmic Trading/ETFs and Beyond with Natalia Bandera, Lisa Borland, and Dinah Chowayou

Friday, April 28, 2017

9:30 – 10:30
Xin Guo (University of California, Berkeley (UC Berkeley))

General Research Directions in Financial Mathematics
PDF Presentation

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11:00 – 11:25
11:30 – 11:55
Alexandra Chronopoulou (University of Illinois at Urbana-Champaign)

Recent Advances in Factional Stochastic Volatility Models
PDF Presentation

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12:00 – 12:50
Linda Kreitzman (University of California, Berkeley (UC Berkeley))

The Impact of Fintech and Data Science on Financial Institutions: The Need for New Skill sets.
PDF Presentation

2:00 – 2:25
Kim Weston (University of Texas at Austin)

Equilibrium with Transaction Costs
PDF Presentation

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2:30 – 2:55
Rohini Kumar (Wayne State University)

Portfolio optimization in a short time horizon

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3:10 – 3:35
Deniz Sezer (University of Calgary)

Illiquidity, Credit risk and Merton’s model
PDF Presentation

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3:40 – 4:05
Yuchong Zhang (Columbia University)

Optimal Reward and Mean Field Game of Racing
PDF Presentation

Why I attend Mathematical Problems in Industry (MPI) workshops


by Hangjie Ji, Duke University

I have found that it is very easy for graduate students to get immersed in their own filtered and narrowed research area without thinking about how to use their expertise in other applications. During my Ph.D. study in mathematics I was an active participant in several industrial workshops where graduate students have the opportunity to work in teams on real problems. In this post I will share my experiences at these workshops.

First of all, these workshops provide an industrial-like environment quite different from the usual academic setting. In the Mathematical Problems in Industry (MPI) series organized by Duke University, University of Delaware, NJIT and other institutes, engineers and scientists from industry introduce challenges that they face in business and the workshop participants form groups to tackle the problems. The initial proposed problems could be ill-posed in the mathematical sense, and require careful clarification and formulation to build workable models. During the weeklong workshop professors, postdocs, and graduate students work together, and usually spend at least the first two days on resolving the confusions of the problem setup and terminologies by communicating with the industrial participants. Once the mathematical model is established, various methods that we have learnt from our own research can then be applied to the problem, and the group members usually form smaller teams to tackle the problem from different perspectives. While at first it seems a little intimidating to speak up in front of a group of senior professors, observing the way people think and discuss is actually a very good learning experience for me, and junior mathematicians do contribute lots of fresh insights to the problems. At the end of the week, each team would present their results and it always amazes me how much work can be accomplished in a single week. One of my favorite moments of the final presentation is a display of Colemanballs quotes or bloopers that happen during the workshop like “F is so big it has to be zero!”


The other two modeling camps that I have participated in are designed for graduate student career development in interdisciplinary problem solving skills. At the Graduate Student Mathematical Modeling Camp (GSMMC) at Rensselaer Polytechnic Institute where graduate student teams get together to solve real problems that arise from industrial applications under the guidance of invited faculty mentors. These modeling camps are usually held one week prior to the MPI workshops, and part of the goal is to better prepare the grad students for the coming MPI workshop, as the weeklong collaboration with team members in a friendly and productive environment quickly adjusts us to more challenging projects at MPI. Another workshop that I participated in, the Industrial Mathematical and Statistical Modeling Workshop (IMSM) is sponsored by the Statistical and Applied Mathematical Science Institute (SAMSI) and NC State University and focuses more on data-driven industrial projects. In the IMSM workshop I worked with a group of seven graduate students from different universities on a project proposed by costal engineers from United States Army Corps of Engineers Costal Lab, and our goal was to estimate the underwater bathymetry in environmental flows. Using the measurement data from several sources and statistical learning methods, we successfully incorporated the data to a wave model that leads to reasonable estimates. In addition to the interesting results that we worked out for the projects, I am also really happy with the training that I received during the workshop on technological tools like Git repository and Python, handling unfiltered dirty data with simulations, and perhaps most importantly, scientific communication skills. For instance, learning how to speak with a costal engineer during the IMSM project turned out to be not so easy. Getting exposed to these skills, or at least being aware of the new tools people are using in industry, is very beneficial for PhD students who have interests in pursuing an industrial career after graduation. The projects during these workshops have always been nice talking points in my conversations with industrial people, and I believe that similar experiences could naturally contribute to a sparkling resume for potential employers as it shows interest and passion outside of one’s academic research work.

As a summary, my personal experience at these workshops has been consistently rewarding and enlightening. I have learnt a lot and have explored areas way beyond my expectation. As an extension of the MPI workshop, I worked on a side project in collaboration with Corning Inc. under the 2015 MPI Fellowship, and presented my results at the 2016 MPI workshop and in a research paper. Two years later, I am still in continuous contact with friends that I met during the first few workshops that I attended, and the connections that we built in those weeks were truly incredible and unforgettable. I am really grateful to the organizers and sponsors who made these workshops possible, and would like to encourage my fellow graduate students to grasp opportunities like these to explore the links between applied mathematics and problems of interest to industry, and to learn more about their own interests.


Photo:  Group work session

For more info:


Opportunity: NASA High Performance Fast Computing Challenge


Overview (from the site above)

Do you want to help aerospace engineers solve problems faster? Does the phrase “nonlinear partial differential equations used for unsteady computations” excite you? Do you want to try yourself with the complex computational software that NASA scientists use? This might be the challenge for you.

NASA’s Aeronautics Research Mission Directorate (ARMD) is responsible for developing technologies that will enable future aircraft to burn less fuel, generate fewer emissions and make less noise.  Every U.S. aircraft and U.S. air traffic control tower has NASA-developed technology on board. It’s why we like to say, NASA is with you when you fly!

We need to increase the speed of computations on the Pleiades supercomputer, specifically for computational fluid dynamics, by orders of magnitude, and could use your help!

This isn’t a quest for the faint of heart. As a participant, you’ll need to gain access to FUN3D software through an application process with the US Government.  Although this software usually runs on the Pleiades supercomputer, you can download and run it locally after applying HERE.



NASA’s Aeronautics Research Mission Directorate (ARMD) is tasked with innovating at the cutting edge of aerospace.  Their work includes Innovation in Commercial Supersonic Aircraft, Ultra-efficient Commercial Vehicles and Transitioning to Low-Carbon Propulsion while also supporting the development of launch vehicles and planetary entry systems.  These strategic thrusts are supported by advanced computational tools, which enable reductions in ground-based and in-flight testing, provide added physical insight, enable superior designs at reduced cost and risk, and open new frontiers in aerospace vehicle design and performance.

The advanced computational tools include the NASA FUN3D software which is used for solving nonlinear partial differential equations, known as Navier-Stokes equations, used for steady and unsteady flow computations including large eddy simulations in computational fluid dynamics (CFD). Despite tremendous progress made in the past few decades, CFD tools are too slow for simulation of complex geometry flows, particularly those involving flow separation and multi-physics (e.g. combustion) applications. To enable high-fidelity CFD for multi-disciplinary analysis and design, the speed of computation must be increased by orders of magnitude.

NASA is seeking proposals for improving the performance of the NASA FUN3D software running on the NASA Pleiades supercomputer.  The desired outcome is any approach that can accelerate calculations by a factor of 10-1000x without any decrease in accuracy and while utilizing the existing hardware platform.

More info HERE.