5 Things I Learned About Working at a National Lab

Sandia_Severa.png

 

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.

NASA_SeveraDOE_Severa

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.

Lost in translation: Academic work beyond academia

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

 

IUSE_Carrie
Listening to interdisciplinary conversations as part of IUSE grant SUMMIT-P
Qubes
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….

 

Opportunity: Graduate Student Modeling Workshop (IMSM 2017), July 2017

 The 23rd Industrial Mathematical & Statistical Modeling (IMSM) Workshop for Graduate Students will take place at North Carolina State University, between 16-26 July 2017.  The workshop is sponsored by the Statistical and Applied Mathematical Sciences Institute (SAMSI) together with the Center for Research in Scientific Computation (CRSC) and the Department of Mathematics at North Carolina State University.
The IMSM workshop exposes graduate students in mathematics, engineering, and statistics to exciting real-world problems from industry and government. The workshop provides students with experience in a research team environment and exposure to possible career opportunities. On the first day, a Software Carpentry bootcamp will bring students up-to-date on their programming skills in Python/Matlab and R, and introduce them to version control systems and software repositories.

Local expenses and travel expenses will be covered for students at US institutions.

The application deadline is April 15, 2017.
Information is available at http://www.samsi.info/IMSM17
and questions can be directed to grad@samsi.info

With best regards,
Mansoor Haider, Ilse Ipsen, Pierre Gremaud, and Ralph Smith

BIG Math Job Titles (hint…usually not mathematical scientist)

The BIG Math Network would like to collect a list of BIG Math Job Titles to help job seekers search for and identify opportunities.  If you don’t see your job title on the list, please email it to bigmathnetwork@gmail.com or ping us on Twitter at @bigmathnetwork with the hashtag #bigmathjob.

Job titles  (often with junior or senior in front)

  • Actuary
  • Analyst
  • Analytics Consultant
  • Analytics Manager
  • Applied Mathematics Researcher
  • Associate Editor
  • Biostatistician
  • Business Analyst
  • Business Intelligence Developer
  • Claims Specialist
  • Consultant
  • Cryptanalyst
  • Cryptographer
  • Data Analyst
  • Data Engineer
  • Data Operations Associate
  • Data Processing Specialist
  • Data Scientist
  • Director of Math Tutorial Curriculum
  • Engineer
  • Forecast Analyst
  • Functional Analyst
  • Game designer/slot game designer/game mathematician
  • Geolocation Engineer
  • Global Pricing Analyst
  • Guidance and Navigation Engineer
  • Informatics Scientist
  • Information Analyst
  • Investment Analytics Quant
  • Manager
  • Math Curriculum Coach
  • Math Curriculum Consultant
  • Mathematician
  • Modeler
  • Modeling Engineer
  • Operations Researcher
  • Operations Support Specialist
  • Pharmacokineticist
  • PK/PD Modeler
  • Planner
  • Principal Scientist
  • Product Manager
  • Program Manager
  • Programmer
  • Project Manager
  • Quality Systems and Compliance Manager
  • Quantitative Analyst
  • Quantitative Developer
  • Quantitative Pharmacologist
  • Quantitative Researcher
  • Quantitative Scientist
  • Quantitative Software Engineer
  • Reporting Engineer
  • Research and Development Engineer
  • Research Analyst
  • Researcher
  • Research Scientist
  • Risk Analyst
  • Risk Strategist
  • Scientist
  • Simulation Engineer
  • Software Engineer
  • Staff Scientist
  • Statistician
  • Strategist
  • Supply Chain Analyst
  • Systems Engineer
  • Technical Staff
  • Tutor