Summer Internship with an Impact

by George Baldini and Kendall Thomas

The end of the school year marks a break from exams, homework, and classes. It’s also a desirable time to dive into experiences outside the classroom, notably in research or an internship. Academic research allows students to explore untrodden intellectual territory and potentially create new knowledge. Business internships allow undergraduates an opportunity to apply their academic learning to business, try their hand in industry, and potentially make connections for future employment. Research or business internship? Many data analytics companies hire rising seniors with a possible job offer coming at summer’s end. We are rising juniors. Internships are possible but difficult to find. What did we decide for our summer? Both!

This summer, we worked at Davidson College with Dr. Tim Chartier in an internship with Athlete Intelligence. Athlete Intelligence is sports technology and data analytics company headquartered in Kirkland, Washington. The company makes wearable devices for athletes, like mouthguards (seen below) and helmet sensors, that track head impacts as well as biometric data. These devices provide instantaneous data for each impact during a session, alerting coaches and training staff if an impact magnitude exceeds a preset threshold or a player surpasses a certain number of hits in a small period of time. Their unique user platform empowers coaches and athletic trainers to access useful insights from this data to help reduce the risk of injury and improve performance.

Baldini_Thomas_1Our group served as a data analytics research branch of the company. As Jesse Harper, CEO of the company stated, “This is a mission to Mars. We’ll know what we find when we find it.”

If you don’t know where you’re going, where do you begin? With analytics, a first step is data. The company supplied impact data from high school and college football teams for one or more seasons. For each impact, Athlete Intelligence devices record the corresponding player, position, head location, magnitude, and time.

Armed with data, we turned to research goals. First, find “coachable moments,” actionable insights which aid coaches. For example, one team’s impacts increased towards the end of the game, possibly from fatigue’s effect on technique. If a coach verified fatigue’s influence, the team could emphasize conditioning and remind players to keep their heads up when tackling late in games.

Our second goal, connecting to the first goal, enrich the data. We wanted to add data that leads to additional insights. Since Athlete Intelligence will partner with Davidson Women’s Soccer team this fall, we asked our soccer coaches what data they currently track and would like to track in the future. In the end, we augmented the data with hours of sleep for each player, weather, elevation, location, and type of event (game or practice).

While the new data came from our soccer coaches’ interests, we added these new data points to our current impact data, which occurred in football. Insight followed. For example, one team’s centers and defensive ends were hit significantly harder in games than in practices; and, a little unnerving, quarterbacks and special teams got hit harder in practice than in games as seen in the graph below. An immediate question followed: why? Presented with such information, coaches could revisit game tape and practice plans to identify these situations and make any necessary adjustments.

Baldini_Thomas_2

To conclude our summer, we visited the company’s office in Kirkland. We presented our research and discussed how it could enrich the company’s user platform. Our research met their business goals and would help the company.

Our summer was fascinating and productive. Our internship introduced us to a new company, exposed us to cutting edge research, and included a trip to the Seattle area. Even better, our work wouldn’t end with the summer. While in Kirkland, our Athlete Intelligence colleagues presented us with more interesting projects. We enter the fall ready to get back to work through our continued collaboration and make more impacts with our analytics research.

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ICME Data Science Workshop

ICME will hold one-day summer workshops on Fundamentals of Data Science from August 14-18th at Stanford.  You can sign up for one workshop, or several, with topics ranging from Machine Learning to Natural Language Processing to Programming in R. Visit our Summer Workshops website for more information and to register.  

Feel free to spread the word! We hope to see you there.

Judy and the ICME team

Judy Logan

Institute for Computational and Mathematical Engineering (ICME)

Stanford University

Women in Data Science (WiDS) Conference

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

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