Data science

A random walk toward a net positive

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by Derek Kane, Deka Research and Development  dgkane64@gmail.com

Today, I am investigating whether magnetic resonance imaging can evaluate cell viability as we attempt to grow replacement organs: hearts, lungs, kidneys, etc., for patients who need transplants. I believe the child I was at five would approve. Of course, I also have a two hour meeting this afternoon to read C++ code to ensure that it not only performs its intended task, but also conforms to DEKA’s formatting standards. Even the coolest job, and I have a very cool job, includes drudgery and paperwork.

Avoiding boredom was my earliest career goal. My undergraduate degree was mechanical engineering, and my brother got me a job with him at Itek Optical Systems. Itek made cameras and telescopes, largely for the Department of Defense. The engineering challenges were fascinating, but the analysis and algorithm aspects of the work excited me much more than traditional mechanical engineering. However, my lack of deep mathematical training limited the analyses and algorithm development I could handle. At this job, I also noticed two career paths: one group of older engineers became middle managers whose work looked unbearably dull and who seemed very vulnerable to layoffs. A smaller group of engineers, including my boss, served as technical experts. When a new and innovative solution was required, or when a program stalled because a physical or computational challenge could not be overcome, these experts were consulted. I wanted this job.

I decided I also wanted to attend graduate school in mathematics. The deeper understanding of mathematics would enable me to comprehend and address a wider range of analytic and algorithmic problems. Additionally, a PhD provides gravitas when working with other engineers in industry. An engineer with a bachelor’s degree must have a large volume of high quality and high visibility work, before their opinions are considered seriously outside of the company where they work. While there are a great many fools who have doctorates, when you are sitting around a table with several PhDs, it is handy to have your own so you are part of the club.

To prepare for graduate school, I took one or two undergraduate math classes every semester for two and a half years while working. In the process, I discovered that math was beautiful as well as useful. The University of Michigan accepted me into their graduate program, and I studied algebraic group theory, intending to become a professor after graduation. Graduate school also proved an ideal environment to enjoy my two small children. However, as I approached my defense the academic job market was drying up. I could look forward to a series of one or two year positions before finding a tenure-track job. With two children, this prospect was unattractive, so I decided to return to industry.

My previous experience with optics enabled me to join a laser-based project at Lockheed Martin. This project offered the opportunity to work with inertial systems, and this experience made me attractive to Deka Research & Development. Deka was developing the iBot (an inertially stabilized wheelchair capable of traversing rough terrain, curbs and stairs) and the Segway (an inertially stabilized, two-wheel vehicle).

Dean Kamen, the founder of Deka, feels that we should only be working on jobs that are hard and that positively affect many people. The range of work I get to join is varied and exciting: mobility for people who can’t walk, prosthetics for people who have lost arms, clean water for people who will never get utilities from their governments, hearing improvement, safe delivery of drugs, improved dialysis for people with kidney failure, several projects I cannot talk about, and most recently growing new organs for people in need of transplants.

The range of disciplines this allows me to sample is equally wide ranging: thermodynamics, electro-magnetics, computer modeling of liquids, exotic signal processing, statistics, optics, big data analysis, synthetic biology, human-machine interfaces, colloidal flows, causality, complexity, numerical solution of differential equations, etc. Mathematical training allows me to move from discipline to discipline, because at its core, each of these topics depends upon a quantitative approach to understanding data, modeling relationships, and predicting outcomes. Grad school supplemented this flexibility by demonstrating that hard work and research can overcome difficult technical problems. You should leave grad school feeling that if another human has managed to solve a problem and write it down, then you can read their work and understand it.

Today, it is almost twenty-one years since I defended my thesis. I anticipate another twenty-one years of professional life, although I am aiming for at least forty more years. At the beginning of my career, my primary concerns were staying employed and working on exciting projects. Now, I am becoming concerned with why I do the work I do, and whether this work is a net good for the world.

I left the defense industry seventeen years ago, primarily for the selfish reason that it had become wearing and grating to put up with the intrusiveness of security clearances, and because commercial industry was tackling more interesting technical challenges than defense. It is absolutely true that there are sound moral arguments for working for defense, but I never really thought about the ethical justification of my work. I have been extraordinarily fortunate to land at a company where I am sure that my work is contributing to society.

I am largely comfortable with what I worked on, but I regret not seriously considering the moral implications of my early projects. Young mathematicians have complex lives; they need to support families, establish reputations and orient themselves in a world bursting with opportunities. However, it is also very valuable to develop an understanding of the non-technical world: history, culture and philosophy. This helps us avoid choices that make it hard to sleep as we get older. Older mathematicians have reputations, authority and time to reflect. It is morally incumbent that we provide opportunities for young mathematicians, guide them to interesting work, and protect them from external forces who would inappropriately exploit their talents.

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Blogpost: Parsa Bakhtary

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It is humbling to address future and current mathematicians, but as a former algebraic geometer myself, I will do my best to share with you my story. I work as a data scientist, which the Harvard Business Review in 2012 dubbed “the sexiest job of the 21st century,” at Facebook, which has been ranked by Glassdoor as one of the best companies for which to work. The path that led me from an eager math student who despised applications to where I am today has been a strange one, but the lessons I learned in my undergraduate and graduate math classes have had a profound impact on my ability to analyze concrete problems in industry.

After earning a B.S. in mathematics at UC Davis, I took a year off in which I decided to pursue a graduate education in the same subject. Seven years later, I finally received my doctorate from Purdue University, having written a thesis in the subject of algebraic geometry, and I was eager to take the path which would lead me towards a professorship somewhere. Unfortunately, I was unable to find a post doc in my home country of the US, so I took a position in Saudi Arabia at King Fahd University of Petroleum & Minerals, teaching calculus to aspiring petroleum engineers and occasionally publishing a paper. After three years there, I missed California and returned unemployed in the summer of 2012.

I quickly realized the job market for math professors wasn’t promising at the time, so I started looking for industry positions that would be suitable for someone with my background. After extensive Googling, I realized “data scientist” sounded like something I could do. I taught myself some Python and SQL, practiced analyzing and visualizing publicly available data sets in R and Excel, then started applying. After six months of unemployment, I caught a break and was offered a position at a startup in Chicago. The rest, as they say, is history.

My job at Facebook is unique in its flexibility and often quite challenging, though perhaps not in the same way as algebraic geometry. I have worked on game ranking, platform ecosystem health, comment ranking, celebrity usage patterns on Instagram, and discussion of TV show content on Facebook. I was lucky to be the first data scientist on Facebook Live when it launched, and our team helped grow it into one of the biggest live-streaming platforms in the world. The problems I work to solve can either be very technical, involving complex modeling and simulation, or it can be investigatory, requiring me to search for an explanation of an unusual phenomenon, or it can even be exploratory, such as trying to answer vague questions like “What makes a mobile game fun?”

The analytical training that we mathematicians receive put us at a unique advantage in the field of data science. The rigor we’re accustomed to help us break down a general question into concrete analytical pieces which we can answer with data. It is easy for us to spot errors in thinking, or situations where the evidence doesn’t actually answer the question. After learning some basic statistics and the familiarity with an analytical data manipulation environment (e.g. R or Excel), any mathematician can rapidly become a data scientist. The field of data science is also vast, as one can focus on subfields such as product analytics, visualization, or machine learning.

The biggest misconception people have about data science is that they think we all know how to program and have spent many years writing code. While some familiarity with SQL and analytical software is often desired, we are not programmers. We are, if anything, the voice of evidence at a company. We are there to help shape our colleagues’ understanding and intuition based on the data that we see, and to give actionable recommendations that will improve existing products and help define the appropriate strategies. It’s a fun job, and a great option for all mathematicians interested in industry.

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Opportunity: Machine Learning Workshop

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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

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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.