Some Thoughts about
 Career Developments in Industry

數學沙龍與⽣生涯探索 (Last modified: 2017/11/24)

Career Developments in Industry

A Personal View

Weichung Wang Department of Mathematics

Institute of Applied Mathematical Sciences National Taiwan University

http://slido.com

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Thinking

100 Years of Fashion

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基因演化 vs 文化演化

人的實體變化很慢，思維可以短時間改變 例如，從君權神授到主權在民

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How Do I Think?

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• Nano

• Big data

• AI

Context

Time Constraints

Algorithm

Insights Imagination

Application

Architecture

Example 1

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• Dynamic wings

• Efficient & robust engines

• Composite materials

• Integration & customization

Boeing 787 Dreamliner

Numerical linear algebra Differential equations Operations research Computational geometry

Optimization Optimal control Statistics

Data management

Applied Mathematics in 787

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• Entirely designed on computers

High-Performance Computing in 787

HPC Boeing 787

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Algorithm

Architecture

Math/Stat

Application

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Example 2

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• Data scientist @ Rakuten, Japan

• Interdisciplinary

• Internation

• Industry

陳嘉宏

Computational Sciences

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Algorithm

Architecture

Math/Stat

What Do Others Think?

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Example 1

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• Economy changes from manufacturing-based to 
 service-based


• Industries hiring mathematical 
 and computational scientists


• Student preparations

SIAM Report on Mathematics in Industry, 2012

Society for Industrial and Applied Mathematics 3600 Market Street, 6th Floor • Philadelphia, PA 19104-2688 USA www.siam.org • [email protected]

Math_in_Industry-2_05-03-12.indd 1 5/7/2012 1:41:13 PM

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• “T-shape” profile

• breadth of knowledge across the mathematical and computational sciences

• technical depth in a relevant discipline


• Interest/experience/enthusiasm for varied challenges or the scientific or business focuses

Qualifications and Skills

• Mathematics

• board in core courses & depth in specialty


• Computation

• Programming & high-performance computing


• Application and real-world problem solving

• Language of domain & bridging the gap


Technical Skills that Graduates Need

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• Graduate-level training outside of their major

Graduate Student Education

Society for Industrial and Applied Mathematics Math in Industry

4 Perspectives on

Graduate Education

“Seek internships in industries you are interested in. Do not focus so much on the degree. Think outside the academia box. Work on people skills. It does you no good to be smart if you can’t communicate or work in teams.”

4.1 PhD Education

The 1996 report made several recommendations for improving graduate education for students interested in taking a job in industry, including coursework in an application, experience with formulating and solving real-world problems, and coursework in computer or computational science.

In the current survey, we asked three related questions to gauge to what extent these

recommendations have become a part of graduate education. First, we asked if the graduates had been involved in industrial-related programs. Of the respondents, 28% participated in industrial internships, 7% had an industrial mentor, 7% participated in problem sessions, and 5%

participated in an industrial workshop. Multiple answers were allowed. However, 59% either did not respond or indicated that the question was not applicable.

On the other side of the coin, many of the companies we visited in on-site interviews stressed the advantage of internships, and many of them offer such internships: for example, Boeing, D.E. Shaw, Cray, IBM, GM, AT&T, Intel, Akamai, HP Labs, Google, and Solidworks. Likewise, workshops have proven to be a successful platform transferring knowledge from academia to industry and giving students experience at solving industrial problems. Leaders include the mathematical problems in industry workshops in the United States, the industrial problem solving workshops of the Pacific Institute for the Mathematical Sciences in Canada, and the European Study Groups with Industry.

In view of the appreciation that companies express for internships and workshops, and the variety of opportunities, it is disappointing that participation in such programs has not become more universal, at least among students considering an industrial career.

“I think it is important to bridge the gap between theory and practical applications—most people do not realize this.”

“Programming experience (preferably on team software project) is very important and frequently omitted in a mathematically oriented curriculum.”

We asked graduates about graduate-level training outside of their major. In this case, 79% of the graduates had at least one such experience, see Table 9.

Often this took the form of training in programming, scientific computing, and other computer sciences. Finally, we asked how valuable this training was for obtaining a job in industry, and 65% of graduates considered it very valuable or valuable. We also asked how important they found the experience of working in a team, and 70% rated this as very valuable or valuable.

We contacted one graduate program that ranks very high in the percent of graduates entering industry to see if that department has any special insights into the preparation of students for industrial jobs, particularly ideas that might be portable to other institutions. The Department of Computational and Applied Mathematics at Rice University sent eight of its twenty graduates (40%) into industry from 2004 to 2008, and six out of eleven from 2009 to 2010. Some points made by department chair Matthias Heinkenschoss were:

• The CAAM department enjoys strong contacts with local companies, such as BP, Shell, ExxonMobil, and Chevron. While the specific companies will differ, the concept is certainly generalizable to other departments.

Encourage graduate degrees that involve dual mentoring by mathematics or computer science and another department, government laboratory, or industry.

Trainingin another discipline

%

programming 65

scientific computing 35 other computer science 28 scientific discipline 58 business discipline 29 engineering discipline 9

other discipline 4

Table 9: Percent of respondents with training in another discipline

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Essential to Annual Review

Math in Industry Society for Industrial and Applied Mathematics

Compared to the earlier survey, fewer respondents cited “modeling and simulation” as essential or important. However, this last finding seems to contradict the answers we received to a question on what metrics were important to the respondents’ annual performance reviews. The leading answer to that question was “mathematical models,” at 67%, followed closely by “presentations to management” at 64%, (see Table 8). Perhaps the apparent contradiction means that mathematical models remain crucial for job performance, but the pedagogy of mathematical modeling is in some way not keeping current.

In the 1996 survey, advanced computation was rated essential or important by 83% of graduates. In the current survey, we explored this in more detail.

Programming (86%), computational science (57%), data mining (40%) and software engineering (34%) were the computational disciplines rated as essential or important. Overall, 65% of the respondents rated the mathematical or computational sciences as highly important to the success of their group.

In our 1996 report, we clearly emphasized the importance of communication skills. Table 8 shows that outputs related to communication skills (“presentations to management,” “preparation of internal reports”) are at least as important for advancement as technical accomplishments (“mathematical models,” “software development”).

Likewise, our on-site interviews returned again and again to the theme of soft skills such as communication, teamwork, flexibility, and willingness to listen.

Some of the comments we received are given below.

“Soft skills make the difference.”

“You often can’t guess who can’t make the transition [to industry]. After the fact, you can see that the successful ones are good listeners who are tolerant of other people’s ideas and willing to make incremental improvements.”

“You have to be able to explain projects to non-experts. PhDs often have more flexibility than MSs, because they have been required to think outside the box.”

“Must be willing to ‘get hands dirty’ to help the firm meet its business imperatives.”

“Must be able to attack and solve unstructured problems.”

Our survey also asked the recent graduates for advice that they would give to graduate students who are now considering careers in industry. We received 21 responses, such as these:

“In banking, the current challenge is to sieve through huge amounts of customer data. Any training in mass data manipulation would be a plus.”

“Be open-minded, attend practitioners’ seminars, learn to program, and you’ll be fine.”

“Pursue internships. Follow your interests. Acquire database skills.”

“PhDs tend to underestimate the quality of science done in industry. You will get to solve challenging problems in industry, too.”

“Get internships and learn to program a computer. From my perspective, programming is essential for almost any industrial mathematician.”

73 Rated essential/important Survey MII96

%

statistics 61 51

probability 60 50

applied mathematics 56 modeling and simulation 49

numerical analysis 42 65

optimization 38 38

discrete mathematics 30 26

differential equations 29 50 Table 7: Percent of mathematical specialty rated as an essential or important

requirement for their job. Multiple answers allowed. Comparison to MII96 included.

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%

Essential/important to annual review

%

mathematical models 67

software development 43

presentations to management 64 preparation of internal reports 59 presentations to customers 53 presentations at conferences 39 publication in the open literature 29 Table 8: Percent of respondents rating task outcomes as essential or important for their review

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• Flexibility and communications skills required to work in an interdisciplinary team


• The discipline to meet time constraints


• Sense for a reasonable solution

Example 2

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•Rajesh Krishnamachari, Vice President of Quantitative and Derivatives Strategy, J.P. Morgan


•“Our team has PhDs in different disciplines – there are a couple with

• PhDs in applied math, who work on computational geometric algorithms,

• PhDs [in computer science] with the deep-learning skills we’re trying to enhance on, and

• PhDs in statistics, who apply classical mathematical techniques to writing algorithms,”

• https://news.efinancialcareers.com/us-en/297540/j-p-morgan-wants-to-hire-phds-who-can-master-machine-learning-and-markets

J. P. Morgan, 2017

• “Our team has PhDs in different disciplines – there are a couple with

• PhDs in applied math, who work on computational geometric algorithms,

• PhDs [in computer science] with the deep-learning skills we’re trying to enhance on, and

• PhDs in statistics, who apply classical mathematical techniques to writing algorithms,” 


Rajesh Krishnamachari

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• “Rigorous mathematics might not lead you to the right answer, so even when you hire people with the backgrounds I mentioned, you have to make sure they have the right markets intuition.”

Rajesh Krishnamachari

What Can You Do?

Find Your Passion!

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infinity and beyond

超越無限

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