Transcript Distinguished Lecture Series Talk at Simon Fraser University

University-Industry Collaboration
in Computer Science Education
and Research
Eugene Fiume
University of Toronto
[email protected]
www.dgp.toronto.edu/~elf/.misc/tech.ppt
Abstract
There is a gulf between biggest questions in “practical” CS and those in
“academic” CS. The primary aim of practical CS is the systematic
development of reliable, economical, efficient, scalable, and usable
software. The primary aim of academic CS lies in the creation of
fundamental algorithms to settle scientific questions and for possible
future application. I fear that despite considerable effort, the gulf
between these two areas is widening, and our students, both
undergraduate and graduate, are falling into it. The academic pressures
of tenure and promotion also tend to reward research of more
immediate scientific impact than the rigours of developing a long-term
multi-disciplinary software research agenda that necessarily involves
human dynamics and communications.
In this talk, we shall use this context as a point of departure to explore
the broader issue of enhancing the collaboration between University
and Industry. While I have no magic solutions to offer, but I will draw
on my experiences in technology transfer, software development, and
research management to illustrate that it may in fact be possible to
bridge the gulf while respecting the separate agendas of university and
industry.
Relevant Biography
• Professor & Past Chair of Dept of CS at U of Toronto.
• Personal involvement in technology transfer projects.
• Directed Research and Usability Engineering at
Alias|Wavefront, and directed University Relationships.
• Advisor to companies and VCs in digital media, internet
services, software strategy, corporate organisation.
• Heavily involved in intellectual property issues.
• Member of many Scientific and Corporate Boards.
– Policy issues on IP repositories.
– “Success” measures for research.
– Matching innovation (research outcomes) to business/societal impact.
Overview
• The two sides: IT in University and Industry.
• The gulf between them.
• Bridging the gulf.
Warning!
Deliberate
oversimplifications
to
follow.
Simply Put
A university’s obligation is to the betterment
of society through discovering, preserving,
and disseminating knowledge.
A corporation is responsible for maximising
shareholder value and increasing the material
prosperity of its employees and of society.
From the beginning, there is a gulf to bridge.
Implications
University education and technology
transfer both tend to be far more finely
focused than the available diversity.
Industry needs broader skills from its
employees and partners to build products
and solutions, not technology.
We have a gulf in practice as well.
University CS (and CE)
Difficult to generalise, but University research:
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is about fundamental algorithms and results.
has multiple year horizon.
aims to affect the future through disruption.
is risk tolerant (but what about tenure?).
has highly orthodox structures.
is peer reviewed.
is not usually “integrative” in the short term.
Industrial CS (and CE)
Difficult to generalise, but Industrial R&D:
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focuses on feasible realisations.
has 1-2 year horizon.
aims to affect the future through evolution.
is risk prudent.
has evolving structures.
hierarchical and peer-based validation.
is “integrative”.
“Integrative?”
• That innovation is mapped out in advance to
accumulate and support a specific objective.
• Ongoing basic research is not in principle
integrative in that new, serendipitous results
can completely change research focus.
• However it is integrative in the long term.
• “Applied” research and development is
integrative as smaller results should
aggregate into a unified, cohesive outcome.
University CS (and CE)
Difficult to generalise, but CS education:
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is about implementing fundamental algorithms.
focuses on “inside-out” software development.
deals with computers as abstractions.
promotes abstract complexity measures over
practical performance and usability.
• emphasises individual performance.
• produces outstanding, focused technical grads.
• whither communication, business, culture?
Industrial CS (and CE)
Difficult to generalise, but needs:
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to develop reliable, robust, usable products.
to focus on outside-in product development.
to see computers as concrete things.
to promote practical performance.
to emphasise team performance.
a combination of technical, business, cultural.
Implications: Technology Transfer
University-based researchers:
• tend to over-value individual focused results.
• do not see intellectual property as accretive.
• have an incomplete understanding of market
and industry.
• underestimate cost of integration/deployment.
• are better scientists than entrepreneurs.
• do not see their universities as helpful in TT.
Implications: Technology Transfer
Industrial R&D:
• even internal TT is difficult!
• underestimates importance of grad students.
• underestimates the analytic skills of academics:
– Academics are paid to think and they do it well!
– Excellent advisors and reviewers.
– Views academics as too “ivory tower”.
• does not appreciate societal role of university.
• does not appreciate the potential of interaction:
– Celebrate the differences!
The Innovation “Pipeline”
Increasing resources spent (but must reduce cost)
University
Industry
Basic
Research
Applied
Research
Outcomes:
• grads
• demo
• papers
• grants
• awards
• press
Industrial
R&D
Outcomes:
• prototype
• patents
• licences
• TT
• matching
funds
Product
Integration
Outcomes:
• prod design
• consult
• financing
• biz plan
Impacts:
• to society
• revenue
• wealth
• well-being
TT happens only when cost, people, timing are right.
Suggestions: Technology Transfer
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Don’t force research to be commercialisable! Disaster!
Believe in the scientific method: it is robust and redundant.
Provide greater opportunity for serendipitous TT.
Research-industry discussion forums without IP preconditions.
New funds for TT separate from basic research funding.
Reward university-industry interaction, especially through
programmes involving people transfer in performance reviews.
• Provide more business mentoring to researchers.
• Reduce barriers to TT through better IP ownership policies.
– Make more money on philanthropy over research royalties!
• Put deployment cost models into seed-funding VC structure:
– Industrial tech transfer is never in VC funding plan. Why not?
Evolution of Industrial Software Products
Browser-based tools
Large,
monolithic
back office
applications
Web services, SOA
Interactive analysis
(Business Intelligence)
Data Analysis, Data
Mining
Greater exposure to software and data components.
Increasingly collaborative, interactive.
“Software Engineering”
We have been speaking here of
Product Development.
However one defines “software engineering” :
SE is a subset of PD!
And
PD is a subset of “Business Models and Processes”.
The Changing Product Landscape
workflow
Support
R&D
Procs
UI
User
Tech
Ops
UI Design
Collateral
QA
DB
Feasibility
and dev
Capacity
Planning
Mktg
CIO
Brand,
position
Assets
Shows
Sales
Deals
Funds
CFO
Governance
M&A DD
CTO,
legal
IP & TT
Only a tiny
subset of
the
linkages
are
indicated.
Departmental Linkages
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They have always existed.
But they’re tighter now.
Many stakeholders, including customers.
More third party arrangements (e.g., TT).
More multi-site engagements (e.g., M&A).
Communication becomes a crucial problem.
Need to share diverse knowledge.
“Agile” management techniques?
Business Implications?
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Different expertise needed.
Far more early communication and discussion.
Fast due diligence and technology transfer.
More “strategic” partnerships.
Less direct development (e.g., outsourcing).
More integration, component based design.
Different management styles, incentives.
Stronger business and customer focus.
Cleaner business and development processes.
Business Expectations of Students
• Cannot assume “out of box” performance.
• More mentorship and apprenticeship (trades).
• More ongoing professional development.
• University training is a beginning, not an end.
• Need broader array of University/Industry
engagements (co-ops, internships, exchanges,
joint research, joint development,
consultancies, contracts).
University Implications?
• There is too much to teach, too little time – continuing educ.
• Students still need strong science/tech core.
• But undergraduates need more communications, business,
project mgmt, design, usability, humanities.
• Separate technical features from real business need.
• Graduate students could use a unit of business school.
• Increased exposure to users, customers, systems integration,
capacity planning, performance, bandwidth, latency, usability.
• Must learn more about corporate structures (SOX).
• More team-based project work.
• More professional UGrad/Grad programmes?
Curriculum Implications
• So many masters, so much diversity!
• More streams to address diversity.
• Need more people in the field (especially women).
• Case-based, “capstone” programmes?
• Differentiated professional programmes.
• Senior management programmes (like EMBA):
– Career path limitations for 2nd/3rd line tech managers.
• Multiple academic unit combined programmes.
“Capstone?”
Definition:
• The top stone of a structure or wall.
• The crowning achievement or final stroke; the
culmination or acme.
Provide design based, team-work oriented
final courses that facilitate transition to
industrial careers. Focus on users, products,
negotiation, “outside-in” development.
Conclusions
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The gulf is wide but can be bridged.
Need a clearer understanding of each side.
Need to look at the obstacles and react to them.
Respect for their traditional roles.
Focus on building grass-roots relationships:
– formal agreements come later.
– discussion is at least as valuable as the technology.
– graduate students are key.