center for data science paris-saclay

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Center for Data ScienceParis-Saclay

DR / CNRS LAL & LRI

CNRS & University Paris-Sud

BALÁZS KÉGL

Pr / UPSud

LRI

CÉCILE GERMAIN

MdC / Telecom ParisTech

LTCI

ALEXANDRE GRAMFORT

MdC / Mines ParisTech

CGS

AKIN KAZAKÇI

Pr / ENSAE

Laboratoire de Statistique

ARNAK DALALYAN

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Meta

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I will not talk about science

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I will talk about management

(of) (data) science


• My eight-year of experience interfacing between high-energy physics and data science

• Our one-year experience of running PSCDS

• Extensive discussions with management scientist and the MI/Mastodons/MaDICS for the last year

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WHERE DOES IT COME FROM?

Center for Data ScienceParis-Saclay6

UNIVERSITÉ PARIS-SACLAY

19 founding partners


UNIVERSITÉ PARIS-SACLAY

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+ horizontal multi-disciplinary and multi-partner initiatives (“lidex”) to create cohesion


Biology & bioinformaticsIBISC/UEvry LRI/UPSudHepatinovCESP/UPSud-UVSQ-Inserm IGM-I2BC/UPSud MIA/AgroMIAj-MIG/INRALMAS/Centrale

ChemistryEA4041/UPSud

Earth sciencesLATMOS/UVSQ GEOPS/UPSudIPSL/UVSQLSCE/UVSQLMD/Polytechnique

EconomyLM/ENSAE RITM/UPSudLFA/ENSAE

NeuroscienceUNICOG/InsermU1000/InsermNeuroSpin/CEA

Particle physics astrophysics & cosmologyLPP/Polytechnique DMPH/ONERACosmoStat/CEAIAS/UPSudAIM/CEALAL/UPSud

The Paris-Saclay Center for Data ScienceData Science for scientific Data

250 researchers in 35 laboratories

Machine learningLRI/UPSud LTCI/TelecomCMLA/Cachan LS/ENSAELIX/PolytechniqueMIA/AgroCMA/PolytechniqueLSS/SupélecCVN/Centrale LMAS/CentraleDTIM/ONERAIBISC/UEvry

VisualizationINRIALIMSI

Signal processingLTCI/TelecomCMA/PolytechniqueCVN/CentraleLSS/SupélecCMLA/CachanLIMSIDTIM/ONERA

StatisticsLMO/UPSud LS/ENSAELSS/SupélecCMA/PolytechniqueLMAS/CentraleMIA/AgroParisTech

Data sciencestatistics

machine learninginformation retrieval

signal processingdata visualization

databases

Domain sciencehuman society

life brain earth

universe

Tool buildingsoftware engineering

clouds/gridshigh-performance

computingoptimization

Data scientist

Applied scientist

Domain scientist

Data engineer

Software engineer


datascience-paris-saclay.fr

@SaclayCDS


A multi-disciplinary initiative to define, structure, and manage the data science ecosystem at the Université Paris-Saclay

http://www.datascience-paris-saclay.fr/

http://www.datascience-paris-saclay.fr/


DATA SCIENCE

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Design of automated methods

to analyze massive and complex data

to extract useful information


CENTER FOR DATA SCIENCE=

DATA CENTER

We are focusing on inference:

data knowledge

Interfacing with HPC, cloud, storage, production


PARAMETERS

• 2 years: April 2014 - June 2016, 1.2M€

• +1 year, conditional on evaluation

• Light management

• executive committee of 17 members

• work groups

• management, strategy (around objectives)

• thematic (around scientific themes),

• open to everyone to propose and to participate


Domain scienceenergy and physical sciences

health and life sciences Earth and environment

economy and society brain

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THE DATA SCIENCE LANDSCAPEData scientist

Data trainer

Applied scientist

Domain scientistSoftware engineer

Data engineer


machine learning information retrieval

signal processing data visualization

databases

Tool building software engineering

clouds/grids high-performance

computing optimization


• Manpower

• especially at the interfaces

• industrial brain-drain

• Incentives

• data scientists are not incentivized to work on domain science

• scientists are not incentivized to work on tools

• Access

• no well-developed channels to identify the right experts for a given problem

• Tools

• few tools that can help domain scientists and data scientists to collaborate efficiently

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CHALLENGES


TOOLS

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We are designing and learning to manage tools to

accompany data science projects with different needs


TOOLS: LANDSCAPE TO ECOSYSTEM

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Data scientist

Data trainer

Applied scientist

Domain expertSoftware engineer

Data engineer

Tool building Data domains


machine learning information retrieval

signal processing data visualization

databases

• interdisciplinary projects • matchmaking tool • design and innovation strategy workshops • data challenges

• coding sprints • Open Software Initiative • code consolidator and engineering projects

software engineeringclouds/grids

high-performancecomputing

optimization

energy and physical sciences health and life sciences Earth and environment

economy and society brain

• data science bootcamps • IT platform for linked data • annotation tool • SaaS data science platform


POSTDOCS, THESES, SABBATICALS

• Common selection criteria

• scientific quality

• expected results both in domain science and data science

• relevance and feasibility

• (real) scientific data, available at the start of the project

• interdisciplinarity (PIs both from domain and data sciences, different LABEXs)

• community building

• organizing and participating in thematic days, bootcamps, workshops


ENGINEERING AND CODE CONSOLIDATING PROJECTS

• No research

• implementation, maintenance, integration

• 1 year engineering projects

• 3-6 month code consolidating projects

• a postdoc or PhD student drops research during the project, implements his/her research code in a professional software, or integrates it into a toolbox


• A window to open data at Paris-Saclay

• We are not storing or handling existing large data sets

• Rather indexing, linking, and mapping, embedding in the worldwide linked data (RDF) ecosystem

• Storing small data sets of small teams is possible

• Subsets of large sets for prototyping

• Or simply store metadata plus pointer

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IT PLATFORM FOR LINKED DATA

http://io.datascience-paris-saclay.fr/

http://io.datascience-paris-saclay.fr/


IT PLATFORM FOR LINKED DATA

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BOOTCAMPS

• Single-day coding sessions

• 20-30 participants

• Goals

• training PhD students, postdocs, engineers, senior researchers for hands-on data science (problem types, tools)

• solving (prototyping) real data science problems

• networking, knowing each other

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BOOTCAMPS


BOOTCAMPS

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BOOTCAMPS

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BOOTCAMPS


• A data challenge is a recently developed unconventional dissemination and communication tool

• a scientific or industrial data producer arrives with a well-defined problem and a corresponding annotated data set

• defines a quantitative goal

• makes the problem and part of the data set (the training set) public on a dedicated site

• data science experts then take the public training data and submit solutions for a test set with hidden annotations

• submissions are evaluated numerically using the quantitative measure

• contestants are listed on a leaderboard

• after a predefined time, typically a couple of months, the final results are revealed and the winners are awarded

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DATA CHALLENGES


• The HiggsML challenge on Kaggle

• https://www.kaggle.com/c/higgs-boson

• 1785 teams, huge publicity

• significant improvement on baseline

• 18 month preparation, yet partially missing the target

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DATA CHALLENGES

https://www.kaggle.com/c/higgs-boson


• Challenges are useful for

• generating visibility in the data science community about novel application domains

• benchmarking in a fair way state-of-the-art techniques on well-defined problems

• finding talented data scientists

• Limitations

• not necessary adapted to solving complex and open-ended data science problems in realistic environments

• emphasizes competition

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DATA CHALLENGES

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DESIGN AND INNOVATION STRATEGY WORKSHOPS


• Putting domain scientists, data scientists, and management scientist in the same room

• Getting them understand each other

• Keeping them collectively creative

• The goal: identifying and defining projects

• low-hanging fruits

• breakthrough projects

• long-term vision

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innovative design

=

interaction and joint expansion of concepts

and knowledge

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C/K design theory



[P]$Project$building!Ini3alisa3on$

[K]$Knowledge$sharing$

Workshops$

[C]$IFM?Design$Workshops$

[RUN]!

DKCP process: linearizing C-K dynamics


TAKE HOME MESSAGE NO1

If you are interested in adapting any of these tools in

your project/site, feel free to contact us, we would be

happy to share our experience


• We need data engineers and trainers to support research

• ideally: 75% research scientists, 25% research engineers

• We are lucky in France that the position exists in public research

• Tasks

• integrating research code into general-purpose professional software (e.g., scikit-learn, Torch)

• providing an interface between computational infrastructure (e.g., clouds) and scientists

• training scientists to use the tools

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WHAT CNRS CAN DO


WHAT CNRS CAN DO

Incentives


WHAT CNRS CAN DOMost data scientists, as other scientists, are trained and incentivized to do research on highly specialized domains. They search scientific visibility in their international community, which is equally highly specialized, because their carrier advancement is almost entirely based on peer-reviewed publications. Even when they would have the expertise, they have little incentive to venture into the tool builder (data engineer) role since software authorship has little value in their evaluation, and it can only serve them implicitly through the visibility they gain in the community of tool users. By the same token, they have little incentive to venture into domain sciences and to tackle economic or societal challenges. It is possible that a domain science or an industrial project requires new techniques which then can be published in data science venues, but this is not guaranteed at all. It usually takes heavy investment of time and effort to be able to understand domain problems, so excursions into domain sciences are highly risky. Even when such collaborations are established, the data scientist has a strong prior to use his/her favorite methodology which is not necessarily the best solution for a given problem. Finally, the data scientist has little incentive to bring the project to full fruition, and he/she often “runs away” with an abstract data science problem (and solution) extracted from the project. Symmetrically, domain scientist and industrial domain experts have no incentive to advance data science and to develop and publish new techniques, as long as their data science problems get solved. When they venture into tool development, they have little incentive in developing general purpose tools.


Affirmative action

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• Overweight (e.g, double count) out-of-domain papers in every evaluation

• Make software tool ownership count

Affirmative action

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Co-locality is important.

It would be desirable that data science for scientific

data in France be grouped into 5-6 sites of similar size.

Resources and experience can and should be shared.


THANK YOU!

center for data science paris-saclay

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