nvidia / pscds / upspr / ensae! laboratoire de statistique arnak dalalyan mdc / telecom paristech!...

Pr / UPSud

LRI

CÉCILE GERMAIN

Pr / ENSAE

Laboratoire de Statistique

ARNAK DALALYAN

MdC / Telecom ParisTech

LTCI

ALEXANDRE GRAMFORT

1

NVIDIA / PSCDS / UPSACLAY MEETING

Center for Data ScienceParis-Saclay

March 30, 2015, LAL

MdC / Mines ParisTech

CGS

AKIN KAZAKÇI

DR / CNRS LAL & LRI

CNRS & University Paris-Sud

BALÁZS KÉGL


• Why we launched this meeting?

• The Paris-Saclay CDS

• challenges, courses, hackatons

• Where are we going?

• the subject of the discussion after the talks

2

OUTLINE


• We do research (both applied and basic) on deep learning

• We are running server-side-execution hackatons which need significant resources for very short periods

• typically 1 day every 6 weeks (not all of them are GPU-intensive)

• mutualization

• Discuss a Saclay-wise initiative on research on GPUs, find the actors, discuss collaboration with NVIDIA

• we, at the CDS, do not do research on GPUs, usually use them through standard ML libraries

• but mutualization can of course go beyond data science

3

WHY WE LAUNCHED THIS MEETING?


VIRTUALDATA@P2IO

4


DATA SCIENCE

5

Design of automated methods

to analyze massive and complex data

to extract useful information

Center for Data ScienceParis-Saclay6

DATA SCIENCE=

BIG DATA

We are focusing on inference:

data knowledge

Interfacing with infrastructure, security, production

7

UNIVERSITÉ PARIS-SACLAY

19 founding partners


UNIVERSITÉ PARIS-SACLAY

8

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



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

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

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

LIST/CEA

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


THE DATA SCIENCE ECOSYSTEM

Data domainsenergy and physical sciences

health and life sciences Earth and environment

economy and society brain

Data 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


TOOLS

11

We are designing and learning to manage

tools

to accompany data science projects

with different needs


TOOLS: LANDSCAPE TO ECOSYSTEM

12

Data scientist

Data trainer

Applied scientist

Domain expertSoftware engineer

Data engineer

Tool building Data domains


machine learning information retrieval

signal processing data visualization

databases

software engineeringclouds/grids

high-performancecomputing

optimization

energy and physical sciences health and life sciences Earth and environment

economy and society brain

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

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

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


TWO ANALYTICS TOOLS

13

RAPID ANALYTICS AND MODEL PROTOTYPING

DATA CHALLENGES


• 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 (predictions) 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

14

DATA CHALLENGES


• 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

• no direct access to solutions and data scientist

• emphasizes competition

15

DATA CHALLENGES


• Single-day coding sessions

• 20-30 participants

• preparation is similar to challenges

• Goals

• focusing and motivating top talents

• promoting collaboration, speed, and efficiency

• solving (prototyping) real problems


17


19


20



ANALYTICS TOOLS TO PROMOTE COLLABORATION AND CODE REUSE



22

2015 Jan 15 replaying the

HiggsML challenge


2015 Feb 9 Mortality prediction in septic patients


23



24

2015 Apr 10 Classifying variable stars



25

2015 May Drug identification from spectra



26

2015 June Insect classification


THANK YOU!

nvidia / pscds / upspr / ensae! laboratoire de statistique arnak dalalyan mdc / telecom paristech!...

Documents