Mats Jirstrand

2 0 1 8 - 0 2 - 0 8

I ndustr ia l M athe m at ic s A ppl ie d to M ac hine L e ar n ing , B ig D ata A na ly t i c s and D ata S c ie nc e

V e h i c l e I C T A r e n a – I n n o v a t i o n B a z a a r

L i n d h o l m e n S c i e n c e P a r k

M a t s J i r s t r a n d , P h DH e a d o f D e p a r t m e n t S y s t e m s a n d D a t a A n a l y s i s

Mats Jirstrand

O U T L I N E

- F R A U N H O F E R - C H A L M E R S C E N T R E ( F C C )

- P R O J E C T S AT S Y S @ F C C

- T O O L S A N D T E C H N O L O G Y

- M O D E O F O P E R AT I O N

Mats Jirstrand

F R AU N H O F E R - C H A L M E RS C E N T R E ( F C C )

Mats Jirstrand

F R AU N H O F E R - C H A L M E RS C E N T R E

- Founded September 2001 by Fraunhofer and Chalmers

- Offers applied mathematics for a broad range of industrial applications

- Projects defined by companies and public institutes on a commercial basis

- Pre-competitive research and marketing with financing from our founders

- Systems and Data Analysis, Geometry and Motion Planning, Computational Engineering

FCC

Public grants

Industry projects

EU grants

Basicfunding

Chalmers

1/6

Fraunhofer

1/6

1/3 1/3

0.00

10.00

20.00

30.00

40.00

50.00

60.00

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Industry projects Governments and UniversitiesEuropean Commission Fraunhofer (Special programs)

Year

MSEK

67 research units& institutes

24 000 employees

2.1 billion € turnover

Fraunhofer-Gesellschaft 2016

Saarbrücken

MagdeburgDortmundSchmallenbergAachen

Euskirchen

Darmstadt

JenaChemnitz

Dresden

Itzehoe

Bremen

HannoverBraunschweig

Kaiserslautern

Freiburg

Würzburg

Holzkirchen

Karlsruhe

Duisburg

Erlangen

Pfinztal

St. Ingbert

MünchenFreising

Oberhausen

Stuttgart

GolmBerlin

St. Augustin

Rostock

Mats Jirstrand

F C C – S YS T E M S A N D DATA A N A LYS I S

- Machine Learning and Data Science

- Big Data Analytics

- Dynamical Systems Modeling

- Pharmacokinetics and Pharmacodynamics

- Systems and Synthetic Biology

- Electrophysiology and Arrhythmia

Development of computational methods, algorithms, and software

tools for systems and data analysis on different levels of abstraction

utilizing time and spatially resolved measurement data

Mats Jirstrand

S A M P L E ( P U B L I C ) P R OJ E C T S AT S YS @ F C C

Mats Jirstrand

▪ Big Automotive Data Analytics (13.7MSEK)

Fleet telematics big data analytics forvehicle Usage Modeling and Analysis (FUMA)

On-board Off-Board Distributed Data Analytics (OODIDA)

Machine Learning for Engineering Knowledge Capture (MALEKC)

▪ Digitalization in Manufacturing (8.6MSEK)

Root Cause Analysis of Quality Deviations in Manufacturing UsingMachine Learning (RCA-ML)

Smart Assembly 4.0

SUstainability, sMart Maintenance and factorydesign Testbed (SUMMIT)

M A C H I N E L E A R N I N G A N D B I G DATA A N A LY T I C S

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O O D I DA – O N - B OA R D O F F - B OA R D D I S T R I B U T E D DATA A N A LY T I C S

- Develop and implement scalable data analysis methods in a distributedenvironment involving vehicle on-board and off-board resources

- Develop platform for distributed data analytics environments involvingboth on-board and off-board analysis

- Methodologies:- Streaming analytics frameworks- Differential privacy- Clustering, classification, regression methods in streaming contexts:

example – federated random forests - Distributed learning algorithms: Erlang/python

- 2016 – 2019, FFI-BADA program, VINNOVA

Entire vehicle fleet

Volvo reference vehicles

A

C

B

Data analysis node

Central computational node

D

Mats Jirstrand

TO O L S A N D T E C H N O LO GY

Mats Jirstrand

M A C H I N E L E A R N I N G – D I F F E R E N T TA S KS

UNSUPERVISED LEARNING SUPERVISED LEARNING REINFORCEMENT LEARNING

Discrete

CLUSTERINGDIMENSION REDUCTION

CLASSIFICATION REGRESSION

MACHINE LEARNING

Find groups of similar individuals

Describe individuals with fewer variables

Categorize into predefined classes

Predict continuous value given input

Take actions given the state of a systemDiscreteContinuous Continuous

Mats Jirstrand

TO O L S & T E C H N O LO GY

▪ Machine Learning

Classification & Prediction (SVM, k-means, LASSO, SOM, MDS, GPR)

Kernel methods

Reinforcement learning

Deep neural networks (AE, CNN, RNN)

Bayesian optimization

Active learning

▪ Systems & Control Theory

DEs, ODEs, SDEs, …

Stability, sensitivity, ...

Feedback & adaptive control

System identification

Optimal control

▪ Python

▪ R – Statistics

▪ Mathematica

Numerics/Symbolics

Wolfram Workbench (IDE)

▪ Matlab – Numerics

▪ BDA

Spark, Storm, MXNet,TensorFlow, …

AWS, Google Cloud, Azure

▪ C++

▪ Java, Scala, JavaScript, Erlang,NoSQL, Apache, Nginx, React, Angular, CUDA, …

▪ Probability & Stochastics

Monte Carlo methods(MCMC, SMC)

Probabilistic programming

Gaussian processes

Bayesian networks (PGM)

▪ Optimization

Gradient based methods

Automatic differentiation

Sparse linear algebra, ...

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M O D E O F O P E R AT I O N

Mats Jirstrand

FCC offers a combination of highly skilled and research educated collaborators with a mixed academic and commercial perspective

FCC vs academia

▪ Focus on application – research as a mean to reach objectives

▪ Direct project funding (industrial partner) or public project funding (Vinnova/SSF/…)

▪ Specific deliverables and short/medium term time horizon compared to a more genericPhD or MSc project where deliverable depends on outcome and interest of scientificresearch

▪ Clearly defined project objectives, deliverables, and cost

▪ Sustainable software support

▪ Not dependent on a single student or post doc

▪ Non-disclosure, Intellectual Property Rights (IPR)

FCC vs consulting company

▪ Highly skilled science trained personell at PhD-level

▪ Well aquainted with the process of applying for public funding

▪ Can work in an academic regime, including the possibility for publications as deliverables if requested

M O D E O F O P E R AT I O N

Project Quote

Grant Proposal