industrial mathematics applied to machine learning, big ... · mats jirstrand 2018- 02- 08...
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Mats Jirstrand
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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
- 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
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10.00
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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
▪ 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
Mats Jirstrand
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
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, ...
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