I ndustrial Mathematics Applied to M a c hine Learning, B ig Data Analytics and Data Science 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 2018-02- 08 M a t s J i r s t r a n d, P h D H e a d of 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
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
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 )
F R AU N H O F E R - C H A L M E RS C E N T R E Basic funding - Founded September 2001 by Fraunhofer and Chalmers Fraunhofer 1/6 Chalmers 1/6 - Offers applied mathematics for a broad range of industrial applications FCC - Projects defined by companies and public institutes on a commercial basis 1/3 1/3 - Pre-competitive research and marketing with financing from our founders Industry projects EU grants Public grants - Systems and Data Analysis, Geometry and Motion Planning, Computational Engineering Fraunhofer-Gesellschaft 2016 67 research units & institutes Bremen Itzehoe Rostock MSEK 60.00 50.00 Industry projects European Commission Governments and Universities Fraunhofer (Special programs) 24 000 employees Hannover Braunschweig Golm Berlin Oberhausen Magdeburg Duisburg Dortmund Aachen Schmallenberg Dresden Euskirchen St. Augustin Jena Chemnitz 40.00 30.00 2.1 billion turnover Darmstadt Kaiserslautern Würzburg Erlangen Saarbrücken St. Ingbert Karlsruhe Pfinztal Stuttgart Freising Freiburg München Holzkirchen 20.00 10.00 0.00 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Year
F C C S YSTEMS AND DATA A N A LYS I S 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 - Machine Learning and Data Science - Big Data Analytics - Dynamical Systems Modeling - Pharmacokinetics and Pharmacodynamics - Systems and Synthetic Biology - Electrophysiology and Arrhythmia
S A M P L E ( P U B L I C ) PROJ E C T S AT S YS @ F C C
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 Big Automotive Data Analytics (13.7MSEK) Fleet telematics big data analytics for vehicle 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 Using Machine Learning (RCA-ML) Smart Assembly 4.0 SUstainability, smart Maintenance and factory design Testbed (SUMMIT)
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O O D I DA ON- 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 distributed environment involving vehicle on-board and off-board resources - Develop platform for distributed data analytics environments involving both 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 Volvo reference vehicles Entire vehicle fleet C A Central computational node B Data analysis node D - 2016 2019, FFI-BADA program, VINNOVA
TO O L S A N D T E C H N O LO GY
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 MACHINE LEARNING UNSUPERVISED LEARNING SUPERVISED LEARNING REINFORCEMENT LEARNING Discrete Continuous Discrete Continuous Take actions given the state of a system CLUSTERING DIMENSION REDUCTION CLASSIFICATION REGRESSION Find groups of similar individuals Describe individuals with fewer variables Categorize into predefined classes Predict continuous value given input
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 Probability & Stochastics Monte Carlo methods (MCMC, SMC) Probabilistic programming Gaussian processes Bayesian networks (PGM) Optimization Gradient based methods Automatic differentiation Sparse linear algebra,... Python R Statistics Mathematica Numerics/Symbolics Wolfram Workbench (IDE) Matlab Numerics BDA C++ Spark, Storm, MXNet, TensorFlow, AWS, Google Cloud, Azure Java, Scala, JavaScript, Erlang, NoSQL, Apache, Nginx, React, Angular, CUDA,
M O D E O F O P E R AT I O N
M O D E O F O P E R AT I O N 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 generic PhD or MSc project where deliverable depends on outcome and interest of scientific research 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 Project Quote Grant Proposal