leiden university. the university to discover. leiden institute of advanced computer science liacs...

Leiden University. The university to discover.

Leiden Institute of Advanced Computer Science

LIACS

Joost N. Kok, Wetenschappelijk Directeur


LIACS

- Het Informatica Instituut van de Universiteit Leiden

- Onderdeel Faculteit Wiskunde en Natuurwetenschappen


LIACS

- Management Team- Scientific Director- Director of Education- Managing Director

- Opleidingscommissie- Instituutsraad- Examencommissie- Raad van Toezicht


Onderzoeksclusters van LIACS

- Algorithms- Foundations of Software Technology- Computer Systems- Imagery and Media- Technology Innovation Management


LIACS Research Clusters

Algorithms - prof.dr. Thomas Bäck & prof.dr. Joost Kok

Computer Systems - prof.dr. Ed Deprettere & prof.dr. Harry Wijshoff

Foundation of Software Technology - prof.dr. Farhad Arbab & prof.dr. Joost Kok

Imaging - dr. Michael Lew & dr.ir. Fons VerbeekTechnology Innovation Management – prof. dr.

Bernhard Katzy


Professors @ LIACS


LIACSFull ProfessorsAssociate ProfessorsAssistant ProfessorsPostdocsPhD studentsSupport staff


Taken

- 40% onderzoek – 40% onderwijs – 20% management

- 80% onderwijs – 20% onderzoek

- 1e, 2e en 3e geldstroom


Onderwijs

- Bachelor Informatica

- Master Computer Science- Master Media Technology- Master ICT in Businesss


Master Degrees Three Masters

Computer Science (including Bioinformatics Track)

Media Technology ICT in Business

Two years


PhD Education 60 PhD students @ LIACS

PromovendiBuiten Promovendi

Graduate SchoolOnderzoeksscholen

IPAACSISIKS

Leiden University. The university to discover.Algorithms Cluster @ LIACS


Natural computing

- Natural computing focuses on computational methods gleaned from natural models, such as evolutionary computation, molecular computing, neural computing, cellular automata, and swarm intelligence.


Natural Computing

- Computers are to Computer Science as Comic Books to Literature


Evolutionary Algorithms for Multi-Parameter Physics- Evolutionary algorithms are applied to

problems in multi-parameter physics, such as e.g. the control of femto-second lasers to impact molecules in a desired way.


The Fourth Paradigm

- Data-Intensive Scientific Discovery

“One of the greatest challenges for 21st-century science is how we respond to this new era of data-intensive science. This is recognized as a new paradigm beyond experimental and theoretical research and computer simulations of natural phenomena—one that requires new tools, techniques, and ways of working.”— Douglas Kell, University of Manchester


Data Mining definitions

- Secondary analysis of data- Induction of understandable useful models and patterns from data

- Algorithms for large quantities of data


-Data Mining is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data

useful

novel, surprising

comprehensible

valid (accurate)


Typical Data Mining Results

-Forecasting what may happen in the future-Classifying people or things into groups by recognizing patterns-Clustering people or things into groups based on their attributes-Associating what events are likely to occur together-Sequencing what events are likely to lead to later events


From “Querying” to “Mining”

Are there any occurrences of GAAT in this string?

How many occurrences of AAT are there in this string?

Which substrings of length 4 occur at least 2 times?

Which substrings (of any length) occur significantly moreoften in the

white string than in the black string?

Standard databasetechnology solves suchquestions

Data mining technologycan sometimes solve suchquestions (computationsmay be (too) heavy)

Science fictionWhy is the virus to the left resistant to my drug, and the one to the right

not?


Subgroup Discovery

- How to find comprehensible subgroups in large amounts of data?

- As an example: subtypes in complex diseases.

- Different types of input.

1

2

3

Class A Class B


Grand Challenges

- Lerende Auto’s


Robosail

- Website


Intelligent Bridge (InfraWatch)


Sensor network to monitor bridge behavior


145 sensors, 100Hz: 5GB/day

Vibration

Strain (longitudinal)

Strain (transverse)

Temperature


What are we looking for?

• Signs of decay (long)• Effects of traffic,

weather,... (medium)• Individual ‘events’

(short)• Relationships

between different signals

and why?

• Plan maintenance

• What kind of forces is bridge subjected to?

• How does bridge respond?

• How does one affect the other? What info do we really need? Fewer/different sensors?


The Challenge

- Sensor network: 145 sensors: 5GB/day, 8TB/4years

- Disk read @50MB/s takes 2 days

- We would like 2 minutes/seconds


Sensor Viewer

- We constructed a “mediaplayer” to view the data over time.


Graph Mining

Internet Map [lumeta.com]

Hyves

Protein Interactions [genomebiology.com]

Friendship Network [Moody]


Graph Mining Tasks- Object-Related

- Link-Based Object Ranking- Link-Based Object Classification- Object Clustering (Subgroup Detection)- Object Identification (Entity Resolution)

- Link-Related- Link Prediction

- Graph-Related- Subgraph Discovery- Graph Classification- Generative Models for Graphs


Visualisation

- Intelligent/Intelligible Data Analysis

- Intelligent = Methods- Intelligent = Human Interaction- Intelligible = Understandable

- First step:

- Visualisation of the data


DNA Visualisation

- Long patterns over small alphabets are hard to find …

- ababababababababababababababababababababababa . . .- (ab)

- abbbababaaababbabbbababaaababbabbbababaaababb . . .- (abbbababaaababb)

- abaaaababbbbabaaaababbbbabaaaababbbbabaaaabab . . .- (abaaaa · babbbb)


DNA Visualisation

- Associate each nucleotide A, C, T, G with a dimension

- Four nucleotides => four dimensions- Build a structure in four dimensions- Project to three dimensions


DNA Visualisation

- Expectation:

- A non-predictable walk for information rich parts of the DNA

- A true random walk for random parts

- Lines (or approximate lines) for repeating parts of the DNA

- Large identical substrings in the DNA can easily detected


DNA Visualisation- Select four three-dimensional vectors.

- The vectors should be of comparable length

- The four vectors should add up to 0

- Every subset of three vectors should be independent.


DNA Visualisation


The first 160,000 nucleotides of the human Y-chromosome


40,000–100,000 of the chromosome 1 (human)

Leiden University. The university to discover.Algorithms Cluster @ LIACS

leiden university. the university to discover. leiden institute of advanced computer science liacs...

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