Visual Guide to AI and ML Research at Lund University through Statistics and Machine Learning

Cluster id

Search terms used to identify 1.345 AI and ML related publications in LUCRIS Lund University research information system. Each dot in the illustration to the right represents one of these publications. In the illustration the publications are

clustered based on relative similarity of the content of their abstracts.

80 60 40 20 0 20 40 60

x

60

40

20

0

20

40

60

y

Cluster map of abstracts

cluster-id

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

80 60 40 20 0 20 40 60

x

60

40

20

0

20

40

60

y

Cluster map of abstracts

cluster-id

0

1

2

3

4

5

6

7

8

9

10

11

12

13

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16

Search terms

Cluster share

Year

Nou

n ph

rase

s

Cluster 5 (9.96%)

Cluster 16 (8.85%)

Cluster 2 (8.40%)

Cluster 0 (7.81%)

Cluster 3 (6.99%)

Cluster 7 (6.62%)

Cluster 8 (5.58%)

Cluster 11 (5.50%)

Cluster 9 (5.28%)

Cluster 15 (4.98%)

Cluster 6 (4.83%)

Cluster 13 (4.83%)

Cluster 12 (4.68%)

Cluster 4 (4.61%)

Cluster 1 (4.61%)

Cluster 10 (3.79%)

Cluster 14 (2.68%)

Cluster share

0 20 40 60 80 100 120 140Count

1977197919811982198319841986198719881989199019911992199319941995199619971998199920002001200220032004200520062007200820092010201120122013201420152016201720182019

Year

Publications per year

Cluster similarity matrix

Cluster id

Clu

ster

id

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16Cluster ID

01

23

45

67

89

1011

1213

1415

16Cluster ID

1.00 0.74 0.64 0.57 0.74 0.87 0.74 0.92 0.70 0.64 0.52 0.71 0.67 0.82 0.66 0.85 0.75

0.74 1.00 0.65 0.66 0.81 0.71 0.74 0.80 0.76 0.66 0.57 0.63 0.77 0.70 0.74 0.81 0.75

0.64 0.65 1.00 0.63 0.64 0.66 0.84 0.70 0.67 0.67 0.84 0.65 0.68 0.74 0.64 0.72 0.80

0.57 0.66 0.63 1.00 0.77 0.61 0.68 0.65 0.72 0.67 0.49 0.88 0.75 0.68 0.70 0.75 0.82

0.74 0.81 0.64 0.77 1.00 0.72 0.77 0.85 0.75 0.77 0.58 0.79 0.86 0.74 0.72 0.85 0.72

0.87 0.71 0.66 0.61 0.72 1.00 0.72 0.87 0.74 0.68 0.51 0.75 0.74 0.85 0.63 0.78 0.85

0.74 0.74 0.84 0.68 0.77 0.72 1.00 0.81 0.76 0.75 0.85 0.71 0.73 0.80 0.66 0.80 0.79

0.92 0.80 0.70 0.65 0.85 0.87 0.81 1.00 0.79 0.72 0.59 0.75 0.82 0.84 0.73 0.85 0.79

0.70 0.76 0.67 0.72 0.75 0.74 0.76 0.79 1.00 0.63 0.64 0.69 0.76 0.73 0.82 0.82 0.81

0.64 0.66 0.67 0.67 0.77 0.68 0.75 0.72 0.63 1.00 0.60 0.76 0.82 0.79 0.64 0.78 0.70

0.52 0.57 0.84 0.49 0.58 0.51 0.85 0.59 0.64 0.60 1.00 0.53 0.56 0.63 0.52 0.61 0.66

0.71 0.63 0.65 0.88 0.79 0.75 0.71 0.75 0.69 0.76 0.53 1.00 0.75 0.84 0.62 0.79 0.82

0.67 0.77 0.68 0.75 0.86 0.74 0.73 0.82 0.76 0.82 0.56 0.75 1.00 0.78 0.75 0.78 0.76

0.82 0.70 0.74 0.68 0.74 0.85 0.80 0.84 0.73 0.79 0.63 0.84 0.78 1.00 0.63 0.83 0.84

0.66 0.74 0.64 0.70 0.72 0.63 0.66 0.73 0.82 0.64 0.52 0.62 0.75 0.63 1.00 0.82 0.73

0.85 0.81 0.72 0.75 0.85 0.78 0.80 0.85 0.82 0.78 0.61 0.79 0.78 0.83 0.82 1.00 0.82

0.75 0.75 0.80 0.82 0.72 0.85 0.79 0.79 0.81 0.70 0.66 0.82 0.76 0.84 0.73 0.82 1.00

Cluster similarity matrix

0.5

0.6

0.7

0.8

0.9

1.0

(“artificial intelligence” OR “machine learning” OR “deep learning” OR “autonomous systems” OR “pattern recognition” OR “image recognition” OR “natural language processing” OR “robotics” OR “image analytics” OR “big data” OR “data mining” OR “computer vision” OR “predictive analytics”)

Publications per yearn=1.699

Weighted publication title noun phrases

0 5 10 15Weight

applicationmotioncontrol

computer visionartificial neural network

robotdevelopment

structureheart transplantation

analysisdesign

classificationrobotic

effectsegmentation

comparisonreconstruction

anticipationmodelingdetection

evaluationexperiment

outcomemultiple view geometry

diagnosismachine learning

patientsystem

minimal problemsmean

attentionline

gynecological diseasemodel

challengesimulation

industrial robotbig datum

methodintegration

deep learningshape

computational modelperformance

pattern recognitionassociative self-organizing map

experiencedatum

interactionresponse

actionview

clinical and ultrasound characteristicconcept

motion estimationknowledge

validationinternal simulation

pointsurveyautism

learningapplications

robotsbox jellyfish

imagesadaptive control

contextarchitecture

unknown focal lengthpredictioncognition

pose estimationtoll-like receptorteachable agent

strategyuse

proceedingtext

relative poseautomate subject classification

approachtracking

revieweuclidean reconstructiondiscrete-event simulationcomputational methods

peoplehand-eye calibration

planar motionmind

extensionradial distortion

haptic perceptionrole

3d reconstructionoptimization

eyegeneralized roof dualitysemantic segmentation

Noun phrase

Weighted publication title noun phrases

Count

Aut

hor

0 25 50 75 100 125Count

Kalle Åström

Christian Balkenius

Rolf Johansson

Anders Heyden

Anders Robertsson

Pierre Nugues

Carl Olsson

Magnus Oskarsson

Birger Johansson

Mattias Ohlsson

Klas Nilsson

Peter Gärdenfors

Jacek Malec

Johan Nilsson

Lil Valentin

Bodil Andersson

Niels Christian Overgaard

Peter Höglund

Håkan Ardö

Mathias Haage

Elin Anna Topp

Mikael Nilsson

Agneta Gulz

Volker Krueger

Cristian Sminchisescu

Gunnar Sparr

Rasmus Bååth

Håkan Eftring

Magnus Haake

Carita Paradis

Mauno Vihinen

Fredrik Bagge Carlson

Stefan Larsson

Erik Bylow

Dennis Medved

Karl Johan Åström

Carsten Peterson

Britt Östlund

Martin Karlsson

Giorgos Nikoleris

Lars Hall

Germund Hesslow

Marcus Klang

Anasua Sarkar

Heiko Herwald

Eric Warrant

Henrik Jörntell

Anna Balkenius

Petter Johansson

Marcus Carlsson

Author

Publications with LU author

Lund University authors

Count

n=1.699

Weight

Cluster map of abstracts combined with word clouds of these abstracts

n=1.699

http://aiml.lu.se/visual-guide

Concept & Technical Design: Marcus Klang and Jonas Wisbrant