fişa de verificare a ȋndeplinirii crieteriilor minimale ... file1 fişa de verificare a...

1

Fişa de verificare a ȋndeplinirii crieteriilor minimale naționale

corespunzătoare domeniului Informatică

a) Etica cercetării

Subsemnata, Adriana Birlutiu, am respectat toate normele de etica a cercetării şi prin urmare perspectiva a) o evaluez cu calificativul: îndeplinit. b) Producţia ştiinţifică

Productia stiintifica Ca

teg

ori

a

N

r.

a

u

to

ri

Punct

aj

1. B. Malli, A. Birlutiu, T. Natschlaeger. Standard-free calibration transfer - An

evaluation of different techniques. Chemometrics and Intelligent Laboratory

Systems, vol. 161, pp. 49–60, 2017

A 3 8

2. T. Geimer, M. Unberath, A. Birlutiu, O. Taubmann, J. Wolfelschneider, C. Bert,

A. Maier. A Kernel-Based Framework for Intra-Fractional Respiratory Motion

Estimation in Radiation Therapy. International Symposium on Biomedical

Imaging (ISBI'17), Australia, April 18 - 21, 2017.

A 7 1,6

3. A. Birlutiu, P. Groot, T. Heskes. Efficiently Learning the Preferences of People.

Machine Learning, ISSN: 0885-6125, pp. 1-28, 2013 A 3 8

4. T.E. De Boer, A. Birlutiu, Z. Bochdanovits, M.J.T.N. Timmerman, T.M.H.

Dikstra, N.M. van Straalen, B. Ylstra, D. Roelofs. Transcriptional Plasticity of a

Soil Arthropod Across Different Ecological Conditions. Molecular Ecology,

ISSN: 0962-1083, vol: 20, issue: 6, pp. 1144-1154, 2011.

A 8 1,33

5. A. Floares, A. Birlutiu. Decision tree models for developing molecular classifiers

for cancer diagnosis. International Joint Conference on Neural Networks

(IJCNN), Brisbane, Australia, June 10-15, 2012, pp. 1-7. Publisher: IEEE. ISSN:

2161-4393, Print ISBN: 978-1-4673-1488-6, 2012.

A 2 8

6. P. Groot, A. Birlutiu, T. Heskes. Bayesian Monte Carlo for the Global

Optimization of Expensive Functions. Proceedings of the 19th European

Conference on Artificial Intelligence (ECAI), Lisbon, Portugal, IOS Press,

Frontiers in Artificial Intelligence and Applications, vol. 215, pp. 249-254, 2010,

ISBN: 978-1-60750-605-8, 2010.

A 3 8

7. A. Birlutiu, T. Heskes. Expectation Propagation for Rating Players in Sports

Competitions. 11th European Conference on Principles and Practice of

Knowledge Discovery in Databases, Warsaw, Poland, Lecture Notes in Computer

Science, vol. 4702, pp. 374-381, Springer, ISBN: 978-3-540-74975-2, 2007.

A 2 8

TOTAL jurnale şi conferinţe din categoria A 42,93 8. T. Grubinger, A. Birlutiu, H. Schoner, T. Natschlager, T. Heskes. Domain

Generalization based on Transfer Component Analysis. IWANN International

Work Conference on Artificial Neural Networks, 13th International Work-

Conference on Artificial Neural Networks, IWANN 2015, Palma de Mallorca,

Spain, June 10-12, 2015. Proceedings, Part I. Series Volume: 9094, pp. 325-334.

Series ISSN 0302-9743, 2015.

B 5 1,33

9. A. Birlutiu, F. d Alche-Buc, T. Heskes. A Bayesian Framework for Combining

Protein and Network Topology Information for Predicting Protein-Protein

Interactions. IEEE/ACM Transactions on Computational Biology and

Bioinformatics, vol. 12(3), pp. 538-550, ISSN: 1545-5963, 2015

B 3 4

2

10. P. Groot, A. Birlutiu, T. Heskes. Learning from Multiple Annotators with

Gaussian Processes. Proceedings of the 21st International Conference on

Artificial Neural Networks (ICANN), Espoo, Finland, Lecture Notes in

Computer Science, Springer, vol 6792, Part II, pp. 159-164, ISBN: 978-3-642-

21737-1, 2011

B 3 4

11. A. Birlutiu, P. Groot, T. Heskes. Multi-Task Preference Learning with Gaussian

Processes. ESANN 2009, 17th European Symposium on Artificial Neural

Networks, Bruges, Belgium, online proceedings, pp. 123-128, 2009.

B 3 4

12. A. Birlutiu, P. Groot, T. Heskes. Multi-Task Preference Learning with an

Application to Hearing-Aid Personalization. Neurocomputing, ISSN: 0925-2312,

vol: 73, issue: 7-9, pp. 1177-1185, 2010.

B 3 4

TOTAL jurnale şi conferinţe din categoria B 17,33 13. A. Birlutiu, A. Burlacu, M. Kadar, D. Onita. Defect Detection in Porcelain

Industry based on Deep Learning Techniques. 19th International Symposium on

Symbolic and Numeric Algorithms for Scientific Computing, Timisoara,

September 21- 24, 2017.

C 4 1

14. T. Grubinger, A. Birlutiu, H. Schoner, T. Natschlager, T. Heskes. Multi-Domain

Transfer Component Analysis for Domain Generalization. Neural Processing

Letters (2017). doi:10.1007/s11063-017-9612-8

C 5 0,66

15. A. Birlutiu, T. Heskes. Using Topology Information for Protein-Protein

Interaction Prediction. In: Comin M., Käll L., Marchiori E., Ngom A., Rajapakse

J. (eds) Pattern Recognition in Bioinformatics. PRIB 2014. Lecture Notes in

Computer Science, vol 8626, pp. 10-22 Springer, Cham.

C 2 2

16. A. Birlutiu, D. Ardevan, P. Bulzu, C. Pintea, A. Floares. Integration of Clinico-

Pathological and microRNA Data for Intelligent Breast Cancer Relapse Prediction

Systems. In: Formenti E., Tagliaferri R., Wit E. (eds) Computational Intelligence

Methods for Bioinformatics and Biostatistics. CIBB 2013. Lecture Notes in

Computer Science, vol 8452, pp. 178-193. Springer, Cham.

C 5 0,66

TOTAL jurnale şi conferinţe din categoria C 4,32 TOTAL jurnale şi conferinţe din categoria A*+A + B + C 64,58 TOTAL jurnale şi conferinţe din categoria A*+A + B 60,26

Conferenţiar / CP II Profesor / CP I

Valori minime 32 56

Praguri A*+A+B>=16 A*+A>=24

A*+A+B>=40

C) Impactul rezultatelor

Nr

publ

icati

ei

care

citea

za

Referinta bibliografica a publicatiei k care citeaza 𝑺𝒌 ∑𝒌𝑺𝒌 𝒏𝒊 ∑𝒌𝑺𝒌 / max(1,𝒏𝒊-2)

3

B. Malli, A. Birlutiu, T. Natschläger. Standard-free calibration transfer - An

evaluation of different techniques Chemometrics and Intelligent Laboratory Systems,

Volume 161, 15 February 2017, Pages 49-60, ISSN 0169-7439

16 3 16

1. D.V. Poerio, S.D. Brown. Dual-Domain Calibration Transfer

Using Orthogonal Projection. Applied spectroscopy, 2017.

A 8

2. KDTM Milanez, TCA Nóbrega, DS Nascimento et al., Selection

of robust variables for transfer of classification models

employing the successive projections algorithm. Analytica

Chimica Acta Volume 984, 1 September 2017, Pages 76-85.

A 8

T Grubinger, A Birlutiu, H Schöner, T Natschläger, T Heskes. Domain

generalization based on transfer component analysis. IWANN 2015. 9 5 3

1. T Grubinger, GC Chasparis. Online transfer learning for climate

control in residential buildings. European Control Conference,

2016.

D 1

2. T Grubinger, GC Chasparis, T Natschläger. Generalized online

transfer learning for climate control in residential buildings.

Energy and Buildings. Volume 139, 15 March 2017, Pages 63-

71, 2017.

A 8

A. Birlutiu, F. d Alche-Buc, T. Heskes. A Bayesian Framework for Combining

Protein and Network Topology Information for Predicting Protein-Protein

Interactions. IEEE/ACM Transactions on Computational Biology and Bioinformatics,

vol. 12(3), pp. 538-550, ISSN: 1545-5963, 2015

14 3 14

1. S. Wang, Y. Zhang, X. Yang, P. Sun, Z. Dong, A. Liu,

Pathological brain detection by a novel image feature—

fractional Fourier entropy, Entropy, 17(12), 8278-8296, 2015

B 4

2. S. Bandyopadhyay, K, Mallick. A new feature vector based on

Gene Ontology terms for protein-protein interaction prediction.

IEEE/ACM Transactions on Computational Biology and

Bioinformatics, 2016.

B 4

3. JRG Manzan, K Yamanaka, IS Peretta, ER Pinto. A

mathematical discussion concerning the performance of

multilayer perceptron-type artificial neural networks through use

of orthogonal bipolar vectors. Computational and Applied

Mathematics, pp 1–22, 2016

C 2

4. P Dutta, S Basu, M Kundu. Assessment of semantic similarity

between proteins using information content and topological

properties of the Gene Ontology graph. IEEE/ACM Transactions

on Computational Biology and Bioinformatics, 2017.

B 4

A. Birlutiu, T. Heskes. Using Topology Information for Protein-Protein Interaction

Prediction. Pattern Recognition in Bioinformatics (PRIB), 9th IAPR International

Conference, volume 8626 of Lecture Notes in Computer Science, pages 10-22,

Springer International Publishing, Stockholm, Sweden, 2014.

11 2 11

1. B. Xia, H. Zhang, Q.M. Li, T. Li, PETs: A Stable and Accurate

Predictor of Protein-Protein Interacting Sites Based on

Extremely-randomized Trees, IEEE Transactions on

NanoBioscience, Volume:14 , Issue: 8, 2015

C 2

2. X. Peng, J. Wang, W. Peng, F.-X. Wu, Y. Pan. Protein–protein

interactions: detection, reliability assessment and applications.

Briefings in bioinformatics, 2016.

A 8

3. MZ Meriem. Approches Bio-inspirées pour la Fouille de

Données en Bioinformatique. PhD Thesis. BADJI MOKHTAR-

ANNABA UNIVERSITY UNIVERSITE BADJI MOKHTAR-

ANNABA, 2015.

D 1

4

A. Birlutiu, P. Groot, T. Heskes. Efficiently Learning the Preferences of People.

Machine Learning, ISSN: 0885-6125, pp. 1-28, 2013. 38 3 38

1. M. Elahi, F. Ricci, N. Rubens, Active learning strategies for

rating elicitation in collaborative filtering: A system-wide

perspective, ACM Transactions on Intelligent Systems and

Technology (TIST) - Special Section on Intelligent Mobile

Knowledge Discovery and Management Systems and Special

Issue on Social Web Mining archive Volume 5 Issue 1,

December 2013 Article No. 13, pp. 2157-6904, 2013

A* 12

2. K. Xu, S.S. Liao, R.Y.K. Lau, J. Leon Zhao, Effective Active

Learning Strategies for the Use of Large-Margin Classifiers in

Semantic Annotation: An Optimal Parameter Discovery

Perspective, INFORMS Journal on Computing, Volume 26,

Issue 3, 2014

D 1

3. Q. Zhao, F. Ye, S. Wang, A New Back-Propagation Neural

Network Algorithm for a Big Data Environment Based on

Punishing Characterized Active Learning Strategy, International

Journal of Knowledge and Systems Science (IJKSS), vol. 4,

Issue 4. 1947-8208, 2013

D 1

4. L. Zagar, Ranking by Multitask Learning, University of

Ljubljana, Doctoral Thesis, 2014 D 1

5. D. Mukhlisullina, A. Passerini, R. Battiti, Learning to diversify

in complex interactive Multiobjective Optimization, MIC 2013:

The X Metaheuristics International Conference, pag. 230-239,

Singapore, August 4–8, 2013

D 1

6. B.S. Jensen, Integration of top-down and bottom-up information

for audio organization and retrieval, PhD Thesis, Technical

University of Denmark, 2012

D 1

7. M Donini, D Martinez-Rego, M Goodson. Distributed variance

regularized Multitask Learning. Neural Networks (IJCNN), 2016

International Joint Conference on, 2016

A 8

8. P Campigotto, C Rudloff, M Leodolter. Personalized and

Situation-Aware Multimodal Route Recommendations: The

FAVOUR Algorithm. IEEE Transactions on Intelligent

Transportation Systems, Volume: 18, Issue: 1, Jan. 2017, pp. 92

– 102, 2017.

A 8

9. A Passerini. Learning Modulo Theories. In: Bessiere C., De

Raedt L., Kotthoff L., Nijssen S., O'Sullivan B., Pedreschi D.

(eds) Data Mining and Constraint Programming. Lecture Notes

in Computer Science, vol 10101. Springer, Cham, 2016.

C 2

10. J Sang, H Zhang, C Xu. Visual BFI: An Exploratory Study for

Image-Based Personality Test. In: Chen E., Gong Y., Tie Y.

(eds) Advances in Multimedia Information Processing - PCM

2016. PCM 2016. Lecture Notes in Computer Science, vol 9916.

Springer, Cham, 2016.

C 2

11. M. Donini. Exploiting the structure of feature spaces in kernel

learning. Ph.D. thesis, Padua, 2016. D 1

A. Floares, A. Birlutiu, Reverse engineering networks as ordinary differential

equations Computational Intelligence. NOVA Science Publishers, New York, 2012 2 2 2

1. C.T.C. Arsene, D. Ardevan, P. Bulzu, Reverse Engineering

Methodology for Bioinformatics Based on Genetic

Programming, Differential Expression Analysis and Other

Statistical Methods, Computational Intelligence Methods for

C 2

5

Bioinformatics and Biostatistics Lecture Notes in Computer

Science, Springer, pp 161-177, 2014

A Birlutiu, D Ardevan, P Bulzu, C Pintea, A Floares. Integration of Clinico-

Pathological and microRNA Data for Intelligent Breast Cancer Relapse Prediction

Systems. International Meeting on Computational Intelligence Methods for

Bioinformatics and Biostatistics. pp. 178-193 Springer, 2013

2 5 0,66

1. C Arsene. Partial Logistic Artificial Neural Network with

automatic relevance determination and Markov Chain Monte

Carlo methods applied in medical survival studies. IEEE

International Meeting on Computational Intelligence Methods

for Bioinformatics and Biostatistics (CIBCB), 2016

C 2

A. Floares, A. Birlutiu. Decision tree models for developing molecular classifiers for

cancer diagnosis. International Joint Conference on Neural Networks (IJCNN),

Brisbane, Australia, June 10-15, 2012, pp. 1-7. Publisher: IEEE. ISSN: 2161-4393,

Print ISBN: 978-1-4673-1488-6, 2012.

9 2 9

1. A.J. Aljaaf, D. Al-Jumeily, A.J. Hussain, D. Lamb, M. Al-

Jumaily, K. Abdel-Aziz, A Study of Data Classification and

Selection Techniques for Medical Decision Support Systems,

Intelligent Computing Methodologies Lecture Notes in

Computer Science Volume 8589, 2014, pp 135-143 , 2014

C 2

2. AJ Aljaaf, D Al-Jumeily, AJ Hussain, Toward an optimal use of

artificial intelligence techniques within a clinical decision

support system, Science and Information Conference (SAI),

IEEE, 2015

D 1

3. Hany A. Elsalamony, Bank Direct Marketing Analysis of Data

Mining Techniques , International Journal of Computer

Applications (0975 – 8887), Volume 85 – No 7, 2014

D 1

4. Hany. A. Elsalamony, Alaa. M. Elsayad, Bank Direct Marketing

Based on Neural Network, International Journal of Engineering

and Advanced Technology (IJEAT), ISSN: 2249 – 8958,

Volume-2, Issue-6, August 2013, 2014

D 1

5. AV Vasilakos, Y Tang, Y Yao. Neural networks for computer-

aided diagnosis in medicine: A review. Neurocomputing

Volume 216, Pages 700-708, 2016.

B 4

A. Birlutiu, Machine learning for pairwise data: applications for preference learning

and supervised network inference, PhD Thesis, Radboud University Nijmegen,

ISBN: 9789088913303, pp. 135, 2011

1 1 1

1. C. Brouard, Inférence de réseaux d’interaction protéine-protéine

par apprentissage statistique, Doctoral Thesis Universite dEvry-

Val dEssonne, France, 2013

D 1

P. Groot, A. Birlutiu, T. Heskes. Learning from Multiple Annotators with Gaussian

Processes. Proceedings of the 21st International Conference on Artificial Neural

Networks (ICANN), Espoo, Finland, Lecture Notes in Computer Science, Springer,

vol 6792, Part II, pp. 159-164, ISBN: 978-3-642-21737-1, 2011

74 3 74

1. C. Long, G. Hua, A. Kapoor. A joint gaussian process model for

active visual recognition with expertise estimation in

crowdsourcing. International Journal of Computer Vision,

Volume 116, Issue 2 , pp 136-160, 2016

A* 12

2. A. Tarasov, S.J. Delany, B. Mac Namee. Dynamic estimation of

worker reliability in crowdsourcing for regression tasks: Making

it work. Expert Systems with Applications Volume 41, Issue 14,

15 October 2014, Pages 6190-6210 0957-417, 2014

B 4

3. F. Rodrigues, F. Pereira, B. Ribeiro. Gaussian Process

Classification and Active Learning with Multiple Annotators. A* 12

6

Proceedings of the 31st International Conference on Machine

Learning (ICML-14), pp. 433-441, 2014

4. F. Rodrigues, F. Pereira, B. Ribeiro Sequence labeling with

multiple annotators. Machine Learning, Volume 95, Issue 2, pp

165-181 0885-6125, 2014

A 8

5. O. Dekel, C. Gentile, K. Sridharan. Selective sampling and

active learning from single and multiple teachers. Journal of

Machine Learning Research, ISSN 15337928, 2012

A* 12

6. F. Rodrigues, F. Pereira, B. Ribeiro. Learning from multiple

annotators: Distinguishing good from random labelers. Pattern

Recognition Letters Volume 34, Issue 12, pp. 1428-1436 0167-

8655, 2013

B 4

7. J.G. Gonzalez, M.A. Alvarez, Á A. Orozco. Automatic

assessment of voice quality in the context of multiple

annotations. Engineering in Medicine and Biology Society

(EMBC), 37th Annual International Conference of the IEEE, pp.

6236 – 6239, 2015

C 2

8. M. Venanzi. Trust-based algorithms for fusing crowdsourced

estimates of continuous quantities, University of Southampton,

Electronics and Computer Science, University of Southampton,

PhD thesis, 2014

D 1

9. A. Tarasov . Dynamic Estimation of Rater Reliability using

Multi-Armed Bandits, PhD Thesis, Dublin Institute of

Technology, 2014.

D 1

10. J. Suutala. Learning Discriminative Models from Structered

Multi-sensor Data for Human Context Recognition, Doctoral

thesis. University of Oulu, Finland, 978-951-42-9848-6, 2012

D 1

11. M. Venanzi, A. Rogers, N.R. Jennings. Crowdsourcing Spatial

Phenomena Using Trust-Based Heteroskedastic Gaussian

Processes. AAAI Conference on Human Computation and

Crowdsourcing, 2013

D 1

12. X Jia, A Khandelwal, J Gerber. Learning large-scale plantation

mapping from imperfect annotators. Big Data (Big Data), 2016

IEEE International Conference on, 2016.

D 1

13. D Padmanabhan, S Bhat, D Garg. A Robust UCB scheme for

active learning in regression from strategic crowds. Neural

Networks (IJCNN), 2016 International Joint Conference on,

2016.

A 8

14. E Besler, P Ruiz, R Molina. Classification of multiple annotator

data using variational Gaussian process inference. Signal

Processing Conference (EUSIPCO), 2016 24th European, 2016.

B 4

15. P Ruiz, E Besler, R Molina. Variational Gaussian process for

missing label crowdsourcing classification problems. Machine

Learning for Signal Processing (MLSP), 2016 IEEE 26th

International Workshop on, 2016.

D 1

16. P Ruiz, E Besler, R Molina. Spatial Resolution Enhancement in

Ultrasound Images from Multiple Annotators Knowledge. In:

Beltrán-Castañón C., Nyström I., Famili F. (eds) Progress in

Pattern Recognition, Image Analysis, Computer Vision, and

Applications. CIARP 2016. Lecture Notes in Computer Science,

vol 10125. Springer, Cham

C 2

T.E. De Boer, A. Birlutiu, Z. Bochdanovits, M.J.T.N. Timmerman, T.M.H. Dikstra,

N.M. van Straalen, B. Ylstra, D. Roelofs. Transcriptional Plasticity of a Soil 121 8 20,16

7

Arthropod Across Different Ecological Conditions. Molecular Ecology, ISSN: 0962-

1083, vol: 20, issue: 6, pp. 1144-1154, 2011.

1. T. Tully, M. Potapov, Intraspecific Phenotypic Variation and

Morphological Divergence of Strains of Folsomia candida

(Willem) (Collembola: Isotomidae), the "Standard" Test

Springtaill, Plos One, 2015

A 8

2. A.D. Biales, M. Kostich, R.M. Burgess, K.T. Ho, D.C. Bencic,

R.L. Flick, L.M. Portis, M.C. Pelletier, M.M. Perron, M. Reiss,

Linkage of Genomic Biomarkers to Whole Organism End Points

in a Toxicity Identification Evaluation (TIE), Environ. Sci.

Technol., 2013, 47 (3), pp 1306-1312, ISSN 0013-936X, 2013

A* 12

3. T. Nakamori, N. Kaneko, Biomarker responses reveal that food

quality affects cadmium exposure in the soil collembolan

Folsomia candida, Environmental Pollution Volume 176, May

2013, Pages 165-170, ISSN 0269-7491, 2013

A 8

4. CR Primmer, S Papakostas, EH Leder, Annotated genes and

nonannotated genomes: cross‐species use of Gene Ontology in

ecology and evolution research, Molecular Ecology, Volume 22,

Issue 12, June 2013, pp. 3216–3241, 2013

A 8

5. S.C. Novais, P. Lopes, T. Vandenbrouck, W. De Coen, et al.

Enchytraeus albidus Microarray: Enrichment, Design,

Annotation and Database (EnchyBASE), PLoS One 1932-6203,

2012

A 8

6. C. L. Richards, U. Rosas, J. Banta, N. Bhambhra, M.D.

Purugganan, Genome-Wide Patterns of Arabidopsis Gene

Expression in Nature, PLoS Genetics, ISSN 1553-7404, 2012

A 8

7. M. Mundry, E. Bornberg-Bauer, M. Sammeth, et al. Evaluating

Characteristics of De Novo Assembly Software on 454

Transcriptome Data: A Simulation Approach, Plos One,

Volume: 7, Issue: 2, 2012

A 8

8. S.C. Novais, M.J.B Amorim, Normal operating range (NOR) in

Enchytraeus albidus–Transcriptional responses to control

conditions, Applied Soil Ecology, Volume 85, pp. 1-10, 2015

A 8

9. A. V. Semenov, M.C. Pereira e Silva, J. Falcao Salles, H.

Schmitt, J.D. van Elsas, Quantitative assessment of soil

functioning across a representative range of Dutch soils,

Ecological Indicators Volume 39, April 2014, Pages 88-93, 2014

B 4

10. M.C. Pereira e Silva, A.V. Semenov, H. Schmitt, J.D. van Elsas,

J. Falcao Salles, Microbe-mediated processes as indicators to

establish the normal operating range of soil functioning, Soil

Biology and Biochemistry Volume 57, February 2013, Pages

995-1002, ISSN 0038-0717, 2013

A* 12

11. L. Stoetefeld, S. Scheu, M. Rohlfs, Fungal chemical defence

alters density‐dependent foraging behaviour and success in a

fungivorous soil arthropod, Ecological Entomology

Volume 37, Issue 5, pages 323–329, 2012

A 8

12. M.C. Pereira e Silva, F. Poly, N. Guillaumaud, J.D. van Elsas,

J.F. Salles, Fluctuations in Ammonia Oxidizing Communities

Across Agricultural Soils are Driven by Soil Structure and pH.

Frontiers in Microbiology, vol.3, nr. 77, ISSN 1664-302X, 2012

A 8

13. O. Sobral, Variability patterns and genetic determination of the

tolerance to metal-rich acid mine drainage by planktonic

invertebrates, PhD thesis, Universidade de Coimbra, Portugal,

2013

D 1

8

14. M. Morris, S.M. Rogers, Integrating Phenotypic Plasticity

Within an Ecological Genomics Framework: Recent Insights

from the Genomics, Evolution, Ecology, and Fitness of

Plasticity, Ecological Genomics Advances in Experimental

Medicine and Biology Volume 781, 2014, pp 73-105, ISSN:

978-94-007-7346-2, 2014

D 1

15. O. Kloeke, Living in a broccoli world:: Design of a decision

matrix for assessing the impact of novel (GM) crops on the soil

ecosytem, PhD thesis, Vrije Universiteit Amsterdam, 2013

D 1

16. TE de Boer, TKS Janssens, J Legler, et al., Combined

Transcriptomics Analysis for Classification of Adverse Effects

As a Potential End Point in Effect Based Screening. Environ.

Sci. Technol., ISSN: 0013-936X, 49 (24), pp 14274–14281,

2015.

A* 12

13. M Qiao, GP Wang, C Zhang, D Roelofs et al., Transcriptional

profiling of the soil invertebrate Folsomia candida in

pentachlorophenol-contaminated soil, Environmental

Toxicology and Chemistry, Wiley Online Library, 2015

B 4

P. Groot, A. Birlutiu, T. Heskes. Bayesian Monte Carlo for the Global Optimization

of Expensive Functions. Proceedings of the 19th European Conference on Artificial

Intelligence (ECAI), Lisbon, Portugal, IOS Press, Frontiers in Artificial Intelligence

and Applications, vol. 215, pp. 249-254, 2010, ISBN: 978-1-60750-605-8, 2010.

1 3 1




D 1

A. Birlutiu, P. Groot, T. Heskes. Multi-Task Preference Learning with Gaussian

Processes. ESANN 2009, 17th European Symposium on Artificial Neural Networks,

Bruges, Belgium, online proceedings, pp. 123-128, 2009.

14 3 14

1. J.B.B. Nielsen, J. Nielsen, J. Larsen, Perception-based

personalization of hearing aids using Gaussian processes and

active learning, IEEE/ACM Transactions on Audio, Speech, and

Language Processing, ISSN: 2329-9290, Volume:23 , Issue: 1,

2015

B 4

2. J.B. Nielsen Efficient individualization of hearing aid processed

sound, Acoustics, Speech and Signal Processing (ICASSP),

2013 IEEE International Conference on 1520-6149 2013

B 4

3. F Aiolli, A Sperduti, Supervised learning as preference

optimization, ESANN 2009 B 4

4. C. Dimitrakakis, C.A. Rothkopf, Bayesian multitask inverse

reinforcement learning. Recent Advances in Reinforcement

Learning 9th European Workshop, EWRL 2011, Athens,

Greece, September 9-11, 2011, Revised Selected Papers, Lecture

Notes in Computer Science, Springer, 2012

C 2

A. Birlutiu, P. Groot, T. Heskes. Multi-Task Preference Learning with an

Application to Hearing-Aid Personalization. Neurocomputing, ISSN: 0925-2312, vol:

73, issue: 7-9, pp. 1177-1185, 2010.

101 3 101

1. X He, G Mourot, D Maquin, J Ragot, P Beauseroy Multi-task

learning with one-class SVM. Neurocomputing Volume 133, 10

June 2014, Pages 416-426, ISSN: 0925-2312, 2014

B 4

2. D. Li, G. Hu, Y. Wang, Z. Pan Network traffic classification via

non-convex multi-task feature learning Neurocomputing

Volume 152, 25 March 2015, Pages 322-332, ISSN: 0925-2312,

2015

B 4

9

3. F. Dinuzzo Learning output kernels for multi-task problems

Neurocomputing Volume 118, 22 October 2013, Pages 119-126,

ISSN: 0925-2312, 2013

B 4

4. M.E. Abbasnejad, E.V. Bonilla, S. Sanner Decision-Theoretic

Sparsification for Gaussian Process Preference Learnin Machine

Learning and Knowledge Discovery in Databases Lecture Notes

in Computer Science Volume 8189, 2013, pp 515-530

(ECML/PKDD), 978-3-642-40990-5, 2013

A 8

5. E Abbasnejad, S Sanner, EV Bonilla, P Poupart Learning

community-based preferences via dirichlet process mixtures of

Gaussian processes, Proceedings of the Twenty-Third

International Joint Conference on Artificial Intelligence 978-1-

57735-633-2, 2013

A* 12

6. N. Houlsby, J.M. Hernandez-Lobato, F. Huszar, Z. Ghahramani,

Collaborative Gaussian Processes for Preference Learning

Advances in Neural Information Processing Systems NIPS, 2012

A* 12

7. S.P. Chatzis, Y. Demiris. A sparse nonparametric hierarchical

Bayesian approach towards inductive transfer for preference

modeling Expert Systems with Applications, 9574174, 2012

B 4

8. G. Skolidis, G. Sanguinetti. Bayesian multitask classification

with gaussian process priors IEEE Transactions on Neural

Networks, 1045-9227, 2011

D 1

9. Z. Xu, K. Kersting, Multi-task learning with task relations.

IEEE International Conference on Data Mining, 2011 A* 12

10. EV Bonilla, S Guo, S Sanner. Gaussian Process Preference

Elicitation. Advances in Neural Information Processing Systems

NIPS, 978-1-617-82380-0, 2010

A* 12

11. M.E. Khan, Y.J. Ko, M. Seeger. Scalable Collaborative

Bayesian Preference Learning Proceedings of the 17th

International Conference on Artificial Intelligence and Statistics,

vol. 33, p. 475-483

A 8

12. S. Guo Bayesian Recommender Systems: Models and

Algorithms, Doctoral Thesis The Australian National University,

PhD thesis, 2011

D 1

13. X. He, G. Mourot, D. Maquin, J. Ragot, P. Beauseroy, A.

Smolarz, E. GrallMaas, Multitask Learning for the Diagnosis of

Machine Fleet. Supervision and Safety of Complex Systems,

978-1-84821-413-2, 2012, Wiley

D 1

14. S. Skolidis Transfer learning with Gaussian processes, PhD

Thesis, University of Edinburgh 2012 D 1

15. S. Hiard, Trimming the complexity of Ranking by Pairwise

Comparison Universite de Liège, PhD Thesis, 2013 D 1




D 1

17. YJ Ko, Applications of Approximate Learning and Inference for

Probabilistic Models, PhD Thesis, École Polytechnique de

Laussane, 2017

D 1

18. L. Lu, Q. Lin, H. Pei, P. Zhong. The aLS-SVM based multi-task

learning classifiers. Applied Intelligence. pp 1–15, 2017. C 2

19. M. Montazery, N. Wilson. Dominance and Optimisation Based

on Scale-Invariant Maximum Margin Preference Learning.

Proceedings of the Twenty-Sixth International Joint Conference

on Artificial Intelligence (IJCAI-17).

A* 12

10

A. Birlutiu, T. Heskes. Expectation Propagation for Rating Players in Sports

Competitions. 11th European Conference on Principles and Practice of Knowledge

Discovery in Databases, Warsaw, Poland, Lecture Notes in Computer Science, vol.

4702, pp. 374-381, Springer, ISBN: 978-3-540-74975-2, 2007.

55 2 55

1. B. Potetz, M. Hajiarbabi, Whitened Expectation Propagation:

Non-Lambertian Shape from Shading and Shadow. Computer

Vision and Pattern Recognition (CVPR), 2013 IEEE Conference

on 1063-6919, 2013

A 8

2. S. Guo, S. Sanner, T. Graepel, W. Buntine, Score-Based

Bayesian skill learning. European conference on Machine

Learning and Knowledge Discovery in Databases - ECML

PKDD 2012, Volume Part I, pp. 106-121 978-3-642-33459-7,

2012

A 8

3. C. DeLong, J. Srivastava. Teamskill evolved: Mixed

classification schemes for team-based multi-player games.

PAKDD'12 Proceedings of the 16th Pacific-Asia conference on

Advances in Knowledge Discovery and Data Mining - Volume

Part I, 2012

A 8

4. C. DeLong, N. Pathak, K. Erickson, E. Perrino, K. Shim, J.

Srivastava, TeamSkill: modeling team chemistry in online multi-

player games. Proceedings of the 15th Pacific-Asia conference

on Advances in Knowledge Discovery and Data Mining -

Volume Part I, 2011

A 8

5. R. Houben, T. Dijkstra, W. Dreschler, Analysis of Individual

Preferences for Tuning of Noise-Reduction Algorithms, Journal

of the Audio Engineering Society, ISSN 1549-4950, 2013

B 4

6. C. DeLong, L. Terveen, J. Srivastava, Teamskill and the nba:

applying lessons from virtual worlds to the real-world,

ASONAM '13 Proceedings of the 2013 IEEE/ACM International

Conference on Advances in Social Networks Analysis and

Mining, pp. 156-161, ACM.

D 1

7. S. Guo, Bayesian Recommender Systems: Models and

Algorithms, Doctoral Thesis, The Australian National

University, 2011

D 1

8. J. Furnkranz, E. Hullermeier, Preference Learning, Encyclopedia

of the Sciences of Learning, Springer, ISBN 978-1-4419-1427-9,

2012

D 1

9. B Cseke, T Heskes. Approximate marginals in latent Gaussian

models. Journal of Machine Learning Research,

12(Feb):417−454, 2011.

A* 12

10. L Zhang, J Wu, ZC Wang. A factor-based model for context-

sensitive skill rating systems. Tools with Artificial Intelligence

(ICTAI), 2010 22nd IEEE International Conference on, 2010.

B 4

TOTAL citări in forumuri de tip A*, A și B 309

TOTAL citări 359,82

Conferenţiar / CP II Profesor / CP I

Valori minime 48 120

Praguri A*+A+B>=12 A*+A+B>=40

11

d) Performanţa academică

Performanța academică Punctaj

i) Cărţi autor/editate şi capitole publicate în edituri de categoria

(conform clasamentului SENSE):

1. A. Birlutiu Machine learning for pairwise data: applications for

preference learning and supervised network inference. Ph.D.

Dissertation, SIKS Dissertation Series, pp. 135, ISBN: 978-90-8891-

330-3, Uitgeverij BOXPress, Oisterwijk, Netherlands, 2011 (cărți

nelistate)

2. A. Floares, A. Birlutiu, Capitol de carte: Reverse Engineering

Networks as Ordinary Differential Equations Systems, Computational

Intelligence, NOVA Publisher, ISBN: 978-1-62081-959-3 (capitole

nelistate)

2 puncte

1 puncte

Total 3 puncte

ii) Editor proceedings la conferinţe de tip: - A | B | C | D

1. Proceedings of the 8th International Conference on Theory and

Applications of Mathematics and Informatics, ICTAMI 2015, Alba

Iulia, Romania, 17th-20th of September, Editors: N. Breaz, A. Birlutiu,

I.-L. Popa, D. Breaz, ICTAMI, Aeternitas Publishing House, 2015

(conferință indexată Zentralblatt MATH)

0,5 puncte

0,5 puncte

iii) Publicarea unui curs universitar in format electronic

1. Masini instruibile si recunoasterea formelor – Note de curs si aplicații.

Adriana Birlutiu, Manuella Kadar, Seria Didactica, UAB, 2018.

2. Baze de date orientate obiect – Note de curs si aplicatii. Adriana

Birlutiu, Manuella Kadar, Corina Rotar, Seria Didactica, UAB, 2018.

3. A. Bîrluțiu, I. Joldeș Programare orientată pe componente - Note de

curs și aplicații, Seria Didactica, UAB, 2014.

4. A. Birlutiu, M. Muntean, O. Domsa, Algoritmi fundamentali. Note si

aplicatii, Seria Didactica, UAB, 2015.

2 puncte

2 puncte

2 puncte

2 puncte

Total 8 puncte

iv) Director/editor al unei reviste de tip: A | B | C | D

1. Managing Editor, Acta Universitatis Apulensis (revista indexata

Zentralblatt, categoria D)

3 puncte

Total 3 puncte

v) Director (coordonator/responsabil), membru al unui

grant/proiect/contract/program de cercetare naţional/international

1. UEFISCDI/ PN-IIIP2-2.1-PED-2016-1835, Modele computationale

pentru reproducerea culorilor in produse ceramice (CMRCC), buget:

483.838,00 lei – membru

2. CNFIS-FDI-2017-0592 Mecanisme si de corelare a ofertei

educationale cu cerintele pietei muncii in cadrul Universitatii “1

DECEMBRIE 1918” din Alba Iulia (PRO-INSERT), buget: 31.777

EUR, – membru

3. UEFISCDI PN-III-P2-2.1-BG-2016-0333, Sistem inteligent bazat pe

învățare automată și vedere artificială pentru optimizarea fluxului de

fabricație a porțelanului (SIVAP), buget: 101.404,44 EUR – director

4. UEFISCDI/PN-II-PT-PCCA-2013-4-1959 IntelCor: Sisteme

Inteligente Non-invazive de Diagnostic si Prognostic in Cancerul

3 puncte

1 punct

6 puncte

5 puncte

12

Colorectal Bazate pe microARN Circulant Integrate in Workflowul

Clinic, buget: 638.000 EUR, – membru

5. UEFISCDI/PN-II-PT-PCCA-2011-3.1-1221: Sisteme inteligente de

predictie a recurentei si progresiei in cancerul superficial de vezica

bazate pe inteligenta artificiala si date microarray: mRNA tumoral si

microRNA plasmatic, buget: 444.000 EUR, – membru

6. HearClip – Personalizarea aparatelor auditive prin determinarea

Bayesiană a preferințelor utilizatorilor, NWO (Netherlands

Organization for Scientific Research) Olanda, 2007-2011, buget:

634,596 EUR - membru

4 puncte

5 puncte

Total 24 puncte

vii) Organizare evenimente stiintifice/școli de vara, in calitate de: -

director I membru in comitetul de organizare

1. Conferinta Recent Trends in Pure and Applied Mathematics , Alba

Iulia, 31 iulie – 5 august, 2017 - membru

2. SATEE Smart Applications & Technologies for Electronic

Engineering, 2016 - membru

3. ICTAMI- International Conference on Theory and Applications in

Mathematics and Informatics 2015 - membru.

4. Joint International Meeting of the American Mathematical Society and

the Romanian Mathematical Society, Alba Iulia, 27-30 iunie 2013, -

membru.

5. Concursul National Studentesc de Matematica Traian Lalescu, Alba

Iulia, 20-22 mai 2013 - membru

6. Sesiune de comunicari stiintifice a studentilor, Universitatea „1

Decembrie 1918” Alba-Iulia, 2017 – membru


Decembrie 1918” Alba-Iulia, 2016- membru







1 punct

1 punct

1 punct

1 punct

1 punct

1 punct

1 punct

1 punct

1 punct

1 punct

Total 10 puncte

viii) Profesor/researcher asociat/visiting la o universitate:

1. Visiting researcher, Telecom ParisTech, Franta, Prof. Florence

d’Alche-Buc, iunie 2017 (world rank: 236)

2. Visiting researcher, Friedrich-Alexander University Erlangen-

Nürnberg, Germania, Prof. Andreas Maier, iunie – iulie 2016 (world

rank: 350)

3. Visiting researcher, Johannes Kepler University, Linz, Austria, Prof.

Susanne Saminger-Platz, august – septembrie 2015 (world rank: 674)

2 puncte

4 puncte

2 puncte

Total 8 puncte

Premii și alte merite (la decizia universitilii sau institutului de cercetare)

1. UEFSCDI Program „Resurse Umane”, Subprogram „Premierea

rezultatelor cercetarii (articole)” 2017





13

TOTAL valori pentru perspectiva d) 56,5 puncte

Conferenţiar / CP

II Profesor / CP 1

Valori minimale pentru perspectiva d) 36

60

Prag: minim un proiect

Centralizator punctaje:

Perspectiva Punctaj realizat Condiții minimale

conferentiar

Condiții minimale

profesor

a) Etica

cercetării

Am respectat normele de etică a cercetării

Se respectă normele de etică a cercetării

Se respectă normele de etică a cercetării

b) Producţia

ştiinţifică

64,58 (A*+A+B>=60,26)

32 puncte (prag: A*+A+B>=16)


c) Impactul

rezultatelor

359,82 (A*+A+B>=309)



d) Performanţa

academică

56,5 puncte 36 puncte 60 puncte

fişa de verificare a ȋndeplinirii crieteriilor minimale ... file1 fişa de verificare a...

Documents