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Petia Radeva’s Homepage

ivanova

Full professor at the Department of Mathematics and Computer Science, Faculty of Mathematics and Computer Science, Universitat de Barcelona; Senior Researcher in Computer Vision Center (CVC);

Head of “Computer Vision and Machine Learning at the University of Barcelona” consolidated research group (www.ub.edu/cvub) SGR 1742; Research Manager of AEI, Ministry of Science and Innovation, Spain.

Collaborator of the State Agency of Research (Agencia Estatal de Investigación) area TIC (INF), División de Coordinación, Evaluación y Seguimiento Científico Técnico, November 2019.

Personal Web: http://www.ub.edu/cvub/petiaradeva,
email: petia dot ivanova at ub dot edu, radevap at gmail dot com
Google Scholar Citations, Microsoft Academic Record, LinkedIn, ResearchGate
 
We often have open postdoc positions on Machine learning and Computer Vision. If you are interested, send an email to radevap at gmail dot com
 
We have a vacant Master and/or PhD Fellowship on Deep learning and applications to real problems! If you are interested, send your CV and Academic transcript to radevap at gmail dot com

Biosketch

Prof. Petia Radeva is a Full professor at the Universitat de Barcelona (UB), Head of the Consolidated Research Group “Computer Vision and Machine Learning” at the University of Barcelona (CVMLUB) at UB (www.ub.edu/cvmlub) and Senior researcher in Computer Vision Center (www.cvc.uab.es). She was PI of UB in 7 European, 3 international and more than 25 national projects devoted to applying Computer Vision and Machine learning for real problems like food intake monitoring (e.g. for patients with kidney transplants and for older people). Petia Radeva is a REA-FET-OPEN vice-chair since 2015 on, and international mentor in the Wild Cards EIT program since 2017.

She is an Associate editor of Pattern Recognition journal (Q1, IP=7.196) and International Journal of Visual Communication and Image Representation (Q2, IP=3.13).

She is a Research Manager of the State Agency of Research (Agencia Estatal de Investigación, AEI) of the Ministry of Science and Innovation of Spain.

Petia Radeva has been awarded IAPR Fellow since 2015, ICREA Academia assigned to the 30 best scientists in Catalonia for her scientific merits since 2014, received several international awards (“Aurora Pons Porrata” of CIARP, Prize “Antonio Caparrós” for the best technology transfer of UB, etc).

She supervised 22 PhD students and published more than 100 SCI journal publications and 250 international chapters and proceedings, her Google scholar h-index is 49 with more than 9000 cites.

Currently, member of:

Research interests

My research interests are in the fields of:

  • Food image recognition by end-to-end learning
  • Food intake monitoring by Deep learning
  • Domain addaptation and multi-task learning
  • Uncertainty modeling
  • Lifelogging and egocentric vision
  • Event detection
  • Object discovery
  • Video segmentation and analysis
  • Image segmentation
  • Multi-class classification

Applications of Computer Vision and Machine Learning to Healthcare deserve special interest to me. In particular:

  • Applying life-logging to Mild Cognitive Impairment patients.
  • Applying life-logging to improve healthy habits of persons.
  • Wireless endoscopy for intestine motility disorders diagnosis.
  • Registration and retrieval of IVUS pullbacks for pre- post-intervention assessment.

Selected Recent Publications

Talks

  • Invited talk at the 12th International Conference on Pattern Recognition Systems (ICPRS) at MINES Saint-Etienne in France from June 7 to June 10, 2022.
  • Invited talk to the International Symposium on Artificial Intelligence (ISAI 2022) February 17 – 19, 2022 at Haldia Institute of Technology (https://hithaldia.in/), Haldia-721657, West Bengal, India.
  • Invited talk to the ICCV Workshop “LargeFineFoodAI: Large-Scale Fine-Grained Food AnalysIs”, October 2021.
  • Invited talk by the Asociación Española de Reconocimiento de Formas y Análisis de Imágenes (AERFAI), CEDI 2021,  (congresocedi.es) in Málaga, special session of AERFAI, 23 de Septiembre de 2021.
  • Invited talk on the Summer school DeepLearn, Gran Canarias, 29 of July 2021.
  • Invited talk on the Summer school Dataethics, on “Explainable and interpretable AI for transparent clinical decision making” 14 of July 2021, Barcelona.
  • Invited talk on “What is common btw Negative Transfer, Uncertainty and Food Recognition?”, 7 of July, 2021, Delta’2021, (online).
  • Invited talk on the conference Stanford Women in Data Science in Barcelona, 22 of June, 2021.
  • Invited talk on “Big Data – is my data the new oil?”, Barcelona 28 of April 2021. https://youtu.be/kdHILUFtaB4
  • Invited talk at FW: Fòrum d’Intel·ligències Artificials – Deep learning meets Healthcare, Hospital Vall d’Hebron, 15 of April 2021.
  • Invited talk at ICMV’2020, 3.11.2020.
  • Invited lecture to India, 24.06.2020, on “Food Image Analysis by Deep Learning” as part of the 5 days FDP programme on “Recent Advances of Machine Learning and its Applications” organised by Amity University Kolkata, India.
  • Invited lecture on the IV Workshop on Data and Knowledge Engineering (https://disc.cl/wdke), el cual forma parte de la XI Conferencia Internacional Infonor Chile 24.09.2020.(http://www.infonor2020.uda.cl/).
  • Invited plenary talk on “Uncertainty Modeling within an End-to-end Framework for Food Image Analysis”, DeLTA, Paris, France, 8 of July 2020.
  • Invited talk on “Uncertainty modeling for food analysis within end-to-end framework” the Summer school AI-DLDA, 29 of June, 2020, Udine, Italy.
  • Invited plenary talk on “Applying Deep Learning for Food Recognition”, ITNT, Samara, Russia, 28 of May 2020.
  • Invited plenary talk on “Uncertainty modelling within an End-to-end framework for Food Image Analysis” at Biostec, 13 of March, 2020, Valetta, Malta.
  • Invited talk on “Uncertainty modelling within an End-to-end framework for Food Image Analysis” at the University of Groningen, The Netherlands, 13 of February, 2020.
  • Invited plenary talk on “Uncertainty Modelling for Improving Food Recognition” at ICMV’2019, 16 of November, 2019, Amsterdam, The Netherlands.
  • Invited talk “Uncertainty-Aware Food Recognition by Deep Learning” in the Data Council, Barcelona, Spain, 29 of September 2019.
  • Invited inauguration talk on “Deep learning – science, technology or society solution?”, Faculty of Mathematics and Computer Science, Universitat de Barcelona, Spain, 25 of September, 2019.
  • Invited talk on “Uncertainty-aware Food image analysis” at the University Kliment Ohridski”, Sofia Bulgaria, 19 of August 2019.
  • Invited talk at the Barcelona.ai on “Uncertainty-aware Food image analysis”, 20 of June, 2019, Barcelona, Spain.
  • Invited talk on “Uncertainty-aware CNNs. Application to food image analysis” at the University of Otago, New Zealand, 17 of April, 2019.
  • Invited plenary talk at the Artificial Intelligence International Conference A2IC’2018, Barcelona, Spain, 22.11.2018.
  • Open lecture on “Uncertainty-aware CNNs. Application to food image analysis” as a conclusion of the second edition of the Deep Learning for Artificial Intelligence course of Master MET, UPC, 17.12.2018.
  • Invited talk on “Deep Learning and Computer Vision” at the Aula Magna, Lima, Peru, 5.11.18.
  • Invited talk on “Uncertainty-aware food analysis by Deep learning” at the International Conference on Machine Vision, ICMV’2018, Munich, Germany, 2.10.2018.

Team

PhD and Master students:

  • Martin Menchon, INTIA, UNICEN, Argentina co-supervised with Dr. Jose Massa.
  • Bhalaji Nagarajan.
  • Giuseppe Pezzano, co-supervised with Dr. Vicent Ribas.
  • Federico Gonzalez, University of Ushuaya, Argentina.
  • Ahmed

PhD. Collaborators:

  • Simone Balocco, Associate professor, UB.
  • Oliver Diaz, Lecturer, UB.
  • Santiago Egea, Postdoc, UB.

External collaborations:

  • Jose Massa & Jose Moreno, Universidad Nacional del Centro de la Provincia de Buenos Aires, Argentina.
  • Maite Garolera, Head of Neuropsychology Unit at Consorci Sanitari de Terrassa.
  • Russell Butson, University of Otago, Otago, New Zealand.
  • Domenec Puig, Associate professor, Universitat Rovira i Virgili (URV).
  • Xavier Giro-i-Nieto, Associate professor, UPC.

Supervised PhD thesis:

22. Marc Bolaños, 2021, Universitat de Barcelona.

21. Eduardo Aguilar, 2020, Universitat de Barcelona.

20. Alejandro Cartas, 2020, Universitat de Barcelona, co-supervised with Mariella Dimiccoli (UPC).

19. Margarida Torre, 2020, Universitat Autonoma de Barcelona, co-supervised with Fernando Martinez (UPC).

18. Estefania Talavera, 2020, University of Groningen, co-supervised with Prof. Nikolai Petkov.

17. Mostafa Kamal Sarker, 2019, URV, co-supervised with Prof. Domenec Puig.

16. Maedeh Aghaei, 2018, UB, co-supervised with Dr. Mariella Dimiccoli.

15. Felip Miralles, 2016, UB

14. Michal Drozdzal, 2014, UB

13. Marina Alberti, 2013, UB, co-supervised with Dr. Simone Balocco.

12. Francesco Ciompi, 2012, UB, co-supervised with Dr. Oriol Pujol.

11. Pierluigi Casale, 2011, UB, co-supervised with Dr. Oriol Pujol.

10. Sergio Escalera, 2009, UAB, co-supervised with Dr. Oriol Pujol.

9. David Rotger, 2008, UAB.

8. Fernando Vilariño, 2006, UAB.

7. Jaume Amores, 2006, UAB.

6. Ignacio Pulido, 2005, University of Zaragoza, co-supervised with Prof. Francisco Serón.

5. Misael Rosales, 2005, UAB

4. Debora Gil, 2004, UAB

3. Oriol Pujol, 2004, UAB

2. Cristina Cañero, 2003, UAB

1. Ricardo Toledo, 2002, UAB, co-supervised with Prof. Juanjo Villanueva.

Supervised Master thesis:

Ongoing:

  • Chenxi Liu, 2020, co-supervised with Federico Gonzalez.
  • Petar Tonchev, 2020, co-supervised with Javier Rodenas.
  • Maria Madalenova, 2020, co-supervised with Javier Rodenas.
  • Jordi Ventura, 2020, co-supervised with Javier Rodenas.
  • Xavier Lopez, 2020, co-supervised with Javier Rodenas.
  • Marc Asenjo, 2020, co-supervised with Eduardo Aguilar.

Defended:

  • Magi Toneu, 2020.
  • Gerard Marrufat, 2019, co-supervised with Eduardo Aguilar.
  • Carolin, Wuerich, 2019, co-supervised with Estefania Talavera.
  • Santos Bringas, 2019.
  • Emanuel Sanchez, 2018, co-supervised with Mariella Dimiccoli.
  • Montserrat Brufau, 2018.
  • Alex Ferrer, 2018.
  • Markos Gavalas, 2018.
  • Maria Leyva, 2017.
  • Pedro Herruzo, 2017.
  • Laura Portell, 2017.
  • Alberto Soto, 2017.
  • Emanuela Mollova, 2017.
  • Chen Zang, BioHealth Master, co-supervised with Laura Igual (UB).
  • Marc Bolaños, UB, 2014.
  • Maedeh Aghaei, UB, 2013, etc.

Teaching

Currently, I’m teaching:

Computer Vision, a course for 3rd year students of Computer Science undergraduate study at the University of Barcelona.

Artificial Vision, a Master course for students of graduate study on Artificial Intelligence, UPC-UB-URV.

Advanced Medical Imaging, a Master course for students of graduate study on Biomedical Engineering.

Graduate course on Data science, UB.

Professional Service

Chair of Visapp, 2020, Malta.

Program committee of MICCAI’2015, CAIP’2015, CCIA’2015, etc.

Local area chair at CVPR’2014.

Local area chair of ICPR’2014.

Special sessions chairwomen of ICME’2014.

Chairwomen of Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT), Workshop at MICCAI’2014.

Program committee member of: CBMS 2019, APPIS 2019, SITIS 2018, MMM 2019, CAIP2019, CAEPIA 2018, CCIA 2018, SITIS 2018, MADiMa2018, CompIMAGE’18, MLDM 2018, APPIS 2018, ISCSA2017, MMWorkshop17, SSPandBE 2017, NFQS3, CCIA 2017, CBMS2017, WBICV2017IBPRIA’17, WiML’2016, CIARP’2016, CCIA’2016, DATRA’2016, CompIMAGE’2016, LTA’2016, MLPRA’2016, CCIA’2015, CIAPR’2014, CIARP’2014, AMDO’2012, CAIP’2013, CCIA’2014, CIARP’2012, CIARP’2014, CAIP’2015, CAIP’2013, JCC’2013, JCC’2014, MCPR’2013, MICCAI-STENT’2013, MICCAI-STENT’2012.

Petia Radeva has been the honorable chair of ICMV 2018, ICMV 2017, ICMV 2016, ICMV 2015, ICMV 2014, and invited speaker at AMDO’2014, CAIP’2015, CEIG’2003, etc.

Press

Petia Radeva: «La transferència de coneixement és un motor de motivació per als investigadors», interview from the Fundació “Bosch i Gimpera”, May, 2016, Barcelona.

El CST rep una beca per la investigació en malalties neurodegeneratives“, terassadigital.cot, 31 d’Octubre, 2014.

“Convertir las 8 horas de película de la endoscopia sin cable en un ‘corto’”, El Mundo, April, 9th, 2014.

elmundo

Algoritmos de visión artificial para la salud coronaria“, 08/01/2014, ingenieros.es, Portal de ingenieros superiores.

“Algorithms of computer vision for coronary health”, UB, 2014.

UB premia estudis de neurolingüística i de nanotecnologia per vèncer càncer“, La Vanguardia, Desember 12, 2013.

La UB reclama més inversió pública per transferir el coneixement de les universitats“, EuropaPress, Desember 12, 2013.