Foteini Liwicki

Biography

Foteini Simistira Liwicki received her Ph.D. diploma from the School of Electrical and Computer Engineering, NTUA, Greece, in the field of Pattern Recognition in 2015 with the title “Recognition of online handwritten mathematical expressions”. From 1997 till 2015, she worked as Research Associate in the Institute of Language and Speech Processing, ATHENA R.C., where she was mainly responsible for research programs in the field of Pattern Recognition, Machine Learning and Natural Language Processing. She was also highly involved in the design and development of innovative educational platforms (targeting mainly high school education in Greece but also in other European countries). From 2015 till June 2019 she worked as a PostDoc fellow in the University of Fribourg (DIVA research group) in the field of Document Image Analysis and Database generation. From June 2018 till June 2019 she worked as a PostDoc fellow with the Machine Learning group at the Luleå University of Technology, Sweden.

From May 2022, she is working as Associate Professor at the Luleå University, in the area of Machine Learning and Artificial Intelligence.

Research interests

• Machine Learning
• Artificial Intelligence (AI)
• Brain understanding
• Inner speech
• Behavior Analysis
• Natural Language Processing (NLP)
• Document Analysis
• Handwriting recognition

Reviewer in: She serves as a reviewer in international scientific journals and conferences (e.g. Pattern Recognition Letters, DAS, ICFHR, ICDAR).

Scientific member

• Institute of Document Analysis and Knowledge Science, Kyoto, Japan
• Education officer of the International Association for Pattern Recognition (IAPR) Technical Committee Number 11: Reading Systems

Medical Neuroscience by Duke University on Coursera. Certificate earned at Wednesday, July 3, 2019 11:09 AM GMT (certificate)

Research activities

Program Committee member

• 14th International Conference on Document Analysis and Recognition (ICDAR2017)
• 1st International Workshop on Open Services and Tools for Document Analysis (ICDAR-OST) , part of the ICDAR2017 conference
• 13th International Workshop on Document Analysis Systems (DAS2018)
• 15th International Conference on Document Analysis and Recognition (ICDAR2019) 
• 2nd International Workshop on Open Services and Tools for Document Analysis (ICDAR-OST), part of the ICDAR2019 conference
• 14th International Workshop on Document Analysis Systems (DAS2020)
• 17th International Conference on Frontiers of Handwriting Recognition (ICFHR2020)
• 25th International Conference on Pattern Recognition (ICPR2020)
• 16th International Conference on Document Analysis and Recognition (ICDAR2021) 

Competition Organiser

• ICDAR2017 Competition on layout analysis for challenging medieval manuscripts, part of ICDAR 2017 
• ICDAR 2019 Historical Document Reading Challenge on Large Structured Family Records (ICDAR-2019-HDRC-Chinese), part of ICDAR2019
• Competition organizer of ICDAR2023 Competition on Recognition of Handwritten Mathematical Expressions CROHME 2023, part of ICDAR 2023 

Competition Chair

• 16th International Conference on Document Analysis and Recognition (ICDAR2021) 

General Chair

• 18th International Conference on Document Analysis and Recognition (ICDAR2024) 

Evaluation of PhD theses

• 2019 - External examiner for Stefano Martina - PhD Thesis - Classification of cancer records with deep learning methods, University of Florence, Italy
• 2019 - External examiner for Qurat ul Ain - PhD Thesis - Segmentation of Urdu Nastalique, University of Engineering and Technology Lahore, Pakistan

Book chapters

1. Simistira Liwicki, F. and Liwicki, M., 2020. Deep learning for historical document analysis. In Handbook Of Pattern Recognition And Computer Vision (pp. 287-303).
2. Simistira Liwicki, F., 2020. DIVA-HisDB A Precisely Annotated Dataset of Challenging Medieval Manuscripts. Handwritten Historical Document Analysis, Recognition, And Retrieval-State Of The Art And Future Trends, 89, p.25.
3. Simistira Liwicki F. et al., Deep Neural Network approaches for Analysing Videos of Music Performances, submitted to Journal of Creative Music Systems 2021, status: under revision

Preprint

1. Simistira Liwicki, Foteini, et al. "Bimodal pilot study on inner speech decoding reveals the potential of combining EEG and fMRI." bioRxiv (2022). pdf

Teaching activities

Advanced Data Mining (D7043E), link
Introduction to Artificial Intelligence (D0030E), link
Program responsible of the national master in Applied Artificial Intelligence TCAIA, link
Program responsible of the international master in Applied Artificial Intelligence TMDIA, link

Presentations