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Keynote Speakers

Prof Jürgen Schmidhuber
Director, AI Initiative, KAUST

Thoughts about Machine Learning

Speaker’s Bio
The New York Times headlined: “When A.I. Matures, It May Call Jürgen Schmidhuber ‘Dad’.” Since age 15, his main goal has been to build a self-improving A.I. smarter than himself, then retire. His lab’s deep learning artificial neural networks based on ideas published in the “Annus Mirabilis” 1990-1991 have revolutionised machine learning and A.I. By 2017, they were on over 3 billion smartphones, and used billions of times per day, for Facebook’s automatic translation, Google’s speech recognition, Google Translate, Apple’s Siri & QuickType, Amazon’s Alexa, etc. He pioneered generative adversarial networks (1990, now widely used), artificial curiosity, Transformers with linearized self-attention (1991 – Transformers are the basis of the famous ChatGPT), and meta-learning machines that learn to learn (since 1987). Today, the most cited neural networks all build on work done in his labs. Elon Musk tweeted: “Schmidhuber invented everything.” He is recipient of numerous awards, Director of the AI Initiative at KAUST in KSA, Scientific Director of the Swiss AI Lab IDSIA, Adj. Prof. of A.I. at Univ. Lugano, and Co-Founder & Chief Scientist of the company NNAISENSE. He is a frequent keynote speaker at major events, and advising various governments on A.I. strategies.

Dr Maria Kamilaki
Acting Director General D.G. for e-Administration, Library & Publications
Hellenic Parliament 

Sharing the past, preparing the future. The digital transformation of the Hellenic Parliament Library.

Speaker’s Bio
Dr Maria Kamilaki is Acting Director-General of e-Administration, Library & Publications of the Hellenic Parliament. She holds a PhD in Sociolinguistics (University of Athens), a MSc in Applied Linguistics & English Language Teaching (University of Edinburgh) and a MSc in Cultural Management (Panteion University of Social & Political Sciences). She teaches at the Hellenic Open University postgraduate course Current trends in Linguistics for Teachers, and works as a Training Program Developer at the National Centre for Public Administration and Local Government. She is co-author of the book Pepper in the mouth! Aspects of taboo words in Standard Modern Greek and author of ‘Words that smile, words that hurt’: Verbal bullying in the school environment. A teacher’s Guide. She has also published a long series of papers in the field of Sociolinguistics and Language Teaching. Her research interests currently lie in parliamentary discourse analysis, carrying out a postdoctoral research, entitled From language attitudes to language policies: Aspects of the Greek Language Question in parliamentary discourse (University of the Aegean). She has a long-standing experience in designing educational programs and outreach activities at the Hellenic Parliament, such as Glossopolis: A multimodal exhibition on Modern Greek linguistic variety.

Prof Cheng-Lin Liu 
Director State Kay Laboratory of Multimodal
Institute of Automation of Chinese Academy of Sciences (CASIA)

Towards Explainable Document Recognition

Speaker’s Bio
Cheng-Lin Liu is a Professor at the State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation of Chinese Academy of Sciences. He is a vice president of the Institute of Automation, a vice dean of the School of Artificial Intelligence, University of Chinese Academy of Sciences. He received the PhD degree in pattern recognition and intelligent control from the Chinese Academy of Sciences, Beijing, China, in 1995. He was a postdoctoral fellow in Korea and Japan from March 1996 to March 1999. From 1999 to 2004, he was a researcher at the Central Research Laboratory, Hitachi, Ltd., Tokyo, Japan. His research interests include pattern recognition, machine learning and document image analysis. He has published over 400 technical papers in journals and conferences. He is an Associate Editor-in-Chief of Pattern Recognition Journal and Acta Automatica Sinica, an Associate Editor of International Journal on Document Analysis and Recognition, Cognitive Computation, IEEE/CAA Journal of Automatica Sinica, Machine Intelligence Research, CAAI Trans. Intelligence Technology, CAAI Artificial Intelligence Research and Chinese Journal of Image and Graphics. He is a Fellow of the CAA, CAAI, the IAPR and the IEEE.


IAPR/ICDAR Young Investigator Award 2024
Vincent Christlein
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)

Unraveling Scribal Authorship: New Frontiers in Writer Retrieval

Speaker’s Bio
Vincent Christlein heads the Computer Vision group at the Pattern Recognition Lab, Friedrich-Alexander University of Erlangen-Nürnberg (FAU), Germany. He received his diploma and Dr.-Ing. degrees from FAU in 2012 and 2018, respectively. His primary research focus is on document analysis, including writer identification and handwriting imitation, as well as environmental projects such as glacier front segmentation and bird detection. In the field of document analysis, his work has earned recognition through several international competition wins and multiple awards.

Abstract of the talk
How have recent advancements in machine learning transformed the field of writer identification and retrieval from handwritten text images? This presentation delves into the evolution from conventional techniques to state-of-the-art deep learning approaches. The first part of the talk dissects the process of writer retrieval, highlighting essential components and discussing significant contributions to the field. It will examine how modern deep learning technologies have significantly improved the accuracy and efficiency of identifying writers.
The second half of the talk shifts focus to the rapidly advancing area of handwriting generation and imitation. Initially facilitated by generative adversarial networks for handwriting synthesis, diffusion-based methods have recently taken the lead as more robust alternatives, capable of producing more diverse and realistic handwritten text. What implications do these emerging technologies hold for the future of document analysis? The discussion will highlight the potential impacts, emphasizing how these developments could reshape the landscape of writer identification and retrieval.