Keynote Speakers

:: Keynote Speakers

Prof. Reza Boostani

Applications of Deep Learning and Signal Processing Methods for the Diagnosis of Neurological and Psychiatric Diseases

Abstract: Electroencephalogram (EEG) signals are spatiotemporal integration of electrical activity of millions of neurons in the brain. Functional changes in the brain, raised by different psychiatric and neurologic diseases, can be detected through analysis of EEG signals and also the disease severity can be quantitatively measured. In this talk, I introduce deep learning and signal processing methods for continuous estimation of the Beck score for depressed patients and also propose efficient methods for classifying four levels of depression. An efficient deep network is introduced to estimate Bispectral Index Scale (BIS) continuously, from EEG signals, as a reliable measure of depth of anesthesia during surgery operation. I also explain how to differentiate five levels of pain by analysis of EEG signals using signal processing methods. A few attempts have been made by my team to estimate Montreal Cognitive Assessment (MOCA) scale and Unified Parkinson’s Disease Rating Scale (UPDRS) continuous score as measures of Parkinson’s severity by applying deep neural networks and also deep fuzzy networks to raw EEG signals.

Prof. Reza Boostani was born in Shiraz in 1973. He got his B.Sc. in Electrical Engineering from Shiraz University in 1996 and then got his M.Sc. and Ph.D. in Biomedical Engineering from Amirkabir University of Technology in 1999 and 2005, respectively. Afterward he joined CSE&IT department of Shiraz University in 2005 as an assistant Professor. He has become selected as distinguished researcher of Shiraz University three times in 2010, 2017 and 2023, respectively. In addition, according to Elsevier criterion, he has been selected in the list of top 2% scientists in the world both in 2022 and 2023. he has published more than 225 papers (including 145 journal papers and the rest are conference papers) and have 27 journal papers under review. Now, he is a full professor at Shiraz University and is the head of Biomedical Engineering Group from 2008 till now. His research area is biomedical signal processing, statistical pattern recognition and deep neural networks.

Dr. Serwah Sabetghadam

Metadata Management in Data-driven Companies

These days organizations rely heavily on data quality for orienting their business strategy to market positioning. Lack of adequate metadata, named as data littering, leads to difficulty in understanding, managing or reusing the data. Dealing with large amount of data in data-driven companies raises the need to know where data originates from, how is the quality, who is the data owner, and what is the history of the changes. Data littering causes companies high expenses by incorrect interpretations, resulting in misguided decisions or strategies. In addition, dealing with low-quality data can result in poor productivity due to low trustability in data-driven decisions. Metadata management helps an organization to find, understand and trust the right information to decide based on the gained insight. It facilitates seamless data sharing between teams, departments, and organizations. Furthermore, it enforces data governance, controlled data quality and facilitated data discovery through data catalogue. In this talk main concepts of metadata management in an enterprise company, including data governance, data lineage, data quality and different roles and responsibilities related to metadata management is presented.

Dr. Serwah Sabetghadam, received her bachelor degree in Software Engineering from Tehran University. She continued her master studies in Computer Architecture at Shahid Beheshti University designing a secure protocol for evaluating mobile applications. Following her interest in data area, she continued her research in the field of Multimodal Information Retrieval in her PhD studies in Technical University of Vienna in Austria. After graduation she worked for a while as freelance researcher in the faculty of Informatics of the same university. Appealing to put her knowledge in industry projects, she switched to industry by working in top two Telecom companies in Vienna. She deals daily now with designing data pipelines as Data Solution Designer at Magenta Telecom company in Vienna. Her favorite areas are Multimedia Information Retrieval, Crowdsouring and working on Meta Data quality in big data projects

Prof. Alireza Nematollahi

Statistics and AI: A winning combination in the age of information explosion

The emergence and growth of data science in recent years and the introduction and rapid expansion of new and attractive sciences and techniques such as machine learning and data mining methods in the age of information explosion and facing big data analysis have raised this question in the minds of scientific communities; where is the role and position of science and statistics in this field? As they say, are statistics left behind by the high-speed train of data science, which mainly deals with the analysis of big data produced in this era? Can interaction and insight between statisticians and experts in the field of artificial intelligence, as two sides of the same coin, lead to their integration? This seminar aims to attempt to provide a suitable and worthy answer to these questions and to provoke an open discussion about the evolving role of statistics in the rapid development of artificial intelligence. We will review some of the top statistical ideas of the past half century that have contributed to the artificial intelligence revolution, and discuss the long-term satisfaction and utility of statistical answers, which are due to the theoretical and mathematical study of statistical methods and analyses. By strengthening and continuing the discussion about the interplay of statistics and AI, it is possible to advance statistical research paradigms to embrace the opportunities that AI presents. The powerful combination of artificial intelligence technologies with modern statistical methods and their synergy can provide a winning and efficient combination in addressing the challenges facing data science researchers in the current era of information explosion.

Prof. Alireza Nematollahi is a full professor of statistics at Shiraz University with over 25 years of experience in academia and research. He has made significant contributions to the advancement of statistics education and research, publishing over 50 research articles in renowned international journals and actively involved in numerous research projects. His research interests are focused on probability theory, time series analysis, stochastic processes, regression, and multivariate statistical analysis. In 2021, he and some of his colleagues founded the Data Science major in the Master of Statistics program in the Department of Statistics at Shiraz University, and he has been also working in this field ever since. From 2016 until 2018, he was the president of the Iranian Statistical Society and currently works as the editor-in-chief of the Iranian Statistical Association Journal.

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