:: Workshops

Workshop Presenter Abstract Goals Duration 
Deep Learning Pooya Mohammadi Kazaj Deep learning is one of the most popular and
widely used fields of artificial intelligence, which currently has the most occupations and
fields of research. This field is composed of
extensive sections, all of which are difficult to
recognize and follow not only for newcomers, but also for experts at the same time. In this
workshop, our goal is to cover different
sections, review the advantages and
disadvantages of different models and reasons for each model during It's time.
Finally, the research and industrial areas of
each section will be introduced and their
libraries and implementation frameworks will
be reviewed accordingly.
1. Introduction of artificial intelligence and the place of deep learning
2. Review and introducing of various areas of deep learning, including machine vision, natural language and sound processing, GAN models and deep reinforcement learning systems
3. Study of the evolution of models, libraries and frameworks
4. Investigating the future of the mentioned fields in industry and research sectors
Analyzing Fnancial Markets
with the
Help of
Machine Learning
Abolfazl Tazari Analysis of complex systems, such as
financial markets, is a field of science that,
due to its extreme complexity, cannot be
analyzed with conventional tools, and for this
reason, scientists have given attention to data
science and machine learning. Reputable
universities in the world conduct research in
this field during their doctoral studies.
In this workshop, we want to develop a machine learning-based system that analyzes
financial markets. Reviewing the data mining
process, feature extraction, preprocessing,
feature selection, modeling, model evaluation,
model aggregation and supermodel creation,
etc. are among the features of this theoretical /
practical workshop. 
1. Using classification algorithms such as Artificial Neural Network, SVM, XGBOOST, Random Forest, etc. in Python language and scikit-learn package
2. Using regression algorithms in Python language and scikit-learn package
3. Practical acquaintance with how to assemble(Ensemble) the algorithms with Bagging, Boosting and Stacking methods
4. Familiarity with data analysis in global financial markets
5. Familiarity with algorithmic traiding and how to implement it
Ethereum Network
DApp Programming
Omid Boushehrian Ethereum Network, As one of the most 
extensive peer-to-peer networks is becoming
the most important platform for providing
decentralized services due to the use of
blockchain technology. This network because of the possibility of the setting up systems
without the need for a central organization,
data transparency, scalability, and the use of
agreement mechanisms that make it almost
impossible for an organization to manipulate
the data is soon to be replaced the current
web and introducing a new web called Web 3.
This platform is currently used in various fields
including: Virtual organizations, financial
systems, the Internet of things, distributed
economy and energy. In this workshop, the
aim is to get acquainted with this network and
learn Web 3 programming and its practical
1. Familiarity with the basics of the Ethereum network and the concept of the global computer
2. Familiarity with its application areas
3. Introduce network components
4. Training on how to join the network and use it
5. Training on DApp engineering structure

Important Dates

  • Submission Deadline: November 30, 2020  December 20, 2020  (Anywhere on Earth)
  • Notification: February 3, 2021
  • Final Papers Due: February 13, 2021
  • Paper Registration: February 18, 2021
  • Conference: March 3-4, 2021