Technical Workshop - New Technology Trends in GenAI
In this workshop, Guest Speaker Naresh R. Jasotani, Principal Technologist at Google, provided an
in-depth analysis of emerging trends in Generative AI (GenAI) and projected future advancements in
the field. Mr. Jasotani elaborated on Google's large language model, Gemini, and introduced tools
such as NotebookLM, GenAI Playground, and Gemini Studio. He emphasized the transformative potential
of GenAI agents and forecasted that the year 2025 would be a critical period for their development
and widespread implementation. The workshop offered valuable insights into the evolving landscape of
GenAI, highlighting its implications for various industries and the innovative applications that are
expected to emerge.
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Technical Workshop - Unlocking the Power of Recommender Systems by: Bahareh Rahmatikargar
This workshop marks the second workshop in a three-part series dedicated to the fascinating field of
social network analysis and its application in recommender systems. Step right into the fascinating
world of recommender systems! Our previous workshop explored various recommender systems, from
traditional models to the latest advancements. We discussed their strengths and limitations, the
data they use, and how to measure their performance.
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Technical Workshop - Advanced Topics in Data Clustering by Ali Abbasi Tadi
Clustering is a way of grouping data points into different clusters consisting of similar data
points. The objects with possible similarities remain in a group that has less or no similarities
with another group. In this workshop, we explore advanced topics in data clustering for high
dimensions. We will define the curse of dimensionality and dimensionality reduction methods like
t-SNE, PCA, and Isomap, as well as their implementations. We will explore the consensus matrix and
how to adjust the best clustering parameters.
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Technical Workshop - Basic Topics on Cloud Computing for IoT by Atefeh Gilvari
This workshop explores the integration of Cloud Computing with the Internet of Things (IoT),
detailing how data from loT devices is managed, analyzed, and utilized for decision-making through
platforms like AWS. It highlights the architecture of AWS for loT, including device connectivity,
security, and the practical applications of cloud technologies in enhancing efficiency and
reliability in IoT ecosystems.
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Technical Workshop - Foundations of Q-Learning by Xiaofeng (Michael) Liu
Reinforcement learning (RL) is training machine learning models to make a sequence of decisions.
Q-learning is a type of RL with a model-free environment which achieves learning by interaction with
the environment instead of transition probabilities. This workshop will introduce the principle of
Q-learning.
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Technical Workshop - Unveiling the Potential of Deep Reinforcement Learning (DRL): Methods and
Applications (1st) by Nahid Abdolrahmanpour
Deep Reinforcement Learning (DRL) has become a groundbreaking paradigm in artificial intelligence,
exhibiting the potential to solve complex decision-making tasks. This workshop aims to provide
participants with a complete understanding of the methods and applications of DRL, highlighting its
significance in various domains, including robotics, gaming, and autonomous systems. Additionally,
the workshop will delve into recent advancements and prospects, empowering participants to unlock
the full capabilities of DRL and its transformative impact on the technological landscape.
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Technical Workshop - Azure Machine Learning (2nd Offering) by Mahshad Hashemi
In this workshop, participants will delve into the basics of Azure Machine Learning, exploring its
comprehensive ecosystem and the role it plays in the Al and ML landscape. We will cover essential
components, the machine learning workflow, and the tools available for efficient model development.
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Technical Workshop - Intro to NLP (2nd Offering) by Soroush
Natural Language Processing (NLP) is a rapidly growing field of Artificial Intelligence that focuses
on enabling machines to understand, interpret, and generate human language. This introductory
workshop will cover the basics of NLP, its applications, and popular techniques, including text
classification, sentiment analysis, named entity recognition, and machine translation.
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Cultural Workshop - Iran: A Journey Through Culture and Tradition (2nd Offering) by: Nasrin
Tavakoli, and Atefeh Gilvari
Embark on a journey through the vibrant cultural tapestry of Iran! This interactive workshop invites
you to delve into the heart of Iran's traditions, spanning its music, dance, and the rich diversity
of its ethnic communities. From the soul-stirring melodies of Persian classical music to the
high-energy folk dances of regions across the country, you'll experience the profound beauty and
variety of each cultural expression. We'll uncover Iran's colourful history, artistic heritage, and
the unique contributions of its many ethnic groups-from the Persian and Azeri to the Kurdish and
Baluchi-each adding a distinct flavour to Iran's cultural mosaic.
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MSc Thesis Defense: HyCAN: A Hybrid Approach for CAN Bus Intrusion Detection using performance
analysis of Machine Learning Techniques.
Electronic Control Units (ECUs) have become important to modern vehicles, enhancing operational
control, driving comfort, and safety. These ECUs communicate using the Controller Area Network (CAN)
protocol, which despite its widespread adoption, is vulnerable to various security threats.
Therefore making CAN protocol more secure is crucial, intrusion detection systems (IDS) offer a
viable path to mitigating cyberattacks on vehicles.
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PhD Defense: Federated Learning: Taxonomy, Challenges, Future Directions, and a Brief Insight into
Graph Neural Networks for Decentralized Collaboration
Deep Learning is one of the most revolutionary concepts in the field of Artificial Intelligence,
allowing us to train a Machine Learning model for almost any type of problem using any type of data.
Federated Learning (FL) is a type of distributed Deep Learning framework in which the model is
trained locally on each device, and only the trained gradients, also known as “local updates”, are
sent to a central server that aggregates them and creates a global model.
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