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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
About me
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Posts
Future Blog Post
Published:
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Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
publications
RPoA: Redefined Proof of Activity
Published in Arxiv, 2022
We propose RPoA, a new consensus protocol that builds on top of some of the best features of the previous protocols, such as PoW, PoS, and PoA, and values active service provided by users on the network. While PoA tried to address some of the issues pertinent to PoS and PoW, it still fell short of solving the issues regarding high energy consumption, high resources needed, high mining latency, and the requirement for private blockchains. Our approach tries to address all the mentioned issues and falls in the service-based protocols category that gives mining credit to users as they serve on the network.
Recommended citation: S. Kamali, S. Shabihi, MT. Fakharian, A. Arbabi, P. Tajmehrabi, M. Saadati, B. Bahrak (2022). "RPoA: Redefined Proof of Activity." Arxiv. https://arxiv.org/pdf/2210.08923
research
Crystalline
Data Analytics Lab, University of Tehran, ECE Department, 2021
At the University of Tehran, my team and I developed a cryptocurrency as a proof of concept using pure Python, introducing a unique Proof of Activity as its main consensus protocol. This innovative protocol was crafted to amalgamate the advantages of both the PoS and PoA protocols. Our approach was informed by extensive research, including numerous papers and courses, and the study has been successfully concluded. For more information, please visit our repo.
Hate Speech Detection
Intelligent Information Systems Lab, University of Tehran, ECE Department, 2022
In our research, we aimed to detect hate speech in texts using a predefined dataset. We jointly trained the GCN (Graph Convolutional Networks) and BERT to model this data. Our model formed a heterogeneous graph with documents represented as nodes using BERT, allowing mutual interactions between local and global information. This interaction created a comprehensive classification representation. The multilayer graph contained connections between words, tweets, and hashtags, with inter-layer edges denoting semantic equivalence across languages. We also incorporated predefined words and tweets to enhance the graph’s relationships. Throughout this process, we reviewed numerous articles on GCN types, their synergy with BERT, and heterogeneous graphs. After extensive experimentation with different structures and state-of-the-art models, our research is now concluded.
Educational Technology
Cognitive Systems Lab, University of Tehran, ECE Department, 2023
Driven by a passion for transforming education using cutting-edge technology, we’ve spearheaded research into Artificial Intelligence, particularly Reinforcement Learning, to create tailored adaptive learning systems for students. Complementing this, we harness the potential of Large Language Models (LLMs), employing techniques such as fine-tuning and Reinforcement Learning from Human Feedback (RLHF), to produce bespoke educational content. Further enhancing our endeavor, we’ve introduced a novel pedagogy model for Deep Learning and designed a state-of-the-art Learning Management System (LMS). This LMS, developed with Python, Django, Bootstrap, and JavaScript, integrates an optimized ChatGPT with Google Classroom, leveraging AI tools like Discord and AI-based robots to offer a rich, personalized learning landscape. Our combined efforts aim for an engaging, accessible, and truly inclusive educational future, details of which can be found on our website.
Transfer Learning in Reinforcement Learning
My Bachelor’s Thesis, University of Tehran, ECE Department, 2024
In my bachelor’s thesis, I explored the application of Transfer Learning to improve the efficiency of Reinforcement Learning, with a particular focus on utilizing Large Language Models. Reinforcement Learning often faces challenges, such as extended learning times and difficulty adapting to new problems. My research aimed to address these challenges by using Transfer Learning to facilitate the transfer of knowledge from past experiences to new tasks. By leveraging Large Language Models, I investigated how identifying similarities and differences between previous knowledge and new problems can enhance learning efficiency. The experiments demonstrated that Large Language Models can significantly accelerate learning and improve decision-making, presenting a more efficient approach than traditional Reinforcement Learning techniques.
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Crystalline
Published:
At the University of Tehran, my team and I developed a cryptocurrency as a proof of concept using pure Python, introducing a unique Proof of Activity as its main consensus protocol. This innovative protocol was crafted to amalgamate the advantages of both the PoS and PoA protocols. Our approach was informed by extensive research, including numerous papers and courses, and the study has been successfully concluded. For more information, please visit our repo.
Hate Speech Detection
Published:
In our research, we aimed to detect hate speech in texts using a predefined dataset. We jointly trained the GCN (Graph Convolutional Networks) and BERT to model this data. Our model formed a heterogeneous graph with documents represented as nodes using BERT, allowing mutual interactions between local and global information. This interaction created a comprehensive classification representation. The multilayer graph contained connections between words, tweets, and hashtags, with inter-layer edges denoting semantic equivalence across languages. We also incorporated predefined words and tweets to enhance the graph’s relationships. Throughout this process, we reviewed numerous articles on GCN types, their synergy with BERT, and heterogeneous graphs. After extensive experimentation with different structures and state-of-the-art models, our research is now concluded.
Educational Technology
Published:
Driven by a passion for transforming education using cutting-edge technology, we’ve spearheaded research into Artificial Intelligence, particularly Reinforcement Learning, to create tailored adaptive learning systems for students. Complementing this, we harness the potential of Large Language Models (LLMs), employing techniques such as fine-tuning and Reinforcement Learning from Human Feedback (RLHF), to produce bespoke educational content. Further enhancing our endeavor, we’ve introduced a novel pedagogy model for Deep Learning and designed a state-of-the-art Learning Management System (LMS). This LMS, developed with Python, Django, Bootstrap, and JavaScript, integrates an optimized ChatGPT with Google Classroom, leveraging AI tools like Discord and AI-based robots to offer a rich, personalized learning landscape. Our combined efforts aim for an engaging, accessible, and truly inclusive educational future, details of which can be found on our website.
Transfer Learning in Reinforcement Learning
Published:
In my bachelor’s thesis, I explored the application of Transfer Learning to improve the efficiency of Reinforcement Learning, with a particular focus on utilizing Large Language Models. Reinforcement Learning often faces challenges, such as extended learning times and difficulty adapting to new problems. My research aimed to address these challenges by using Transfer Learning to facilitate the transfer of knowledge from past experiences to new tasks. By leveraging Large Language Models, I investigated how identifying similarities and differences between previous knowledge and new problems can enhance learning efficiency. The experiments demonstrated that Large Language Models can significantly accelerate learning and improve decision-making, presenting a more efficient approach than traditional Reinforcement Learning techniques.