Mohsen Ghasemizade
About
Computer science professional with a strong background in NLP, machine learning, and data analytics. Skilled in developing end-to-end NLP pipelines and machine learning models using state-of-the-art techniques such as BERT. Passionate about leveraging data and AI to drive solutions and insights. Proven ability in uncovering patterns in complex datasets, specifically within the context of social media behavior and network security.
Advisee of Dr. Jeremiah Onaolapo
University Of Vermont
Research Interests
Conspiracy Theory
Honeypots
Social network security
Cybersecurity
Projects
:
Developing a Hierarchical Model for Unraveling Conspiracy Theories
Pre-print
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Developed a scientifically structured 'family tree' of conspiracy theories, categorizing and illustrating the connections among various conspiracies to enhance community understanding.
- Created the dataset by scraping articles from fact-checking websites and efficiently labeling them using Keyphrase Extraction, simplifying the process of identifying the main themes in each article.
- Developed a binary classifier using various machine learning methods, and our RoBERTa model achieved the highest performance with an F1 score of 87%, effectively distinguishing between conspiracy-related and non-conspiracy articles.
- Utilized the HDBSCAN + UMAP algorithm to facilitate effective data clustering and exploration, generating labels to be added to the main family tree.
Hacker Detector with Honey Documents
- Developed Google documents filled with simulated hacking methods for distribution acrosspaste sites.
- Utilized Cutlly API, Google App Script, and a self-controlled domain to examine visitor metrics such as visit count, edits made, geolocation, browser type, operating system, and device used.
- Differentiated between bots and non-bots accesses.
- Performed support vector machine (SVM), multilayer perceptron (MLP), convolutional neural network (CNN), decision trees, k-fold cross validation, linear and logistic regression on metadata of over 1 million songs to classify the genre, based on 7 different features.
- Simulated a secure, distributed cryptocurrency system, ensuring transparency and trust by enabling each participant to control and validate the ledger, effectively preventing fraudulent activities such as double spending.
- Leveraging a robust peer-to-peer network framework, our simulation demonstrates the essential processes of a cryptocurrency operation including transaction signing, block mining, broadcasting, and validation, culminating in a dynamic and decentralized digital currency ecosystem.
The Conspiracy Pipeline
Music Genre Classification Simulation of Distributed Cryptocurrency System
