As a skilled Software Engineer specializing in Python development, I am dedicated to building robust and scalable software solutions. Passionate about Software Engineering, Data Science, Machine Learning, Coding, Mathematics, and History, I thrive as a Software Engineer specializing in AI and Computer Vision. Leveraging cutting-edge technologies and methodologies, I develop innovative solutions that address complex challenges in the field. With expertise in Python, Object-Oriented Programming (OOP), Web Development, database management using SQL, version control with Git, and containerization using Docker, I build robust and scalable software systems. I am well-versed in Linux environments and have a strong understanding of cloud computing technologies.
During my Master's in Data Science at the Sapienza University of Rome, I had the privilege to collaborate with esteemed professors in Rome, as well as Prof. Fosca Giannotti and Prof. Riccardo Guidotti at KDD Lab - CNR at Pisa. Throughout this program, I embarked on various projects in fields such as Data Science, Machine Learning, Deep Learning, IoT, Explainable AI, and Statistics. These experiences provided me with a solid foundation in developing advanced AI models and leveraging data-driven insights for effective problem-solving. Remarkably, for my master's thesis project, I focused on eXplainable AI (XAI) projects and developed a novel data and model-agnostic dialogue system. This system enables end-users to interact with prediction models and their explanations effectively.
One notable achievement includes publishing a paper that explores the intersection of IoT and Machine Learning, uncovering opportunities for driving innovation and solving real-world challenges. As a proactive and collaborative team member, I am dedicated to continuous learning and professional growth. Currently working as a Software Developer at Aylook, specializing in AI and Computer Vision, I actively contribute to the development of cutting-edge solutions. I leverage my expertise in software engineering to push the boundaries of technology. Additionally, I have made significant contributions to the growth of Namaad Tosse Arya Co. in Tehran, Iran, where I dedicated approximately 2 years to Python-based web development and successfully implemented ERP (Enterprise Resource Planning) projects.
Research on eXplainable Artificial Intelligence (XAI) projects focusing on Natural Language Processing (NLP) and Dialogue Systems
Developed Python-based Web Development projects and ERP (Enterprise Resource Planning) solutions
Relevant Coursework: Advanced Machine Learning, Data Management, Data Mining, Digital Epidemiology, Neural Networks, Statistical Methods, Cloud Computing, Networking for Big Data.
Relevant Coursework: Advanced Programming, Algorithms Design, Data Structures, Database Design, Digital Image Processing, Software Engineering, Statistical Methods.
Bachelor Thesis: Group Constraints Clustering based on Ensemble Learning
Abstract: The interest in the Internet of Things (IoT) is increasing both as for research and market perspectives. Worldwide, we are witnessing the deployment of several IoT networks for different applications, spanning from home automation to smart cities. The majority of these IoT deployments were quickly set up with the aim of providing connectivity without deeply engineering the infrastructure to optimize the network efficiency and scalability. The interest is now moving towards the analysis of the behavior of such systems in order to characterize and improve their functionality. In these IoT systems, many data related to device and human interactions are stored in databases, as well as IoT information related to the network level (wireless or wired) is gathered by the network operators. In this paper, we provide a systematic approach to process network data gathered from a wide area IoT wireless platform based on LoRaWAN (Long Range Wide Area Network). Our study can be used for profiling IoT devices, in order to group them according to their characteristics, as well as detecting network anomalies. Specifically, we use the k-means algorithm to group LoRaWAN packets according to their radio and network behavior. We tested our approach on a real LoRaWAN network where the entire captured traffic is stored in a proprietary database. Quite important is the fact that LoRaWAN captures, via the wireless interface, packets of multiple operators. Indeed our analysis was performed on 997, 183 packets with 2169 devices involved and only a subset of them were known by the considered operator, meaning that an operator cannot control the whole behavior of the system but on the contrary has to observe it. We were able to analyze clusters’ contents, revealing results both in line with the current network behavior and alerts on malfunctioning devices, remarking the reliability of the proposed approach.
Keywords: IoT, LoRa, LoRaWAN, Machine Learning, k-means, Anomaly Detection, Cluster Analysis
Developed a novel data and model-agnostic dialogue system to enable effective interaction with prediction models and their explanations.