I am currently pursuing a Master's degree in Data Science at TU Freiberg, fueled by a deep passion for utilizing data to create impactful solutions.
My journey has led me to a position as a Working Student Senior Software Engineer, where I am eager to apply my expertise and contribute to image-based data coordination.
In addition, I have a strong interest in Artificial Intelligence and its vast potential. My goal is to specialize in this domain, leveraging my foundation in Data Science to uncover insights that enhance operational efficiency and drive strategic innovation.
Bringing together Artificial Intelligence, Data Science, Software, and IT Engineering defines my professional vision. I believe that this interconnected approach is the key to developing groundbreaking solutions and making a lasting difference.
I’d love to connect and explore new opportunities where data-driven insights lead to transformative possibilities!
Part-time - 8 Months
Freiberg, Saxony, Germany
On-site
Full-time - 1 Year 4 Months
Tehran, Iran
On-site
Part-time - 3 Years 3 Months
Karaj, Iran
Hybrid
Part-time - 1 year
Karaj, Iran
On-site
Master's Degree – Data Science and Data Processing & AI
Technology
Freiberg, Germany
October 2023 – Present
Bachelor's Degree – Computer Engineering
Karaj, Iran
October 2018 – April 2023
Microsoft
Issued July 2025
Credential ID: 64228f9b79adc...
Harvard University
Issued March 2024
Udemy
Issued December 2022
Credential ID: 0004
Harvard University
Issued April 2024
With thanks to TUBAF, I led a seminar on AI in blockchain security, covering attack vectors, vulnerabilities, and AI-driven prevention methods.
Delivered a workshop on Supply Chain Management for SMEs, comparing strategies in Hungary and Indonesia, analyzing improvements and implementation best practices.
Ran three workshops on Ethernet virtualization with STP protocol—reducing costs, improving efficiency, and enhancing network security.
Led a seminar on “CAPTCHA Unmasked,” dissecting image-processing defenses and exploring ML-based enhancements like biometric and dynamic CAPTCHA challenges.
Developed a Scientific ML model using PySINDy for financial forecasting, identifying governing equations and predicting token prices for optimal profit/loss.
Developed during tenure at SAPCO, this method introduces a modular AI-driven framework to evaluate, compare, and optimize supply chain strategies using performance metrics, inventory simulation, and demand forecasting. The system helps large-scale automotive supply chains dynamically adapt policies based on operational data, constraints, and efficiency metrics. This innovation supports strategic SCM decision-making in industrial production pipelines.
Developing an advanced Large Language Model (LLM)-based framework capable of recognizing and predicting human emotions from diverse data modalities, including text, speech, images, and video. The system leverages cross-modal embeddings and attention-based fusion mechanisms to interpret emotional cues with high accuracy across varying formats. This model enables real-time affective computing in human-computer interaction, mental health monitoring, and adaptive user experience applications. Designed to operate robustly even when certain modalities are missing or degraded, using techniques like modality dropout, knowledge distillation, and temporal context tracking.
Awarded for presenting an original research concept titled “Financial Forecasting Equations with Scientific Machine Learning,” integrating sparse identification techniques (SINDy) with domain knowledge in dynamic economic modeling. Recognized for scientific rigor, clarity, and real-world forecasting potential.
Awarded for designing and implementing an ML-based optimization pipeline that significantly improved production-line efficiency.