I am a recent Telecommunication Engineering master's graduate from Politecnico di Milano, driven by a deep passion for leveraging data-driven methods across diverse domains, such as communication systems, Edge-AI sensors, and biomedical applications.
I specialize in Explainable AI (XAI) and aim to develop transparent and interpretable AI models for stakeholders and end-users. My expertise includes AI/ML, deep learning, and XAI, complemented by hands-on experience in designing scalable, domain-specific solutions.
Most recently, I worked as an AI Researcher at STMicroelectronics, where I applied TinyML and Quantization-Aware Training techniques to develop innovative MEMS calibration solutions. I also utilized XAI techniques to make complex models more understandable in various applications, including communication networks, medical diagnostics, and sensor systems.
- 🌱 Currently expanding my expertise in ML/DL applications, XAI, and multidisciplinary AI/ML solutions.
- 🤝 Open to collaborations on AI projects focused on communication systems, IoT sensors, and biomedical data.
- 💬 Feel free to ask me about Explainable AI, Deep Learning, Signal Processing, or Computer Vision.
- 📫 Reach out: LinkedIn | Email
- ⚡ Fun fact: I’m passionate about bridging the gap between black-box models and human understanding.
Deep Learning: TensorFlow, PyTorch, scikit-learn
XAI Tools: SHAP, LIME, GradCAM
Data Science: Pandas, NumPy, Matplotlib, PySpark
Others: OpenCV, Scipy, statmodels
- Published in MDPI Electronics Journal, October 2024
- Published in IEEE Xplore, proceedings of the 8th International Conference on Research and Technologies for Society and Industry (IEEE RTSI 2024)
- Accepted at STMicroelectronics TechWeek 2024 internal conference and proposed as an innovative MEMS calibration solution
- Poster presentation at the 4th National Conference on Contemporary Issues in Computer Information and Science (CICIS2019)
- Built a real-time vehicle detection and counting system using YOLOv8 and BYTETrack.
- Focused on smart transportation systems and high-density environments.
- Developed ML models to detect signal anomalies in optical transponders using constellation diagrams.
- Conducted a comparative study on classifiers for biomedical image analysis using XAI techniques like GradCAM and SHAP.
- Analyzed KPIs for 20 companies to optimize e-marketing strategies, a project proposed by Google Italy.
Polytechnic University of Milan
- Specialization: Signals and Data Analysis
- Thesis: Continuous IMU-MEMS Self-Calibration Process by Means of Tiny Neural Networks
Kharazmi University of Tehran
- Thesis: Automatic Architecture Design of CNNs using Genetic Algorithm and Reinforcement Learning (MetaQNN)
Here are some of the main courses I completed during my MSc in Telecommunication Engineering at Politecnico di Milano:
- Network Measurement and Data Analysis Lab (30/30 Cum Laude)
- Fundamentals of Signals and Transmission (30/30 Cum Laude)
- Applied AI in Biomedicine (30/30)
- Recommender Systems (30/30)
- Game Theory (29/30)
- Wireless Internet (28/30)
- Image Analysis and Computer Vision (27/30)
- Advanced Digital Signal Processing (27/30)
- Member of PoliMi Data Science Association.