teaching
Teaching Assistant
I serve as a teaching assistant for courses at Mälardalen University focusing on computer science fundamentals and embedded systems:
2024
Data Communication for Embedded Systems 1
Theoretical and practical knowledge of data communication and networking for embedded systems.
Data Structures, Algorithms and Programming Construction with Python
Abstract data types, dynamic data structures, searching and sorting algorithms, and time complexity analysis.
Supervised Theses
I have co-supervised the following master’s theses:
2024
Leveraging Machine Learning for Fast Performance Prediction for Industrial Systems: Data-Driven Cache Simulator
Sharifeh Yaghoobi Master’s thesis (Two Years), 20 credits / 30 HE credits
This thesis presents a novel solution for CPU architecture simulation with a focus on cache miss prediction using machine learning (LSTM) techniques. The work demonstrates the viability of ML-based methods in enhancing the fidelity of CPU architecture simulations.
2025
Multi-Objective Reinforcement Learning based Algorithm for Dynamic Testbed Scheduling in C-RAN
Shruthi Puthiya Kunnon and Supriya Rajendiran Master’s thesis (One Year), 80 credits / 120 HE credits
This thesis addresses the complex, multi-objective problem of testbed scheduling in Cloud Radio Access Network (C-RAN) environments by proposing a Deep Reinforcement Learning (DRL)-based approach. The proposed models significantly outperform traditional FCFS scheduling by reducing makespan, operational costs, and test request waiting times.