I’m an Electrical Engineering student at Amirkabir University of Technology, specializing in Control Systems and Artificial Intelligence. My work bridges classical control theory with cutting-edge machine learning, deep learning, and reinforcement learning to build intelligent, autonomous systems. From robotics and autonomous driving to speech and image-based emotion recognition, I focus on creating real-world AI-powered solutions through research and development.
Featured Projects
IL-RL-ObstacleAvoidance
Autonomous driving system using Imitation Learning and Reinforcement Learning for obstacle avoidance, powered by computer vision and LiDAR in Webots simulator.
Speech-Emotion-Recognition-using-Wav2Vec2
Deep learning-based Speech Emotion Recognition system using Wav2Vec2 to detect emotions like happy, sad, angry from raw audio data.
Twitter-Emotion-Classifier-using-Transformer-Encoder
Transformer-based model for emotion detection in tweets, using GloVe embeddings and custom text preprocessing for high-accuracy classification.
ResNetInception-CNN-Classifier-For-TinyImageNetDataset
CNN-based image classification using a ResNet-Inception hybrid model on TinyImageNet, with extensive hyperparameter tuning and performance analysis.
MNIST-Deep-Learning-Saliency-Maps-and-FGSM-Attacks
Deep learning project for MNIST digit classification, featuring saliency map visualization and robustness evaluation using FGSM adversarial attacks.
RISC-V-Single-Cycle-Processor
VHDL single-cycle RISC-V processor supporting R-type (ADD, SUB, AND, OR) and I-type (ADDI, ANDI, ORI, LW, SW) instructions, with modular design.
Bachelor Thesis
BLIP-FusePPO Framework
A vision-language deep reinforcement learning framework for autonomous lane keeping using BLIP (Bootstrapped Language-Image Pretraining) and Proximal Policy Optimization (PPO). The system learns to drive in simulation by understanding visual inputs and high-level commands.
Let's Connect!
I'm always interested in new opportunities and collaborations. Feel free to reach out!