ML

Processed ERP segments from the PhysioNet EEG dataset (109 subjects) for motor imagery classification. Evaluated RF, LSTM, CNN, EEGNet via cross-validation and full-dataset training. Reached 74.34% accuracy with EEGNet, outperforming traditional and sequence models.

A deep learning pipeline for automatic annotation and retrieval of scientific figures. The system builds on CLIP and BiomedCLIP — including fine-tuned variants — to embed biomedical images and text into a shared semantic space, and integrates Wikidata-based knowledge graphs to enrich that space with structured domain knowledge. The result is stronger semantic alignment between figures and their descriptions, along with interpretable visualisations that make the annotation and retrieval behaviour of the models easier to inspect.

A medical image denoising system that fuses vision and language: a UNet-based denoiser is conditioned on CLIP text embeddings derived from the VQA-RAD dataset, letting textual context guide the reconstruction of noisy scans. Compared to a vision-only baseline, the vision-language fusion improved reconstruction quality by 12.5% in PSNR and 4.6% in SSIM.

Analyzed passing networks with graph-based metrics and Node2Vec embeddings. Identified tactics and key players using Louvain clustering and temporal trends.

Designed an LSTM-based model for 14-day forecasting with real-time data pipelines. Improved financial modeling for short-term predictions.

Boosted DenseNet121 classification accuracy to 98.67% via PCA, DCT, and noise-based augmentation (baseline - 93.6%).

Used MediaPipe and CLAHE for preprocessing. Achieved 10.47 MAE using deep regression (ResNet, Inception-v4).

An end-to-end machine learning pipeline for estimating the market price of used cars. Listings were collected via web scraping and cleaned into a structured dataset, with categorical features encoded for model consumption. Several regression models were trained and compared, with a Random Forest regressor delivering the most accurate price estimates and forming the core of the final predictive system.

A forecasting study of Tehran’s Air Quality Index built on a decade of historical pollutant measurements. Decision Tree, Support Vector Machine, and Random Forest models were trained and evaluated on the dataset to predict AQI levels, comparing how well each captures the temporal patterns and pollutant interactions that drive air quality in the city.