AI and Machine Learning engineering student with a strong foundation in data analysis, Python, and applied machine learning. Experienced in developing data-driven solutions from concept to implementation, with a strong i…
AI and Machine Learning engineering student with a strong foundation in data analysis, Python, and applied machine learning. Experienced in developing data-driven solutions from concept to implementation, with a strong interest in applying AI in real-world, industrial and societal contexts. Motivated, analytical, and eager to contribute while continuously learning.
Represent BTH at recruitment and outreach events by communicating academic programs, student life, and career opportunities to diverse audiences.
Strengthened presentation skills, stakeholder communication, and relationship building in a part-time role alongside studies.
Represent BTH at recruitment and outreach events by communicating academic programs, student life, and career opportunities to diverse audiences. Strengthened presentation skills, stakeholder communication, and relationship building in a part-time role alongside studies.
Led planning and day-to-day operations of the association.
Organized and delivered events with 100+ attendees.
Collaborated with university stakeholders and external partners.
Strengthened the association’s structure and visibility to increase student engagement.
Led planning and day-to-day operations of the association. Organized and delivered events with 100+ attendees, collaborated with university stakeholders and external partners, and strengthened the association’s structure and visibility to increase student engagement.
Support individuals with special needs in structured and regulated environments, promoting independence and inclusion.
Contribute through teamwork, responsibility, structured problem-solving, and clear communication.
Support individuals with special needs in structured and regulated environments, promoting independence and inclusion. Contribute through teamwork, responsibility, structured problem-solving, and clear communication.
Evaluated robustness of facial recognition systems against digital and physically realizable adversarial attacks. Fine-tuned ResNet-18/101 and tested FGSM, PGD, DeepFool, and patch attacks, with defenses including adversarial training and feature squeezing. Key insight: accuracy ≠ robustness; adversarial training improved security, while physical conditions reduced attack effectiveness.
Built an end-to-end ML pipeline to predict revenue, profitability, and ratings from metadata and title-based features. Applied preprocessing, feature engineering, and sentiment analysis (NLTK), and evaluated XGBoost, Random Forest, and KNN using stratified k-fold CV and statistical tests. Budget emerged as the strongest predictor.
Conducted exploratory data analysis on a real-world manga dataset to identify factors linked to success. Performed data cleaning, outlier filtering, and statistical analysis of score, popularity, ranking, and chapter length. Found popularity and ranking to be stronger indicators of success than chapter count.
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