Publications
Publications
"Exploring the Impact of Robot Appearance in Human Compliance". This thesis investigates how robot appearance influences compliance among dementia patients. Hospitals in the United States face persistent understaffing, making it important to explore technological solutions that can assist caregivers. Social robots are already known as companions for dementia patients, and they may also support medication adherence by providing reminders.
In this study, one Pepper robot will be recolored to appear darker. Drawing from long-standing tropes in media and literature—where darkness is often associated with evil and lightness with goodness—this research examines whether such associations extend to human–robot interaction. Through a combination of literature review and experimental design, the study addresses the following research questions:
RQ1: Does the color of a humanoid robot (black vs white) influence the level of compliance it can gain from human participants when paired with AI generated conversations ?
RQ2: Does the robot’s surface color (light vs. dark) affect patient's perceived trustworthiness, reliability, and sociability during the interaction?
Fine-Tuning BanglaBERT for Sentiment Analysis in Bangla dataset : A sentiment analysis system for Bangla, a low-resource language, was developed by fine-tuning BanglaBERT on the benchmark SentNoB dataset. Traditional machine learning methods were compared with pretrained language models, and BanglaBERT was found to significantly outperform baseline approaches while challenges in handling noisy, informal social media text were highlighted.
Predicting Concrete Compressive Strength from Mix Ingredients Using Multiple Linear Regression: A multiple linear regression model was developed to predict concrete compressive strength using ingredients such as cement, water, slag, fly ash, and superplasticizer. Extensive diagnostics, transformations, and outlier analyses were performed, resulting in a model that explained 83% of the variability (R² = 0.83) with an RMSE of 1.54.
Exploring Echo Chamber effect in r/depression subreddit : The echo chamber effect was explored in the r/depression subreddit using a year-long dataset of over 150K posts and 428K comments. Semantic similarity and sentiment analysis were applied to detect homogeneous discussions, revealing echo chamber patterns in topics like hopelessness and medication, while highlighting more diverse viewpoints in conditions such as ADHD and PTSD.
In Class protocol development for project-based learning: A structured protocol for project-based learning (PBL) was designed to guide undergraduates in conducting in-class projects using EEG signal analysis. The protocol was demonstrated through a study comparing male and female cognitive responses across tasks such as chess, robotics interaction, and memory games, where EEG wave patterns were analyzed but no statistically significant sex-based differences were detected.
Grasp-and-Lift Detection from EEG Signal Using Convolutional Neural Network
Field: Biomedical Engineering, Machine Learning
Tools: Python