Master of Science

Robotics & AI

GPA: 3.91/4.00

Bachelor of Engineering

Electronics and Telecommunication Engineering

GPA: 8.23/10.00 (Graduated with first class with distinction)

Computer Vision Research Assistant

Developed a cutting-edge object detection model using ResNet50 with Faster R-CNN and MobileNetV3 on HPC, optimizing for real-time performance with PyTorch and CUDA. Managed a 256 GB dataset of six classes and led data preprocessing, converting raw images to 16-bit TIFF format through debayering and transforming to linear and log spaces for accurate model evaluation.

Global Student Mentor

Supported new international student's cultural transition and academic success through one-on-one and group meetings, fostering an inclusive community and promoting intercultural competence.

Algorithms & Machine Learning Co-op

Implemented advanced visualization and contour-based segmentation for shape transformation assessment, automating the annotation of biomedical shapes into four categories. Utilized Hu Moments and XGBoost for classification, achieving high accuracy in correlating shape changes with blood pressure.

Employed Vision Transformer and LSTM models to capture spatial and temporal aspects, while developing algorithms for dual IMU sensor data and creating a dashboard for visualizing analysis. Enhanced model interpretability using SHAP and refined protocols for improved detection of cardiovascular events.

AI/ML Intern

Created a distracted driving detection model using the State Farm Dataset, achieving 98% accuracy with CNN and 99% with ResNet-101 through transfer learning and hyperparameter tuning. Utilized feature extraction and visualized results with Class Activation Mapping (CAM).

Utilized 5 feature extraction techniques: HOG, SURF, Color Histograms, and applied PCA for dimensionality reduction

Research Assistant

Engineered a wireless surveillance bot utilizing YOLOv4 for object detection, achieving an accuracy of 92% across different areas and 92.17% at different times of the day.

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Software Engineer

Developed an end-to-end ML pipeline for sentiment analysis using LSTM networks, achieving 89% accuracy, and a predicting API for real-time sentiment classification. Implemented CI/CD using CircleCI and AWS for scalable deployment and automated updates of the best-performing model.

Parsed over 100 resumes using NLTK for Named Entity Recognition, achieving an F1-score of 0.89 in predicting job roles with Random Forest and Gradient Boosting models through skill extraction and feature transformation.

Extracted resume skills and applied supervised learning random forest, gradient boosting with 0.89 F1-score to predict job.