I am a Ph.D. student in Computing at Queen’s University with a focus on Machine Learning, Natural Language Processing, and Computer Vision. I am passionate about innovating in AI and working on projects involving human activity recognition.
Python, SQL, JavaScript, HTML, CSS, Node JS, Jupyter Notebook, AWS, Machine Learning, Deep Learning, Web Design, UI/UX, Python-Django, Flask, TensorFlow, PyTorch, Git, Firebase, Tableau, Pandas, Seaborn, Natural Language Processing (NLP), NLTK, Unix, Data Structures, Algorithms.
Utilized state-of-the-art deep learning algorithms and edge computing techniques to develop a high-speed image recognition system achieving 94% accuracy. Investigated the impact of network architecture and hyperparameter tuning on model performance, identifying optimal settings for real-world deployment. Conducted experiments using multiple datasets and image types to demonstrate the effectiveness and scalability of the proposed approach. Developed a user-friendly web-based interface for easy deployment and management of the system on edge servers. Published multiple papers based on the research, receiving recognition from both academic and industry communities.
Designed and implemented a GPS-based routing algorithm to optimize package delivery routes, resulting in a 20% reduction in delivery time. Utilized telemetry data to enable real-time tracking of drone location and package status, enhancing package security and customer satisfaction. Developed a custom-built drone with a payload capacity of up to 5 kg and rigorously tested the prototype in various weather and wind conditions to ensure robustness and stability. Conducted extensive safety testing to ensure compliance with FAA regulations and industry standards. Presented the project at multiple conferences and received positive feedback from industry experts.
Authored clear, concise technical documentation for a non-technical audience, demonstrating a client-focused approach and strong written communication skills. Worked independently and as part of a team, adapting to various collaborative environments and handling projects with minimal supervision. Managed tight deadlines and responded to user inquiries, showcasing the ability to work under pressure and handle stressful situations effectively. Utilized learning management systems (like OnQ) to organize and present information, aligning with the program’s technical requirements.
Contributed to a team of 4 Graduate Teaching Assistants in assisting over 100 Undergraduate Students with mathematical topics such as Linear Algebra, Calculus, Statistics, Coordinate Geometry, and more. Our focus was not only on helping students understand and excel in math but also on fostering their ability to perceive and visualize mathematics.
Collaborated with a team of 6 experienced IT professionals in managing and executing the technical preparation of Docker containers and laptops. This critical function ensured the seamless integration of technology into the day-to-day operations of faculty and staff members at ISU. Leveraged in-depth understanding of computer systems to optimize performance and reliability, contributing to the productivity of the entire educational institution.
Spearheaded a team of 7 members to design, develop, and deploy a highly scalable web application using the Python-Django web framework. Created social media web applications capable of accommodating over 3000+ users, leveraging machine learning and data analysis techniques. Developed user-friendly interfaces with HTML, CSS, and Bootstrap 5 for frontend, and designed a secure and efficient MySQL database for backend storage. Utilized AWS S3 for deployment services, ensuring high availability and easy accessibility for users. Analyzed extensive user data using Python and SQL, implementing machine learning models to provide personalized recommendations that led to a 25% increase in user engagement. Collaborated closely with stakeholders to understand business requirements, resulting in a 30% reduction in development time. Conducted A/B testing on various features and functionalities, making data-driven decisions to enhance the user experience and optimize performance.
Collected news articles from various sources and topics using Python libraries like newspaper and BeautifulSoup. Preprocessed the text data with NLTK and spaCy libraries. Trained a machine learning model to classify news articles into categories such as sports, politics, technology, and more, achieving an 80% accuracy rate. Visualized the classification results using Python libraries such as Matplotlib and Seaborn.
Implemented a functional model of a Dietary Monitoring System to track a person's calorie intake by analyzing their eating habits, food quality, and the types of food consumed. Achieved an impressive accuracy rate of 91-92% in food recognition. Leveraged computer vision and image segmentation techniques to classify food items in images.
Queen’s University - Sept 2023 - Present
Indiana State University - Aug 2021 - May 2023
Amity University - Aug 2017 - May 2021