Boston, Massachusetts, United States
I am a passionate and dedicated Master of Science in Computer Science student at The Pennsylvania State University, set to graduate in August. I have a robust background in Software Development, Research, Artificial Intelligence, Deep Learning, and Blockchain Technology. My academic journey has been enriched by diverse experiences, including teaching assistant and research assistant positions, where I honed my technical and instructional skills. Throughout my studies, I have engaged in several research projects that have expanded my expertise and practical knowledge. Notably, I am currently working on a thesis focused on object tracking in 3D space using monocular vision, aiming to contribute innovative solutions to the field of computer vision. Additionally, I have published a research paper on "Blockchain for Criminal Forensics and Security Systems" in the Journal of Discrete Mathematical Sciences and Cryptography, highlighting my ability to conduct impactful research. During my undergraduate studies, I interned as a Software Development Engineer at Deep LogicTech, working extensively with Python to develop ML solutions to preemptively detect and stop potential cyberattacks on databases and SSH servers. In addition to my technical expertise, I served as a Mentor for International Students, leveraging cross-cultural communication and peer support skills to help international students navigate their academic and social lives at the university. I thrive in collaborative environments and am eager to contribute to cutting-edge projects that push the boundaries of technology. I am actively seeking internship and job opportunities where I can leverage my skills and knowledge to drive innovative solutions in the field of computer science. Let's connect and explore how we can work together to shape the future of technology.
* Pioneered an AI-driven automation solution to detect ROI in low-contrast monochromatic images, reducing processing time by 85%. * Developed a neural network to detect data matrices in low-contrast images of machine surfaces, achieving an accuracy of 93%. * Generated a dataset of 50, 000+ images to improve model robustness, adapting to diverse lighting and contrast conditions. * Implemented efficiency enhancements to an established production line, resulting in a reduction of up to 6 months in production time and cost savings of up to $100, 000/yr.