Coursework: Natural Language Processing, Computer Vision, Pattern Recognition, Deep Learning, Algorithmic Graph Theory, Distributed Systems

Coursework: Data Structures & Algorithms, Operating Systems, Computer Networks, Algorithm Engineering, Web Application Development, Database Systems

Refactored legacy C# AI chatbot logic into modular Python functions, enhancing code maintainability and enabling easier future development

Engineered and deployed AWS Lambda functions to expose business logic as scalable, reusable APIs across chatbot platforms

Automated the generation of SAP Business Process Documents (BPDs) using Python APIs, reducing manual workload and increasing consistency

Analyzed and translated complex monolithic codebases to ensure accurate cross-language functionality and alignment with business requirements

Developed CNN-based vision models using PyTorch to detect tumors in MRI scans, improving diagnostic accuracy and assisting doctors in providing patients with better outcomes

Leveraged transfer learning with pretrained models (ImageNet) to enhance model precision and reduce training time

Utilized YOLOv8 for image segmentation to precisely localize tumor regions within MRI scans, leading to a 10% increase in detection accuracy by focusing on relevant areas

Evaluated assignments to uphold rigorous academic standards in the Web Application Development course

Assisted students with helpful insight on GitLab by describing the uses for HTML, CSS, and JavaScript in front-end design, including topics such as responsive sizing and dynamic retrieval of data

Improved students’ understanding of Flask for back-end development by 7% through hosting regularly scheduled office hour sessions

Developed a secure web application for hospitals powered by AWS services (Amplify, RDS), allowing doctors to verify tumors detected in patient MRI scans

Implemented encryption, role-based access control, and multifactor authentication to ensure patient data security

Designed backend architecture using Flask to manage communications between MySQL database and React frontend

Collaborated with colleagues to enhance MSU Federal Credit Union’s mobile banking apps using Flutter, Dart, and SQL

Designed an aesthetically pleasing user interface using Flutter and Dart to enhance customers’ banking experience

Implemented a modern peer-to-peer transfer system featuring usernames, QR codes, and NFC to increase usability

Created a system using Google Places API to notify users of deals at local businesses based on shopping patterns