Inshad Rahman Noman

Experienced professional skilled in C, C++, Python, neural networks, machine learning and object-oriented programming. Currently a Teaching Associate and Graduate Assistant at California State University, Los Angeles contributing to instruction, research and administrative tasks. Proficient in developing machine learning models, designing software systems and managing data infrastructures. Effective mentor and collaborator with a strong ability to manage multiple responsibilities. Seeking a challenging role in data science, software development, AI or cybersecurity to apply technical expertise and drive innovation.

Geo Health Analysis Using Machine Learning

Conducted a comprehensive capstone project focused on Geo Health Analysis utilizing machine learning techniques. Developed predictive models to analyze geographical health data, identifying patterns and trends related to public health issues. Leveraged advanced machine learning algorithms to process and interpret large datasets, providing valuable insights into the correlation between geographic factors and health outcomes. The project involved data cleaning, feature engineering, model training and evaluation, ultimately delivering actionable recommendations for health policy and intervention strategies.

Avian Guard - A Drone-Based Approach for Bird Identification and Risk Mitigation

Completed a master's thesis titled "Avian Guard" which focused on developing a drone-based system for bird identification and risk mitigation specially at the airport areas. The project integrated advanced computer vision and machine learning techniques to accurately identify bird species in real-time using drone-mounted cameras. The system aimed to address challenges in bird-aircraft collisions and agricultural crop protection by providing automated, efficient monitoring and deterrent strategies.

Project: American Sign Language Translator

Developed an innovative project focused on creating an American Sign Language (ASL) translator. Utilized advanced computer vision and machine learning algorithms to interpret ASL gestures and translate them into written language in real-time.