Farheen Akhter

Adaptable Software Engineer ready to support operations from conception through post-release support. Skilled creator of efficient code and exciting user experiences. Eager to elevate ongoing development projects or create novel software solutions geared towards driving increased usership.

• Machine learning popular libraries such as Scikit-learn, Numpy, Pandas, TensorFlow, Keras API, Generative AI with Lang chain (LLM).
• Firebase, Azure DevOps, Atlassian, G Suite (now Google Workspace), Microsoft 365 Administration
• Software Development Lifecycle, Continuous Integration and Continuous Delivery (CI/CD), Agile development methodologies
• Java, ReactJS/TS, HTML, CSS, JavaScript, C++, Python, NodeJS with MongoDB, GraphQL, React Native, Eclipse, Visual Studio Code, Android Studio.

• Active Member of the CSUSB CSE Club, conducting Hackathons and code competitions
• Attended the RC/Adriano Workshop in the Women in Computer science and Engineering club
• Worked as volunteer in the Office of Student Research(OSR) for Research Week Kickoff event.

• Generative AI, Artificial Intelligence, Machine Learning
• Preparing for Machine Learning Practioner Certification from Data Bricks

• Completed comprehensive coursework covering fundamental concepts and advanced topics in artificial intelligence and machine learning. Studied algorithms, neural networks, and data science techniques to solve complex problems in Generative Ai domain with Lang chain (LLM).
• Data processing and feature engineering techniques to handle missing data, feature scaling and selection
• Created machine learning models using supervised learning algorithms such as linear, logistic regression, decision trees, random forest and support vector machines
• Worked to explore unsupervised learning techniques such as k-means, hierarchial clustering and density based clustering
• Learned to assess the performance of models using various evaluation metrics and cross-validation techniques, concepts such as over fitting, under fitting, bias variance trade off methods to select a model
• Introduction to deep learning, artificial neural network by exploring the topics like Feed forward networks, CNN, Recurrent neural network(RNN)
• Gained hands on experience by working on real world machine learning problems with the popular libraries and frameworks such as scikit-learn, Numpy, Pandas, TensorFlow, Keras API to build and deploy machine learning models.

• Dr. Ronald Salloum, ronald.salloum@csusb.edu
• Dr. Abdul Motin Howlader, abdul.motihowlader@csusb.edu
• Srikant Bollaru, Purple Talk, India, srikant.bollaru@xcubelabs.com, +91 9985599891
• Wasim Akram, M2pfintech, India, wasimakram.b@m2pfntech.com, +91 9573575873