About
Computer Science and Engineering (AI & ML) undergraduate with a strong academic foundation and hands-on experience in Machine Learning, Deep Learning, Natural Language Processing, and Web Development. Proficient in Python, C++, JavaScript, SQL, and familiar with tools and frameworks such as TensorFlow, Keras, OpenCV, Pandas, NumPy, AWS, Git, and Google Colab.Currently serving as a Teaching Assistant at NxtWave, providing support in Python programming and debugging Experience includes developing CNN-based facial expression recognition systems, legal text classifiers using BiLSTM and causal inference (GFCI), and privacy-preserving facial recognition via machine unlearning.Completed internships at Swecha and IBM SkillsBuild, contributing to AI research, data handling, and model optimization. Published research in IEEE and ScienceDirect, demonstrating strong technical writing and implementation skills. Seeking opportunities to apply AI and software engineering skills to real-world problem-solving in dynamic environments
- Birthday: 7 June 2005
- Phone: +91 7793977136
- City: Hyderabad
- Email: ruthick765@gmail.com
Interests
Software Development
Machine Learning
Full stack Devlopment
Natural Language Processing
Education
B V Raju Institute of Technology
BTech in CSE (Artificial Intelligence & Machine Learning)
2022 - 2026
- CGPA 8.47 (till 5th semester)
Publications
Securing Personal Identity in Facial Recognition: The Shift to Machine Unlearning
Dec 2024
- 4th International Conference on Sustainable Expert Systems (ICSES), Kaski, Nepal,2024, 1115-1121, 10.1109/ICSES63445.2024.10763384
- Publisher: IEEE
Integrating NAS for Human Pose Estimation
To Be Published
- International Conference on Emerging Methods in Computer Science and Network (ICEMCSN 2024)
- Publisher: ScienceDirect
Projects
Emotion-Aware Recommendation System Using Machine Learning
Python, Scikit-learn, Natural Language Processing (NLP)
- Developed a CNN-based facial expression recognition model using Python, OpenCV, and TensorFlow to classify emotions and provide personalized activity and quote recommendations.
- Extracted facial landmarks and emotion features using deep learning
Handwritten Digit Recognition using LeNet
Technologies: Python, TensorFlow, Keras
- Built a Convolutional Neural Network (CNN) model based on the LeNet-5 architecture for handwritten digit classification.
- Trained the model on the MNIST dataset, achieving high accuracy in recognizing digits from 0 to 9.
- Performed model evaluation, tuning, and optimization to improve prediction performance.
To-Do List Web Application
Technologies: HTML, CSS, JavaScript
- Developed an interactive and responsive To-Do List application to efficiently manage tasks.
- Implemented dynamic task addition, deletion, and completion features using JavaScript.
- Integrated local storage functionality to persist user data across browser sessions.
Skills
Languages and Databases
Web Technologies
Frameworks
Tools
Technologies
Resume & Links
Contact
Social Profiles
ruthick765@gmail.com
Contact
+91 7793977136