Aspiring Data Scientist with ML & DL Focus

MANOGNA RAYALA

+1-979-***-**** — *************@****.*** — LinkedIn — GitHub

EDUCATION

Texas A&M University Aug 2025 – Present

Master of Science in Data Science

SRM Institute of Science and Technology Sep 2021 – Jul 2025 Bachelor of Technology in Computer Science Engineering (AI & ML) INTERNSHIPS

Shiash Info Solutions, Chennai Nov 2024 – May 2025 Data Science Intern

• Performed data collection, cleaning, and preprocessing using Python, Pandas, NumPy, and SQL to support data- driven business analysis.

• Built and evaluated supervised machine learning models for classification and regression tasks using scikit-learn.

• Conducted exploratory data analysis (EDA) and statistical analysis to identify trends and actionable insights.

• Developed data visualizations using Matplotlib to communicate results to technical and non-technical stakeholders. AICTE – AWS Academy (Remote) May 2023 – Jul 2023

AI-ML Intern

• Designed, trained, and evaluated machine learning models, improving model accuracy and computational efficiency.

• Applied supervised learning techniques, cross-validation, and performance metrics including precision, recall, and F1-score.

• Explored cloud-based machine learning workflows using AWS services for scalable model development and deployment. PROJECTS

Advanced Transfer Learning Techniques for Detection of Down Syndrome in Pediatric Patients Using Facial Image Analysis IEEE, Jul 2025

• Developed a non-invasive AI-based diagnostic system using Python, ResNet50, and YOLOv12 for pediatric Down Syndrome detection from facial images.

• Implemented deep learning pipelines including data preprocessing, feature extraction, model training, and evaluation using PyTorch and TensorFlow.

• Achieved 97.11% classification accuracy, F1-score of 0.96, and mAP@0.5 of 0.987 through optimized transfer learning techniques.

• Applied Grasshopper Optimization Algorithm (GOA) for hyperparameter tuning, improving model convergence and generalization.

• Deployed the trained model as a Flask-based web application enabling real-time inference and clinical decision support. Detection and Classification of Zebrafish Embryonic Phenotypes to Signaling Pathways using Enhanced YOLOv8 IEEE, Jun 2025

• Built an enhanced deep learning framework using Python and YOLOv8 to detect and classify 17 zebrafish embryonic phenotypes.

• Integrated biological shape descriptors and fine-tuned convolutional neural network architectures for improved feature representation.

• Achieved F1-score of 0.90, precision of 1.00, and mAP@0.5 of 0.92, outperforming ResNet50, VGG16, EfficientNet, and RCNN.

• Conducted comparative model evaluation, performance benchmarking, and error analysis across multiple deep learn- ing models.

• Enabled scalable, automated phenotypic screening for biomedical research, drug discovery, and developmental biology applications.

SKILLS

Programming: Python, SQL, Object-Oriented Programming, Quantum Computing(Foundational) Machine Learning: Supervised Learning, Regression, Classification, Deep Learning, Transfer Learning, Model Eval- uation, Cross-Validation

Frameworks & Tools: Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, Git, Jupyter Notebook Data Analysis: Data Cleaning, Exploratory Data Analysis (EDA), Feature Engineering, Statistical Analysis, Data Visualization (Matplotlib)

CERTIFICATIONS

NPTEL – Database Management Systems Mar 2024

NPTEL – Google Cloud Computing Dec 2023

AWS Cloud Foundations Aug 2023

TCS iON Career Edge – Young Professional Jul 2023

ISRO – Geodata Processing using Python Feb 2023

Supervised Machine Learning: Regression and Classification Dec 2022

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