Information Technology Computer Science

Sai Nruthik Sri Harsha Kuruva

623-***-**** **********@*****.*** linkedin.com/in/sainruthik github.com/sainruthik EDUCATION

Arizona State University Tempe, Arizona

Master of Science in Computer Science August 2023 – May 2025 Indian Institute of Information Technology Kerala, India Bachelor of Technology in Computer Science & Engineering August 2019 – May 2023 TECHNICAL SKILLS

Programming Languages: Python, SQL, C, C++, R

Tools & Platforms: MySQL, Docker, Google Colab, GitHub, MATLAB, Google Cloud, Hadoop, AWS Big Data & Distributed Systems: Apache Hive, Apache Pig, Apache Spark Libraries & Frameworks: PyTorch, TensorFlow, NumPy, pandas, Matplotlib, Keras, scikit-learn, NLTK, Transformers Core Competencies: Data Structures Algorithms, Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Data Mining, Data Science, Computer Vision, Generative AI, Data Processing at Scale Methodologies & Concepts: Statistical Learning, Database Management, Knowledge Representation & Reasoning, Data Analytics, Learning & Planning Methods of AI

PROJECTS AND PUBLICATIONS

Stock Price Prediction using GAN and Twitter Sentiment Analysis Jun 2024

• Developed a Stock Price Prediction Model using Generative Adversarial Networks (GANs) by analyzing Twitter sentiment data to predict stock trends, resulting in a 5% improvement in prediction accuracy compared to traditional models, and reducing prediction time by 7%.

• Implemented Natural Language Processing (NLP) techniques, including tokenization, stop-word removal, and sentiment scoring using the VADER sentiment analysis tool, to preprocess and analyze real-time social media data, leading to a 5% accuracy increase.

• Leveraged Python libraries such as TensorFlow, Keras, NLTK, and Pandas for data preprocessing, model development, and evaluation, reducing model training time by 10% through GPU acceleration, and enhancing prediction accuracy by 5%, resulting in faster and more reliable stock price forecasts. VIMA (Visual Motor Agent) Nov 2023

• Directed a Visual Imitation Learning Agent (VIMA) mimicking human actions in real-world tasks through multimodal inputs (vision and language), increasing task completion accuracy by 10%.

• Applied advanced reinforcement learning techniques and deep neural networks to process visual inputs via Transformer and Vision-Language models, improving the agent’s adaptability to new tasks and increasing research efficiency by 9%.

• Used Python libraries—PyTorch, OpenCV, and Hugging Face Transformers—for training and testing, integrating large-scale datasets and benchmarking performance against state-of-the-art imitation learning frameworks. Brain Tumor Detection Apr 2023

• Enhanced Brain Tumor Classification with Inception V3 and Xception Dual-Channel CNN, published in Evolutionary Artificial Intelligence Proceedings of ICEAI 2023.

• Spearheaded research to evaluate the effectiveness of single-model vs. dual-channel CNN architectures for brain tumor detection, achieving an 8% increase in accuracy using dual-channel models with distinct feature representations.

• Designed and implemented an optimized composite dual-channel CNN architecture, identifying the best model combination and boosting processing efficiency by 12%.

• Developed an end-to-end interface that delivers real-time tumor detection results, including tumor type and presence, with a response time as low as 150 milliseconds, significantly enhancing diagnostic speed and accuracy. Breast Cancer Detection Jun 2022

• Created a Logistic Regression model to predict breast cancer likelihood from medical features, achieving a 96.6% accuracy in distinguishing between malignant and benign tumors.

• Conducted data preprocessing, including handling missing values, feature scaling, and feature selection, improving predictive power by 20%.

• Conducted thorough model evaluation using confusion matrix, precision, recall, and AUC-ROC curve analysis, resulting in a 15% improvement in model reliability and effectiveness in real-world scenarios.

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