I am a passionate Data Scientist and Machine Learning Engineer with expertise in AI, Data Analytics, and Software Engineering. I am currently pursuing my Master’s in Computer Software Engineering at Northeastern University, Boston. My experience spans quantitative research, data engineering, predictive modeling, and AI-driven solutions.
- Looking for collaborations in GenAI, LLMs, Data Science, and AI-driven innovations.
- Connect with me on LinkedIn or via email at [email protected].
- Research Assistant Platform using Airflow, FastAPI, Streamlit, Snowflake, AWS S3, Docker, and NVIDIA Multi-Modal RAG
Developed an automated data ingestion and analytics platform, reducing processing times by 60%, accelerating user interactions by 70%, and enabling intuitive exploration of CFA Institute publications. - Implementing Graph RAG Using KGs and Vector Databases
Built a Graph RAG model that enhances retrieval-based question-answering systems with knowledge graphs.
- Brain Tumor Segmentation Using U-Net
Developed a U-Net model for MRI-based brain tumor segmentation, improving Dice Score from 0.475 to 0.650. - Meteorological Forecasting over Lake Michigan
Built a hybrid ConvLSTM2D-LSTM model for multi-class precipitation forecasting, achieving 85% accuracy. - Optimizing Credit Scoring with Reinforcement Learning
Developed a Q-learning-based RL model to optimize credit score acceptance thresholds, increasing lender profits by 15%. - Global Emission Analysis
Analyzed 500,000+ data points on greenhouse gas emissions and predicted future trends with 85% accuracy.
- Motor Vehicle Collision Analytics
Engineered a data pipeline for analyzing traffic collisions, improving query performance by 35%. - Food Inspection Data Engineering
Optimized food safety data processing and visualized insights using Power BI. - Data Profiling & Visualization for 311 Call Centers
Analyzed and visualized 500,000+ service requests using Alteryx, Power BI, and SQL.
- Blockchain-based Voting System in a Democratic Environment
N. S. Varma Indukuri, S. Reddy Eguram, A. Nichena, S. Ulvalapudi, and R. Gajula
📖 IEEE ICAISS 2023 | DOI: 10.1109/ICAISS58487.2023.10250609
- AWS Cloud Foundation
- AWS AI-Machine Learning Foundations
- Logistic Regression, ARIMA, Support Vector Machines (SVM)
- k-Nearest Neighbors (k-NN), Random Forest, XGBoost
- Naive Bayes, K-means & Hierarchical Clustering
- Principal Component Analysis (PCA), Recommendation Systems
- Deep Neural Networks (DNN), Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM)
- Detection Transformer (DeTR)
- Topic Modeling, Sentiment Analysis, Text Summarization
- Machine Translation, Text Generation, Named Entity Recognition (NER)
- Medical Image Segmentation
🔗 LinkedIn
📧 [email protected]
📁 GitHub