Satyanarayana Repala

Experienced data scientist with a strong background in fraud detection, recommendation systems, visual search, and natural language processing (NLP). Skilled in both classical machine learning and deep learning techniques. Led projects to stop fraud and account takeovers, and built smart systems that recommend products and understand images or text. Good at working with cross-functional teams, bringing in third-party models, and making sure models are accurate, fair, and well-governed. Also experienced in using large language models (LLMs) to find trends and help businesses make better decisions.

Machine Learning: statistical models , feature selection using correlation and data normalization, transforming data, hyper parameter tuning, deciding loss functions and optimizers, ensemble models , bagging and Gradient boosting methods,PCA(principle component analysis)

Deep Learning: CNN , activation function, different layers, , regularization techniques, data preparation, data preprocessing, training models and

NLP: NER(named entity recognition), sentiment analysis, word2vec,CBOW,SKIPGRAM,Glove, BERT, TFIDF, N-gram,Transformers, Fast text embedding(char embedding).

Validation Techniques : AUC (Area under curve analysis) / ROC ( receiver operating characteristic curve),confusion matrix, Precision, Recall, F1 score.

Project : personalized recommendations to the user based on user history and demographics, using Graph neural networks (deep graph library).

Project : detecting products,shelves and positions in Retail store, using deep neural network

Project: Extracting invoice parameters , invoice number, date,account number,vendor,ship,and remit addressed

Project: extract contact entities , person name, title and company name email address from email threads and populate these details into sales force contact management screen

Project: Build, person ,facemask detection and social distance monitoring Video analytics application and deployed in SHINOBI CCTV monitoring app, Live Detection and streaming

Project : Text classification , intention detection in email threads, is email contains complaint, feedback, query, compliment, information.

Project : user activity monitoring tool , deep neural network to monitor user system , how much time spent on which application, browser , command line, media player, excell

Project : text classification, monitor user screen and classify the amount of improper text present on screen , to measure how much a child is exposed to abuse,hatred on social media platforms

Neural Networks And Deep Learning: coursera.org/verify/SLTJ3EGKFWQQ

Phase II, Amrita Nagar, Choodasandra, Bengaluru, Karnataka 560035