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Rajamanogaran Murugan

Associate Professor, Department of Computer Science and Engineering, Amrita School of Computing, Chennai

manogaran248@gmail.com
Ph: +91 8056696551
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Bio

Rajamanogaran Murugan currently serves as Associate Professor in the Department of Computer Science and Engineering, School of Computing, Chennai.

Innovative and research-driven Data Scientist with 7+ years of experience in Artificial Intelligence, Deep Learning, and Computer Vision. Specialized in architecting end-to-end machine learning solutions for real-time accident detection, biometric systems, medical imaging, and predictive analytics in healthcare and finance. Patent holder and published researcher with expertise in CNN, YOLO frameworks, LLMs, and federated learning.

Qualification
  • Ph.D. (Computer Science) – (under Thiruvalluvar University), Thesis Objective: Automating and optimizing accident identification for faster emergency response and traffic safety management
  • M.Sc. (Information Technology, Integrated) – Annamalai University, Chidambaram (2012–2017)
Skills
  • Languages: Python, C, SQL
  • Machine Learning: Regression, SVM, KNN, Random Forest, PCA, Clustering, Ensemble Methods
  • Deep Learning: CNN, RNN, LSTM, Transfer Learning (InceptionV3, VGG, MobileNet, YOLOv3), LLMs, TensorFlow, Keras
  • Computer Vision & NLP: OpenCV, Image Processing, Spacy, NLTK, Sentiment Analysis, Word2Vec
  • Cloud & Big Data: Azure ML Studio, Cosmos DB, PySpark, MongoDB, MySQL
  • Frameworks: Django, Flask
  • Tools: Git, Jupyter Notebook, Pandas, NumPy, Matplotlib, Scikit-learn
Experience
  • Data Scientist – Derisk Solutions (May 2018 – 2020)
    • Developed AI-driven solutions using Azure Cloud for real-time analytics and storage
    • Designed predictive models with deep learning for diverse client applications
    • Optimized neural architectures for faster inference in real-time systems
  • Python Developer – SVJ Technology (May 2017 – Mar 2018)
    • Built backend logic and web applications using Python frameworks
    • Collaborated on early-stage ML integrations and data pipelines
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