Jayasankar Santhosh

Part of Endowed Chair of Artificial Intelligence in Zeppelin University focusing on exploring how humans and AI can collaborate most effectively. Contributing to the conceptual and theoretical development and methodological implementation of Chairs's research agenda. My current research focus is on advancing human-AI collaboration by exploring how AI can act as an active, collaborative member within teams. The goal is to design intelligent agent-based systems that enhance group productivity and creativity.

A passionate PhD researcher in educational technology, specializing in Deep Learning and sensor-based analytics to enhance learning experiences. Expertise in AI and Machine Learning is applied to develop adaptive learning systems powered by Generative AI, dynamically responding to real-time data to optimize educational environments and drive personalized learning. Collaborated with interdisciplinary teams to design and implement AI systems addressing real-world challenges in learning environments.

- Teaching Assistant for Neurocognitive computing lecture in RPTU Kaiserslauten-Landau - Neurocognitive Computing explores how humans and computers process information, bridging gap between computer science and neuroscience. - Assisted and delivered lectures to Master’s students focusing on state-of-the-art techniques for detecting and analyzing and cognitive states using machine learning and AI. - Designed hands-on tasks enabling students to apply machine learning models to analyze datasets.

- Research on document analysis by integrating text and sensor data using Machine Learning and Deep Learning. - Data collection, Data cleaning, Exploratory data analysis, Time series analysis, Statistical analysis, Data modelling, Feature engineering, Data visualization, Data optimization. - Classification, Regression, Dimensionality reduction, Anomaly detection, Clustering.

- Generating Artificial Eye Movements over document to determine the reading difficulty of documents. - Data set creation and analysis by conducting experiments. - Predicting Readability of Unknown Documents using Machine Learning - Text processing for efficient feature extraction using Natural Language Processing (NLP). - Sensor data features Integration with text features for personalized readability measurement of documents

Intelligent Systems, Software Sytems, Visualization, Deep Learning, Machine Learning, Embedded Intelligence

Neural Networks, Unix Shell programming, Distributed Computing, Algorithms deduction