👋 Hi there! I am Data Scientist with a PhD in Astrophysics and over 8 years of experience in developing innovative machine learning solutions, particularly in time series analysis, deep learning, and signal processing.

🔭 My research has focused on the detection and characterization of nascent planets, with published works on planet detection techniques using advanced time series analysis and statistical methods.

👨‍💻 I'm proficient in Python, Scikit-learn, Keras, TensorFlow, and have significant experience in data wrangling, signal processing, dynamic time warping (DTW), Gaussian Process modelling, and Bayesian optimization.

🚀 I enjoy tackling complex data problems, translating research into deployable solutions, and working with cross-functional teams to drive AI innovation.

• Programming Languages: Python, R
• Machine Learning Frameworks: TensorFlow, Keras, Scikit-learn
• Data Manipulation & Analysis: Pandas, NumPy, SQL/MySQL, Excel
• Time Series Analysis: Dynamic Time Warping (DTW), Fourier Analysis, Gaussian Processes, Bayesian Optimization
• Deep Learning: CNNs, RNNs, LSTMs, Time Series and Image Classification, Forecasting
• Development Tools: Git, GitHub

1. Deep Learning for Chest X-ray Image Classification

   • A deep learning project using convolutional neural networks (CNN) for classifying chest X-ray images.
   • View Project

2. Planet Detection using Time Series Analysis

   • Detection limit of nascent planets in the presence of stellar spot activity using statistical methods.
   • View Project
   • Modeling bisectors using cross-correlation functions to illustrate the effect of Doppler shifts.
   • https://github.com/rajeevzar/bisector_modelling_CITAU

3. Time Series Forecasting using LSTM

   • LSTM-based model for time series forecasting.
   • View Project

4. Earth Environment Analysis by country

   • In this project, I utilized environmental data from NASA Earthdata (https://search.earthdata.nasa.gov/search) to develop a codebase that enables users to analyze environmental data for different countries.
   • The code categorizes and classifies the data into three levels—Good, Moderate, and Severe—based on predefined thresholds, providing valuable insights into the environmental health and performance of each country.
   • We can plot time series data to observe the trends in environmental indicators over time, helping to assess the amelioration or deterioration of environmental conditions by country.
   • View Project

• Detecting Planets Around Young Stars Using Time Series Analysis
  Link to paper

• Other Publications
  Link to paper

Feel free to reach out if you'd like to collaborate on AI projects or discuss any exciting opportunities in data science and machine learning.

• 📧 Email: [email protected]
• 🔗 LinkedIn: https://www.linkedin.com/in/rajeev-manick-ph-d-a3284b44/