澳门六合彩开奖记录

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Dr Nachiketa Chakraborty

dr-nachiketa-chakraborty portrait

Welcome Week Lead, Open Days, Visit Days, and Offer Holders Days Joint Coordinator 

Office

Room 145 

Building location

Polly Vacher

Areas of interest

  • Mathematics of Data Science
           - Causality
           - Signal processing / Time-Series Analysis
           - Machine Learning & Artificial Intelligence
           - Graphical Models and Bayesian Inference
  • Physics
           - Solar and Astrophysics
           - Dynamical Systems Studies with Observations
  • Environmental Sciences
           - Space Weather
           - Climate

Teaching

Research centres and groups

  • Computer Science : Data Science and Artificial Intelligence, Advanced Computing for Environmental Science.
  • Meteorology : Data Assimilation Research Centre (DARC), Solar Physics/Space Weather.

Research projects

My research is at the intersection of mathematics of data science research and domains like physics and geosciences. I am interested in time-series studies, causal inference and artificial intelligence applied to learning from observational (time-domain) data.
A central theme of my work is to develop and combine novel mathematical methods (modern statistical and machine-learning techniques) for extracting insights from data with conventional modelling approaches (theoretical and phenomenological) in aforementioned domains. With this unique foundation, I aim to build and train interdisciplinary researchers to tackle complex problems in this data-driven era of scientific research.

Background

  • Education:

           -University of Illinois (UIUC) Urbana, IL, USA

            Ph.D. in Astronomy August 2013

            Dissertation: 鈥淪tandard Model Foregrounds in Nuclear and Particle Astrophysics鈥,

            Supervised by Prof. B. D. Fields

            -Tata Institute of Fundamental Research (TIFR) Mumbai, MH, India

             M.Sc. in Physics July 2007

            Dissertation: 鈥淟ow Frequency GMRT Imaging of the field of RXCJ1516.3+0015鈥,

             Supervised by Prof. R. Nityananda

  • Teaching:

            -Undergraduate (past)

             Mathematical Methods II (Advanced 鈥 for engineers and physicists)

             Advanced Topics in Statistics

           -Postgraduate (past)

             Introduction to Machine Learning

             Introduction to Statistical Methods in Data Science

Academic qualifications

 

Professional bodies/affiliations

• Chair of Reading Local Group in Royal Statistical Society (RSS), Fellow of RSS
• Fellow of Royal Astronomical Society
• Alumni of Alexander von Humboldt Foundation

Websites/blogs


Publications

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