Dr Nachiketa Chakraborty
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Lecturer in Computer Science
Welcome Week Lead, Open Days, Visit Days, and Offer Holders Days Joint Coordinator
Office
Room 145Building location
Polly VacherAreas 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
- Undergraduate (present):
– Data Integration and Information Visualisation – Module Convenor - Postgraduate (past and present):
– Artificial Intelligence and Machine Learning – Module Convenor
– Causal Inference and Decision
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: “Standard Model Foregrounds in Nuclear and Particle Astrophysics”,
Supervised by Prof. B. D. Fields
-Tata Institute of Fundamental Research (TIFR) Mumbai, MH, India
M.Sc. in Physics July 2007
Dissertation: “Low Frequency GMRT Imaging of the field of RXCJ1516.3+0015”,
Supervised by Prof. R. Nityananda
- Teaching:
-Undergraduate (past)
Mathematical Methods II (Advanced – 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