Aditi Kathpalia

About

I am currently a postdoctoral researcher at the Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences. My research interests include causal inference, causal and biologically-inspired machine learning, signal processing, complex systems, applications of causality, information theory and non-linear dynamics in complex systems (particularly brain and climate).

I completed my PhD thesis on ‘Theoretical and Experimental Investigations into Causality, its Measures and Applications’ from the National Institute of Advanced Studies (NIAS), IISc Campus, Bengaluru, India. My bachelors and masters degree are in the field of Biomedical Engineering from Indian Institute of Technology (BHU), Varanasi, India.

Employment

• Czech Academy of Sciences, Institute of Computer Science Postdoctoral researcher
  2021 - Present
• Norwegian University of Science and Technology Research Assistant
  2015 - 2016
• GE India Technology Centre Pvt Ltd Research Intern
  2014 - 2014

Education

• National Institute of Advanced Studies PhD
  2016 - 2021
• Indian Institute of Technology (BHU) Varanasi IDD, B.Tech. in Bioengineering with M.Tech. in Biomedical Technology
  2010 - 2015

Projects & Funding

Projects & funding information is unavailable.

Publications (12)

• Granger causality for compressively sensed sparse signals
  Physical Review E 2023 DOI: 10.1103/physreve.107.034308
  +Save
• Compression complexity with ordinal patterns for robust causal inference in irregularly sampled time series
  Scientific Reports 2022 DOI: 10.1038/S41598-022-18288-4
  +Save
• Measuring Causality The Science of Cause and Effect
  Resonance 2021 DOI: 10.1007/S12045-021-1119-Y
  +Save
• Time-Reversibility, Causality and Compression-Complexity
  Entropy 2021 DOI: 10.3390/E23030327
  +Save
• A Novel Compression Based Neuronal Architecture for Memory Encoding
  ICDCN: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING AND NETWORKING 2019 DOI: 10.1145/3288599.3295575
  +Save
• Causal stability and synchronization
  Chaos 2019 DOI: 10.1063/1.5121193
  +Save
• ChaosNet: A chaos based artificial neural network architecture for classification
  Chaos 2019 DOI: 10.1063/1.5120831
  +Save
• Data-based intervention approach for Complexity-Causality measure
  PeerJ Computer Science 2019 DOI: 10.7717/PEERJ-CS.196
  +Save
• Structured Compressed Sensing Matrices for Causality Detection
  IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) 2018 DOI: 10.1109/RTEICT42901.2018.9012521
  +Save
• Adaptive Spectral Envelope Estimation for Doppler Ultrasound
  IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 2016 DOI: 10.1109/TUFFC.2016.2587381
  +Save