Research

My interest in Artificial Intelligence (AI) and Machine Learning begins when I was doing my BTech Degree at Integral University, Lucknow, India in the academic year 2007-2008. Since then I have been working on several areas of AI. The word cloud given below highlights the keywords related to my research directions.

The picture that changed my life is given below. The picture is from the book Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig. The first chapter of the book is an introduction to AI and I read it in 2007. I would say no one can explain it much better than this book.


The following are my areas of research:


1. EEG/MEG-based Brain-Computer Interfacing

A major issue in bringing real-world applications of machine learning outside the laboratory is the difference in the data distributions between training and testing stages or domains. The diverging statistical properties in different domains can lead to decay the prediction performance. The technical term for a change in the distribution of features is covariate shift, which also happens to be a common challenge in electroencephalography (EEG) and Magnetoencephalography (MEG) based brain-computer interface (BCI); this is due to the presence of non-stationarities in the EEG/MEG signals. The non-stationary nature of EEG/MEG signals makes an EEG/MEG-based BCI a dynamic system, thus improving its performance is a challenging task. A lot of adaptive machine learning approaches have been developed and used previously to tackle this challenge such as passive and active approach for learning, domain adaptation, transfer learning, covariate shift minimization, deep learning, and more. I am particularly interested in developing methods for covariate shift adaptation in both EEG/MEG datasets. To visualize the covariate shift generally scatter plot and histogram are used. I am also interested in exploring new ways of analysing, visualizing, and measuring the covariate shift in data.

Here is a list of key publications:


2. Healthcare and Predictive Analytics

Healthcare analytics is the combination of data science and healthcare, where data analytics is used on healthcare data for offering insights into hospital management, patient records, costs, diagnoses, and more. The field covers a broad area of the healthcare industry, offering insights on both the macro and micro level. When combined with artificial intelligence and machine learning methods and data visualisation tools, healthcare analytics helps managers operate better by providing real-time information that can support decisions, predictive power, and deliver actionable insights. I am particularly interested in the following themes:

  • AI & data-driven SMART triage system
  • AI Pathway with an intelligent decision support system
  • Technology and digital transformation for future healthcare
  • Predictive Modelling using Electronic Health Records

3. Environmental Sciences and Agriculture

Advances in AI could be one of the solutions to solving major global environmental crises–from climate change to agriculture. With advances in machine learning and deep learning, we can now tap the predictive power of AI to make better data-driven models of environmental processes to improve our ability to study current and future trends, including forest-fire, water availability, crop-monitoring, and ecosystems wellbeing. I am interested in bringing AI and ML to play a key role in enhancing environmental decision and policy-making work, by bringing algorithmic solutions to the following themes:

  • Developing forest-fire prediction algorithm using IoT
  • Image analysis for monitoring crops

Highlights, 2020