Study what you love

The massive technological disruptions that have taken place in the past few decades have been stimulated by the advances in computational technologies. This has led to an exponential growth in data availability paralleled with rapid advances in computational speed. The combination of the two has prompted the rise of a new methodology: data science which is being increasingly utilised to advance conventional fields of knowledge such as the sciences, business, social sciences, engineering etc. While traditional data analysis methods continue to be relevant, new methodologies have emerged to analyse, understand, and act on the knowledge gleaned from the data. No area of human affairs has been immune from this data revolution. Given its importance, conventional disciplines require a new type of researcher who is proficient in the use of latest methodoslogies of data analysis to advance not only the existing boundaries of current knowledge but also to create new knowledge.

The FLAME PhD program with data science aims to train researchers who:

  • extend the boundaries of current knowledge and also are able to create new knowledge.
  • are adept in the use of traditional methodologies and statistical tools and build on these to develop new techniques to answer questions that transcend disciplinary boundaries. For example, use Artificial /ML tools in Marketing; deploy highly optimised solutions to logistics; advanced computational tools in Finance; Natural Language Processing and facial recognition applied in Psychology.
  • can communicate clearly and effectively to the expert and the public at large the insights gleaned from the research.
  • can apply the skills and knowledge in practical contexts.

The inter-disciplinary nature of Data science and analytics aligns with the liberal education ethos of FLAME University. As one of the thrust areas of the university, it aspires to be recognised for the quality and impact of its work in data science.  Towards this goal, the institution seeks to build an environment that where cutting-edge methodologies are developed and applied to a wide variety of contexts. Moving towards goal, PhD scholars will, along with faculty, outside experts and industry, work towards creating an ecosystem known for its quality of its output.  PhD scholars will work, through internships and joint projects, on real-world projects in close association with outside organisations and apply relevant and innovative data science methodologies to address problems. This will create a virtuous cycle where academic research will enable effective data driven decision and governance processes and in return real-world issues will stimulate the invention of new methodologies.

The structure of the PhD program will entail coursework and internships while working on the research problem. Students will be encouraged to take a wide variety of courses in order to cultivate a cross-disciplinary outlook. The topic of the dissertation is expected to be practical, inspired by or be sponsored by industry and the output should address a gap or add something new to the existing literature.  In addition, students are expected to gain valuable teaching skills by tutoring and/or independently teaching a course.

PhD guides and Research Interests

Prof. Kaushik Gopalan

Prof. Kaushik Gopalan
Ph.D. - Electrical Engineering (Satellite Remote Sensing) | University of Central Florida, USA

Remote Sensing, Image Processing, Data Science, Machine Learning

Prof. Swapnajit Chakraborti

Prof. Swapnajit Chakraborti
FPM - Information Systems | Indian Institute of Management - Indore

Predictive Analytics, Text Mining, Data Mining, E-commerce, Marketing Analytics, Machine Learning, Natural Language Processing

Prof. Chiranjoy Chattopadhyay

Prof. Chiranjoy Chattopadhyay
Ph.D. - Computer Science | Indian Institute of Technology Madras

Image Processing, Computer Vision, Augmented And Virtual Reality, Applied Machine Learning, Computational Studies On Images, Computational Studies On Migration

Prof. Chiranjoy Chattopadhyay

Prof. Renu Dhadwal
Ph.D. - Industrial Mathematics | Technical University of Kaiserslautern - Germany

Physics Informed Neural Networks, Machine Learning, Differential Equations, Numerical simulations of ODEs and PDEs, Polymer processes modelling

Prof. Balaji Kalluri

Prof. Balaji Kalluri
Ph.D. - Building Sciences | National University of Singapore, Singapore

Research Interests: Design-thinking and Innovation Management in Complex Socio-Technical Systems (e.g. Smart Building, Smart Cities, e-Governance)