Dr. Saurabh Bhardwaj

Dr. Saurabh Bhardwaj

Designation:

Associate Professor

Specialization:

Statistical and machine learning based solutions for the problems which involves recognition, classification, clustering, modeling, estimation and information retrieval

Email:

saurabh.bhardwaj@thapar.edu

Web page link:

https://sites.google.com/site/saurabhshomeonweb/

Biography

Dr. Saurabh Bhardwaj received the Ph.D. degree in Instrumentation and Control Engineering from Netaji Subhas Institute of Technology (NSIT), New Delhi, India in 2013 and the M.E. degree in Instrumentation Engineering from Panjab University, Chandigarh in 2008. Currently, he is working as an Associate Professor in Thapar Institute of Engineering & Technology, Patiala. Dr. Bhardwaj has about 13 years of teaching and research experience. His current research interests include Biometrics, machine learning and its applications in different fields i.e. automatic speaker recognition, time series prediction and in solar radiation estimation.

Phone/Mobile Number +917528981415
Email ID saurabh.bhardwaj@thapar.edu


Membership of Professional Institutions, Associations, Societies:

  1. IEEE Membership (Membership Number: 92447824)
  2. Metrology Society of India

Publications and other Research Outputs

SCI-6; Non-SCI- 2

Awards and Honours:

  1. Best Paper Award in Springer Conference: G. Chaudhary, S. Srivastava, S. Bhardwaj, “Multi-level fusion of palm print and dorsal hand vain,” 3 rd International Conference on Information Systems Design and Intelligent Applications,” on Jan 9, 2016, Visakhapatnam, India.

  2. Best Presentation Award in IEEE workshop: S. Bhardwaj, S. Srivastava, Sanidhya, Mani, “Multi-Environment Dataset for Speaker Identification,” Computational Intelligence: Theories, Applications and Future Directions on July 14, 2013 at IIT Kanpur, India.

Description of Research Interests

My research interests are focused on development and improvement of statistical and machine learning based solutions for the problems which involves recognition, classification, clustering, modeling, estimation and information retrieval.