1.International Level :
1.Anasua Sarkar and Ujjwal Maulik, “Parallel Point Symmetry Based Clustering for Gene Microarray Data”, in the Proceedings of Seventh International Conference on Advances in Pattern Recognition – 2009 (ICAPR, 2009), Kolkata, IEEE Computer Society, Conference Publishing Services (CPS), pp. 351-354, 2009.
2.Anasua Sarkar and Ujjwal Maulik, “Parallel Clustering Technique Using Modified Symmetry Based Distance”, In the Proceedings of 1st International Conference on Computer, Communication, Control and Information Technology (C3IT 2009), MacMillan Publishers india Ltd., pp. 611-618, 2009.
2.National Level :
1.Anasua Sarkar and Ujjwal Maulik, “An Efficient Parallel Point Symmetry Based Clustering Algorithm”, in the Proceedings of IEEE National Conference on Computing and Communication Systems (CoCoSys-09), published by University Institute of Technology, Burdwan University, pp. 131-136, 2009.
Joint convenor of National Seminar on “Data Mining and Knowledge Discovery – Identifying Potential Information”, GCETT, Serampore, 2006.
A reviewer in the journals of Parallel and Distributed Computing (JPDC), Elsevier, IEEE SMCC-C.
A member of member of the groups : Magnome - Models and Algorithms for the Genome, INRIA Bordeaux Sud-Ouest research center and MaBIOVIS (Modeles et Algorithmes pour la Bioinformatique et la Visualisation) groups in University Bordeaux 1, France. MAGNOME is a joint team with CNRS (LaBRI), University Bordeaux 1 and ENSEIRB with INRIA theme “Biologie numérique et bioinformatique”.
Student member for International Society of Computational Biology (ISCB) for 2010-2011. A member of Computer Science Teacher Association(CSTA), ACM Chapter.
Scholarship Won : National Scholarship, from the Ministry of Education & Culture, Govt. of India, for the year 1995, for Madhyamik Examination .
Intersted fields for research works :
1.Bioinformatics, computational biology, parallel computing applications of molecular biology, parallel compiler designing, embedded systems, real-time systems.
Class discriminator-based EMG classification approach for detection of neuromuscular diseases using discriminator-dependent decision rule (D3R) approach