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Associate Professor & Warden (Women's Hostel)


Education : Ph.D. (University of California Davis)

Professor Nair obtained her Ph.D. in Computer Science from University of California, Davis; after a B.Tech in Aerospace Engineering from IIT-Madras and an M.S. in Computational Engineering from Mississippi State University. Prior to joining IIITB, she worked as a scientific programmer at Enthought Inc. Austin and as a research associate at Texas Advanced Computing Center, the University of Texas at Austin. Her areas of interest are visualization, scientific computing, computer graphics, and computational geometry.

She leads the  Graphics-Visualization-Computing Lab at IIITB. She is also the core team member of the E-Health Research Center at IIITB. 

[Curriculum Vitae]

Spatial big data analytics (multivariate/tensor/network data modeling and visual analytics) with a focus on applications in earth observations (LiDAR point clouds, ocean data), geographical networks (e.g. transport), biological networks (genomic, brain) and survey data pertaining to population health and public health.

Early Career Research Award, SERB (2017-20) IBM SUR Award (2018-19)

Recent publications: [complete list of publications, GVCL publications]

  1. J. Sreevalsan-Nair, A. Jindal, and B. Kumari, ``Contour Extraction in Buildings in Airborne LiDAR Point Clouds Using Multi-scale Local Geometric Descriptors and Visual Analytics,'' IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), Early Access, June 2018. (doi)
  2. K. Lukose, S. Agarwal, V. N. Rao, and J. Sreevalsan-Nair, ``Design Study for Creating Pathfinder: A Visualization Tool for Generating Software Test Plans Using Model Based Testing,'' in the Proceedings of the 13th International Joint Conference on Computer Vision, Imaging, and Computer Graphics Theory and Applications (VISIGRAPP 2018), vol 3: IVAPP, SCITEPRESS, 2018. (doi)(pdf)
  3. J. Sreevalsan-Nair, N. Murthy, S. Agarwal, R. R. Vangimalla, and S. Ramesh, ``Collaborative Design of Visual Analytics Techniques for Survey Data for Community-based Research in Public Health,'' (accepted as poster) in the 8th Workshop on Visual Analytics in Healthcare, affiliated with IEEE VIS 2017. (pdf)
  4. J. Sreevalsan-Nair, and A. Jindal, ``Using Gradients and Tensor Voting in 3D Local Geometric Descriptors for Feature Detection in Airborne LiDAR Point Clouds in Urban Regions,'' in the Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2017), July 2017. (doi)(pdf)
  5.  J. Sreevalsan-Nair, and B. Kumari, ``Local Geometric Descriptors for Multi-Scale Probabilistic Point Classification of Airborne LiDAR Point Clouds,'' in ``Modeling, Analysis and Visualization of Anisotropy,'' Mathematics and Visualization Series, Springer, Cham, pp 175-200, October 2017. (from Proceedings of Dagstuhl Seminar 16142) (doi)(pdf)
  6. J. Sreevalsan-Nair, and S. Agarwal, ``NodeTrix-CommunityHierarchy: Techniques for Finding Hierarchical Communities for Visual Analytics of Small-world Networks,'' in the Proceedings of 12th International Joint Conference on Computer Vision, Imaging, and Computer Graphics Theory and Applications (VISIGRAPP 2017), vol 3: IVAPP, pp 140-151, SCITEPRESS, 2017. (Nominated for Best Paper Award). (doi)(pdf)
  7. S. Agarwal, A. Tomar, and J. Sreevalsan-Nair, ``NodeTrix-Multiplex: Visual Analytics of Multiplex Small World Networks,'' in Complex Networks & Their Applications V, Studies in Computational Intelligence, vol. 693, pp 579-591, Springer International Publishing, 2017. (doi)(pdf)

[In Term I 2018-19 (Fall 2018)]

  • CS855: Data Visualization
  • ESS201: Programming II (Theory and lab modules on C++)
  • DT107: Application Development for a Connected Society

[In Term II 2017-18 (Spring 2018)]

  • CS714: Advanced Computer Graphics

Current R&D projects:

ACM Member, IEEE Senior Member