Selected publications: [complete list of publications - HTML, Google Sites][Supervised Masters/PhD Theses]
V. S. Bitra, R. R. Vangimalla, and J. Sreevalsan-Nair, “Network-based Diseasome Construction from Multiomics Data and RadTrix Visualization,” IEEE Transactions on Computational Biology and Bioinformatics (to appear), 2025. https://doi.org/10.1109/TCBBIO.2025.3599771
K. Sama, J. Sreevalsan-Nair, S. Choudhary, S. Nagendra, P. V. Reddy, A. Cohen, U. M. Mehta, and J. Torous, “mindLAMPVis as a Co-designed Clinician-facing Data Visualization Portal to Integrate Clinical Observations from Digital Phenotyping in Schizophrenia: User-centered Design Process and Pilot Implementation,” JMIR Formative Research, vol. 9:e70073, 2025, PMID: 40493647. https://doi.org/10.2196/70073. [Online]. Available: https://formative.jmir.org/2025/1/e70073
R. N. Laveti, J. Sreevalsan-Nair, and T. Srikanth, “EAMF: An Entropy-enhanced Attention-based Ensemble Metric Few-Shot Learning for MRI Image Classification,” in Proceedings of the 2025 47th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (accepted), IEEE, 2025.
B. Gnanaraj, S. Manivasagam, and J. Sreevalsan-Nair, “To the Point: From Dynamic Heatmap Video to Gaze Points,” in Proceedings of the 2025 Symposium on Eye Tracking Research and Applications, ser. ETRA ’25, New York, NY, USA: ACM, 2025. https://doi.org/10.1145/3715669.3725873
J. Sreevalsan-Nair, “Data-Driven Framework for Enhanced Flash Flood Preparedness and Building Urban Resilience,” in Proceedings of the 2025 IEEE Bangalore Humanitarian Technology Conference (B-HTC), IEEE, 2025, pp. 1–6. https://doi.org/10.1109/B-HTC64616.2025.11116113
J. Sreevalsan-Nair, A. Mundayatt, B. Gnanaraj, A. Thomas, N. C. Kumar, G. G. Sabhahit, S. Joshi, and T. K. Srikanth, “Mental Healthcare in the Times of Climate Change Action and Data Science,” in Data-Driven Insights and Analytics for Measurable Sustainable Development Goals, Elsevier, 2025, pp. 59–82. https://doi.org/10.1016/B978-0-443-33044-5.00010-3
P. Nileshbhai Butani, J. Sreevalsan-Nair, and N. Kamat, “CMA: An End-to-End System for Reverse Engineering Choropleth Map Images,” IEEE Geoscience and Remote Sensing Letters, vol. 21, pp. 1–5, 2024, presented in the GRSL Special Stream at the 37th Conference on Graphics, Patterns and Images (SIBGRAPI 2024). https://doi.org/10.1109/LGRS.2024.3444600 [Online]. Available: https://ieeexplore.ieee.org/ document/10637448
S. Mathai, P. Krishnan, and J. Sreevalsan Nair, “Understanding Graphical Literacy Using School Students’ Comprehension Strategies,” Contemporary Education Dialogue, vol. 22, no. 1, pp. 1–35, 2024. https://doi.org/1177/09731849241242855
V. Jaisankar and J. Sreevalsan-Nair, “SuP-SLiP: Subsampled Processing of Large-scale Static LIDAR Point Clouds,” in Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data, ser. GeoSearch ’24, ACM, 2024, 40–47. https://doi.org/10.1145/3681769.3698585
D. Katkoria, J. Sreevalsan-Nair, M. Sati, and S. Karunakaran, “WBF-ODAL: Weighted Boxes Fusion for 3D Object Detection from Automotive LiDAR Point Clouds,” in Proceedings of 2024 International Conference on Vehicular Technology and Transportation System (ICVTTS), IEEE, 2024, 1–6, Best Paper Award. https://doi.org/10.1109/ICVTTS62812.2024.10763933
D. Katkoria, J. Sreevalsan-Nair, M. Sati, and S. Karunakaran, “ME-ODAL: Mixture-of-Experts Ensemble of CNN Models for 3D Object Detection from Automotive LiDAR Point Clouds,” in Deep Learning Theory and Applications, 5th International Conference DeLTA 2024, Dijon, France, July 10-11, 2024, Proceedings, Part II, CCIS, vol. 2172, Springer Cham, 2024, pp. 279–300. https://doi.org/10.1007/978-3-031-66705-3
A. Mundayatt and J. Sreevalsan-Nair, “Scaling up Study Area Size in Flood Susceptibility Mapping,” in Proceedings of 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, 2024, pp. 3211–3214. https://doi.org/10.1109/IGARSS53475.2024.10640798
L. S. Liang, J. Sreevalsan-Nair, and B. S. D. Sagar, “Multispectral Data Mining: A Focus on Remote Sensing Satellite Images,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, e1522, October 2023. https://doi.org/10.1002/widm.1522 eprint: https://wires.onlinelibrary.wiley.com/doi/pdf/10.1002/widm.1522 [Online]. Available: https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/widm.1522
J. Sreevalsan-Nair, A. Mubayi, J. Chhabra, R. R. Vangimalla, and P. R. Ghogale, “Evaluating Early Pandemic Response through Length-of-Stay Analysis of Case Logs and Epidemiological Modeling: A Case Study of Singapore in Early 2020,” Computational and Mathematical Biophysics, vol. 11, no. 1, p. 20 230 104, October 2023. https://doi.org/10.1515/cmb-2023-0104. [Online]. Available: https://www.degruyter.com/ document/doi/10.1515/cmb-2023-0104/html
D. Katkoria and J. Sreevalsan-Nair, “Evaluating and Improving RoSELS for Road Surface Extraction from 3D Automotive LiDAR Point Cloud Sequences,” in Deep Learning Theory and Applications: Revised Selected Papers from Third International Conference DeLTA 2022, Portugal, Chapter 6, CCIS volume 1858, Springer Cham, 2023.
B. Gnanaraj and J. Sreevalsan-Nair, “EyeExplore: An Interactive Visualization Tool for Eye-Tracking Data for Novel Stimulus-Based Analysis,” in Proceedings of the 2023 Symposium on Eye Tracking Research and Applications, ser. ETRA ’23, Tubingen, Germany: ACM, 2023.
J. Sreevalsan-Nair, Co-Association Matrices in Ensemble Clustering: Diverse Applications and Extensions, Preprint available at SSRN, May 2023.
H. Ravindra and J. Sreevalsan-Nair, “A Methodology for Integrating Population Health Surveys Using Spatial Statistics and Visualizations for Cross-sectional Analysis,” SN Computer Science, vol. 4, no. 224, pp. 1–19, 2023.
S. Singh and J. Sreevalsan-Nair, “Visual Exploration of LiDAR Point Clouds,” in Advances in Scalable and Intelligent Geospatial Analytics: Challenges and Applications, Chapter 12, K. Kurte, S. Durbha, J. Sanyal, L. Yang, S. Chaudhari, U. Bhangale, and U. Bharambe, Eds., Florida, USA: CRC Press, 2023, p. 19.
J. Sreevalsan-Nair, “On Metavisualization and Properties of Visualization,” in Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Vol 3, IVAPP, INSTICC, SciTePress, 2023, pp. 230–239, ISBN: 978-989-758-634-7.
S. C. Daggubati, J. Sreevalsan-Nair, and K. Dadhich, “BarChartAnalyzer: Data Extraction and Summarization of Bar Charts from Images,” SN Computer Science, 3(500), 1–19, 2022.
J. Sreevalsan-Nair and A. Jakher, “CAP-DSDN: Node Co-association Prediction in Communities in Dynamic Sparse Directed Networks and a Case Study of Migration Flow,” in Proceedings of the 14th International Conference on Knowledge Discovery and Information Retrieval, INSTICC, SciTePress, 2022, pp 63--74. ISBN : 978-989-758-614-9.
R. R. Vangimalla and J. Sreevalsan-Nair, “Communities and Cliques in Functional Brain Network Using Multiscale Consensus Approach,” IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE), vol. 30, pp. 1951–1960, 2022.
V. Sridhar, J. Sreevalsan-Nair, P. R. Ghogale, and R. R. Vangimalla, “Sharing and Use of Non-Personal Health Information: Case of the COVID-19 Pandemic,” in Data Centric Living: Algorithms, Digitization and Regulation, V. Sridhar, Ed., 1st ed., Routledge India, 2022, ch. 8, ISBN : 9780367536534.
R. Thangavel and J. Sreevalsan-Nair, “CV4FEE: Flood Extent Estimation Using Consensus Voting in Ensemble of Methods for Change Detection in Sentinel-1 GRD SAR Images,” in 7th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR 2021), IEEE, 2021, pp. 1–6.
A. C. Victor and J. Sreevalsan-Nair, “Building 3D Virtual Worlds from Monocular Images of Urban Road Traffic Scenes,” in International Symposium on Visual Computing (ISVC 2021), Part II, Lecture Notes in Computer Science LNCS 13018, Bebis, George et al., Springer Nature Switzerland AG, pp 1-14, 2021.
R. R. Vangimalla and J. Sreevalsan-Nair, “HCNM: Heterogeneous Correlation Network Model for Multi-level Integrative Study of Multi-omics Data for Cancer Subtype Prediction,” in Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, 2021, pp. 1880–1886.
S. Singh, and J. Sreevalsan-Nair, “Adaptive Multiscale Feature Extraction in a Distributed System for Semantic Classification of Airborne LiDAR Point Clouds,” IEEE Geoscience and Remote Sensing Letters, July 2021.
S. Singh and J. Sreevalsan-Nair, “A distributed system for multiscale feature extraction and semantic classification of large-scale LiDAR point clouds,” in Proceedings of the 2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS), IEEE, 2020, 74–77. Best Paper Award. https://doi.org/10.1109/InGARSS48198. 2020.9358938
U. M. Mehta, D. Shadakshari, P. Vani, S. S. Naik, Kiran Raj V., R. R. Vangimalla, Y. C. J. Reddy, J. Sreevalsan-Nair, and R. D. Bharath, "Case Report: Obsessive compulsive disorder in posterior cerebellar infarction - illustrating clinical and functional connectivity modulation using MRI-informed transcranial magnetic stimulation," Wellcome Open Research 2020, 5:189
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,'' in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), 11(7), pp 2320-2335, June 2018.
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, pp. 289-300, 2018.
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.
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)
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.
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.
B. Kumari, and J. Sreevalsan-Nair, ``An interactive visual analytic tool for semantic classification of 3D urban LiDAR point cloud,'' In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems 2015 (p. 73:1--73:4), ACM.
