Topic-wise Grouping of GVCL Publications


Themes at GVCL


Consolidated List

Lab Theme: Human-Centric Spatio-Temporal Data Science
Topics involved:

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Geospatial Data:

B. B. Desai, Y. A. Rajapur, A. Mundayatt, and J. Sreevalsan-Nair, CityAQVis: Integrated ML-Visualization Sandbox Tool for Pollutant Estimation in Urban Regions Using Multi-Source Data (Software Article), Under editorial hold at arXiv Preprints, 2025.

A. Mundayatt and J. Sreevalsan-Nair, Large-scale hazard susceptibility mapping using machine learning and deep learning techniques, (accepted) as the Graduate Forum, the Sixth Indian Symposium on Machine Learning (IndoML 2025), 2025.

K. K. Htay and J. Sreevalsan-Nair, “Attention- and Uncertainty-based Enhancements to U-Net Model for Semantic Segmentation of Aerial Imagery for Land Cover Classification,” in (accepted) in the IEEE International Conference on Robotics and Mechatronics, IEEE, 2025.

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. [Online]. Available: Elsevier webpage

S. Kothari, S. Murali, S. Kothari, U. Verma, and J. Sreevalsan-Nair, Adversarial Robustness of Deep Learning Models for Inland Water Body Segmentation from SAR Images, arXiv Preprints, 2025. [Online]. Available: https://arxiv.org/abs/2505.01884 

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), pp 1-6, IEEE, 2025. https://doi.org/10.1109/B-HTC64616.2025.11116113

J. Sreevalsan-Nair and A. Mundayatt, Evolution of Data-driven Single- and Multi-Hazard Susceptibility Mapping and Emergence of Deep Learning Methods, arXiv Preprints, 2025. [Online]. Available: https://arxiv.org/abs/2502.09045

V. Arora, S. Gupta, A. Kudupu, A. Priyadarshi, A. Mundayatt, and J. Sreevalsan-Nair, CCESAR: Coastline Classification-Extraction From SAR Images Using CNN-U-Net Combination, arXiv Preprints, 2025. [Online]. Available: https://arxiv.org/abs/2501.12384

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 (accepted),” in Data- Driven Insights and Analytics for Measurable Sustainable Development Goals, Elsevier, 2024.

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. (doi)(URL)

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, also presented in the GRSL Special Stream at the 37th Conference on Graphics, Patterns and Images (SIBGRAPI 2024). (doi)(URL)

J. Sreevalsan-Nair, A. Kiran, A. Bhattacharya, B. D. Sagar, G. KN, U. Verma, K. Lanka, and S. K. Meher, “InGARSS 2023 in Bangalore: Striking a Balance,” IEEE Geoscience and Remote Sensing Magazine, vol. 12, no. 3, pp. 180–187, 2024. (doi)

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) (accepted), IEEE, 2024.

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. (doi)(eprint)

L.-T. Tay and J. Sreevalsan-Nair, “Disaster Susceptibility Analysis in Remote Sensing,” in Cognitive Sensing Technologies and Applications, G. R. Sinha, B. Subudhi, C.-P. Fan, and H. Nisar, Eds., Stevenage, UK: Institute of Engineering and Technology (IET), 2023 (doi)(url).
 
J. Sreevalsan-Nair, Co-Association Matrices in Ensemble Clustering: Diverse Applications and Extensions, Preprint available at SSRN, May 2023. (url)

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. (doi)

J. Sreevalsan-Nair, “Interpolation,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)
J. Sreevalsan-Nair, “Eigenvalues and Eigenvectors,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)
J. Sreevalsan-Nair, “Independent Component Analysis,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)
J. Sreevalsan-Nair, “Laplace Transform," in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)
J. Sreevalsan-Nair, “Expectation-Maximization Algorithm,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)
J. Sreevalsan-Nair, “Simulated Annealing,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)
J. Sreevalsan-Nair, “K-Medoids,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)
J. Sreevalsan-Nair, “Fuzzy C-Means Clustering," in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)
J. Sreevalsan-Nair, “Proximity Regression,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)
J. Sreevalsan-Nair, “Normal Distribution,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)

J. Sreevalsan-Nair, “Virtual Globe,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)

J. Sreevalsan-Nair, “K-Means Clustering,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)

J. Sreevalsan-Nair, “K-Nearest Neighbors,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)

J. Sreevalsan-Nair, P. Mohapatra, and S. Singh, “IMGD: Image-based Multiscale Global Descriptors of Airborne LIDAR Point Clouds Used for Comparative Analysis,” in Smart Tools and Apps in Graphics (STAG 2021) - Eurographics Italian Chapter Conference, P. Frosini, D. Giorgi, S. Melzi, and E. Rodolá, Eds., The Eurographics Association, October 2021, pp 61--72, ISBN: 978-3-03868-165-6.(doi)

 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.
(doi)

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. (doi)(preprint)

J. Sreevalsan-Nair, “Maximum Likelihood,” in Encyclopedia of Mathematical Geosciences, Encyclopedia of Earth Sciences Series, B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Cham: Springer International Publishing, 2022. (doi)
J. Sreevalsan-Nair, “Minimum Entropy Deconvolution,” in Earth Sciences Series, Encyclopedia of Mathematical Geosciences (accepted), B. S. D. Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Springer International Publishing, 2022. (doi)
 
J. Sreevalsan-Nair, “Data Visualization,” in Earth Sciences Series, Encyclopedia of Mathematical Geosciences (accepted), B. S. D. Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Springer International Publishing, 2022. (doi)
 
J. Sreevalsan-Nair, “Multiscaling,” in Earth Sciences Series, Encyclopedia of Mathematical Geosciences (accepted), B. S. D. Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Springer International Publishing, 2022. (doi)
 
J. Sreevalsan-Nair, “LiDAR,” in Earth Sciences Series, Encyclopedia of Mathematical Geosciences (accepted), B. S. D. Sagar, Q. Cheng, J. McKinley, and F. Agterberg, Eds., Springer International Publishing, 2022. (doi)

S. Singh, and J. Sreevalsan-Nair, "A Distributed System for Optimal Scale Feature Extraction and Semantic Classification of Airborne LiDAR Point Clouds," in Distributed Computing and Internet Technology, Proceedings of the 17th International Conference on Distributed Computing and Internet Technology (ICDCIT), January 2021, Sequence Number 18, Lecture Notes in Computer Science, Springer International Publishing. (doi)(preprint)

S. Singh, and J. Sreevalsan-Nair, "A Distributed System for Multiscale Feature Extraction and Semantic Classification of Airborne LiDAR Point Clouds," accepted at the IEEE International India Geoscience and Remote Sensing Symposium (InGARSS) 2020, pp 74-77, December 2020. (doi)(pdf) (Best Paper Award)

J. Sreevalsan-Nair, and P. Mohapatra, “Augmented Semantic Signatures of Airborne LiDAR Point Clouds for Comparison,”  in arXiv (2020), May 2020, https://arxiv.org/abs/2005.02152

J. Sreevalsan-Nair and P. Mohapatra, “Influence of Aleatoric Uncertainty on Semantic Classification of Air-borne LiDAR Point Clouds: A Case Study with Random Forest Classifier Using Multiscale Features”, accepted in the Proceedings of the 2020 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2020), pp 1066-1070, September 2020. (doi)(pdf)

J. Sreevalsan-Nair, "Visual Analytics of 3D Airborne LiDAR Point Clouds in Urban Regions," in Sarda, N., Acharya, P., Sen, S. (eds) Geospatial Infrastructure, Applications and Technologies: India Case Studies, pp 313-325, Springer Singapore, November 2018. (doi)(pdf)

 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. (doi)(pdf)

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)

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)

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. (doi)(pdf)

B. Kumari, A.Ashe, and J. Sreevalsan-Nair, ``Remote Interactive Visualization of Parallel Implementation of Structural Feature Extraction of Three-dimensional Lidar Point Cloud,'' in the Proceedings of the Third International Conference on Big Data Analytics, Lecture Notes in Computer Science (LNCS) Series, Vol. 8883, 2014, pp 129-132, Springer. (doi)(pdf)

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Population/Biomedical Data

V. S. Bitra, R. R. Vangimalla, and J. Sreevalsan-Nair, “Network-based Diseasome Construction from Multi-omics Data and RadTrix Visualization,” IEEE Transactions on Computational Biology and Bioinformatics (to appear), 2025. https://doi.org/10.1109/TCBBIO.2025.3599771

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. [Online]. Available: Elsevier webpage

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; Preprint at https://preprints.jmir.org/preprint/70073  DOI: https://doi.org/10.2916/70073  [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), pp 1-6, IEEE, 2025. https://doi.org/10.1109/B-HTC64616.2025.11116113

S. Mathai, P. Krishnan, and J. Sreevalsan Nair, “Understanding Graphical Literacy Using School Students’ Comprehension Strategies,” Contemporary Education Dialogue, pp. 1–35, 2024. (doi)
 
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. (doi)(open access)

J. Sreevalsan-Nair, Co-Association Matrices in Ensemble Clustering: Diverse Applications and Extensions, Preprint available at SSRN, May 2023. (url)

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. (full-text view) (doi)

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. (doi). (Best Paper Award Nomination)

H. Ravindra and J. Sreevalsan-Nair, Spatial and Visual Analytics for Grouped Analysis of Population Survey Data, presented at the doctoral research workshop at the 26th International Conference on Information Visualization IV2022, July 2022.

H. Ravindra and J. Sreevalsan-Nair, "Composition of Geospatial Visualizations for Scale-aware Views of Multiple Outcome Variables in Population Surveys," in Proceedings of the 26th International Conference on Information Visualization IV2022, IEEE, pp 432-441, July 2022. (doi)

S. Agarwal, F. Beck, U. Ghosh, and J. Sreevalsan-Nair, CiteVis: Visual Analysis of Overlapping Citation Intents as Dynamic Sets, accepted for poster presentation at the 15th IEEE Pacific Visualization Symposium (PacificVis) 2022, April 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. (doi)

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 (doi)

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. (doi)
H. Ravindra, and J. Sreevalsan-Nair, "Integrating Population Surveys Using Spatial Visual Analytics: A Case Study on Nutrition and Health Indicators of Children under Five in India," in the Proceedings of the 7th International Conference on Geographical Information Systems Theory, Applications, and Management (GISTAM 2021), pp 203--213, April 2021, SCITEPRESS. (doi)(preprint)

A. Jakher, and J. Sreevalsan-Nair, “Community Detection in Migration Flow Networks,” accepted for oral presentation at the Urban Complex Sysems 2020, a satellite event at the annual Conference on Complex Systems 2020 (CCS 2020), December 9-10, 2020.

J. Sreevalsan-Nair, R. R. Vangimalla, and P. R. Ghogale, "Influence of COVID-19 Transmission Stages and Demographics on Length of In-Hospital Stay in Singapore for the First 1000 Patients," accepted as poster in COVID-19 COSI at the 28th Conference on Intelligent Systems for Molecular Biology (ISMB), July 2020. (abstract)(doi)

J. Sreevalsan-Nair, R. R. Vangimalla, and P. R. Ghogale, “Estimation of Length of In-Hospital Stay Using Demographic Data of the First 1000 COVID-19 Patients in Singapore,” medRxiv (2020), April 2020. (doi)

R. R. Vangimalla, and J. Sreevalsan-Nair, “Comparing Community Detection Methods in Brain Functional Connectivity Networks,” in Balusamy S., Dudin A.N., Graña M., Mohideen A.K., Sreelaja N.K., Malar B. (eds) Computational Intelligence, Cyber Security and Computational Models. Models and Techniques for Intelligent Systems and Automation. ICC3 2019. Communications in Computer and Information Science, vol 1213. Springer, Singapore, October 2020. (doi); preprint at bioRxiv (2020), February 2020.(doi)

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. (doi)

R. R. Vangimalla, and J. Sreevalsan-Nair, "Construction and Visualization of Diseasome of Lung Diseases Associated with COVID-19 from Co-association Networks of Multi-omics Data," accepted as poster in NetBio COSI at the 28th Conference on Intelligent Systems for Molecular Biology (ISMB), July 2020. (abstract)(doi)

R. R. Vangimalla, and J. Sreevalsan-Nair, “A Multiscale Consensus Method Using Factor Analysis to Extract Modular Regions in the Functional Brain Network,” in the Proceedings of the 42nd Annual Conferences of the IEEE Engineering in Medicine and Biology Society, pp 2824-2828, July 2020. (pdf with correction in axis labels in Fig 2(i))(doi)

R. R. Vangimalla, and J. Sreevalsan-Nair, “Consensus Methods for Network Analysis of Biomedical Data: Case Studies on Brain Functional Connectivity Network and Gene-Gene Association Networks,” presented at the doctoral colloquium at the 4th International Conference on Computational Intelligence and Networks (CINE 2020), February 2020. (pdf)(researchgate)
 
R. R. Vangimalla, and J. Sreevalsan-Nair, "RadTrix: A Composite Hybrid Visualization for Unbalanced Bipartite Graphs in Biological Datasets," accepted as poster in the 9th Eurographics Workshop on Visual Computing for Biology and Medicine, September 2019. (conference-proceedings)(pdf)(poster-pdf)

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)

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. (doi)(pdf) (Best Paper Award Nomination)

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)

J. Sreevalsan-Nair, ``A Survey of Requirements of Multivariate Data and its Visualizations for Analysis of Child Malnutrition in India,'' Data Science Communications, vol. 1, IIITB Press, 1--26, October 2016. (pdf)

S. Parveen, and J. Sreevalsan-Nair, ``Visualization of Small World Networks Using Similarity Matrices,'' in the Proceedings of the Second International Conference on Big Data Analytics, Lecture Notes in Computer Science, Volume 8302, 2013, pp 151-170, Springer. (doi)(pdf)


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Urban Scenario Data:


B. B. Desai, Y. A. Rajapur, A. Mundayatt, and J. Sreevalsan-Nair, CityAQVis: Integrated ML-Visualization Sandbox Tool for Pollutant Estimation in Urban Regions Using Multi-Source Data (Software Article), Under editorial hold at arXiv Preprints, 2025.

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), pp 1-6, IEEE, 2025. https://doi.org/10.1109/B-HTC64616.2025.11116113

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. (doi)(URL)

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.(doi)(URL)
 
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. (doi)(URL)
 
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. (doi) (book-link)

D. Katkoria and J. Sreevalsan-Nair, “RoSELS: Road Surface Extraction for 3D Automotive LiDAR Point Cloud Sequence,” in Proceedings of the 3rd International Conference on Deep Learning Theory and Applications (DeLTA), INSTICC, SciTePress, 2022, pp 55–67. ISBN : 978-989-758-584-5. (doi). (Best Paper Award Nomination)

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. (doi)(Easychair preprint)

A. C. Victor and J. Sreevalsan-Nair, "Scene Editing Using Synthesis of Three-Dimensional Virtual Worlds From Monocular Images of Urban Road Traffic Scenes", accepted for spotlight session and poster at the ACM SIGGRAPH European Conference on Visual Media Production (CVMP) on December 2019.(pdf)(conference-proceedings)
 

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HCI Data
 
B. B. Desai, Y. A. Rajapur, A. Mundayatt, and J. Sreevalsan-Nair, CityAQVis: Integrated ML-Visualization Sandbox Tool for Pollutant Estimation in Urban Regions Using Multi-Source Data (Software Article), Under editorial hold at arXiv Preprints, 2025.

 V. S. Bitra, R. R. Vangimalla, and J. Sreevalsan-Nair, “Network-based Diseasome Construction from Multi-omics 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; Preprint at https://preprints.jmir.org/preprint/70073  DOI: https://doi.org/10.2916/70073  [Online]. Available: https://formative.jmir.org/2025/1/e70073

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

P. Rastogi, K. Singh, and J. Sreevalsan-Nair, “SunburstChartAnalyzer: Hierarchical Data Retrieval from Images of Sunburst Charts for Tree Visualization,” in Computer Graphics & Visual Computing (CGVC), P. Vangorp and D. Hunter, Eds., The Eurographics Association, 2023, pp. 97–101, ISBN: 978-3-03868-231-8. (doi)

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. (doi)

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. (doi)

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. (full-text view) (doi)

S. C. Daggubati and J. Sreevalsan-Nair, “ACCirO: A System for Analyzing and Digitizing Images of Charts with Circular Objects,” in Proceedings of the 22 nd International Conference, Part III, chapter 50, Cham: Springer International Publishing, 2022, pp. 605–612. (doi)

J. Sreevalsan-Nair, K. Dadhich, and S. C. Daggubati, "Tensor Fields for Data Extraction from Chart Images: Bar Charts and Scatter Plots," in Topological Methods in Data Analysis and Visualization VI, Ingrid Hotz, Talha Bin Masood, Filip Sadlo, and Julien Tierny (Eds.). Springer, Cham, 2021, pp 219-241. (doi). Preprint at arXiv (2020), October 2020, https://arxiv.org/abs/2010.02319

K. Dadhich, S. C. Daggubati, and J. Sreevalsan-Nair, “ScatterPlotAnalyzer: Digitizing Images of Charts Using Tensor-based Computational Model,” in International Conference on Computational Science, Computational
Science -- ICCS 2021, Part V, Lecture Notes in Computer Science, volume 12746, M. Paszynski, D. Kranzlmüller, V. V. Krzhizhanovskaya, and P. M. Dongarra Jack J. and Sloot, Eds., Cham: Springer International Publishing, 2021, pp. 70–83, ISBN : 978-3-030-77977-1. (doi)(preprint)

K. Dadhich, S. C. Daggubati, and J. Sreevalsan-Nair, "BarChartAnalyzer: Digitizing Images of Bar Charts," in the Proceedings of the International Conference on Image Processing and Vision Engineering (IMPROVE 2021), pp 17--28, April 2021, SCITEPRESS. (doi)(preprint)  (Best Paper Award Nomination)

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. (doi)(pdf)


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