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ORCIDVarun Chandola
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- affiliation: State University of New York (SUNY) at Buffalo
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2020 – today
- 2024
[j24]Suchismit Mahapatra, Varun Chandola:
Learning manifolds from non-stationary streams. J. Big Data 11(1): 42 (2024)- [c40]Namit Juneja, Varun Chandola, Jaroslaw Zola, Olga Wodo, Parth Desai:
Resource Efficient Bayesian Optimization. CLOUD 2024: 12-19 - 2023
- [j23]Amol Salunkhe
, Dwyer Deighan, Paul E. DesJardin, Varun Chandola:
Physics informed machine learning for chemistry tabulation. J. Comput. Sci. 69: 102001 (2023) - [j22]Sreelekha Guggilam, Varun Chandola, Abani K. Patra:
Large Deviations Anomaly Detection (LAD) for collection of multivariate time series data: Applications to COVID-19 data. J. Comput. Sci. 72: 102101 (2023) - [e11]Ashwin Shashidharan, Martin Werner, Krishna Karthik Gadiraju, Varun Chandola, Ranga Raju Vatsavai:
Proceedings of the 11th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2023, Hamburg, Germany, 13 November 2023. ACM 2023 [contents] - [i21]Sreelekha Guggilam, Varun Chandola, Abani K. Patra:
Large Deviations for Accelerating Neural Networks Training. CoRR abs/2303.00954 (2023) - 2022
- [j21]Sreelekha Guggilam, Varun Chandola, Abani K. Patra:
Tracking clusters and anomalies in evolving data streams. Stat. Anal. Data Min. 15(2): 156-178 (2022) - [j20]Ashwin Shashidharan, Krishna Karthik Gadiraju, Ranga Raju Vatsavai, Varun Chandola:
The 10th ACM SIGSPATIAL International Workshop on Analytics for Big Spatial Data (BigSpatial 2022). ACM SIGSPATIAL Special 14(1): 43-44 (2022) - [c39]Amol Salunkhe, Dwyer Deighan, Paul E. DesJardin, Varun Chandola:
ChemTab: A Physics Guided Chemistry Modeling Framework. ICCS (1) 2022: 75-88 - [c38]Sreelekha Guggilam, Varun Chandola, Abani K. Patra:
Classifying Anomalous Members in a Collection of Multivariate Time Series Data Using Large Deviations Principle: An Application to COVID-19 Data. ICCS (1) 2022: 133-149 - [c37]Pranav Sankhe, Seventy F. Hall, Melanie D. Sage, Maria Y. Rodriguez, Varun Chandola, Kenneth Joseph:
Mutual Information Scoring: Increasing Interpretability in Categorical Clustering Tasks with Applications to Child Welfare Data. SBP-BRiMS 2022: 165-175 - [e10]Ashwin Shashidharan, Krishna Karthik Gadiraju, Varun Chandola, Ranga Raju Vatsavai:
Proceedings of the 10th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2022, Seattle, Washington, 1 November 2022. ACM 2022, ISBN 978-1-4503-9531-1 [contents] - [i20]Amol Salunkhe, Dwyer Deighan, Paul E. DesJardin, Varun Chandola:
ChemTab: A Physics Guided Chemistry Modeling Framework. CoRR abs/2202.09855 (2022) - [i19]Pranav Sankhe, Seventy F. Hall, Melanie D. Sage, Maria Y. Rodriguez, Varun Chandola, Kenneth Joseph:
Mutual Information Scoring: Increasing Interpretability in Categorical Clustering Tasks with Applications to Child Welfare Data. CoRR abs/2208.01802 (2022) - [i18]Amol Salunkhe, Dwyer Deighan, Paul E. DesJardin, Varun Chandola:
Physics Informed Machine Learning for Chemistry Tabulation. CoRR abs/2211.03022 (2022) - [i17]Amol Salunkhe, Georgios Georgalis, Abani K. Patra, Varun Chandola:
An Ensemble-Based Deep Framework for Estimating Thermo-Chemical State Variables from Flamelet Generated Manifolds. CoRR abs/2211.14098 (2022) - [i16]Syed Mohammed Arshad Zaidi, Varun Chandola, Eun-Hye Enki Yoo:
Geo-Adaptive Deep Spatio-Temporal predictive modeling for human mobility. CoRR abs/2211.14885 (2022) - 2021
- [j19]Syed Mohammed Arshad Zaidi, Varun Chandola, Eun-Hye Enki Yoo:
DST-Predict: Predicting Individual Mobility Patterns From Mobile Phone GPS Data. IEEE Access 9: 167592-167604 (2021) - [c36]Jialiang Jiang, Sharon Hewner, Varun Chandola:
Explainable Deep Learning for Readmission Prediction with Tree-GloVe Embedding. ICHI 2021: 138-147 - [c35]Marc Böhlen, Raunaq Jain, Wawan Sujarwo, Varun Chandola:
From images in the wild to video-informed image classification. ICMLA 2021: 656-661 - [c34]Namit Juneja, Jaroslaw Zola, Varun Chandola, Olga Wodo:
Graph-based Strategy for Establishing Morphology Similarity. SSDBM 2021: 169-180 - [i15]Marc Böhlen, Varun Chandola, Wawan Sujarwo, Raunaq Jain:
From images in the wild to video-informed image classification. CoRR abs/2109.12040 (2021) - [i14]Sreelekha Guggilam, Varun Chandola, Abani K. Patra:
Anomaly Detection for High-Dimensional Data Using Large Deviations Principle. CoRR abs/2109.13698 (2021) - 2020
- [j18]Frank Schoeneman, Varun Chandola, Nils Napp, Olga Wodo, Jaroslaw Zola:
Learning Manifolds from Dynamic Process Data. Algorithms 13(2): 30 (2020) - [j17]Ashwin Shashidharan, Varun Chandola, Ranga Raju Vatsavai:
The 9th ACM SIGSPATIAL International Workshop on Analytics for Big Spatial Data (BigSpatial 2020): November 3, 2020. ACM SIGSPATIAL Special 12(3): 15-16 (2020) - [e9]Varun Chandola, Ranga Raju Vatsavai, Ashwin Shashidharan:
BIGSPATIAL '20: Proceedings of the 9th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial@SIGSPATIAL 2020, Seattle, WA, USA, November 3, 2020. ACM 2020, ISBN 978-1-4503-8162-8 [contents]
2010 – 2019
- 2019
- [j16]Duc Thanh Anh Luong, Prerna Singh, Mahin Ramezani, Varun Chandola:
longSil: an Evaluation Metric to Assess Quality of Clustering Longitudinal Clinical Data. J. Heal. Informatics Res. 3(4): 441-459 (2019) - [j15]Ashwin Shashidharan, Varun Chandola, Ranga Raju Vatsavai:
The Eighth ACM SIGSPATIAL International Workshop on Analysis for Big Spatial Data: Chicago, IL, USA - November 5, 2019. ACM SIGSPATIAL Special 11(3): 38-39 (2019) - [c33]Jialiang Jiang, Sharon Hewner, Varun Chandola:
Tree-based Regularization for Interpretable Readmission Prediction. AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering 2019 - [c32]Sreelekha Guggilam, Syed Mohammed Arshad Zaidi, Varun Chandola, Abani K. Patra:
Integrated Clustering and Anomaly Detection (INCAD) for Streaming Data. ICCS (4) 2019: 45-59 - [c31]Duc Thanh Anh Luong, Varun Chandola:
Learning Deep Representations from Clinical Data for Chronic Kidney Disease. ICHI 2019: 1-10 - [e8]Varun Chandola, Ranga Raju Vatsavai, Ashwin Shashidharan:
Proceedings of the 8th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial@SIGSPATIAL 2019, Chicago, IL, USA, November 5th, 2019. ACM 2019, ISBN 978-1-4503-6966-4 [contents] - [i13]Sreelekha Guggilam, Syed Mohammed Arshad Zaidi, Varun Chandola, Abani K. Patra:
Integrated Clustering and Anomaly Detection (INCAD) for Streaming Data (Revised). CoRR abs/1911.00184 (2019) - 2018
- [j14]Ting Xie, Varun Chandola, Oliver Kennedy:
Query Log Compression for Workload Analytics. Proc. VLDB Endow. 12(3): 183-196 (2018) - [j13]Varun Chandola, Ranga Raju Vatsavai:
The seventh ACM SIGSPATIAL international workshop on analysis for big spatial data seattle, wa, USA - November 6, 2018. ACM SIGSPATIAL Special 10(3): 12-13 (2018) - [j12]Gökhan Kul, Duc Thanh Anh Luong, Ting Xie, Varun Chandola, Oliver Kennedy, Shambhu J. Upadhyaya:
Similarity Metrics for SQL Query Clustering. IEEE Trans. Knowl. Data Eng. 30(12): 2408-2420 (2018) - [c30]Niyazi Sorkunlu, Duc Thanh Anh Luong, Varun Chandola:
dynamicMF: A Matrix Factorization Approach to Monitor Resource Usage in High Performance Computing Systems. IEEE BigData 2018: 1302-1307 - [c29]Frank Schoeneman, Varun Chandola, Nils Napp, Olga Wodo, Jaroslaw Zola:
Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes. IEEE BigData 2018: 1655-1660 - [c28]Arun Sharma, Syed Mohammed Arshad Zaidi, Varun Chandola, Melissa R. Allen, Budhendra L. Bhaduri:
WebGIobe - A cloud-based geospatial analysis framework for interacting with climate data. BigSpatial@SIGSPATIAL 2018: 42-46 - [c27]Gökhan Kul, Shambhu J. Upadhyaya, Varun Chandola:
Detecting Data Leakage from Databases on Android Apps with Concept Drift. TrustCom/BigDataSE 2018: 905-913 - [e7]Varun Chandola, Ranga Raju Vatsavai:
Proceedings of the 7th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial@SIGSPATIAL 2018, Seattle, WA, USA, November 6, 2018. ACM 2018, ISBN 978-1-4503-6041-8 [contents] - [i12]Frank Schoeneman, Varun Chandola, Nils Napp, Olga Wodo, Jaroslaw Zola:
Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes. CoRR abs/1802.06823 (2018) - [i11]Jialiang Jiang, Sharon Hewner, Varun Chandola:
Hospital Readmission Prediction - Applying Hierarchical Sparsity Norms for Interpretable Models. CoRR abs/1804.01188 (2018) - [i10]Suchismit Mahapatra, Varun Chandola:
Learning Manifolds from Non-stationary Streaming Data. CoRR abs/1804.08833 (2018) - [i9]Gökhan Kul, Shambhu J. Upadhyaya, Varun Chandola:
Detecting Data Leakage from Databases on Android Apps with Concept Drift. CoRR abs/1805.11780 (2018) - [i8]Ting Xie, Oliver Kennedy, Varun Chandola:
Query Log Compression for Workload Analytics. CoRR abs/1809.00405 (2018) - [i7]Niyazi Sorkunlu, Duc Thanh Anh Luong, Varun Chandola:
dynamicMF: A Matrix Factorization Approach to Monitor Resource Usage in High Performance Computing Systems. CoRR abs/1809.10624 (2018) - [i6]Duc Thanh Anh Luong, Varun Chandola:
An Empirical Evaluation of Time-Aware LSTM Autoencoder on Chronic Kidney Disease. CoRR abs/1810.00490 (2018) - 2017
- [j11]Varun Chandola, Ranga Raju Vatsavai:
The Sixth ACM SIGSPATIAL International Workshop on Analysis for Big Spatial Data: Redondo Beach, CA, USA - November 7, 2017. ACM SIGSPATIAL Special 9(3): 20-21 (2017) - [c26]Suchismit Mahapatra, Varun Chandola:
S-Isomap++: Multi manifold learning from streaming data. IEEE BigData 2017: 716-725 - [c25]Niyazi Sorkunlu, Varun Chandola, Abani K. Patra:
Tracking System Behavior from Resource Usage Data. CLUSTER 2017: 410-418 - [c24]Prerna Singh, Varun Chandola, Chester H. Fox:
Automatic Extraction of Deep Phenotypes for Precision Medicine in Chronic Kidney Disease. DH 2017: 195-199 - [c23]Duc Thanh Anh Luong, Varun Chandola:
A K-Means Approach to Clustering Disease Progressions. ICHI 2017: 268-274 - [c22]Frank Schoeneman, Suchismit Mahapatra, Varun Chandola, Nils Napp, Jaroslaw Zola:
Error Metrics for Learning Reliable Manifolds from Streaming Data. SDM 2017: 750-758 - [e6]Varun Chandola, Ranga Raju Vatsavai:
Proceedings of the 6th ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data, BigSpatial@SIGSPATIAL 2017, Redondo Beach, CA, USA, November 7, 2017. ACM 2017, ISBN 978-1-4503-5494-3 [contents] - [r1]Varun Chandola, Arindam Banerjee, Vipin Kumar:
Active Learning. Encyclopedia of Machine Learning and Data Mining 2017: 42-56 - [i5]Niyazi Sorkunlu, Varun Chandola, Abani K. Patra:
Tracking System Behaviour from Resource Usage Data. CoRR abs/1705.10756 (2017) - [i4]Suchismit Mahapatra, Varun Chandola:
S-Isomap++: Multi Manifold Learning from Streaming Data. CoRR abs/1710.06462 (2017) - [i3]Suchismit Mahapatra, Varun Chandola:
Modeling Graphs Using a Mixture of Kronecker Models. CoRR abs/1710.07231 (2017) - [i2]Marc Böhlen, Varun Chandola, Amol Salunkhe:
Server, server in the cloud. Who is the fairest in the crowd? CoRR abs/1711.08801 (2017) - 2016
- [j10]Ranga Raju Vatsavai, Varun Chandola:
Guest editorial: big spatial data. GeoInformatica 20(4): 797-799 (2016) - [c21]Gökhan Kul, Duc Luong, Ting Xie, Patrick Coonan, Varun Chandola, Oliver Kennedy, Shambhu J. Upadhyaya:
Ettu: Analyzing Query Intents in Corporate Databases. WWW (Companion Volume) 2016: 463-466 - [e5]Varun Chandola, Ranga Raju Vatsavai:
Proceedings of the 5th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial@SIGSPATIAL 2016, Burlingame, California, USA, October 31, 2016. ACM 2016, ISBN 978-1-4503-4581-1 [contents] - [i1]Gökhan Kul, Duc Luong, Ting Xie, Patrick Coonan, Varun Chandola, Oliver Kennedy, Shambhu J. Upadhyaya:
Summarizing Large Query Logs in Ettu. CoRR abs/1608.01013 (2016) - 2015
- [j9]Varun Chandola, Ranga Raju Vatsavai:
The Third ACM SIGSPATIAL International Workshop on Analysis for Big Spatial Data Dallas, Texas, USA - November 4, 2014. ACM SIGSPATIAL Special 7(2): 44 (2015) - [c20]Suchismit Mahapatra, Varun Chandola:
Modeling graphs using a mixture of Kronecker models. IEEE BigData 2015: 727-736 - [c19]Zhi Yang, Varun Chandola:
Surface Reconstruction from Intensity Image Using Illumination Model Based Morphable Modeling. ICVS 2015: 117-127 - [p1]Varun Chandola, Jack C. Schryver, Sreenivas R. Sukumar:
Fraud Detection in Healthcare. Healthcare Data Analytics 2015: 577-598 - [e4]Varun Chandola, Ranga Raju Vatsavai:
Proceedings of the 4th International ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data, BigSpatial@SIGSPATIAL 2015, Bellevue, WA, USA, November 3-6, 2015. ACM 2015, ISBN 978-1-4503-3974-2 [contents] - 2014
- [j8]Varun Chandola, Varun Mithal, Vipin Kumar:
A reference based analysis framework for understanding anomaly detection techniques for symbolic sequences. Data Min. Knowl. Discov. 28(3): 702-735 (2014) - [j7]Varun Chandola, Ranga Raju Vatsavai:
BigSpatial-2013 workshop report: 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data (BigSpatial-2013): Nov 5, 2013, Orlando, Florida, USA. ACM SIGSPATIAL Special 6(1): 18-19 (2014) - [c18]Paul T. Bauman, Varun Chandola, Abani K. Patra, Matthew D. Jones:
Development of a computational and data-enabled science and engineering Ph.D. program. EduHPC@SC 2014: 21-26 - [e3]Varun Chandola, Ranga Raju Vatsavai:
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial@SIGSPATIAL 2014, Dallas, Texas, USA, November 4, 2014. ACM 2014, ISBN 978-1-4503-3132-6 [contents] - 2013
- [c17]Varun Chandola, Sreenivas R. Sukumar, Jack C. Schryver:
Knowledge discovery from massive healthcare claims data. KDD 2013: 1312-1320 - [e2]Varun Chandola, Ranga Raju Vatsavai:
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial@SIGSPATIAL 2013, Nov 4th, 2013, Orlando, FL, USA. ACM 2013, ISBN 978-1-4503-2534-9 [contents] - 2012
- [j6]Jordan Graesser, Anil M. Cheriyadat, Ranga Raju Vatsavai, Varun Chandola, Jordan Long, Eddie A. Bright:
Image Based Characterization of Formal and Informal Neighborhoods in an Urban Landscape. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 5(4): 1164-1176 (2012) - [j5]Varun Chandola, Arindam Banerjee, Vipin Kumar:
Anomaly Detection for Discrete Sequences: A Survey. IEEE Trans. Knowl. Data Eng. 24(5): 823-839 (2012) - [c16]Ranga Raju Vatsavai, Auroop R. Ganguly, Varun Chandola, Anthony Stefanidis, Scott Klasky, Shashi Shekhar:
Spatiotemporal data mining in the era of big spatial data: algorithms and applications. BigSpatial@SIGSPATIAL 2012: 1-10 - [e1]Varun Chandola, Ranga Raju Vatsavai, Chetan Gupta:
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial@SIGSPATIAL 2012, Redondo Beach, CA, USA, November 6, 2012. ACM 2012, ISBN 978-1-4503-1692-7 [contents] - 2011
- [j4]Varun Chandola, Ranga Raju Vatsavai:
A scalable gaussian process analysis algorithm for biomass monitoring. Stat. Anal. Data Min. 4(4): 430-445 (2011) - [c15]Ranga Raju Vatsavai, Eddie A. Bright, Varun Chandola, Budhendra L. Bhaduri, Anil M. Cheriyadat, Jordan Graesser:
Machine learning approaches for high-resolution urban land cover classification: a comparative study. COM.Geo 2011: 11:1-11:10 - [c14]Varun Chandola, Ranga Raju Vatsavai, Budhendra L. Bhaduri:
iGlobe: an interactive visualization and analysis framework for geospatial data. COM.Geo 2011: 21:1-21:6 - [c13]Ranga Raju Vatsavai, Mark A. Tuttle, Budhendra L. Bhaduri, Edward Bright, Anil M. Cheriyadat, Varun Chandola, Jordan Graesser:
Rapid damage assessment using high-resolution remote sensing imagery: Tools and techniques. IGARSS 2011: 1445-1448 - [c12]Varun Chandola, Ranga Raju Vatsavai:
Implementing a gaussian process learning algorithm in mixed parallel environment. ScalA@SC 2011: 3-6 - [c11]Varun Chandola, Ranga Raju Vatsavai:
A Gaussian Process Based Online Change Detection Algorithm for Monitoring Periodic Time Series. SDM 2011: 95-106 - [c10]Ranga Raju Vatsavai, Christopher T. Symons, Varun Chandola, Goo Jun:
GX-Means: A model-based divide and merge algorithm for geospatial image clustering. ICCS 2011: 186-195 - 2010
- [j3]Varun Chandola, Olufemi A. Omitaomu, Auroop R. Ganguly, Ranga Raju Vatsavai, Nitesh V. Chawla, João Gama, Mohamed Medhat Gaber:
Knowledge discovery from sensor data (SensorKDD). SIGKDD Explor. 12(2): 50-53 (2010) - [c9]Varun Chandola, Ranga Raju Vatsavai:
Scalable Time Series Change Detection for Biomass Monitoring Using Gaussian Process. CIDU 2010: 69-82 - [c8]Varun Chandola, Ranga Raju Vatsavai:
Multi-temporal remote sensing image classification - A multi-view approach. CIDU 2010: 258-270 - [c7]Varun Chandola, Shyam Boriah, Vipin Kumar:
A reference based analysis framework for analyzing system call traces. CSIIRW 2010: 33 - [c6]Varun Chandola, Dafeng Hui, Lianhong Gu, Budhendra L. Bhaduri, Ranga Raju Vatsavai:
Using Time Series Segmentation for Deriving Vegetation Phenology Indices from MODIS NDVI Data. ICDM Workshops 2010: 202-208
2000 – 2009
- 2009
- [j2]Varun Chandola, Arindam Banerjee, Vipin Kumar:
Anomaly detection: A survey. ACM Comput. Surv. 41(3): 15:1-15:58 (2009) - [c5]Varun Chandola, Shyam Boriah, Vipin Kumar:
A Framework for Exploring Categorical Data. SDM 2009: 187-198 - 2008
- [c4]Varun Chandola, Varun Mithal, Vipin Kumar:
Comparative Evaluation of Anomaly Detection Techniques for Sequence Data. ICDM 2008: 743-748 - [c3]Shyam Boriah, Varun Chandola, Vipin Kumar:
Similarity Measures for Categorical Data: A Comparative Evaluation. SDM 2008: 243-254 - 2007
- [j1]Varun Chandola, Vipin Kumar:
Summarization - compressing data into an informative representation. Knowl. Inf. Syst. 12(3): 355-378 (2007) - [c2]Jon B. Weissman, Vipin Kumar, Varun Chandola, Eric Eilertson, Levent Ertöz, György J. Simon, Seonho Kim, Jinoh Kim:
DDDAS/ITR: A Data Mining and Exploration Middleware for Grid and Distributed Computing. International Conference on Computational Science (1) 2007: 1222-1229 - 2005
- [c1]Varun Chandola, Vipin Kumar:
Summarization - Compressing Data into an Informative Representation. ICDM 2005: 98-105
Coauthor Index
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