Contact Information

Firstname = sunil

Lastname = gupta

Firstname.Lastname@deakin.edu.au

Current Position

I am a Lecturer in School of Information Technology at Deakin University, Australia. I am a founding member of the Strategic Research Centre for Pattern Recognition and Data Analytics (PRaDA). 

Research Interests

Machine Learning | Data Mining | Healthcare Analytics | Pervasive Computing | Social Media Analytics

  • Transfer learning, Multi-task learning.
  • Building predictive models for healthcare data.
  • Dimensionality reduction, Clustering,
  • Shared subspace learning: Modeling multiple data sources jointly.
  • Bayesian hierarchical modeling, Bayesian nonparametrics using Dirichlet and Beta processes.
  • Pervasive/Ubiquitous computing, Social media retrieval.

Education

PhD (Computing) at IMPCA, Curtin University, Australia  (2012) (PhD Thesis)

ME (Signal Processing) at Indian Institute Of Science, Bangalore, India  (2008)

B. Tech. (Electronics and Communication Engg.) at Harcourt Butler Technological Institute, Kanpur, India  (2001)

Work Experience

  • From Feb 2012, I am a lecturer at Deakin university, Geelong (Australia).
  • From Sep 2011 to Jan 2012, I worked at Curtin university, Perth (Australia) as a research fellow.
  • From Apr 2002 to May 2009, I worked at LRDE, Bangalore (India) as a research scientist.

Achievements
  • Recipient of "Best Paper Awardat the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Springer 2015 for Multi- relational MTL paper [14].
  • Intervention paper [21] was selected among Best Papers of SDM, SIAM Data Mining Conference 2014.
  • Recipient of Chancellor’s Commendation for excellence for my PhD thesis at Curtin University, 2012.

  • Recipient of KDD Travel Awards, ACM SIGKDD Data Mining Conference 2010.

  • Recipient of CIPRS Scholarship, Curtin University, Australia, 2009.

Recent Publications

My external research profiles:  Google Scholar and ResearchGate.

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Book Chapters

[1] 

S. Gupta, D. Phung, B. Adams, S. Venkatesh. “A Matrix Factorization Framework for Jointly Analyzing Multiple Nonnegative Data Sources”. In: Data Mining for Service. Ed. by Katsutoshi Yada. Springer, 2014, pp. 151–170.

[2] 

D. Phung, T. Nguyen,  S. Gupta, S. Venkatesh. “Learning Latent Activities from Social Signals with Hierarchical Dirichlet Process”. In: Handbook on Plan, Activity, and Intent Recognition. Ed. by Gita Sukthankar, Christopher Geib, David V. Pynadath, Hung Bui, and Robert P. Goldman. Elsevier, 2013, pp. 149–174.

Journal Articles

[3] 

I. Kamkar,  S. Gupta, D. Phung, S. Venkatesh. “Stable feature selection for clinical prediction: Exploiting ICD tree structure using Tree-Lasso”. In: Journal of Biomedical Informatics  53 (2015), pp. 277–290.

[4] 

W Luo, T Nguyen, M Nichols, T Tran, S Rana, S Gupta, D Phung, S Venkatesh, S Allender. “Is Demography Destiny? Application of Machine Learning Techniques to Accurately Predict Population Health Outcomes from a Minimal Demographic Dataset”. In:  PLoS ONE (2015). doi10.1371/journal.pone.0125602.

[5] 

T Nguyen, T Truyen, W Luo, S Gupta, S Rana, D Phung, M Nichols, L Millar, S Venkatesh, S Allender. “Web search activity data accurately predicts population chronic disease risk in the United States”. In: Journal of Epidemiology & Community Health (2015), (accepted on 26 Jan 2015). issn: 1949-3045. doi10.1136/jech-2014-204523.

[6] 

T. C. Nguyen,  S. Gupta, S. Venkatesh, D. Phung. “Continuous discovery of co-location contexts from Bluetooth data”. In: Pervasive and Mobile Computing 16 (2015), pp. 286–304.

[7] 

S Rana, S Gupta, D Phung, S Venkatesh. “A predictive framework for modeling healthcare data with evolving clinical interventions”. In: Statistical Analysis and Data Mining: The ASA Data Science Journal 8.3 (2015), pp. 162–182.

[8] 

S Gupta, D Phung, S Venkatesh. “Modelling multilevel data in multimedia: A hierarchical factor analysis approach”. In: Multimedia Tools and Applications (2014), pp. 1–23. doi10.1007/s11042-014- 2394-3.

[9] 

S. Gupta, T. Tran, W. Luo, D. Phung, R. Kennedy, A. Broad, D. Campbell, D. Kipp, M. Singh, M. Khasraw, L. Matheson, D. Ashley, S. Venkatesh. “Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry”. In: BMJ Open (DOI:10.1136/bmjopen-2013-004007)(2014).

[10] 

B Saha, S Gupta, D Phung, S Venkatesh. “Multiple Task Transfer Learning with Small Sample Sizes”. In: Knowledge and Information Systems (2014), pp. 1–28. doi10.1007/s10115-015-0821-z.

[11] 

T Tran, W Luo, D Phung, S Gupta, S Rana, L Kennedy, A Larkins, S Venkatesh. “A framework for feature extraction from hospital medical data with applications in risk prediction”. In: BMC bioinformatics 15.1 (2014), p. 6596.

[12] 

S Gupta, D Phung, B Adams, S Venkatesh. “Regularized nonnegative shared subspace learning”. In: Data mining and knowledge discovery 26.1 (2013), pp. 57–97.

[13] 

D. Phung,  S. Gupta, T. Nguyen, S. Venkatesh. “Connectivity, Online Social Capital and Mood: A Bayesian Nonparametric Analysis”. English. In: IEEE Transactions on Multimedia 15 (2013), pp. 1316–1325. issn: 0219-1377.

Conference Papers

[14] 

S. Gupta, S. Rana, D. Phung, S. Venkatesh. “Collaborating Differently on Different Topics: A Multi-Relational Approach to Multi-Task Learning”. In: Advances in Knowledge Discovery and Data Mining. Ho Chi Minh City, Vietnam: Springer-Verlag Berlin Heidelberg, 2015, pp. 303–316. (RECIPIENT OF THE BEST PAPER AWARD)

[15] 

S. Gupta, S. Rana, D. Phung, S. Venkatesh. “What shall I share and with Whom? - A Multi-Task Learning Formulation using Multi-Faceted Task Relationships”. In: Proceedings of the SIAM International Conference on Data Mining. Vancouver, Canada, 2015, pp. 703–711.

[16] 

I Kamkar,  S Gupta, D Phung, S Venkatesh. “Exploiting Feature Relationships towards Stable Feature Selection”. In:  IEEE International Conference on Data Science and Advanced Analytics. Paris, France: IEEE, 2015, (accepted on 23rd July, 2015).

[17] 

I Kamkar, S Gupta, D Phung, S Venkatesh. “Stable Feature Selection with Support Vector Machines”. In: 28th Australasian Joint Conference on Artificial Intelligence, Lecture Notes in Artificial Intelligence. Canberra, Australia: Springer, 2015, (accepted on 1st September, 2015).

[18] 

S. Rana,  S. Gupta, S. Venkatesh. “Differentially-private Random Forest with High Utility”. In: IEEE International Conference on Data Mining. NJ, USA: IEEE, 2015, (accepted on 7th August, 2015).

[19] 

B Saha, S Gupta, S Venkatesh. “Improved Risk Predictions via Sparse Imputation of Patient Conditions in Electronic Medical Records”. In:  IEEE International Conference on Data Science and Advanced Analytics. Paris, France: IEEE, 2015, (accepted on 23rd July, 2015).

[20] 

B. Saha,  S. Gupta, S. Venkatesh. “Prediction of Emergency Events: A Multi-task Multi-label learning Approach”. In: Advances in Knowledge Discovery and Data Mining. Ho Chi Minh City, Vietnam: Springer-Verlag Berlin Heidelberg, 2015, pp. 226–238.

[21] 

S. Gupta, S. Rana, D. Phung, S. Venkatesh. “Keeping up with Innovation: A Predictive Framework for Modeling Healthcare Data with Evolving Clinical Interventions”. In:  Proceedings of the SIAM International Conference on Data Mining. Philadelphia, USA, 2014, pp. 235–243.

[22] 

T. C. Nguyen, S. Gupta, S. Venkatesh, D. Phung. “Fixed-lag Particle Filter for Continuous Context Discovery Using Indian Buffet Process”. In: 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom). Budapest, Hungary, 2014, pp. 20–28.

[23] 

T. Nguyen,  S. Gupta, S. Venkatesh, D. Phung. “A Bayesian Nonparametric Framework for Activity Recognition using Accelerometer Data”. In: Proceedings of 22nd International Conference on Pattern Recognition (ICPR). 2014, pp. 2017–2022.

[24] 

S. Rana, S. Gupta, D. Phung, S. Venkatesh. “Intervention-Driven Predictive Framework for Modeling Healthcare Data”. In: Advances in Knowledge Discovery and Data Mining. Tainan, Taiwan: Springer-Verlag Berlin Heidelberg, 2014, pp. 497–508.

[25] 

S Gupta, D. Phung, S. Venkatesh. “Factorial Multi-Task Learning: A Bayesian Nonparametric Approach”. In: International Conference on Machine Learning. Atlanta, USA, 2013, pp. 657–665.

[26] 

T. Nguyen, D. Phung, S. Gupta, S. Venkatesh. “Extraction of Latent Patterns and Contexts from Social Honest Signals Using Hierarchical Dirichlet Processes”. In:  2013 IEEE International Conference on Pervasive Computing and Communications (PerCom 2013). San Diego, USA, 2013, pp. 47–55.

[27] 

T. Nguyen, D. Phung, S. Gupta, S. Venkatesh. “Interactive Browsing System for Anomaly Video Surveillance”. In:  2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). Melbourne, Australia, 2013, pp. 384–389.

[28] 

S. Venkatesh, D. Phung, T. Tran, S. Gupta. “Capitalising on the data deluge: Data analytics for healthcare”. In: Big Data. Health Informatics Society of Australia (HISA), 2013.

[29] 

S Gupta, D Phung, S Venkatesh. “A Bayesian Nonparametric Joint Factor Model for Learning Shared and Individual Subspaces from Multiple Data Sources”. In: Proceedings of the SIAM International Conference on Data Mining. 2012, pp. 200–211.

[30] 

S. Gupta, D. Phung, S. Venkatesh. “A Slice Sampler for Restricted Hierarchical Beta Process with Applications to Shared Subspace Learning”. In:  Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence. Catalina Island, CA, USA, 2012, pp. 316–325.

[31] 

S. Gupta, S. Phung, S. Venkatesh. “A nonparametric Bayesian Poisson gamma model for count data”. In: Proceedings of the 21st International Conference on Pattern Recognition, ICPR 2012. Tsukuba, Japan, 2012, pp. 1815–1818.

[32] 

S. Gupta, D. Phung, B. Adams, S. Venkatesh. “A Bayesian Framework for Learning Shared and Individual Subspaces from Multiple Data Sources”. In:  Advances in Knowledge Discovery and Data Mining. Shenzhen, China, 2011, pp. 136–147.

[33] 

S. Gupta, D. Phung, B. Adams, S. Venkatesh. “A Matrix Factorization Framework for Jointly Analyzing Multiple Nonnegative Data Sources”. In: Procs. of Text Mining Workshop, in conjuction with SIAM Int. Conf. on Data Mining. Arizona, USA, 2011.

[34] 

Y. Kumar, S. Gupta, B. Kiran, K. Ramakrishnan, C. Bhattacharyya. “Automatic summarization of broadcast cricket videos”. In: Consumer Electronics (ISCE), 2011 IEEE 15th International Symposium on. IEEE. 2011, pp. 222–225.

[35] 

S. Gupta, D. Phung, B. Adams, T. Tran, S. Venkatesh. “Nonnegative shared subspace learning and its application to social media retrieval”. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM. 2010, pp. 1169–1178.

[36] 

S. Gupta, Y. Kumar, K. Ramakrishnan. “Learning Feature Trajectories Using Gabor Filter Bank for Human Activity Segmentation and Recognition”. In: Proceedings of the 6th IEEE Indian Conference on Computer Vision, Graphics & Image Processing (ICVGIP) 2008. Bhubaneswar, India, 2008, pp. 111–118.

Student Supervision
Thanh Dai Nguyen (ongoing)
Iman Kamkar (ongoing)
Sarvanan Subramanian (ongoing)
Cong Thuong Nguyen (completed)