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A/Prof. Sunil Gupta

STAFF PROFILE

Position

Associate Professor

Faculty

Applied Artificial Intel Inst

Department

A2I2P

Campus

Geelong Waurn Ponds Campus

Qualifications

Doctor of Philosophy, Curtin University, 2012

Biography

Dr. Gupta graduated with a PhD in computer science from Curtin University in Jan 2012. He completed his PhD in a period of 2.5 years receiving the Chancellor's Commendation for excellence for his exceptional doctoral work under the supervision of Prof Svetha Venkatesh. Prior to his PhD, he completed a Master of Engineering degree in Signal Processing from Indian Institute of Science, Bangalore. Since completing his PhD, Dr. Gupta has been working at Deakin University, where he works at the Strategic Research Centre for Pattern Recognition and Data Analytics (PRaDA). His current research interests lie in machine learning and data mining.

Read more on Sunil's profile

Research interests

Machine Learning, Bayesian optimisation, Healthcare Analytics, Data Mining, Privacy preserving algorithms 

Knowledge areas

Machine learning, Transfer Learning, Bayesian Optimisation, Data mining, Pattern recognition, Healthcare data analytics, Computer vision, Programming skills, Signal processing

Awards

  • 2017: Vice Chancellor’s Award for Outstanding Contribution through Innovation that spans the Value Promise
  • 2017: Best Student Paper Award at Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2017
  • 2017: Professional Poster Award for our superalloy design work at 4th World Congress on Integrated Computational Materials Engineering, 2017
  • 2016: Best Paper RunnerUp and Best Poster awards at Asian Conference on Machine Learning (ACML) 2016
  • 2016: Finalists INTEL Track 5 Student Paper Award at International Conference of Pattern Recognition (ICPR), 2016
  • 2015: Best Paper Award, at PAKDD 2015
  • 2014: Recipient of Best Papers of SDM, at SIAM Data Mining Conference 2014
  • 2012: Recipient of Chancellor’s Commendation for excellence for my PhD thesis at Curtin University
  • 2010: Recipient of KDD Travel Awards, ACM SIGKDD Data Mining Conference 2010
  • 2009: Recipient of CIPRS Scholarship, Curtin University, Australia, 2009

Publications

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2020

Incorporating expert prior in Bayesian optimisation via space warping

A Ramachandran, S Gupta, S Rana, Cheng Li, S Venkatesh

(2020), Vol. 195, pp. 1-11, Knowledge-based systems, Amsterdam, The Netherlands, C1

journal

Bayesian optimisation in unknown bounded search domains

J Berk, S Gupta, S Rana, V Nguyen, S Venkatesh

(2020), Vol. 195, pp. 1-9, Knowledge-based systems, Amsterdam, The Netherlands, C1

journal

Bayesian optimization for adaptive experimental design: a review

S Greenhill, S Rana, S Gupta, P Vellanki, S Venkatesh

(2020), Vol. 8, pp. 13937-13948, IEEE access, Piscataway, N.J., C1

journal
2019

A flexible transfer learning framework for Bayesian optimization with convergence guarantee

T Theckel Joy, S Rana, S Gupta, S Venkatesh

(2019), Vol. 115, pp. 656-672, Expert systems with applications, Amsterdam, The Netherlands, C1

journal

Information-theoretic transfer learning framework for Bayesian optimisation

A Ramachandran, S Gupta, S Rana, S Venkatesh

(2019), Vol. 11052, pp. 827-842, ECML-PKDD 2018 : Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2018, Dublin, Ireland, E1

conference

Exploration enhanced expected improvement for Bayesian optimization

J Berk, V Nguyen, S Gupta, S Rana, S Venkatesh

(2019), Vol. 11052, pp. 621-637, ECML-PKDD 2018 : Proceedings of the e European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2018, Dublin, Ireland, E1

conference

Incomplete conditional density estimation for fast materials discovery

P Nguyen, T Tran, S Gupta, S Rana, M Barnett, S Venkatesh

(2019), pp. 549-557, 2019 SIAM : Proceedings of the 2019 SIAM International Conference on Data Mining, Calgary, Alta., E1

conference

Bayesian functional optimisation with shape prior

Pratibha Vellanki, Santu Rana, Sunil Gupta, David Leal, Alessandra Sutti, Murray Height, Svetha Venkatesh

(2019), pp. 1617-1624, AAAI 2019 : Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, E1

conference

Optimizing a high-entropy system: software-assisted development of highly hydrophobic surfaces using an amphiphilic polymer

S Subianto, C Li, D Rubin De Celis Leal, S Rana, S Gupta, R He, S Venkatesh, A Sutti

(2019), Vol. 4, pp. 15912-15922, ACS omega, Washington, D.C., C1

journal

Examining conductivity, current density, and sizings applied to carbon fibers during manufacture and their effect on fiber-to-matrix adhesion in epoxy polymers

A Hendlmeier, F Stojcevski, R Alexander, S Gupta, L Henderson

(2019), Vol. 179, pp. 1-8, Composites Part B: Engineering, Amsterdam, The Netherlands, C1

journal

Efficient bayesian function optimization of evolving material manufacturing processes

D Rubn De Celis Leal, D Nguyen, P Vellanki, C Li, S Rana, N Thompson, S Gupta, K Pringle, S Subianto, S Venkatesh, T Slezak, M Height, A Sutti

(2019), Vol. 4, pp. 20571-20578, ACS omega, Washington, D.C., C1

journal
2018

Filtering Bayesian optimization approach in weakly specified search space

T Nguyen, Sunil Gupta, Santu Rana, Cheng Li, Svetha Venkatesh

(2018), pp. 1-29, Knowledge and information systems, London, Eng., C1

journal

Exploiting strategy-space diversity for batch Bayesian optimization

S Gupta, Alistair Shilton, Santu Rana, Svetha Venkatesh

(2018), Vol. 84, pp. 538-547, AISTATS 2018 : Proceedings of the International Conference on Artificial Intelligence and Statistics, Playa Blanca, Lanzarote, Canary Islands, E1

conference

A privacy preserving bayesian optimization with high efficiency

T Nguyen, Sunil Gupta, Santu Rana, Svetha Venkatesh

(2018), Vol. 10939, pp. 543-555, PAKDD 2018 : Advances in Knowledge Discovery and Data Mining : Proceedings of 22nd Pacific-Asia Conference, Melbourne, Victoria, E1

conference

Prescriptive analytics through constrained bayesian optimization

Haripriya Harikumar, Santu Rana, Sunil Gupta, Thin Nguyen, Kaimal Ramachandra, Svetha Venkatesh

(2018), Vol. 10937, pp. 335-347, PAKDD 2018 : Advances in Knowledge Discovery and Data Mining : Proceedings of 22nd Pacific-Asia Conference, Melbourne, Victoria, E1

conference

New bayesian-optimization-based design of high-strength 7xxx-series alloys from recycled aluminum

A Vahid, S Rana, S Gupta, P Vellanki, S Venkatesh, T Dorin

(2018), Vol. 70, pp. 2704-2709, JOM, New York, N.Y., C1

journal

Efficient Bayesian optimisation using derivative meta-model

A Yang, C Li, S Rana, S Gupta, S Venkatesh

(2018), Vol. 11013, pp. 256-264, PRICAI 2018: Proceedings of the 15th Pacific Rim International Conference on Artificial Intelligence, Nanjing, China, E1

conference

Selecting optimal source for transfer learning in Bayesian optimisation

A Ramachandran, S Gupta, S Rana, S Venkatesh

(2018), Vol. 11012, pp. 42-56, PRICAI 2018 : Trends in artificial intelligence : Proceedings of the 15th Pacific Rim International Conference on Artificial Intelligence, Nanjing, China, E1

conference

Multi-target optimisation via Bayesian optimisation and linear programming

A Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh

(2018), UAI 2018: Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, Monterey, Calif., E1

conference

Expected hypervolume improvement with constraints

M Abdolshah, Alistair Shilton, Santu Rana, Sunil Gupta, svetha Venkatesh

(2018), pp. 3238-3243, ICPR 2018 : Proceedings of the 24th International Conference on Pattern Recognition, Beijing, China, E1

conference

Sparse approximation for Gaussian process with derivative observations

A Yang, C Li, S Rana, S Gupta, S Venkatesh

(2018), Vol. 11320, pp. 507-518, AI 2018: Proceedings of the 31st Australasian Joint Conference on Artificial Intelligence, Wellington, N.Z., E1

conference

Accelerating experimental design by incorporating experimenter hunches

C Li, R Santu, S Gupta, V Nguyen, S Venkatesh, A Sutti, D De Celis Leal, T Slezak, M Height, M Mohammed, I Gibson

(2018), Vol. 2018-November, pp. 257-266, IEEE ICDM 2018 : International Conference on Data Mining, Singapore, E1

conference

Differentially private prescriptive analytics

H Harikumar, S Rana, S Gupta, T Nguyen, R Kaimal, S Venkatesh

(2018), Vol. 2018-November, pp. 995-1000, ICDM 2018 : Proceedings of the IEEE International Conference on Data Mining, Singapore, E1

conference

Algorithmic assurance: an active approach to algorithmic testing using Bayesian optimisation

Shivapratap Gopakumar, Sunil Gupta, Santu Rana, Nguyen Vu, Svetha Venkatesh

(2018), Vol. 31, pp. 1-9, NeurIPS 2018 : Proceedings of the 32nd Conference on Neural Information Processing Systems, Montreal, Canada, E1

conference
2017

Effective sparse imputation of patient conditions in electronic medical records for emergency risk predictions

B Saha, S Gupta, Q Phung, S Venkatesh

(2017), Vol. 53, pp. 179-206, Knowledge and information systems, London, Eng., C1

journal

A framework for mixed-type multi-outcome prediction with applications in healthcare

B Saha, S Gupta, D Phung, S Venkatesh

(2017), Vol. 21, pp. 1182-1191, IEEE journal of biomedical and health informatics, Piscataway, N.J., C1

journal

Nonparametric discovery and analysis of learning patterns and autism subgroups from therapeutic data

P Vellanki, T Duong, S Gupta, S Venkatesh, D Phung

(2017), Vol. 51, pp. 127-157, Knowledge and information systems, London, Eng., C1

journal

Regret bounds for transfer learning in Bayesian optimisation

A Shilton, S Gupta, S Rana, S Venkatesh

(2017), Vol. 54, pp. 1-9, AISTATS 2017 : Machine Learning Research : Proceedings of the 20th Artificial Intelligence and Statistics International Conference, Fort Lauderdale, Florida, E1

conference

Stable bayesian optimization

T Nguyen, S Gupta, S Rana, S Venkatesh

(2017), Vol. 54, pp. 578-591, PAKDD 2017 : Advances in Knowledge Discovery and Data Mining : Proceedings of the 21st Pacific-Asia Conference, Jeju, South Korea, E1

conference

High dimensional bayesian optimization with elastic gaussian process

S Rana, C Li, S Gupta, V Nguyen, S Venkatesh

(2017), pp. 1-9, ICML 2017 Proceedings of the International Conference in Machine Learning, Sydney, New South Wales, E1

conference

Rapid Bayesian optimisation for synthesis of short polymer fiber materials

C Li, D Rubn de Celis Leal, S Rana, S Gupta, A Sutti, S Greenhill, T Slezak, M Height, S Venkatesh

(2017), Vol. 7, pp. 1-10, Scientific reports, London, Eng., C1

journal

A framework for mixed-type multioutcome prediction with applications in healthcare

B Saha, S Gupta, D Phung, S Venkatesh

(2017), Vol. 21, pp. 1182-1191, IEEE journal of biomedical and health informatics, Piscataway, N.J., C1

journal

High dimensional bayesian optimization using dropout

C Li, S gupta, S Rana, V Nguyen, S Venkatesh, A Shilton

(2017), pp. 2096-2102, IJCAI 2017 : Proceedings of the 26th International Joint Confrerence on Artificial Intelligence, Melbourne, Victoria, E1

conference

Bayesian optimization in weakly specified search space

V Nguyen, S Gupta, S Rana, C Li, S Venkatesh

(2017), pp. 347-356, ICDM 2017 : Proceedings of the IEEE International Conference on Data Mining, New Orleans, La., E1

conference

Process-constrained batch Bayesian optimisation

P Vellanki, S Rana, S Gupta, D Rubin, A Sutti, T Dorin, M Height, P Sandars, S Venkatesh

(2017), Vol. 2017-December, pp. 3415-3424, NIPS 2017 : Proceedings of the 31st Conference of Neural Information Processing Systems, Long Beach, California, E1

conference

Regret for expected improvement over the best-observed value and stopping condition

V Nguyen, S Gupta, S Rana, C Li, S Venkatesh

(2017), Vol. 77, pp. 279-294, Journal of Machine Learning Research, Seoul, Korea, E1

conference
2016

Multiple task transfer learning with small sample sizes

B Saha, S Gupta, Q Phung, S Venkatesh

(2016), Vol. 46, pp. 315-342, Knowledge and information systems, Berlin, Germany, C1

journal

Stabilizing l1-norm prediction models by supervised feature grouping

I Kamkar, S Gupta, Q Phung, S Venkatesh

(2016), Vol. 59, pp. 149-168, Journal of biomedical informatics, Amsterdam, The Netherlands, C1

journal

A new transfer learning framework with application to model-agnostic multi-task learning

S Gupta, S Rana, B Saha, Q Phung, S Venkatesh

(2016), Vol. 49, pp. 933-973, Knowledge and information systems, London, England, C1

journal

Nonparametric discovery of movement patterns from accelerometer signals

T Nguyen, S Gupta, S Venkatesh, Q Phung

(2016), Vol. 70, pp. 52-58, Pattern recognition letters, Amsterdam, The Netherlands, C1

journal

Flexible transfer learning framework for bayesian optimisation

T Joy, S Rana, S Gupta, S Venkatesh

(2016), Vol. 9651, pp. 102-114, Advances in knowledge discovery and data mining: 20th Pacific-Asia Conference, PAKDD 2016 Auckland, New Zealand, April 19-22, 2016 proceedings, part I, Berlin, Germany, B1

chapter

Toxicity prediction in cancer using multiple instance learning in a multi-task framework

C Li, S Gupta, S Rana, W Luo, S Venkatesh, D Ashley, Q Phung

(2016), Vol. 9651, pp. 152-164, Advances in knowledge discovery and data mining: 20th Pacific-Asia Conference, PAKDD 2016 Auckland, New Zealand, April 19-22, 2016 proceedings, part I, Berlin, Germany, B1

chapter

Privacy aware K-means clustering with high utility

T Nguyen, S Gupta, S Rana, S Venkatesh

(2016), Vol. 9652, pp. 388-400, Advances in knowledge discovery and data mining: 20th Pacific-Asia Conference, PAKDD 2016 Auckland, New Zealand, April 19-22, 2016 proceedings, part I, Berlin, Germany, B1

chapter

Differentially private multi-task learning

S Rana, S Gupta, S Venkatesh

(2016), Vol. 9650, pp. 101-113, PAISI 2016 : Intelligence and Security Informatics : Proceedings of the 11th Pacific-Asia Workshop, Auckland, New Zealand, E1

conference

Transfer learning for rare cancer problems via discriminative sparse gaussian graphical mode

B Saha, S Gupta, Q Phung, S Venkatesh

(2016), pp. 537-542, 2016 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, E1

conference

Extracting key challenges in achieving sobriety through shared subspace learning

H Harikumar, T Nguyen, S Rana, S Gupta, R Kaimal, S Venkatesh

(2016), Vol. 10086, pp. 420-433, ADMA 2016 : Proceedings of the 12th Advanced Data Mining and Applications International Conference, Gold Coast, Queensland, E1

conference

Understanding behavioral differences between short and long-term drinking abstainers from social media

H Harikumar, T Nguyen, S Gupta, S Rana, R Kaimal, S Venkatesh

(2016), Vol. 10086, pp. 520-533, ADMA 2016 : Proceedings of the 12th Advanced Data Mining and Applications International Conference, Gold Coast, Queensland, E1

conference

Guidelines for developing and reporting machine learning predictive models in biomedical research : a multidisciplinary view

W Luo, Q Phung, T Tran, S Gupta, S Rana, C Karmakar, A Shilton, J Yearwood, N Dimitrova, T Ho, S Venkatesh, M Berk

(2016), Vol. 18, pp. 1-10, Journal of medical internet research, Toronto, Ont., C1

journal

Cascade Bayesian optimization

T Nguyen, S Gupta, S Rana, V Nguyen, S Venkatesh, K Deane, P Sanders

(2016), Vol. 9992, pp. 268-280, AI 2016: Advances in Artificial Intelligence : Proceedings of the Australasian Joint Conference, Hobart, Tasmania, E1

conference

Budgeted batch Bayesian optimization

V Nguyen, S Rana, S Gupta, C Li, S Venkatesh

(2016), pp. 1107-1112, ICDM 2016: Proceedings of the 16th IEEE International Conference on Data Mining, Barcelona, Spain, E1

conference

Hyperparameter tuning for big data using Bayesian optimisation

T Theckel Joy, S Rana, S Gupta, S Venkatesh

(2016), pp. 2574-2579, ICPR 2016: Proceedings of the 23rd International Conference on Pattern Recognition, Cancun, Mexico, E1

conference

Multiple adverse effects prediction in longitudinal cancer treatment

C Li, S Gupta, S Rana, T Nguyen, S Venkatesh, D Ashley, P Livingston

(2016), pp. 3156-3161, ICPR 2016: Proceedings of the 23rd International Conference on Pattern Recognition, Cancun, Mexico, E1

conference

Bayesian nonparametric Multiple Instance Regression

S Subramanian, S Rana, S Gupta, S Venkatesh, P Sivakumar, C Velayutham

(2016), pp. 3661-3666, ICPR 2016: Proceedings of the 23rd International Conference on Pattern Recognition, Cancun, Mexico, E1

conference

A bayesian nonparametric approach for multi-label classification

T Nguyen, S Gupta, S Rana, C Li, S Venkatesh

(2016), Vol. 63, pp. 254-269, ACML 2016: Proceedings of the 8th Asian Conference on Machine Learning, Hamilton, New Zealand, E1

conference

Stable clinical prediction using graph support vector machines

I Kamkar, S Gupta, C Li, D Phung, S Venkatesh

(2016), pp. 3332-3337, 2016 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, E1

conference

Molecular epidemiological analysis of three hepatitis C virus outbreaks in Jammu and Kashmir State, India

S Chadha, U Sharma, A Chaudhary, C Prakash, S Gupta, S Venkatesh

(2016), Vol. 65, pp. 804-813, Journal of medical microbiology, London, Eng., C1-1

journal
2015

Stable feature selection for clinical prediction: Exploiting ICD tree structure using Tree-Lasso

I Kamkar, S Gupta, D Phung, S Venkatesh

(2015), Vol. 53, pp. 277-290, Journal of biomedical informatics, Amsterdam, The Netherlands, C1

journal

Continuous discovery of co-location contexts from Bluetooth data

T Nguyen, S Gupta, S Venkatesh, Q Phung

(2015), Vol. 16, pp. 286-304, Pervasive and mobile computing, Amsterdam, The Netherlands, C1

journal

Web search activity data accurately predict population chronic disease risk in the USA

T Nguyen, T Tran, W Luo, S Gupta, S Rana, Q Phung, M Nichols, L Millar, S Venkatesh, S Allender

(2015), Vol. 69, pp. 693-699, Journal of epidemiology and community health, London, Eng., C1

journal

A predictive framework for modeling healthcare data with evolving clinical interventions

S Rana, S Gupta, Q Phung, S Venkatesh

(2015), Vol. 8, pp. 162-182, Statistical analysis and data mining, London, Eng., C1

journal

Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset

W Luo, T Nguyen, M Nichols, T Tran, S Rana, S Gupta, Q Phung, S Venkatesh, S Allender

(2015), Vol. 10, pp. 1-13, PLoS One, San Francisco, Calif., C1

journal

Collaborating differently on different topics: a multi-relational approach to multi-task learning

S Gupta, S Rana, Q Phung, S Venkatesh

(2015), Vol. 9077, pp. 303-316, Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Vietnam, B1

chapter

Prediciton of emergency events: a multi-task multi-label learning approach

B Saha, S Gupta, S Venkatesh

(2015), Vol. 9077, pp. 226-238, 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part I, Vietnam, B1

chapter

Improved risk predictions via sparse imputation of patient conditions in electronic medical records

B Saha, S Gupta, S Venkatesh

(2015), pp. 1-10, DSAA 2015: IEEE International Conference on Data Science and Advanced Analytics, Paris, France, E1

conference

Stable feature selection with support vector machines

I Kamkar, S Gupta, Q Phung, S Venkatesh

(2015), Vol. 9457, pp. 298-308, AI 2015: Advances in artificial intelligence. 28th Australasian Joint Conference Canberra, ACT, Australia, November 30 - December 4, 2015 Proceedings, Berlin, Germany, B1

chapter

Differentially private random forest with high utility

S Rana, S Gupta, S Venkatesh

(2015), pp. 955-960, ICDM 2015: Proceedings of the 15th IEEE International Conference on Data Mining, Atlantic City, New Jersey, E1

conference

What shall i share and with whom? A multi-task learning formulation using multi-faceted task relationships

S Gupta, S Rana, Q Phung, S Venkatesh

(2015), pp. 703-711, SDM 2015: Proceedings of the 15th SIAM International Conference on Data Mining, Vancouver, British Columbia, E1

conference
2014

Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry

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

(2014), Vol. 4, pp. 1-7, BMJ open, London, England, C1

journal

Fixed-lag particle filter for continuous context discovery using Indian Buffet Process

C Nguyen, S Gupta, S Venkatesh, Dinh Phung

(2014), pp. 20-28, PerCom 2014 : proceedings of the IEEE Pervasive Computing and Communications 2014 international conference, Budapest, Hungary, E1

conference

Learning latent activities from social signals with hierarchical dirichlet processes

D Phung, T Nguyen, S Gupta, S Venkatesh

(2014), pp. 149-174, Plan, activity, and intent recognition : theory and practice, Boston, Mass., B1

chapter

Intervention-driven predictive framework for modeling healthcare data

S Rana, S Gupta, D Phung, S Venkatesh

(2014), Vol. 8443 Part 1, pp. 497-508, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Berlin, Germany, B1

chapter

Modelling multilevel data in multimedia : A hierarchical factor analysis approach

S Gupta, D Phung, S Venkatesh

(2014), pp. 1-23, Multimedia Tools and Applications, New York, New York, C1

journal

A bayesian nonparametric framework for activity recognition using accelerometer data

T Nguyen, S Gupta, S Venkatesh, D Phung

(2014), pp. 2017-2022, ICPR 2014 : Proceedings of the 22nd International Conference on Pattern Recognition, Stockholm, Sweden, E1

conference

A framework for feature extraction from hospital medical data with applications in risk prediction

T Truyen, W Luo, P Dinh, S Gupta, S Rana, R Kennedy, A Larkins, S Venkatesh

(2014), Vol. 15, pp. 1-9, BMC Bioinformatics, London, Eng., C1

journal

Keeping up with innovation: a predictive framework for modeling healthcare data with evolving clinical interventions

S Gupta, S Rana, Q Phung, S Venkatesh

(2014), pp. 235-243, SDM 2014: Proceedings of the 14th SIAM International Conference on Data Mining 2014, Philadelphia, Pennsylvania, E1-1

conference

A matrix factorization framework for jointly analyzing multiple nonnegative data sources

S Gupta, Q Phung, B Adams, S Venkatesh

(2014), Vol. 3, pp. 151-170, Data mining for service, Berlin, Germany, B1-1

chapter
2013

Regularized nonnegative shared subspace learning

S Gupta, D Phung, B Adams, S Venkatesh

(2013), Vol. 26, pp. 57-97, Data Mining and Knowledge Discovery, C1-1

journal

Interactive browsing system for anomaly video surveillance

T Nguyen, D Phung, S Gupta, S Venkatesh

(2013), Vol. 1, pp. 384-389, Proceedings of the 2013 IEEE 8th International Conference on Intelligent Sensors, Sensor Networks and Information Processing: Sensing the Future, ISSNIP 2013, E1

conference

Extraction of latent patterns and contexts from social honest signals using hierarchical Dirichlet processes

T Nguyen, D Phung, S Gupta, S Venkatesh

(2013), pp. 47-55, 2013 IEEE International Conference on Pervasive Computing and Communications, PerCom 2013, E1

conference

Connectivity, online social capital, and mood: A bayesian nonparametric analysis

D Phung, S Gupta, T Nguyen, S Venkatesh

(2013), Vol. 15, pp. 1316-1325, IEEE Transactions on Multimedia, C1

journal

Factorial multi-task learning : a Bayesian nonparametric approach

S Gupta, Q Phung, S Venkatesh

(2013), pp. 1694-1702, ICML 2013 : Proceedings of the Machine Learning 2013 International Conference, Atlanta, Ga., E1-1

conference
2012

A bayesian nonparametric joint factor model for learning shared and individual subspaces from multiple data sources

S Gupta, D Phung, S Venkatesh

(2012), pp. 200-211, Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012, E1

conference

A nonparametric Bayesian Poisson gamma model for count data

S Gupta, D Phung, S Venkatesh

(2012), pp. 1815-1818, Proceedings - International Conference on Pattern Recognition, E1

conference

A slice sampler for restricted hierarchical beta process with applications to shared subspace learning

S Gupta, D Phung, S Venkatesh

(2012), pp. 316-325, Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012, E1

conference
2011

A Bayesian framework for learning shared and individual subspaces from multiple data sources

S Gupta, D Phung, B Adams, S Venkatesh

(2011), pp. 136-147, PAKDD 2011 : Advances in knowledge discovery and data mining : 15th Pacific-Asia Conference, Shenzhen, China, May 24-27, 2011, proceedings, part II, Shenzhen, China, E1-1

conference

A matrix factorization framework for jointly analyzing multiple nonnegative data source

S Gupta, D Phung, B Adams, S Venkatesh

(2011), pp. 6-15, Proceedings of the 9th Workshop on Text Mining, in conjunction with the 11th SIAM International Conference on Data Mining, Mesa, Ariz., E1-1

conference

Automatic summarization of broadcast cricket videos

Y Kumar, S Gupta, B Kiran, K Ramakrishnan, C Bhattacharyya

(2011), pp. 222-225, ISCE 2011 : Proceedings of the IEEE International Symposium on Consumer Electronics, Singapore, E1-1

conference
2010

Nonnegative shared subspace learning and its application to social media retrieval

S Gupta, D Phung, B Adams, T Tran, S Venkatesh

(2010), pp. 1169-1178, KDD 2010 : Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, D. C., E1-1

conference
2008

Learning feature trajectories using Gabor Filter Bank for human activity segmentation and recognition

S Gupta, Y Kumar, K Ramakrishnan

(2008), pp. 111-118, ICVGIP 2008 : Proceedings of the 6th Indian Conference on Computer Vision, Graphics and Image Processing, Bhubaneswar, India, E1-1

conference

Funded Projects at Deakin

Australian Competitive Grants

ARC Research Hub for Digital Enhanced Living

Prof Kon Mouzakis, Prof Svetha Venkatesh, Prof Anthony Maeder, Prof Alison Hutchinson, Prof Michael Berk, Prof Ralph Maddison, Prof Abbas Kouzani, Prof Rajesh Vasa, Prof Helen Christensen, Prof Patricia Williams, Prof John Yearwood, Prof Susan Gordon, Prof David Powers, A/Prof Niranjan Bidargaddi, A/Prof Santu Rana, A/Prof Truyen Tran, A/Prof Sunil Gupta, Dr Wei Luo, A/Prof Mohamed Abdelrazek, Dr Felix Tan, Prof Henning Langberg, A/Prof Lars Kayser, Prof Finn Kensing, Prof Freimut Bodendorf, Prof James Warren, Dr Roopak Sinha, Prof A Smeaton, Mr Fonda Voukelatos, Mr Jeffrey Fiebig, Mr John Fouyaxis, Prof John Grundy, Dr Kit Huckvale, Nicole Cockayne, Dr Leonard Hoon, David Varley, Dr Tanya Petrovich, Dr Hermant Ghayvat, Mr Voytek Szapirko, Dr Anju Kissoon Curumsing, Matthew Macfarlane, Dr Tom McClean, Prof Deborah Parker, Ms Sharon Grocott, Dr Scott Barnett, Mr Steven Strange, Prof Jean-Guy Schneider, A/Prof Carsten Rudolph, Prof Nilmini Wickramasinghe, Dr Jordan Vincent

ARC Industrial Transformation Research Hubs

  • 2020: $299,126
  • 2019: $399,716
  • 2018: $449,083
  • 2017: $601,698

Other Public Sector Funding

Defence Applied Al Experiential CoLab

Prof Svetha Venkatesh, A/Prof Sunil Gupta, A/Prof Santu Rana, A/Prof Truyen Tran

  • 2020: $773,495

Industry and Other Funding

ARC Research Hub for Digital Enhanced Living

Prof Kon Mouzakis, Prof Svetha Venkatesh, Prof Anthony Maeder, Prof Alison Hutchinson, Prof Michael Berk, Prof Ralph Maddison, Prof Abbas Kouzani, Prof Rajesh Vasa, Prof Helen Christensen, Prof Patricia Williams, Prof John Yearwood, Prof Susan Gordon, Prof David Powers, A/Prof Niranjan Bidargaddi, A/Prof Santu Rana, A/Prof Truyen Tran, A/Prof Sunil Gupta, Dr Wei Luo, A/Prof Mohamed Abdelrazek, Dr Felix Tan, Prof Henning Langberg, A/Prof Lars Kayser, Prof Finn Kensing, Prof Freimut Bodendorf, Prof James Warren, Dr Roopak Sinha, Prof A Smeaton, Mr Fonda Voukelatos, Mr Jeffrey Fiebig, Mr John Fouyaxis, Prof John Grundy, Dr Kit Huckvale, Nicole Cockayne, Dr Leonard Hoon, David Varley, Dr Tanya Petrovich, Dr Hermant Ghayvat, Mr Voytek Szapirko, Dr Anju Kissoon Curumsing, Matthew Macfarlane, Dr Tom McClean, Prof Deborah Parker, Ms Sharon Grocott, Dr Scott Barnett, Mr Steven Strange, Prof Jean-Guy Schneider, A/Prof Carsten Rudolph, Prof Nilmini Wickramasinghe, Dr Jordan Vincent

  • 2020: $248,750
  • 2019: $378,745

iCetana - Phase 1 Examine and compare state-of-art methods in background/foreground separation

Prof Svetha Venkatesh, A/Prof Santu Rana, A/Prof Sunil Gupta, Dr Budhaditya Saha

  • 2020: $50,000
  • 2018: $100,000
  • 2017: $200,000

Supervisions

Executive Supervisor
2020

Anil Ramachandran

Thesis entitled: Harnessing Auxiliary Knowledge Towards Efficient Bayesian Optimisation

Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins

2019

Thanh Dai Nguyen

Thesis entitled: Addressing Practical Challenges of Bayesian Optimization

Doctor of Philosophy (Information Technology), School of Information Technology

2016

Iman Kamkar

Thesis entitled: Building Stable Predictive Models for Healthcare Applications: A Data-Driven Approach

Doctor of Philosophy (Information Technology), School of Information Technology

Co-supervisor
2019

Tinu Theckel Joy

Thesis entitled: Efficient Hyperparameter Tuning using Bayesian Optimization

Doctor of Philosophy (Information Technology), School of Information Technology

2018

Haripriya Harikumar

Thesis entitled: Machine learning to fight addiction using social media

Doctor of Philosophy (Information Technology), School of Information Technology

Associate Supervisor
2015

Cong Thuong Nguyen

Thesis entitled: Bayesian nonparametric learning of contexts and activities from pervasive signals

Doctor of Philosophy (Information Technology), School of Information Technology