A/Prof. Santu Rana

STAFF PROFILE

Position

Associate Professor

Faculty

Applied Artificial Intel Inst

Department

A2I2P

Campus

Geelong Waurn Ponds Campus

Contact

santu.rana@deakin.edu.au
+61 3 522 71253

Publications

Filter by

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 article

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 article

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 article

Improving the tensile properties of wet spun silk fibers using rapid Bayesian algorithm

Ya Yao, Benjamin Allardyce, Rangam Rajkhowa, Dylan Hegh, Alessandra Sutti, Surya Subianto, Sunil Gupta, Santu Rana, S Greenhill, Svetha Venkatesh, Xungai Wang, Joselito Razal

(2020), Vol. 6, pp. 3197-3207, ACS biomaterials science and engineering, Washington, D.C., C1

journal article

Level set estimation with search space warping

Manisha Senadeera, S Rana, S Gupta, S Venkatesh

(2020), Vol. 12085, pp. 827-839, PAKDD 2020 : Advances in Knowledge Discovery and Data Mining : Proceedings of the 24th Pacific-Asia Conference 2020, Singapore, E1

conference
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 article

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 article

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 article

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
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 article

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 article

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

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

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 article

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

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

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

book 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

book 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

book chapter

Data clustering using side information dependent Chinese restaurant processes

C Li, S Rana, Q Phung, S Venkatesh

(2016), Vol. 47, pp. 463-488, Knowledge and information systems, Berlin, Germany, C1

journal article

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 article

Hierarchical Bayesian nonparametric models for knowledge discovery from electronic medical records

C Li, S Rana, D Phung, S Venkatesh

(2016), Vol. 99, pp. 168-182, Knowledge-based systems, Amsterdam, The Netherlands, C1

journal article

Dirichlet process mixture models with pairwise constraints for data clustering

C Li, S Rana, Q Phung, S Venkatesh

(2016), Vol. 3, pp. 205-223, Annals of data science, Berlin, Germany, C1

journal article

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 article

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

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

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
2015

Small-variance asymptotics for bayesian nonparametric models with constraints

C Li, S Rana, Q Phung, S Venkatesh

(2015), Vol. 9078, pp. 92-105, Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Vietnam, B1

book chapter

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

book chapter

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 article

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 article

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 article

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

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

book chapter

Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data

S Rana, T Tran, W Luo, D Phung, R Kennedy, S Venkatesh

(2014), Vol. 38, pp. 377-382, Australian health review, Melbourne, Vic., C1

journal article

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 article

Regularizing topic discovery in emrs with side information by using hierarchical bayesian models

C Li, S Rana, D Phung, S Venkatesh

(2014), pp. 1307-1312, ICPR 2014 : Proceedings of the 22nd International Conference on Pattern Recognition, Stockholm, Sweden, E1

conference

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
2013

Split-merge augmented Gibbs sampling for hierarchical dirichlet processes

S Rana, D Phung, S Venkatesh

(2013), pp. 546-557, PAKDD 2013 : Advances in knowledge discovery and data mining : 17th Pacific-Asia Conference, Gold Coast, Australia, April 14-17, 2013 : proceedings, Gold Coast, Queensland, E1

conference

Exploiting side information in distance dependent Chinese restaurant processes for data clustering

C Li, D Phung, S Rana, S Venkatesh

(2013), Proceedings - IEEE International Conference on Multimedia and Expo, E1

conference
2012

Large-scale statistical modeling of motion patterns: A Bayesian nonparametric approach

S Rana, D Phung, S Pham, S Venkatesh

(2012), ACM International Conference Proceeding Series, E1

conference

Multi-modal abnormality detection in video with unknown data segmentation

T Nguyen, D Phung, S Rana, D Pham, S Venkatesh

(2012), pp. 1322-1325, ICPR 2012 : Proceedings of 21st International Conference on Pattern Recognition, Tsubuka Science City, Japan, E1

conference
2009

A unified tensor framework for face recognition

S Rana, W Liu, M Lazarescu, S Venkatesh

(2009), Vol. 42, pp. 2850-2862, Pattern Recognition, C1-1

journal article
2008

Recognising faces in unseen modes: a tensor based approach

S Rana, W Liu, M Lazarescu, S Venkatesh

(2008), pp. 1-8, CVPR 2008 : Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska, E1-1

conference

Efficient tensor based face recognition

S Rana, W Liu, M Lazarescu, S Venkatesh

(2008), pp. 1-4, ICPR 2008 : Proceedings of the 2008 19th International Conference on Pattern Recognition, Tampa, Florida, 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, Matthew Macfarlane, Dr Anju Kissoon Curumsing, Dr Tom McClean, Ms Sharon Grocott, Prof Deborah Parker, Dr Scott Barnett, Mr Steven Strange, Prof Jean-Guy Schneider, Prof Nilmini Wickramasinghe, A/Prof Carsten Rudolph, Dr Jordan Vincent

ARC Industrial Transformation Research Hubs

  • 2020: $481,066
  • 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, Matthew Macfarlane, Dr Anju Kissoon Curumsing, Dr Tom McClean, Ms Sharon Grocott, Prof Deborah Parker, Dr Scott Barnett, Mr Steven Strange, Prof Jean-Guy Schneider, Prof Nilmini Wickramasinghe, A/Prof Carsten Rudolph, Dr Jordan Vincent

  • 2020: $309,375
  • 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

Other Funding Sources

The CRC-P for Advanced Hybrid Batteries

Prof Patrick Howlett, Prof Maria Forsyth, Dr Robert Kerr, Prof Svetha Venkatesh, A/Prof Santu Rana

  • 2020: $95,314

Supervisions

Executive 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

Co-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

Associate Supervisor
2015

Cheng Li

Thesis entitled: Exploiting side information in Bayesian nonparametric models and their applications

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