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 in Machine Learning and AI. 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 at Deakin University. He currently works as a Professor and the Head of AI Optimisation and Materials Discovery at the Applied Artificial Intelligence Institute (A2I2). His research interests lie in broad areas of machine learning and artificial intelligence.
Read more on Sunil's profileResearch interests
AI, Machine learning, Bayesian optimisation, Data mining, Pattern recognition, Deep learning, Active learning, Transfer learning, Reinforcement learning, Healthcare data analytics, Computer vision, AI safety and assurance, Adaptive Clinical Trials, AI-driven materials design and discovery
Affiliations
Adjunct Professsor, Indian Institute of Technology, Kanpur, India.
Knowledge areas
AI, Machine learning, Bayesian optimisation, Data mining, Pattern recognition, Deep learning, Active learning, Transfer learning, Reinforcement learning, Healthcare data analytics, Computer vision, AI safety and assurance, Adaptive Clinical Trials, AI-driven materials design and discovery
Awards
- Winner of the “Best Paper Award” at Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2018.
- 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
Prescriptive analytics with differential privacy
H Harikumar, S Rana, S Gupta, T Nguyen, R Kaimal, S Venkatesh
(2022), Vol. 13, pp. 123-138, International Journal of Data Science and Analytics, C1
Verification of integrity of deployed deep learning models using Bayesian Optimization
D Kuttichira, S Gupta, D Nguyen, S Rana, S Venkatesh
(2022), Vol. 241, pp. 1-12, Knowledge-Based Systems, Amsterdam, The Netherlands, C1
Dual-frame spatio-temporal feature modulation for video enhancement
P Patil, S Gupta, S Rana, S Venkatesh
(2022), Vol. 130, pp. 1-14, Pattern Recognition, Amsterdam, The Netherlands, C1
Asymmetry in cerebral perfusion from circle of Willis arterial variations in normal population
C Jain, A Kumar, S Vyas, A Kumar, P Singh, V Bhatia, C Ahuja, J Sahu, S Gupta, N Khandelwal
(2022), Vol. 36, pp. 31-37, Neuroradiology Journal, United States, C1-1
S Sahoo, S Gupta, P Salunke, S Dhandapani, A Aggarwal, N Singla, M Karthigeyan, A Singh, M Tripathi, C Gendle, R Singla, R Chhabra, S Mohindra, M Tewari, R Rekhapalli, P Kokkula, T Gupta
(2022), Vol. 70, pp. 612-617, Neurology India, Mumbai, India, C1
S Gupta, D Gupta, A Savardekar
(2022), Vol. 70, pp. 115-121, Neurology India, Mumbai, India, C1
D Chatterjee, B Radotra, R Madan, S Gupta
(2022), Vol. 70, pp. 285-288, Neurology India, Mumbai, India, C1
M Parkhi, D Chatterjee, A Bal, P Vias, B Yadav, G Prakash, S Gupta, B Radotra
(2022), Vol. 130, pp. 82-94, APMIS, London, Eng., C1
K Deane, Y Yang, J Licavoli, V Nguyen, S Rana, S Gupta, S Venkatesh, P Sanders
(2022), Vol. 12, pp. 1-19, Metals, Basel, Switzerland, C1
Adapting to transradial approach in cerebral angiography: Factors influencing successful cannulation
M Yaser Arafath, V Bhatia, A Kumar, R Chauhan, A Prabhakar, S Gupta, P Singh
(2022), Neuroradiology Journal, C1-1
M Mohanty, R Kaur, S Gupta, M Tripathi, A Sharma, S Munjal
(2022), Vol. 70, pp. 948-952, Neurology India, India, C1-1
A Batish, S Gupta, M Mohanty, M Tripathi, P Salunke, A Aggarwal
(2022), Vol. 70, pp. 897-904, Neurology India, India, C1-1
M Kumar, R Rajput, A Ralta, L Quintans-Júnior, S C Gutierrez, J Barbosa-Filho, D Shekhawat, B Radotra, S Gupta, B Medhi
(2022), Vol. 70, pp. 1601-1609, Neurology India, India, C1-1
Case Report: Plantar Cyst Caused by Phaeoacremonium inflatipes in an Immunocompetent Male, Resolving with Combination Potassium Iodide and Itraconazole Therapy
J Sandhu, J Kohli, S Gupta, V Gupta
(2022), Vol. 107, pp. 113-116, American Journal of Tropical Medicine and Hygiene, C1-1
Real-Time Skill Discovery in Intelligent Virtual Assistants
P Gopal, S Gupta, S Rana, V Le, T Nguyen, S Venkatesh
(2022), Vol. 13280, pp. 315-327, PAKDD 2022 : Proceedings of the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining 2022, Chengdu, China, E1
Sympathy-based Reinforcement Learning Agents
M Senadeera, T Karimpanal, S Gupta, S Rana
(2022), Vol. 2, pp. 1164-1172, Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, E1
Coupling machine learning with 3D bioprinting to fast track optimisation of extrusion printing
K Ruberu, M Senadeera, S Rana, S Gupta, J Chung, Z Yue, S Venkatesh, G Wallace
(2021), Vol. 22, Applied Materials Today, C1
Predictive Model Based on Health Data Analysis for Risk of Readmission in Disease-Specific Cohorts
M Ansari, A Alok, D Jain, S Rana, S Gupta, R Salwan, S Venkatesh
(2021), Vol. 18, pp. 1-11, Perspectives in health information management, Chicago, Ill., C1
Fairness improvement for black-box classifiers with Gaussian process
D Nguyen, S Gupta, S Rana, A Shilton, S Venkatesh
(2021), Vol. 576, pp. 542-556, Information Sciences, C1
Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient
H Harikumar, T Quinn, S Rana, S Gupta, S Venkatesh
(2021), Vol. 14, BioData Mining, England, C1
J Joseph, M Senadeera, Q Chao, K Shamlaye, S Rana, S Gupta, S Venkatesh, P Hodgson, M Barnett, D Fabijanic
(2021), Vol. 888, Journal of Alloys and Compounds, C1
Identification of predictors and model for predicting prolonged length of stay in dengue patients
M Shahid Ansari, D Jain, H Harikumar, S Rana, S Gupta, S Budhiraja, S Venkatesh
(2021), Vol. 24, pp. 786-798, Health Care Management Science, Netherlands, C1
Adaptive cost-aware Bayesian optimization[Formula presented]
P Luong, D Nguyen, S Gupta, S Rana, S Venkatesh
(2021), Vol. 232, Knowledge-Based Systems, C1
An Unified Recurrent Video Object Segmentation Framework for Various Surveillance Environments
P Patil, A Dudhane, A Kulkarni, S Murala, A Gonde, S Gupta
(2021), Vol. 30, pp. 7889-7902, IEEE Transactions on Image Processing, Piscataway, N.J., C1
J Sandhu, S Garg, S Gupta, B Garg
(2021), Vol. 31, pp. 720-724, Journal of Pakistan Association of Dermatologists, C1
N Rani, B Singh, N Kumar, P Singh, P Hazari, S Vyas, M Hooda, A Chitkara, A Shekhawat, S Gupta, B Radotra, A Mishra
(2021), Vol. 36, pp. 568-578, Cancer Biotherapy and Radiopharmaceuticals, New Rochelle, N.Y., C1
Feasibility of Conducting a Virtual Exit Exam in Neurosurgery during the SARS-COV19 Pandemic
P Salunke, S Sahoo, A Chacko, B Baishya, M Tripathi, R Chabbra, M Karthigeyan, A Aggarwal, A Singh, S Gupta
(2021), Vol. 69, pp. 698-702, Neurology India, Mumbai, India, C1
S Gupta, R Narayanan, A Aggarwal, M Mohanty, C Ahuja, N Verma, K Praneeth, V Agarwal
(2021), Vol. 69, pp. 620-627, Neurology India, Mumbai, India, C1
The collateral fallout of COVID19 lockdown on patients with head injury from north-west India
M Karthigeyan, S Dhandapani, P Salunke, S Sahoo, M Kataria, A Singh, C Gendle, C Panchal, R Chhabra, K Jain, S Gupta
(2021), Vol. 163, pp. 1053-1060, Acta Neurochirurgica, Berlin, Germany, C1
V Agarwal, P Singh, C Ahuja, S Gupta, A Aggarwal, R Narayanan
(2021), Vol. 63, pp. 563-572, Neuroradiology, Berlin, Germany, C1
N Patil, S Dhandapani, S Sahoo, R Chhabra, A Singh, P Dutta, R Walia, R Verma, R Gupta, R Virk, C Ahuja, M Dhandapani, H Chaudhary, K Jangra, S Gupta
(2021), Vol. 44, pp. 2291-2298, Neurosurgical Review, Berlin, Germany, C1
Bayesian Optimization with Missing Inputs
P Luong, D Nguyen, S Gupta, S Rana, S Venkatesh
(2021), Vol. 12458, pp. 691-706, ECML PKDD 2020 : Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Belgium, Ghent, E1
Scalable Backdoor Detection in Neural Networks
H Harikumar, V Le, S Rana, S Bhattacharya, S Gupta, S Venkatesh
(2021), Vol. 12458, pp. 289-304, ECML PKDD 2020 : Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Belgium, Ghent, E1
Sparse Spectrum Gaussian Process for Bayesian Optimization
A Yang, C Li, S Rana, S Gupta, S Venkatesh
(2021), Vol. 12713, pp. 257-268, PAKDD 2021 : Advances in Knowledge Discovery and Data Mining : 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11–14, 2021, Proceedings, Part I, Virtual Event, E1
Factor screening using Bayesian active learning and gaussian process meta-modelling
C Li, D Nguyen, S Rana, S Gupta, A Gill, S Venkatesh
(2021), pp. 3288-3295, ICPR 2020 : Proceedings of the 25th International Conference on Pattern Recognition, Online from Milan, Italy, E1
High Dimensional Level Set Estimation with Bayesian Neural Network
Huong Ha, Sunil Gupta, Santu Rana, Svetha Venkatesh
(2021), Vol. 35, pp. 12095-12103, AAAI 2021 : Proceedings of the 35th AAAI Conference on Artificial Intelligence, Virtual Event, E1
A New Representation of Successor Features for Transfer across Dissimilar Environments
Majid Abdolshah, Hung Le, Thommen George, Sunil Gupta, Santu Rana, Svetha Venkatesh
(2021), Vol. 139, pp. 1-14, ICML 2021 : Proceedings of the International Conference of Machine Learning, Virtual Conference, E1
Distributional Reinforcement Learning via Moment Matching
Thanh Nguyen, Sunil Gupta, Svetha Venkatesh
(2021), Vol. 35, pp. 9144-9152, AAAI 2021 : Proceedings of the 35th AAAI Conference on Artificial Intelligence, Virtual Event, E1
Knowledge Distillation with Distribution Mismatch
D Nguyen, S Gupta, T Nguyen, S Rana, P Nguyen, T Tran, K Le, S Ryan, S Venkatesh
(2021), Vol. 12976, pp. 250-265, ECML PKDD 2021 : Machine Learning and Knowledge Discovery in Databases. Research Track, Bilbao, Spain, E1
Fast Conditional Network Compression Using Bayesian HyperNetworks
P Nguyen, T Tran, K Le, S Gupta, S Rana, D Nguyen, T Nguyen, S Ryan, S Venkatesh
(2021), Vol. 12977, pp. 330-345, ECML PKDD 2021 : Machine Learning and Knowledge Discovery in Databases. Research Track, Bilbao, Spain, E1
Variational Hyper-encoding Networks
P Nguyen, T Tran, S Gupta, S Rana, H Dam, S Venkatesh
(2021), Vol. 12976, pp. 100-115, ECML PKDD 2021 : Machine Learning and Knowledge Discovery in Databases. Research Track, Bilbao, Spain, E1
Kernel Functional Optimisation
A Arun Kumar, A Shilton, S Rana, S Gupta, S Venkatesh
(2021), Vol. 6, pp. 4725-4737, NeurIPS 2021 : Proceedings of the 35th Conference on Neural Information Processing Systems, Virtual Conference, E1
Batch Bayesian optimization using multi-scale search
T Joy, S Rana, S Gupta, S Venkatesh
(2020), Vol. 187, Knowledge-Based Systems, C1
S Allender, J Hayward, S Gupta, A Sanigorski, S Rana, H Seward, S Jacobs, S Venkatesh
(2020), Vol. 3, npj Digital Medicine, England, C1
Incorporating expert prior in Bayesian optimisation via space warping
A Ramachandran, S Gupta, S Rana, C Li, S Venkatesh
(2020), Vol. 195, Knowledge-Based Systems, C1
Bayesian optimisation in unknown bounded search domains
J Berk, S Gupta, S Rana, V Nguyen, S Venkatesh
(2020), Vol. 195, Knowledge-Based Systems, C1
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
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
Fast hyperparameter tuning using Bayesian optimization with directional derivatives
T Joy, S Rana, S Gupta, S Venkatesh
(2020), Vol. 205, Knowledge-Based Systems, C1
A scrap-tolerant alloying concept based on high entropy alloys
M Barnett, M Senadeera, D Fabijanic, K Shamlaye, J Joseph, S Kada, S Rana, S Gupta, S Venkatesh
(2020), Vol. 200, pp. 735-744, Acta Materialia, Amsterdam, The Netherlands, C1
B Radotra, M Parkhi, D Chatterjee, B Yadav, N Ballari, G Prakash, S Gupta
(2020), Vol. 11, pp. 1-9, Surgical Neurology International, New York, N.Y., C1
R Sihag, S Gupta, D Sahni, A Aggarwal
(2020), Vol. 68, pp. 1313-1320, Neurology India, Mumbai, India, C1
S Sahoo, S Dhandapani, A Singh, C Gendle, M Karthigeyan, P Salunke, A Aggarwal, N Singla, R Singla, M Tripathi, R Chhabra, S Mohindra, M Tewari, M Mohanty, H Bhagat, A Chakrabarti, S Gupta
(2020), Vol. 49, pp. 1-8, Neurosurgical Focus, Rolling Meadows, IL., C1
N Rani, B Singh, N Kumar, P Singh, P Hazari, A Jaswal, S Gupta, R Chhabra, B Radotra, A Mishra
(2020), Vol. 41, pp. 848-857, Nuclear Medicine Communications, Philadelphia, Pa., C1
Intracranial Aneurysm Biomarker Candidates Identified by a Proteome-Wide Study
T Sharma, K Datta, M Kumar, G Dey, A Khan, K Mangalaparthi, P Saharan, S Chinnapparaj, A Aggarwal, N Singla, S Ghosh, A Rawat, S Dhandapani, P Salunke, R Chhabra, D Singh, A Takkar, S Gupta, T Prasad, H Gowda, K Mukherjee, A Pandey, H Bhagat
(2020), Vol. 24, pp. 483-492, OMICS: A Journal of Integrative Biology, New Rochelle, N.Y., C1
V Williams, A Bansal, M Jayashree, J Ismail, A Aggarwal, S Gupta, S Singhi, P Singhi, A Baranwal, K Nallasamy
(2020), Vol. 34, pp. 258-263, British Journal of Neurosurgery, London, Eng., C1
A Singla, P Mathew, K Jangra, S Gupta, S Soni
(2020), Vol. 68, pp. 141-145, Neurology India, Mumbai, India, C1
A Gupta, M Tripathi, A Umredkar, R Chauhan, V Gupta, S Gupta
(2020), Vol. 68, pp. 132-140, Neurology India, Mumbai, India, C1
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
Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation
Julian Berk, Sunil Gupta, Santu Rana, Svetha Venkatesh
(2020), pp. 2284-2290, IJCAI-PRICAI-20 : Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Yokohama, Japan, E1
Distributionally Robust Bayesian Quadrature Optimization
Tang Thanh, Sunil Gupta, Huong Ha, Santu Rana, Svetha Venkatesh
(2020), Vol. 108, pp. 1921-1930, AISTATS 2020 : Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, Online from Palermo, Italy, E1
Accelerated Bayesian Optimization through Weight-Prior Tuning
Alistair Shilton, Sunil Gupta, Santu Rana, Pratibha Vellanki, Laurence Park, Cheng Li, Svetha Venkatesh, Thomas Dorin, Alessandra Sutti, David Rubin, Teo Slezak, Alireza Vahid, Murray Height
(2020), Vol. 108, pp. 1-10, AISTATS 2020 : Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, Online from Palermo, Italy, E1
Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning
T Karimpanal, S Rana, S Gupta, T Tran, S Venkatesh
(2020), pp. 1-10, IJCNN 2020 : Proceedings of the 2020 International Joint Conference on Neural Networks, Glasgow, Scotland, E1
Unsupervised Anomaly Detection on Temporal Multiway Data
D Nguyen, P Nguyen, K Do, S Rana, S Gupta, T Tran
(2020), pp. 1059-1066, SSCI 2020 : Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence, Canberra, Australian Capital Territory, E1
DeepCoDA: Personalized interpretability for compositional health data
T Quinn, D Nguyen, S Rana, S Gupta, S Venkatesh
(2020), Vol. PartF168147-11, pp. 7833-7842, ICML 2020 : Proceedings of the 37th International Conference on Machine Learning, Online, E1
Trading convergence rate with computational budget in high dimensional Bayesian optimization
H Tran The, S Gupta, S Rana, S Venkatesh
(2020), pp. 2425-2432, AAAI-20 : Proceedings of the Thirty-fourth AAAI Conference on Artificial Intelligence, New York, N.Y., E1
Bayesian optimization for categorical and category-specific continuous inputs
D Nguyen, S Gupta, S Rana, A Shilton, S Venkatesh
(2020), pp. 5256-5263, AAAI-20 : Proceedings of the Thirty-fourth AAAI Conference on Artificial Intelligence, New York, N.Y., E1
Filtering Bayesian optimization approach in weakly specified search space
V Nguyen, S Gupta, S Rana, C Li, S Venkatesh
(2019), Vol. 60, pp. 385-413, Knowledge and Information Systems, C1
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, C1
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, United States, C1
A Hendlmeier, F Stojcevski, R Alexander, S Gupta, L Henderson
(2019), Vol. 179, Composites Part B: Engineering, C1
Efficient Bayesian Function Optimization of Evolving Material Manufacturing Processes
D Rubín 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, United States, C1
M Karthigeyan, S Dhandapani, P Salunke, P Singh, B Radotra, S Gupta
(2019), Vol. 67, pp. 1439-1445, Neurology India, Mumbai, India, C1
R Irrinki, M Bawa, S Hegde, R Chhabra, V Gupta, S Gupta
(2019), Vol. 14, pp. 65-69, Journal of Pediatric Neurosciences, Philadelphia, Pa., C1
V Williams, M Jayashree, A Bansal, A Baranwal, K Nallasamy, S Singhi, P Singhi, S Gupta
(2019), Vol. 35, pp. 1371-1379, Child's Nervous System, Berlin, Germany, C1
Hodgkin lymphoma in a case of chronic myeloid leukemia treated with tyrosine kinase inhibitors
S Gajendra, A Sharma, R Sharma, S Gupta, N Sood, R Sachdev
(2019), Vol. 35, pp. 74-78, Türk Patoloji Dergisi / Turkish Journal of Pathology, C1
N Yagnick, R Singh, M Tripathi, S Mohindra, H Deora, A Suri, S Gupta
(2019), Vol. 121, pp. 222-226, World Neurosurgery, Amsterdam, The Netherlands, C1
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
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
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
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
Explaining black-box models using interpretable surrogates
D Kuttichira, S Gupta, C Li, S Rana, S Venkatesh
(2019), Vol. 11670, pp. 3-15, PRICAI 2019: Trends in Artificial Intelligence 16th Pacific Rim International Conference on Artificial Intelligence, Cuvu, Yanuca Island, Fiji, August 26-30, 2019, Proceedings,, Cuvu, Fiji, E1
Information-Theoretic Multi-task Learning Framework for Bayesian Optimisation
A Ramachandran, S Gupta, S Rana, S Venkatesh
(2019), Vol. 11919, pp. 497-509, AI 2019: Advances in artificial intelligence : Proceedings of the 32nd Australasian Joint Conference on Artificial Intelligence 2019, Adelaide, South Australia, E1
Bayesian Optimization with Discrete Variables
P Luong, S Gupta, D Nguyen, S Rana, S Venkatesh
(2019), Vol. 11919, pp. 473-484, AI 2019 : Advances in Artificial Intelligence : Proceedings of the 32nd Australian Joint Conference, Adelaide, South Australia, E1
Detection of Compromised Models Using Bayesian Optimization
D Kuttichira, S Gupta, D Nguyen, S Rana, S Venkatesh
(2019), Vol. 11919, pp. 485-496, AI 2019 : Advances in Artificial Intelligence : Proceedings of the 32nd Australian Joint Conference, Adelaide, South Australia, E1
Efficient bayesian optimization for uncertainty reduction over perceived optima locations
V Nguyen, S Gupta, S Rana, M Thai, C Li, S Venkatesh
(2019), Vol. 2019-November, pp. 1270-1275, ICDM 2019 : Proceedings of the 19th IEEE International Conference on Data Mining, Beijing, China, E1
Bayesian optimization with unknown search space
H Ha, S Rana, S Gupta, T Nguyen, H Tran-The, S Venkatesh
(2019), Vol. 32, pp. 1-10, NeurIPS 2019 : Proceedings of the 33rd Conference on Neural Information Processing Systems, Vancouver, British Columbia, E1
Multi-objective Bayesian optimisation with preferences over objectives
M Abdolshah, A Shilton, S Rana, S Gupta, S Venkatesh
(2019), Vol. 32, pp. 1-11, NeurIPS 2019 : Proceedings of the 33rd Conference on Neural Information Processing Systems, Vancouver, British Columbia, E1
Sub-linear regret bounds for Bayesian optimisation in unknown search spaces
H Tran-The, S Gupta, S Rana, H Ha, S Venkatesh
(2019), Vol. 2020-December, pp. 1-25, NeurIPS 2019 : Proceedings of the 33rd Conference on Neural Information Processing Systems, Vancouver, British Columbia, E1
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
Comparative genetic variability in HIV-1 subtype C p24 Gene in early age groups of infants
U Sharma, S Gupta, S Venkatesh, A Rai, A Dhariwal, M Husain
(2018), Vol. 54, pp. 647-661, Virus genes, [New York, N.Y.], C1
Comparative Genetic Variability in HIV-1 Subtype C vpu Gene in Early Age Groups of Infants
U Sharma, P Gupta, S Gupta, S Venkatesh, M Husain
(2018), Vol. 16, pp. 64-76, Current HIV Research, Sharjah, United Arab Emirates, C1
S Dhandapani, A Singh, N Singla, K Praneeth, A Aggarwal, H Sodhi, S Pal, S Goudihalli, P Salunke, S Mohindra, A Kumar, V Gupta, R Chhabra, K Mukherjee, M Tewari, N Khandelwal, S Mathuriya, V Khosla, S Gupta
(2018), Vol. 49, pp. 2890-2895, Stroke, Philadelphia, Pa., C1
Data of phosphoproteomic analysis of non-functioning pituitary adenoma
A Rai, B Radotra, K Mukherjee, S Gupta, P Dutta
(2018), Vol. 18, pp. 781-786, Data in Brief, Amsterdam, The Netherlands, C1
A Shahid, M Mohanty, N Singla, B Mittal, S Gupta
(2018), Vol. 128, pp. 229-235, Journal of Neurosurgery, Rolling Meadows, Ill., C1
Independent impact of plasma homocysteine levels on neurological outcome following head injury
S Dhandapani, A Bajaj, C Gendle, I Saini, I Kaur, I Chaudhary, Jasandeep, J Kaur, G Kalyan, M Dhandapani, S Gupta
(2018), Vol. 41, pp. 513-517, Neurosurgical Review, Heidelberg, Germany, C1
A Aggarwal, S Dhandapani, K Praneeth, H Sodhi, S Pal, S Gaudihalli, N Khandelwal, K Mukherjee, M Tewari, S Gupta, S Mathuriya
(2018), Vol. 41, pp. 241-247, Neurosurgical Review, Heidelberg, Germany, C1
Exploiting strategy-space diversity for batch Bayesian optimisation
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
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
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
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
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
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
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
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
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
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
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
B Saha, S Gupta, D Phung, S Venkatesh
(2017), Vol. 53, pp. 179-206, Knowledge and Information Systems, C1
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
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, C1
Rapid Bayesian optimisation for synthesis of short polymer fiber materials
C Li, D Rubín De Celis Leal, S Rana, S Gupta, A Sutti, S Greenhill, T Slezak, M Height, S Venkatesh
(2017), Vol. 7, Scientific Reports, England, C1
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, United States, C1
Comparative genetic variability in HIV-1 subtype C nef gene in early age groups of infants
U Sharma, P Gupta, M Singhal, S Singh, S Gupta, S Venkatesh, A Rai, M Husain
(2017), Vol. 89, pp. 1606-1619, Journal of medical virology, Chichester, Eng., C1
T Gupta, D Sahni, R Gupta, S Gupta
(2017), Vol. 66, pp. 58-66, Journal of the Anatomical Society of India, Amsterdam, The Netherlands, C1
M Dhandapani, S Gupta, M Mohanty, S Gupta, S Dhandapani
(2017), Vol. 24, pp. 105-110, Annals of Neurosciences, Basel, Switzerland, C1
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
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
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
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
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
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
Regret for expected improvement over the best-observed value and stopping condition
V Nguyen, Sunil Gupta, Santu Rana, Cheng Li, Svetha Venkatesh
(2017), pp. 1-16, ACML 2017 : Proceedings of the Ninth Asian Conference on Machine Learning, Seoul, Korea, E1
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
Toxicity Prediction in Cancer Using Multiple Instance Learning in a Multi-task Framework
Cheng Li, Sunil Gupta, Santu Rana, Wei Luo, Svetha Venkatesh, David Ashely, Dinh Phung
(2016), pp. 152-164, Advances in Knowledge Discovery and Data Mining, B1
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
Modelling multilevel data in multimedia: A hierarchical factor analysis approach
S Gupta, D Phung, S Venkatesh
(2016), Vol. 75, pp. 4933-4955, Multimedia Tools and Applications, C1
Multiple task transfer learning with small sample sizes
B Saha, S Gupta, D Phung, S Venkatesh
(2016), Vol. 46, pp. 315-342, Knowledge and Information Systems, C1
Stabilizing l
I Kamkar, S Gupta, D Phung, S Venkatesh
(2016), Vol. 59, pp. 149-168, Journal of Biomedical Informatics, United States, C1
A new transfer learning framework with application to model-agnostic multi-task learning
S Gupta, S Rana, B Saha, D Phung, S Venkatesh
(2016), Vol. 49, pp. 933-973, Knowledge and Information Systems, C1
Nonparametric discovery of movement patterns from accelerometer signals
T Nguyen, S Gupta, S Venkatesh, D Phung
(2016), Vol. 70, pp. 52-58, Pattern Recognition Letters, C1
W Luo, D Phung, T Tran, S Gupta, S Rana, C Karmakar, A Shilton, J Yearwood, N Dimitrova, T Ho, S Venkatesh, M Berk
(2016), Vol. 18, Journal of Medical Internet Research, Canada, C1
S Chadha, U Sharma, A Chaudhary, C Prakash, S Gupta, S Venkatesh
(2016), Vol. 65, pp. 804-813, Journal of Medical Microbiology, England, C1-1
T Gupta, D Sahni, R Tubbs, S Gupta
(2016), Vol. 64, pp. 943-946, Neurology India, Mumbai, India, C1
A Tiwari, D Arora, R Dara, P Dorwal, N Sood, R Misra, S Gupta, V Raina, A Vaid
(2016), Vol. 37, pp. 168-173, Indian Journal of Medical and Paediatric Oncology, Mumbai, India, C1
P Salunke, S Sahoo, S Sood, K Mukherjee, S Gupta
(2016), Vol. 145, pp. 19-27, Clinical Neurology and Neurosurgery, Amsterdam, The Netherlands, C1
Differentially private multi-task learning
S Rana, S Rana, S Gupta, S Gupta, S Venkatesh, 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
Transfer learning for rare cancer problems via Discriminative Sparse Gaussian Graphical model
B Saha, S Gupta, Q Phung, S Venkatesh
(2016), pp. 537-542, 2016 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, E1
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Regularized nonnegative shared subspace learning
S Gupta, D Phung, B Adams, S Venkatesh
(2013), Vol. 26, pp. 57-97, Data mining and knowledge discovery, Boston, Mass., C1-1
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, Piscataway, N.J., C1
Interactive browsing system for anomaly video surveillance
T Nguyen, D Phung, S Gupta, S Venkatesh
(2013), pp. 384-389, ISSNIP 2013 : Sensing the future : Proceedings of the IEEE 8th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, Victoria, E1
T Nguyen, D Phung, S Gupta, S Venkatesh
(2013), pp. 47-55, PerCom 2013 : Proceedings of the 11th IEEE International Conference on Pervasive Computing and Commmunications, San Diego, California, E1
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
S Gupta, D Phung, S Venkatesh
(2012), pp. 200-212, SDM 2012 : Proceedings of the 12th SIAM International Conference on Data Mining, Anaheim, Calif., E1
A nonparametric Bayesian Poisson Gamma model for count data
S Gupta, D Phung, S Venkatesh
(2012), pp. 1815-1818, ICPR 2012 : Proceedings of 21st International Conference on Pattern Recognition, Tsubuka Science City, Japan, E1
S Gupta, D Phung, S Venkatesh
(2012), pp. 316-325, UAI 2012 : Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, Catalina Island, California, E1
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
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
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
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
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
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, 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 John Fouyaxis, Prof John Grundy, Dr Kit Huckvale, David Varley, Dr Leonard Hoon, Nicole Cockayne, Dr Tanya Petrovich, Matthew Macfarlane, Dr Anju Kissoon Curumsing, Dr Scott Barnett, Ms Sharon Grocott, Prof Deborah Parker, Dr Tom McClean, Mr Steven Strange, Prof Jean-Guy Schneider, Prof Nilmini Wickramasinghe, Dr Jessica Rivera Villicana, A/Prof Carsten Rudolph, Mr Fernando Escorcia, Dr Gnana Bharathy
ARC Industrial Transformation Research Hubs
- 2021: $388,477
- 2020: $385,381
- 2019: $399,716
- 2018: $449,083
- 2017: $601,698
A Generic Framework for Verifying Machine Learning Algorithms
Prof Svetha Venkatesh, Prof Sunil Gupta, A/Prof Santu Rana, A/Prof Truyen Tran
ARC - Discovery Projects
- 2023: $37,272
- 2022: $125,478
- 2021: $122,286
Optimising treatments in mental health using AI
Prof Helen Christensen, Prof Svetha Venkatesh, Prof Henry Cutler, Ms Ros Knight, Dr Martin Laverty, Prof Sunil Gupta, A/Prof Santu Rana, A/Prof Truyen Tran, Dr Thomas Quinn, Prof Rajesh Vasa, Prof Kon Mouzakis
MRFF (DISER) - Applied Artificial Intelligence Research in Health
- 2023: $254,910
- 2022: $999,770
- 2021: $1,431,416
Other Public Sector Funding
Al Algorithmic Assurance
Prof Svetha Venkatesh, Prof Sunil Gupta, A/Prof Santu Rana, A/Prof Truyen Tran, Dr Anh Cat Le Ngo, Dr Phuoc Nguyen, Mr Stephan Jacobs, Dr Dang Nguyen
Department of Defence
- 2021: $248,140
- 2020: $208,820
- 2019: $80,640
Defence Applied Al Experiential CoLab
Prof Svetha Venkatesh, Prof Sunil Gupta, A/Prof Santu Rana, A/Prof Truyen Tran
DSTO Grant - Research - Defence Science & Technology Organisation
- 2021: $100,000
- 2020: $873,495
In relation to Assuring an off-the-shelf AI algorithm
Prof Sunil Gupta, A/Prof Truyen Tran, A/Prof Santu Rana, Prof Svetha Venkatesh, Dr Phuoc Nguyen, Mr Tiep-Trong Nguyen, Mr Stephan Jacobs
Defence Science and Technology Group - Department of Defence
- 2022: $85,000
- 2021: $168,034
Coupled self-supervised learning and deep reasoning for improved processing of noisy and dynamic multimodal data from multiple sources.
A/Prof Truyen Tran, A/Prof Shannon Ryan, Prof Sunil Gupta, A/Prof Santu Rana, Prof Svetha Venkatesh
Department of Defence
- 2022: $105,364
Machine-learning based trajectory modelling.
A/Prof Shannon Ryan, Prof Sunil Gupta, Mr Stephan Jacobs, Mr Mahad Rashid
Department of Defence
- 2022: $44,828
Coupled self-supervised learning and deep reasoning for improved processing of noisy and dynamic multimodal data from multiple sources
A/Prof Truyen Tran, A/Prof Shannon Ryan, Prof Sunil Gupta, A/Prof Santu Rana, Prof Svetha Venkatesh
Defence Science and Technology Group - Department of Defence
- 2023: $73,000
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, 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 John Fouyaxis, Prof John Grundy, Dr Kit Huckvale, David Varley, Dr Leonard Hoon, Nicole Cockayne, Dr Tanya Petrovich, Matthew Macfarlane, Dr Anju Kissoon Curumsing, Dr Scott Barnett, Ms Sharon Grocott, Prof Deborah Parker, Dr Tom McClean, Mr Steven Strange, Prof Jean-Guy Schneider, Prof Nilmini Wickramasinghe, Dr Jessica Rivera Villicana, A/Prof Carsten Rudolph, Mr Fernando Escorcia, Dr Gnana Bharathy
Uniting AgeWell, Unisono Pty Ltd, goAct, Aged Care & Housing Group Inc, NeoProducts Pty Ltd, Health Metrics, Interrelate Limited, Black Dog Institute, Dementia Australia (Alzheimer's Australia) Vic Inc, Cancer Council Victoria Grant - Research, Uniting NSW.ACT
- 2022: $793,130
- 2020: $553,025
- 2019: $378,745
iCetana - Phase 1 Examine and compare state-of-art methods in background/foreground separation
Prof Svetha Venkatesh, A/Prof Santu Rana, Prof Sunil Gupta, Dr Budhaditya Saha
iCetana Pty Ltd
- 2020: $50,000
- 2018: $100,000
- 2017: $200,000
Identification of Blood RNA Biomarkers to Measure Disease Progression in Parkinson's Disease.
Prof Sunil Gupta, Prof Svetha Venkatesh, Dr Thin Nguyen
Parkinson's NSW Limited
- 2021: $42,500
- 2020: $42,500
Scale Faciliation CoLab
Dr Scott Barnett, Dr Yasmeen George, Mr Andrew Vouliotis, Dr Srikanth Thudumu, Dr Leonard Hoon, Prof Sunil Gupta, A/Prof Santu Rana, A/Prof Truyen Tran, Prof Rajesh Vasa, Prof Kon Mouzakis, Prof Svetha Venkatesh, Dr Rena Logothetis
Third Chapter Pty Ltd (Sanitex)
- 2022: $600,000
- 2021: $400,000
SaniteX - Re-open
Dr Scott Barnett, Mr Andrew Vouliotis, Dr Srikanth Thudumu, Dr Rena Logothetis, Dr Leonard Hoon, Prof Sunil Gupta, A/Prof Santu Rana, A/Prof Truyen Tran, Prof Rajesh Vasa, Prof Kon Mouzakis, Prof Svetha Venkatesh
Sanitex Global Pty Ltd
- 2022: $1,050,000
Scale Facilitation AI Colab.
Prof Svetha Venkatesh, Prof Sunil Gupta, A/Prof Santu Rana, A/Prof Truyen Tran, A/Prof Shannon Ryan, Prof Kon Mouzakis, Prof Rajesh Vasa, Dr Scott Barnett, Dr Anj Simmons, Dr Rena Logothetis
Sanitex Global Pty Ltd
- 2023: $500,000
Other Funding Sources
The CRC-P for Advanced Hybrid Batteries
Prof Patrick Howlett, Prof Maria Forsyth, A/Prof Robert Kerr, Prof Svetha Venkatesh, Prof Sunil Gupta, A/Prof Santu Rana
Cooperative Research Centres (CRC) Projects Program, Department of Industry, Innovation and Science.
- 2023: $137,788
- 2022: $189,722
- 2021: $295,583
- 2020: $170,546
Supervisions
Deepthi Praveenlal Kuttichira
Thesis entitled: Tackling Practical Challenges in Neural Network Model Deployment
Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins
Tang Thanh Nguyen
Thesis entitled: On Practical Reinforcement Learning: Provable Robustness, Scalability, and Statistical Efficiency
Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins
Huu Phuc Luong
Thesis entitled: Bayesian Optimization for Discrete, Missing and Cost-sensitive Inputs
Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins
Julian Maxwell Andrew Berk
Thesis entitled: A Distributional Approach towards Efficient and Versatile Bayesian Optimisation
Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins
Anil Ramachandran
Thesis entitled: Harnessing Auxiliary Knowledge Towards Efficient Bayesian Optimisation
Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins
Thanh Dai Nguyen
Thesis entitled: Addressing Practical Challenges of Bayesian Optimization
Doctor of Philosophy (Information Technology), School of Information Technology
Iman Kamkar
Thesis entitled: Building Stable Predictive Models for Healthcare Applications: A Data-Driven Approach
Doctor of Philosophy (Information Technology), School of Information Technology
Majid Abdolshah
Thesis entitled: Multi-objective Bayesian Optimisation and Its Applications
Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins
Ang (Leon) Yang
Thesis entitled: Scalable Bayesian Optimization with Sparse Gaussian Process Models
Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins
Tinu Theckel Joy
Thesis entitled: Efficient Hyperparameter Tuning using Bayesian Optimization
Doctor of Philosophy (Information Technology), School of Information Technology
Haripriya Harikumar
Thesis entitled: Machine learning to fight addiction using social media
Doctor of Philosophy (Information Technology), School of Information Technology
Arun Kumar Anjanapura Venkatesh
Thesis entitled: Accelerating Bayesian Optimisation with Advanced Kernel Learning Methods
Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins
Cong Thuong Nguyen
Thesis entitled: Bayesian nonparametric learning of contexts and activities from pervasive signals
Doctor of Philosophy (Information Technology), School of Information Technology