Researcher

Keywords

Fields of Research (FoR)

Neural, Evolutionary and Fuzzy Computation, Earth Sciences, Environmental Technologies

Biography

Dr Rohitash Chandra is a Senior Lecturer in Data Science at the UNSW School of Mathematics and Statistics.  He leads a program of research encircling methodologies and applications of artificial intelligence. The methodologies include  Bayesian deep learning, neuro-evolution,  ensemble learning, and data augmentation. The applications include climate extremes, geoscientific models, mineral exploration, biomedicine, and  COVID-19 where the...view more

Dr Rohitash Chandra is a Senior Lecturer in Data Science at the UNSW School of Mathematics and Statistics.  He leads a program of research encircling methodologies and applications of artificial intelligence. The methodologies include  Bayesian deep learning, neuro-evolution,  ensemble learning, and data augmentation. The applications include climate extremes, geoscientific models, mineral exploration, biomedicine, and  COVID-19 where the focus has been drug selection, infection forecasting, and social media-based language modelling.  Dr Chandra has also pioneered the area of language models for studying ancient religious-philosophical texts.  Dr Chandra has developed novel methods for machine learning inspired by neural systems and learning behaviour that include transfer and multi-task learning, with the goal of modular deep learning.  Dr Chandra  used machine learning and remote sensing for mineral exploration, and is currently focusing on critical metals. He has been using machine learning for climate extremes and currently developing a model for decadal forecasts of high category cyclones.

Dr Chandra has attracted multi-million dollar funding with a leading international interdisciplinary team. He is the Data Theme Lead of the Australian Research Council (ARC ITTC) Training Centre for Data Analytics in Minerals and Resources (2020-2025).  Dr Chandra is one of the Chief Investigators of the NHMRC Medical Research Future Fund (2021-2022) project on the use of machine learning for COVID-19 drug repurposing.

Apart from science, Dr Chandra takes a lot of interest in literature and humanities and has edited and published poetry collections.  Dr Chandra is a strong advocate of human rights and diversity and is a UNSW Cultural Diversity Champion (2021-2023).

Prior to joining UNSW, Dr Chandra held Sydney Research Fellowship at the University of Sydney (2017 - 2019). Prior to this, he has taken roles as Research Fellow in Machine Learning at Rolls Royce @Corp Lab, Nanyang Technological University, Singapore; Postdoctoral Research Fellow in Bioinformatics at Victoria University of Wellington (January to June 2012), and Lecturer in Computing Science at the University of the South Pacific (2013- 2015). Dr Chandra is originally from Fiji with a Girmit Indian heritage.

Dr Chandra is an Associate Editor (Topical Editor) for Geoscientific Model Development, Neurocomputing (Elsevier), and  IEEE Transactions on Neural Networks and Learning Systems. Dr Chandra is a Senior Member of IEEE and an Associate Fellow of the British Higher Education Academy (HEA). Dr Chandra is the founding director of the Transitional Artificial Intelligence Research Group (t-AI) based at UNSW Sydney


My Grants

 

  1. S. Vasan and R. Chandra et al., "The sySTEMs initiative: systems biology-augmented, stem cell-derived, multi-tissue panel for rapid screening of approved drugs as potential COVID-19 treatments," NHMRC - Medical Research Future Fund  (MRFF), July 2021- June 2022 ($1,000,000), 
  2. S. Cripps, R. Chandra, et al., "ARC training centre in data analytics for resources and environments (ARC ITTC DARE)," 2020 - 2024: https://darecentre.org.au/ ($4,000,000 from ARC and $6,500,000 in-kind support from industry)
  3. R. Chandra, Sydney Fellowship Awards, DVC Research, University of Sydney,  2017 -2019 
  4. D. Muller, R. Chandra,  et al., Understanding the deep carbon cycle from icehouse to greenhouse climates, Sydney Research Excellence Initiative (SREI), DVC Research, University of Sydney, 2017 - 2018 ($300,000)

My Qualifications

  1. PhD in Artificial Intelligence, Victoria University of Wellington (2012)
  2. MSc. in Artificial Intelligence, University of Fiji (2008)
  3. BSc. in Computer Science and Engineering Technology, University of the South Pacific (2006)

My Awards

  1. Doctoral Completion Award, Victoria University of Wellington (2012)
  2. Sydney Fellowship Award, University of Sydney (2017-2019)
  3. UNSW Science Silverstar Award 2022, Faculty of Science, UNSW

My Research Activities

Methodology Research

  1. Bayesian deep learning: Markov Chain Monte Carlo (MCMC) methods provide a probabilistic approach for the estimation of the free parameters in a wide range of models. Parallel tempering is an MCMC method that features parallelism with enhanced exploration capabilities. We have developed a Bayesian neural network framework that features parallel tempering MCMC with parallel computing [1]. We have addressed the challenge of applying MCMC methods for deep learning network architectures that features millions of parameters with Bayesian Autoencoders [2] and Bayesian Graph Convolutional Neural Networks [3]. We have also presented a framework that provides a synergy of multi-source transfer learning with Bayesian neural networks powered by MCMC [4]. Our current focus is on their application to other deep learning methods, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-term Memory (LSTM) networks. 
  2. Generative adversarial networks: The major challenge is to develop machine learning models given a low number of training examples. In this area, we use generative adversarial networks (GANs) with machine learning models to generate data in scenarios with space and limited data. Our current focus is on pattern classification problems [5],  but the method can be used for spatiotemporal problems, and also augmented with Bayesian inference for robust uncertainty quantification. 
  3. Surrogate-assisted and Bayesian optimisation: Surrogate-assisted optimization considers the estimation of an objective function for models given computational inefficiency or difficulty to obtain clear results. Surrogate-assistance inference addresses the inefficiency of parallel tempering MCMC for large-scale problems by combining parallel computing features with surrogate assisted estimation of likelihood function that describes the plausibility of a model parameter value, given specific observed data [6][7].  The challenge is to have a good estimation by the surrogates when the actual model features hundreds of free parameters.  
  4. Neuroevolution and modular learning algorithms: Neuroevolution features evolutionary algorithms that provide a gradient-free and black-box approach to learning in neural networks. Hence, the learning algorithm is not constrained to the architecture of the network and does not face the limitations of gradient descent such as local minima and vanishing gradients. We have developed novel neural network learning algorithms using neuro-evolution with motivations from transfer learning, multi-task learning and reinforcement learning [8] [9] [10] [11].  The challenge is in problems that have missing information, noise and inconsistencies in the organisation of data.   

Applications

  1. COVID-19 drug repurpose and modelling: We focus on long-COVID19 and vaccine testing using machine learning methods such as Bayesian optimisation and deep learning.  The goal is to repurpose drugs for COVID-19 using human tissue and organoid models in an ongoing project funded by the NHMRC. We use artificial intelligence and machine learning (AIML) techniques to characterise the systems biology responses better in a follow-on project with the US FDA. This brings together deep learning and tissue/organoid models along with experimental design using Bayesian optimisation. The approach can be extended to other diseases and enable AIML-based management of future pandemics.
  2. Cyclone modelling and prediction: The drastic effect of climate change is visible with extreme weather conditions such as tropical storms and cyclones. In this research, we use machine learning methods for forecasting cyclone formation for decades to come given drastic changes in the climate. We use global circulation models with machine learning methods to estimate cyclone categories decades ahead in the future. 
  3. Language models: In this research, we use novel deep learning models to develop language models via social media to understand public behaviours in events such as COVID-19.  We have applied deep learning-enabled language models for COVID-19 sentiment analysis with a case study of the Indian first wave of the pandemic. Currently, we are extending the methodology with topic modelling for comparing the three major waves of COVID-19 along with emerging topics in India. Furthermore, we are also reviewing anti-vaccine tweets during COVID-19 and sentiments related to them as the peak was reached in the first and the second wave around the world. Deep learning-based language models with sentiment analysis have also been used to model US 2020 Presidential elections. 
  4. Artificial intelligence for philosophy of religion: It is well known that artificial intelligence methods have immense success in their applications in areas of science and technology. It is important to uncover the potential of these methods in areas of arts and humanities. The Bhagavad Gita is a Hindu sacred and philosophical text which has been one of the most translated texts over the course of history. We use artificial intelligence methods to analyse the sentiments uncovered with philosophical issues presented in the Bhagavad Gita. We show that artificial intelligence methods powered by deep learning can be used to guide the study of religious and philosophical texts. We show that artificial intelligence can be used for understanding sentiments expressed in ancient philosophical texts. We use novel language models to analyse selected translations of the Bhagavad Gita (from Sanskrit to English) using semantic and sentiment analyses which help in the evaluation of translation quality.
  5. Mineral exploration and remote sensing: The extraction of geological lineaments from digital satellite data is a fundamental application in remote sensing. We use computer vision techniques for extracting geological lineaments using optical remote sensing data. Furthermore, in another research direction, we provide a synergy of deep learning methods with remote sensing for lithological mapping which is a critical aspect of geological mapping that can be useful in studying the mineralization potential of a region. Currently, we are using variational autoencoders and remote sensing for the identification of lineaments along with novel clustering methods. We would like to extend these methods for space exploration projects with the study of the Moon and Mars using satellite sate.
  6. Reef modelling and remote sensing: Geological reef models such as Py-Reef-Core provide insights into the flux of carbon by analysing carbonate platform growth and demise through time, and modelling their evolution using landscape dynamics and reef modelling. We estimate and provide uncertainty quantification of free model parameters using Bayesian inference with Py-Reef-Core. This can help us understand reef evolution on a geological timescale that can help in predicting the future evolution of coral reefs. The challenge here is in the estimation of the parameters which involves highly non-separable and constrained optimisation.  Currently, we are utilising remote sensing and machine learning method to study reef areas using satellite and drone datasets.
  7. Solid Earth evolution: Bayesian inference has been a popular methodology for the estimation and uncertainty quantification of parameters in geological and geophysical forward models. Badlands is a basin and landscape evolution forward model for simulating topography evolution at a large range of spatial and time scales. Our solid Earth evolution projects consider Bayesian inference for parameter estimation and uncertainty quantification for a landscape evolution model (Bayeslands). The challenge is in parameter estimation for computationally expensive models which are being addressed by high-performance computing and surrogate-assisted Bayesian inversion.  
  8. Paleoclimate reconstruction: The reconstruction of paleoclimate precipitation can provide light to Earth’s climate history of millions of years in the past. Although global circulation models have been used with success for the reconstruction of precipitation in the Miocene period, their application to an era back in time is a major challenge due to limited data. We use an alternate approach that features machine learning methods to predict precipitation that defines paleoclimate that spans up to 400 million years in the past. The data features a range of geological indicators including sedimentary deposits (coal, evaporates, glacial deposits). The challenge has been in addressing missing values in the dataset and providing rigorous uncertainty quantification in order to develop paleo-maps of forests and vegetation.   

Seminars

  1. R. Chandra, “Machine learning for paleo-geology and mineral exploration: A spatiotemporal odyssey”, ARC ITTC Data Analytics in Resources and Environments, December 2021. Youtube
  2. R. Chandra, “BERT-based language models for US Elections, COVID-19, and Bhagavad Gita”, UNSW Statistics Seminar Series, December 2021. Youtube
  3. R. Chandra, “Revisiting Bayesian deep learning with advancements in MCMC”, University of Auckland, Department of Statistics, April 2021. Youtube
  4. R. Chandra, “Unravelling Earth’s geological history with geoscientific models powered by artificial intelligence” University of the South Pacific, Public Seminar Series, September 2019. Youtube
  5. R. Chandra, ``Bayesian inference for Geoscientific models'', School of Computer Science, University of Wollongong, February 2019.
  6. R. Chandra, ``Bayesian inference for computationally expensive Earth evolution models'', School of Computer Science, University of Adelaide, October 2018. Youtube
  7. R. Chandra, ``Bayesian inference for modelling geo-coastal, basin and landscape evolution'', Basin Genesis Hub Workshop, The University of Sydney, February 2018.
  8. R. Chandra, `` Tackling climate change problems with machine learning '', EarthByte Group, School of Geosciences, The University of Sydney, July 2017. Youtube
  9. R. Chandra, ``Competitive neuroevolution with applications'', Seminar, School of Computing, Information and Mathematical Sciences, University of South Pacific, August 2015. Youtube
  10. R. Chandra, ``Open source software for education in Fiji'', Seminar, South Pacific Computer Society, University of South Pacific, Suva, Fiji, April 2013.
  11. R. Chandra, ``Chaotic time series prediction using recurrent networks", Seminar, School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand, August, 2011.

Available Research Projects 

  1. Bayesian deep learning for protein function detection (PhD), Co-supervised by Prof. Alok Sharma (RIKEN, Japan)
  2. Cyclone path and intensity prediction with deep insight based deep learning (Masters/Honours)
  3. Indoor path navigation for disabled persons in large buildings (Masters/Honours) 
  4. Detection of electric cable hazards from Cyclones using  drones and  remote sensing and deep learning (Masters/Honours) 
  5. Dynamic Earth models, landscape dynamics and basin evolution (PhD), Co-supervised by Prof. Dietmar Muller (University of Sydney)
  6. Machine learning for reef modelling and Optimisation, Co-supervised by Prof. Jody Webster (University of Sydney)
  7. Deep learning for the reconstruction of 3D Ore-bodies, Co-supervised by  Dr Ehsan Farahbakhsh
  8. Memory in Recurrent Neural Networks and Neural Turing Machines, Honours/PhD
  9. Bayesian deep learning with incomplete information, Honours/PhD
  10. Variational Bayes for Spatio-temporal modelling (Honours/Masters/PhD) with Prof. Robert Kohn
  11. Bayesian deep learning for language models  (Honours/Masters/PhD)
  12. Sentiment analysis with deep learning during natural disasters and extreme events (Honours/Masters/PhD)
  13. Deep learning for monitoring abuse in social media  (Honours/PhD)
  14. Ensemble learning for class imbalanced problems (Honours/PhD)  with Dr Rodney Beard 
  15. Bayesian deep learning for hydrological models (Honours/PhD) with Prof. Lucy Marshall (UNSW) and A/Prof Willem Vervoort (University of Sydney)
  16. COVID-19 related vaccine research  (Honours/PhD with Prof. Seshadri Vasan (CSIRO and University of York)
  17. Knowledge-based Recurrent Neural Networks (Honours/PhD) with Prof. Christian Omlin (University of Agder, Norway)
  18. Deep learning for bio-diversity and ecology (Honours/PhD) with Prof. Glenda Wardle and Dr Aaron Greenville (University of Sydney)

Access research papers: https://github.com/rohitash-chandra/research

 


My Research Supervision


Supervision keywords


Areas of supervision

Bayesian inference, deep learning,  machine learning, MCMC methods, Environmental informatics, Earth evolution models, reef evolution models, mineral exploration


Currently supervising

PhD research students 

  1. Arpit Kapoor, ''Bayesian deep learning for hydrological models``, School of Mathematics and Statistics,  UNSW Sydney, from September 2022 (Joint Principal Supervisor with Dr Sahani Pathiraja, External supervisor- Prof. Lucy Marshall)
  2. Ratneel Deo, ''Deep learning for understanding geo-coastal and reef development``, University of Sydney, from July 2021 (External Supervisor   with Prof. Jody Webster and Dr Tristan Salles)
  3. Megan Nguyen, " Bayesian deep learning with variational inference and Reversible Jump MCMC", University of Sydney,  from July 2021 (External Supervisor with A/Prof. Minh-Ngoc Tran and Dr Tongliang Liu)
  4. Mahsa Tavakoli, "Synergy of language models and time series models for credit rate forecasting", Western University, Canada, from March 2022 (External supervisor with A/Prof. Cristian  Roman)

Masters  and Honours

  1. Sean Luo, Honours in Data Science, from T2 2022 (Jointly Supervised with Dr Sahani Pathiraja)
  2. Royce Chen, Honours in Data Science, from T1 2022 (Jointly Supervised with Dr Sahani Pathiraja)
  3. George Maksour, Honours in Data Science, from T2 2022 (Jointly Supervised with Dr Sahani Pathiraja)
  4. Benjamin Jackson, Honours in Data Science, from T1 2023
  5. Albert Demskoy, Honours in Data Science, from T1 2023
  6. Honghui Wang, Masters by Research, from T2 2022 (Jointly Supervised with A/Prof Gustavo Batista)
  7. Hamish Haggerty, “ Self supervised deep learning”, Masters in Statistics, from September 2022, (Primary Supervisor)

 

Completions (Ph.D)

  1. Amit Kumar, "Machine learning with physics-based neural networks for lung air-flow modelling", Ph.D, Beijing Institute of Technology, 2022 (External Supervisor)
  2. Ehsan Farahbakhsh, “Machine learning for mineral prospecting”, Ph.D, Amirkabir University of Technology, Tehran, 2020 (External Supervisor)

 Completions (Honours thesis)

  1. Jim Ng, “ Conditional ensemble deep learning for modelling Australian climate extremes: streamflow and floods“, Honours thesis, UNSW Sydney, 2022 (Primary Supervisor with A/Prof Willem Vervoot)
  2. Eric Chen, “Deep learning for modelling historic ground-water levels via stream-flow and precipitation data”, Honours thesis, UNSW Sydney, 2022 (Primary Supervisor with A/Prof Martin Anderson)
  3. George Bai, "Bayesian neural ensemble learning with parallelized  and tempered Langevin MCMC",  Honours thesis,  2021 (Principal Supervisor)
  4. Jodie Pall, “ Bayesreef: Reef evolution using Bayesian inference”, Honours Thesis, School of Geosciences, University of Sydney, 2018 (Received University Medal) (Secondary Supervisor with Prof. Jody Webster and Dr Tristan Salles)

 Completions (Masters by Research) 

  1. Chaarvi Bansal, “Machine learning Framework for COVID-19 Drug Repurposing”, M.Sc. Biological Sciences, Birla Institute in Technology and Science Pilani and UNSW Sydney, 2022 (Principal Supervisor with Prof. P. R. Deepa )
  2. Julian Rodriguez, "Machine learning for spatial-temporal mineral prospecting using plate tectonic models, M.Phil, University of Sydney (External Supervisor with Prof. Dietmar Muller, 2019-2020)
  3. Ratneel Deo, “Neural network methodologies for cyclone wind intensity and path prediction”, Master of Science in Computing Science, University of the South Pacific, Suva, Fiji, December 2017 Download thesis from USP Library (Primary Supervisor - External Supervisor) (Nominated for Best Thesis - Gold Medal)
  4. Shonal Chaudhary, “Mobile Based Face Recognition for Visually Impaired Persons”, Master of Science in Computing Science, University of the South Pacific, Suva, Fiji, August 2015 Download thesis from USP Library (Primary Supervisor)
  5. Swaran Ravindra, “Health Information Systems in Fijian Hospitals”, Master of Science in Information Systems (Minor Thesis), University of the South Pacific, Suva, Fiji, 2015 Download thesis from USP Library (Primary Supervisor)
  6. Kavitesh Bali, “Competitive Island Cooperative Coevolution for Real Parameter Global Optimization”, Master of Science in Computing Science, University of the South Pacific, Suva, Fiji, September 2015 Download thesis from USP Library (Awarded PhD Scholarship at Nanyang Technological University - Singapore, 2016) (Awarded Gold Medal for Best MSc Thesis at USP) (Primary Supervisor)
  7. Ravneil Nand, “Competitive Island Cooperative Neuro-Evolution for Time Series Prediction”, Master of Science in Computing Science, University of the South Pacific, Suva, Fiji, January 2016 Download thesis from USP Library (Primary Supervisor)
  8. Shamina Hussein, “Multi-step ahead prediction using Recurrent Neural Networks”, Master of Science in Computing Science, University of the South Pacific, Suva, Fiji, 2015 Download thesis from USP Library (Primary Supervisor - External Supervisor)
  9. Shelvin Chand, “Multi-Objective Cooperative Neuro-Evolution for Chaotic Time Series Prediction”, Master of Science in Computing Science, University of the South Pacific, Suva, Fiji, August 2014 Download thesis from USP Library (Awarded PhD Scholarship at UNSW Australia, 2015) (Primary Supervisor)

Recent completions - Masters by Coursework (Minor Thesis)

  1. Tianyi Wang, Revisiting world economic outlook post-COVID-19 with deep learning",     Master of Statistics,  UNSW Sydney, 2022, (Primary Supervisor) 
  2. Yuhao Ke, “Machine learning for NBA”,  Master of Statistics, School of Mathematics and Statistics, UNSW Sydney, 2022 (Primary Supervisor)  
  3. Mingyue Kang, "COVID-19 mutation over time", Master of Statistics,  UNSW Sydney, April 2022 (Principal Supervisor in collaboration with Prof. Seshadri Vasan (CSIRO)
  4. Jiaxin Cathy Yu, "COVID-19 diagnosis study with big data", Master of Statistics,  UNSW Sydney, April 2022 (Principal Supervisor in collaboration with Prof. Seshadri Vasan (CSIRO)
  5. Kelin Liu, "Clustering methods for vessel tracking with satellite data", Master of Statistics,  UNSW Sydney, April 2022 (Principal Supervisor in collaboration with Dr Rodney Beard (FFA)
  6. Zhilin Wei, "Computer vision for aerial tracking of coastal plastic waste", Master of Statistics, UNSW Sydney, December 2021 (Principal Supervisor) 
  7. Dizhou Feng, "Graph neural networks for spatiotemporal forecasting", Master of Statistics,  UNSW Sydney, December 2021 (Principal Supervisor) 
  8. Yueyang Zhang, "Gradient Boosting LSTM for reducing model uncertainty", Master of Statistics,  School of Mathematics and Statistics  (Principal Supervisor, 2021) 
  9. Shaodong Lin, "World economic outlook post-COVID-19 with deep learning", Master of Statistics,  School of Mathematics and Statistics  (Principal Supervisor, 2021) 
  10. Yixuan He, "Bayesian neural learning for  financial prediction", Master of Financial Mathematics,  UNSW Sydney, August 2020 (Principal Supervisor) 

Research engineer 

  • Danial Azam. ARC Basin Genesis Hub, University of Sydney, Cosupervision with Prof. Dietmar Muller (Jan 2018 - December 2020)

Research interns 

      *Email your CV if you wish to do an online research internship in machine learning. Open to local and international researchers/students.

2022

  1. Azal Khan, Indian Institute of Technology, Guwahati, India (January - April 2022)  - jointly supervised with Prof. Jody Webster (University of Sydney)
  2. Saharsh Bharve, Manipal Institute of Technology, India (January - May 2022) - jointly supervised with Prof. Jody Webster (University of Sydney)
  3. Shirin Jain,  Indian Institute of Technology, Guwahati, India  (May - August 2022)
  4. Snigdha Jain, Indian Institute of Technology, Guwahati, India  (May - August 2022)
  5. Janhavi Lande, Indian Institute of Technology, Guwahati, India (January - March 2022)  - jointly supervised with Ms. Arti Pillay (Fiji National University)
  6. Chaarvi Bansal, Birla Institute of Technology and Science, Pilani, Rajasthan, India (January - May 2022)  - jointly supervised with Prof. Seshadri Vasan (CSIRO)
  7. Pranjal Singh,  Indian Institute of Technology, Guwahati, India (January - April 2022)  - jointly supervised with Prof. Seshadri Vasan (CSIRO)
  8. Gunjan Dhanuka, Indian Institute of Technology, Guwahati, India (January - April 2022) - jointly supervised with Prof. Seshadri Vasan (CSIRO)
  9. Suryansh Shrivastava, Indian Institute of Technology, Guwahati, India (January - April 2022)  - jointly supervised with Prof. Seshadri Vasan (CSIRO)
  10. Pranshu  Kandoi,  Indian Institute of Technology, Guwahati, India (January - April 2022)
  11. Pandaya  Pranshu, Indian Institute of Technology, Guwahati, India (January - April 2022)

2021

  1. Sweta Rathi, Indian Institute of Technology Guwahati, India (June - August 2021)
  2. Mukul Ranjan, Indian Institute of Technology Guwahati, India (June - August 2021)
  3. Amandeep Singh, Indian Institute of Technology Bombay, India (June - August 2021)
  4. Ritam Manabendra, Indian Institute of Technology Guwahati, India (June - August 2021)
  5. Anshul Negi, Indian Institute of Technology Roorkee, India (June - August 2021)
  6. Rishabh Sharma, Indian Institute of Technology Guwahati, India (June - August 2021)
  7. Sahil Bohra, Indian Institute of Technology Delhi, India (June - August 2021)
  8. Ayush Bhagat, Manipal Institute of Technology, India (April 2021 - June 2021)
  9. Venkatesh Kulkarni, Indian Institute of Technology Guwahati, India (November 2021 - February 2022)
  10. Sandeep Nagar, International Institute of Information Technology, Hyderabad, India (November 2021 - February 2022)

2020

  1. Ritij Saini, Indian Institute of Technology Bombay, India (December 2020 - February 2021)
  2. Aswin Krishna, Indian Institute of Technology Guwahati, India (December 2020 - February 2021)
  3. Prabhat Singh, Indian Institute of Technology Guwahati, India (December 2020 - February 2021) - supervised jointly with Dr Anurag Sharma (University of South Pacific)
  4. Jiaxin Yu, School of Mathematics and Statistics, UNSW Sydney, (December 2020 - February 2021)
  5. Jiaxi Zhao, School of Mathematics and Statistics, UNSW Sydney, (December 2020 - February 2021)
  6. Animesh Renanse, Indian Institute of Technology Guwahati, India (May 2020 - August 2020)
  7. Shaurya Goyal, Indian Institute of Technoritamlogy Delhi, India (May 2020 - August 2020)
  8. Yash Sharma, Indian Institute of Technology Roorkee, India (May 2020 - August 2020)
  9. Ashish Gupta, Indian Institute of Technology Delhi, India (May 2020 - August 2020)
  10. Manavendrasinh Maharana, Manipal Institute of Technology, India (Jan 2020 - June 2020)
  11. Animesh Tiwari, Indian Institute of Technology Guwahati, India (May 2020 - August 2020)
  12. Eshwar Nukala, Indian Institute of Technology Guwahati, India (May 2020 - August 2020)
  13. Arya Arya Indian Institute of Technology Jammu, India ( August 2020 - December 2020) - supervised jointly with Prof Alok Sharma (RIKEN, Japan)
  14. Mahir Jain, Manipal Institute of Technology, India ( August 2020 - December 2020)
  15. Ayush Bhagat, Manipal Institute of Technology, India ( August 2020 - December 2020)
  16. Ayush Jain, Indian Institute of Technology Guwahati, India (May 2020 - August 2020)
  17. Divyanshu Singh, Indian Institute of Technology Guwahati, India ( August 2020 - December 2020)
  18. Kousik Rajesh, Indian Institute of Technology Guwahati, India (May 2020 - August 2020)

2019

  1. Aakarsh Yadav, Indian Institute of Technology, India (June 2019 - August 2019)
  2. Ashray Aman, Indian Institute of Technology Delhi, India (June 2019 - August 2019)
  3. Rishab Gupta, Indian Institute of Technology, India (June 2019 - August 2019)

 

2018

  1.  Konark Jain, Indian Institute of Technology, India (May 2018 - July 2018)
  2.  Arpit Kapoor, SRM Institute of Technology, India (June 2018 - August 2018)
  3.  Ratneel Deo, University of the South Pacific, Fiji (December 2017 - February 2018)
  4.  Wil Grebner, University of Sydney, Australia (February 2018 -  June 2018)

My Teaching

Master of Data Science: 

  1. ZZSC5836 - Data Mining and Machine Learning (Online), https://studyonline.unsw.edu.au/online-programs/master-data-science
  2. MATH5836 - Data Mining, Trimester 3: https://www.maths.unsw.edu.au/courses/math5836-data-mining  Github repo: https://github.com/rohitash-chandra/dataminingMATH5836

Programming Bootcamp:

  1. Resources - code and exercises: https://github.com/rohitash-chandra/python-bootcamp
  2. Youtube Videos available

 

 

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Location

School of Mathematics and Statistics
UNSW Sydney
NSW 2052
The Red Centre
Room 4110

Contact

0413071839
9385 7091