If you are interested in joining our Research group, drop an email at ar.bhowmick@ictmumbai.edu.in. We always look forward to motivated students having Masters degree in Mathematics/Statistics/Computer Science. If you have any research plan in your mind, you are always welcome to discuss it. However, at this moment, we are primarily looking forward to Ph.D. aspirants having an interest in Machine Learning and Deep Learning with potential applications in Natural Sciences. And certainly, you must fulfill the requirement for admission to the Ph.D. program in ICT Mumbai. CSIR NET/DST INSPIRE/OTHER FELLOWSHIP candidates can apply any time during the year.
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Md Aktarul Karim (CSIR Research Fellow, joined 2019, Thesis Defended - October 2024)
Title: Mathematical Analysis of Biological Growth Models with Continuously and Stochastically Varying Parameters with Applications to Real Data.
Email: mdaktarulkarim829@gmail.com
Personal Webpage: https://sites.google.com/view/md-aktar-ul-karim
Quantitative assessment of the growth of biological populations has produced many mathematical equations. It is a challenging problem to select the best estimating model from a set of models as one model may serve as a close approximation of the other by appropriate choice of the parameter(s). The objective of our work is:
To develop inter-connections between growth equations in the literature using a continuous variation of parameters.
To develop statistical methods to detect whether some parameters of the model have undergone continuous or stochastic variation with time.
To extend the idea to higher dimensional and multiphasic population growth models and apply it to real data sets.
Dipali Vasudev Mestry (DST Inspire Fellow, joined 2020) (Ph.D. colloquium delivered - 9th January 2025)
Title: Uncertainty Quantification of Parameters for Extended Families of Biological Growth Models and Associated Model Selection Problems using Bayesian Framework.
Email: dipalimestry96@gmail.com
Personal Webpage: https://sites.google.com/view/dipalimestry
Innovation in modeling the dynamics of natural populations often gives rise to heavily parametrized mathematical equations. The inclusion of parameters is biologically motivating but simultaneously poses a substantial statistical challenge to learning about the parameters from the data. Frequentist statistical estimation methods are of limited use in such a scenario. Therefore, the aim of this thesis is to develop efficient computational techniques employing Bayesian statistics and machine learning algorithms for model estimation. The methods will be applied to estimate the nonlinear trends in natural populations under deterministic and stochastic environments. Both single and multiple population dynamics will be studied. I mainly use computational methods related to
Bayesian computation and simulation of posterior samples using Gibb’s algorithm and grid approximation.
Different types of Markov Chain Monte Carlo methods and their use in Bayesian inference.
Riddhi Bharani (Teacher Category, joined 2021)
Title: Broad area: Computational Statistics
Email: riddhi.bharani@ves.ac.in
I am currently working on the development of a new testing procedure to compare multiple ratios of means of multivariate data from different locations. Initial investigations deal with the computation of asymptotic distribution, simulating power functions, approximation by multivariate delta method, etc. In the initial step, we are investigating the problem for multivariate normal distributions, which can be potentially studied for other distributions as well. This problem is related to analyzing real data sets coming from soil science.
In a broader sense, my research activities are planned to develop efficient computational testing procedures and applications to real data.
Jyoti Jagdish Prajapati (joined 2021)
Title: Use of Machine Learning Models to Assess the Risk of Biological Invasion Under Climate Change: A Case Study with Indian Alien Flora
Email: prajapatijyoti23@gmail.com
(Joint supervision with Dr. Abhishek Mukherjee, Indian Statistical Institute, Giridih)
I am involved in Data Science research activities focusing on a better understanding of the biological invasion problems in India. I am working on the efficient use of Machine Learning techniques in building species distribution models. The work involves significant use of Machine Learning tools in R and Python, Geospatial Data Handling, and Spatial Statistics. I am currently working on data curation and data validation in the management of the ILORA database and automation of the pipeline using Python. I also enjoy writing learning materials related to the Statistical Modelling of Species Distribution. Ecological Niche Modelling using Python: https://github.com/prajapatijyoti23/SDM_2020
Masters Students
2020 – 2021: Pratik Singh, Analysis and Dynamic of variable Caputo Fabrizio Cholera model with exponential memory effect by q-HAETM method (Ph.D. candidate at Heidelberg University)
2020 – 2021: Ankita M. Jaitapkar, Bayesian Networks and Its Application. (Aging Order Analyst at Accurate Background)
2020 – 2021: Rutuja Sharad Bhawar, Bayesian Networks and Its Application. (Management Trainee at NSE India)
2020 – 2021: Subhamoy Kundu, Understanding of Neural Network in a Bayesian Setting. (Associate Process Manager at eClerx)
2020 – 2021: Vaishnavi Parab, Forecasting Population Dynamics of Chitala Fish Catch Data using Time Series Models. (Associate Process Manager at eClerx)
2020 – 2021: Aditi Abhijit Kulkarni, Bayesian Modelling and Application using INLA. (Data Scientist Intern at ACC Limited)
2020 – 2021: Nikita Sudesh Bhingarde, Bayesian Modelling and Application Using JAGS.
2019 – 2020: Urbi Datta, Some aspects of Bayesian Model Averaging and Application to Natural Sciences (Associate Process Manager at eClerx).
2019 – 2020: Ankita Murlidhar Malu, s2s Representation for Low or Imbalance Dataset (Jointly with Venkata Reddy, Statinfer)
2019 – 2020: Supriya Ramdas Bhagat, Approximating The Intervalspecific Estimate And Sampling Distribution Of The Parameters For Biological Growth Models (Associate Data scientist ProjectPro).
2019 – 2020: Vinaya Uttam Deshmukh, Approximation of moments of quasi-equilibrium distribution of stochastic population process: a review and implantation in Python (Associate Process Manager at eClerx).
2018 – 2019: Dipali Vasudev Mestry, Estimation of Parameters in Population Dynamics Models using Grid Approximation: A Bayesian Approach (Pursuing Ph.D. in ICT as DST Inspire Fellow)
2018 – 2019: Pal Rahul Chandraprakash, Natural Language Processing Using Python (with Venkata Reddy, StatInfer)
2018 – 2019: Keshavarao Boosayya Entenika, Natural Language Processing Using Python (with Venkata Reddy, StatInfer) (Instructor in UNIVERSAL LEARNING AID ( ULA) - LetsTute)
2018 – 2019: Kshitij Anand Patil, Uncertainty Modelling Using Polynomial Chaos in Spatial Population Dynamical System (Project assistant in NCL Pune)
2018 – 2019: Jyoti Jagdish Prajapati, Modeling Species Distribution Using Python (Recipient of the UDCT Alumni Association Scholarship for extension of the work) (Pursuing Ph.D. in ICT, Project Fellow at Indian Statistical Institute, Giridih, jointly with Dr. Abhishek Mukherjee)
2018 – 2019: Nilesh Naganath Kokate, Data Analysis using Principal Component Regression and Factor Regression using Python (Intern at Adya Enterprises)
2017 – 2018: Amir Rezza Khan, Study of growth curve models with continuously varying parameters with application to real data.
2017 – 2018: Harish Nagula, Study of growth curve models with randomly varying parameters with application to real data (Pursuing Ph.D. in ICT-IOC Bhubaneswar).
2016 – 2018: Shobhana Gopal Iyer, Ecological Niche Modeling using Logistic, Lasso and Ridge Logistic Regression: A case study using Mikania micrantha kunth. (The work is published in Ecological Indicators, Elsevier) (Business Analyst at Ernst & Young)
2016 – 2018: Sunil Kumar Gupta, Prediction of Animal Movement using Hidden Markov Model (Data Analyst at NCR Corporation)
2015 – 2016: Steffi D'Souza, Some resampling techniques and their application in Artificial Neural Network using R (Associate in Whitehat Jr.)
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