The Pingla Institute offers unique opportunities for researchers and interns to contribute to groundbreaking studies, participate in workshops, and engage in interdisciplinary collaborations.
We are open to externally supervising HDR PhD/Masters/Honours students in our group provided you can fund your study programme. Below are some of the research directions and topics that can be pursued:
Team: Rodney Beard, John Hawkins, Karan Sutariya
Determine whether Sobol sensitivity analysis and global sensitivity analysis or conformal prediction is more robust as an approach to uncertainty quantification for a variety of machine learning methods.
Team: Rodney Beard, John Hawkins, Yogendra Meena, Vishal Gupta
Develop a retrieval augmented generation solution for an expert system based on LLMs and the WCPFC conservation management measures for tuna fishing
Team: Rodney Beard
Conduct a large scale comparison of different diversity measures for feature selection when building machine learning systems. Include evaluation of explainable Artificial Intelligence (xAI) applications of diversity measures.
Team: John Hawkins
Extending work on building literature filtering systems to improve efficiency in systematic reviews. Selection of papers for deeper review on the basis of titles and abstracts using semantically sensitive machine learning models like pre-trained BERT systems.
Team: John Hawkins
Extending our work on GDP forecasting to use time lagged information about investment in energy infrastructure, energy mix and consumption trends to improve GDP forecasting.
Team: Aditya Pramar, John Hawkins
There is an emerging literature on methods used to circumvent the safety methods of LLMs and solicit undesirable content. These methods are called ‘jailbreaks’ and involve using certain phrases and structures inside a prompt to elicit the desired response from the LLM. This project involves experimenting with machine learning approaches to identify potential jailbreak attempts and analyse what drives them.
Team: Rodney Beard
Explore ways to implement submodular function learning. The topic relates to questions of fairness, distributive justice, diversity but from a machine learning perspective. Possible other applications relate to substitutability and complementarity of goods in markets.
Team: Rodney Beard
The Incas used mysterious stringy objects called ‘khipus’ to record data. This project is a data science/machine learning for scientific progress project with elements of mathematical language analysis.
Please apply for a Research Internship.