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Abira Sengupta
PhD
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About | Research areas | Research projects | Publications | Teaching | CV |
About Me
Hi, I am Abira Sengupta, a researcher and educator driven by a passion for leveraging Artificial Intelligence to solve real-world challenges. I recently completed my PhD in Information Science at the University of Otago, New Zealand, where my research focused on developing computational models to address complex problems in multi-agent systems. My work bridges AI, logical reasoning, and data-driven approaches to tackle a variety of issues across domains, including but not limited to climate science.
Currently, my research delves into Explainable AI (XAI) for climate-related challenges, particularly wildfire behavior, spread prediction, and post-wildfire methane emissions. I am an Affiliated Researcher and Visiting Scientist at the Indian Statistical Institute (ISI) and collaborate with international teams to enhance the reliability and transparency of AI-driven climate models. My work integrates multimodal systems and advanced machine learning techniques to predict and understand complex phenomena like greenhouse gas emissions and extreme weather events.
I am a proud member of IEEE, CAIPP, SINZ, and AIRA, and an enthusiastic tutor at the School of Computing, University of Otago. I have a rich teaching history spanning subjects like machine learning, data science, and software development.
My pre-doctoral research emphasized computer vision and deep learning for biometrics, including face recognition and postal automation. During this period, I served as a guest lecturer at renowned Indian institutions.
Originally from Kolkata, India, I relocated to Dunedin, New Zealand, in 2019 for my PhD. Outside my academic pursuits, I find joy in gardening, cycling, music, and constantly learning new skills.
Feel free to explore my research, publications, and collaborative projects through my website.
Research Areas
Research projects
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Post-PhD projects: [1] 2023-2024 University of Otago | Collaboration Study [2] 2024 Indian Statistical Institute | Collaboration Study [3] 2023: University of Otago | Postdoctoral research assistant |
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PhD project:My work focuses on the use of logical reasoning regarding expectations and generalised representation to help computational agents to solve a range of collective action problems. The different applications of expectation-related reasoning, such as expectation, team reasoning norms, norm, and norm emergence from emotion, were explained in this work. The central claim of my research is to build a generalised computational model in which symbolic representations of norms are used as an input and an Expectation Event Calculus (Prolog-based) is used to represent how explicit reasoning of expectation is generated and how it is fulfilled or violated. |
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Other Research work[1] 2021 Univ. of Otago, NZ | Research Assistant- A computational analysis of text data for emotion recognition. [2] April 2017-December 2018: Indian Statistical Institute | Pre-Doctoral Research - ConvNet-based Face Recognition for Biometric Authentication and Pincode detection of multilingual behaviour. [3] July 2011-June 2013: West Bengal University of Technology | Masters Research - Localized Algorithm for Coverage in Wireless Sensor Networks. [4] July 2009-June 2010: West Bengal University of Technology | Bechelors Research - Developing an Academics Management System. |
Publications
Papers
- Abirs Sengupta, Fathima Nuzla Ismail, Shanika Amarasoma, "Modeling Methane Emissions from Rice Paddies with Explainable AI”, Nature Computational Science, 2024 (Submitted)
- Abirs Sengupta, Fathima Nuzla Ismail, "Predicting methane emission for Canadian peatlands using explainable AI”, ICML, 2025 (Submitted)
- Fathima Nuzla Ismail, Abirs Sengupta, "Teaching and Learning Experience with the Kahoot feedback system”, Asia-Pacific Journal of Teacher Education, 2024 (submitted)
- Abirs Sengupta, Fathima Nuzla Ismail, "Software as a Medical Device: Design and Compliance”, ICCAE, 2025 (Accepted)
- Ankan Bhattacharya, Abirs Sengupta, Sarbani Palit, "Interpretable and explainable AI model for precipitation prediction”, IJCNN, 2025 (Submitted)
- Kaushik Sarkar, Abirs Sengupta, Sarbani Palit, Rajat Kumar Pal, "A Review on Smart Weather Prediction using Machine Learning Approaches”, ICWI, 2025 (Submitted)
- Fathima Nuzla Ismail, Abirs Sengupta, Shanika Amarasoma, "Using Pangenome Graphs to Improve Variant Detection in Bacteria”, WIBI, 2025 (Submitted) (GitHub Project)
- Abirs Sengupta, Fathima Nuzla Ismail, "Modelling methane emissions from rice paddies using machine learning", IVCNZ, 2024. [paper]
- Abirs Sengupta, Brendon J. Woodford, "Recent Advances in Explainable Machine Learning Models for Wildfire Prediction", Applied Computing and Geosciences 2024 (submitted)
- Abira Sengupta, Stephen Cranefield, Jeremy Pitt, "Generalising Axelrod's Metanorms Game through the use of explicit domain-specific norms", COINE 2023. [paper]
- Fathima Nuzla Ismail, Abira Sengupta,, Brendon Woodford, Sherlock Licorish, "A Comparison of One-class versus Two-class Machine Learning Models for Wildfire Prediction in California", AusDM 2023. Link [paper]
- Abira Sengupta, Stephen Cranefield, Jeremy Pitt, "Solving social dilemmas by reasoning about expectations", COINE 2021: 1-18. [paper]
- Abira Sengupta, Saurabh Malgaonkar, Nikita Mehrotra, Tejas Hirave, " A Preliminary Investigation of LEACH, TEEN and DEEC Towards Wireless Sensing Application", ICCICT 2021. [paper]
- Abhijit Das, Abira Sengupta, Muhammad Saqib, Umapada Pal, and Michael Blumenstein, "More Realistic and Efficient Face-Based Mobile Authentication using CNN", IJCNN, 2018. [paper]
- Nabin Sharma, Abira Sengupta, Rabi Sharma, Umapada Pal, and Michael Blumenstein, "Pincode detection using Deep CNN for postal Automation", IVCNZ, 2017. [paper]
- Abhijit Das, Abira Sengupta, Umapada Pal, and Michael Blumenstein, "Linking Challenging Face Images Captured from the Optical Phenomenon in the Wild for Forensic Science", IJCB, 2017. [paper]
Posters
- Abira Sengupta, Fathima Nuzla Ismail, "Modelling methane emissions from rice paddies using machine learning", IVCNZ, NZ, 2024
- Abira Sengupta, Stephen Cranefield, Jeremy Pitt, "Generic representation of expectations can help computational agents to solve social dilemmas", AIRA, NZ, https://www.ainz.ai/2022-posters
- Abira Sengupta, Stephen Cranefield, Jeremy Pitt, "Normative Reasoning Based On Emotions In Multi-Agent System To Solve Social Dilemmas", Student Research Symposium of Otago
Awards and Funding
- [1] Commerce Research Grant (2023).
- [2] Marsden scholarship for 3 years PhD studies in NZ (2019 - 2022).
- [3] Technical Education Quality Improvement Programme for MTech scholarship in India (2012 - 2013).
Teaching
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[1] Tutor/Demonstrator at the University of Otago, NZ (2019 to 2023) |
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