Dr Ashim Chakraborty

Ashim Chakraborty For media enquiries please contact the Press Team
Senior Lecturer
Faculty:
Faculty of Science and Engineering
School:
Computing and Information Science
Location:
Cambridge
Areas of Expertise:
Computer Science
Research Supervision:
Yes

Ashim is a Senior Lecturer and researcher primarily working in the areas of computer vision, medical image processing, machine learning, and intelligent systems.

His broader research interests focus on applied artificial intelligence and machine learning, image processing, robotics, and the implementation of AI in immersive technology.

[email protected]

Connect with Ashim on LinkedIn

Visit Ashim's ResearchGate profile

Background

Dr Ashim Chakraborty is a Senior Lecturer and researcher in Artificial Intelligence at Anglia Ruskin University (ARU). His expertise spans AI, medical image processing, computer vision, biomedical informatics, trustworthy AI, generative AI, and intelligent systems, focusing on solutions that address clinical and societal needs.

His research portfolio includes AI applications for neonatal jaundice assessment, ventilation risk stratification, lung tumour analysis, climate modelling, autonomous driving, and mobile diagnostics. He is a PI for projects such as SecuraNext (Innovate UK), LocANTs AI (NIHR), and Co-I in UK biobank project and QuantoSniff (quantum-safe cybersecurity). He collaborates extensively with industry and clinical partners to ensure translational impact.

Recognised with awards including the Vice Chancellor’s Award (2025), he actively contributes to research through high quality journals, IEEE conferences and journal reviews, advancing AI for healthcare and digital security.

Spoken Languages
  • English
  • Bangla
Research interests
  • Medical informatics
  • Applied artificial intelligence
  • Computer vision and pattern recognition
  • Smart data transformation
  • Deep reinforcement learning
  • Eye tracking technology
  • Immersive technology
Awards
  • Vice-Chancellor’s Award for Early Career Excellence in Education, Anglia Ruskin University, 2025
  • Dean’s Award (in recognition of outstanding and distinguished contribution), Anglia Ruskin University, 2022
  • Peer Recognition Award (awarded on two occasions), Anglia Ruskin University
  • Above and Beyond Award (awarded on two occasions), Anglia Ruskin University
Areas of research supervision

Razieh Ehsaniamrei: Modelling and optimizing carbon emission factors in the food supply chain using artificial intelligence (completed).

Teaching
Course Leader

BSc Data Science Degree Apprenticeship

Modules taught
  • System Architecture and Automation
  • Computer Graphics Programming
  • Database Application Programming
  • Software Implementation (C# Programming)
Qualifications
  • PhD in Machine Learning and Medical Image Processing
  • BSc and MSc in Pure and Applied Mathematics
  • MSc in Business Management
  • PGCert Learning & Teaching in Higher Education
Memberships, editorial boards
  • Fellow of Higher Education Academy, UK (FHEA)
Research grants, consultancy, knowledge exchange
  • van der Linde, I., Chakraborty, A., Sapkota, R. (2022) 'How People Look at Pictures: A Health and Wellbeing Perspective using Machine Learning'. £10k
  • Chakraborty, A., Cristina, L., Ball, G. (2023) ‘Advanced Cloud-Based Neonatal Risk Assessment: Automated Classification via Data Fusion of Skin Colour and Ventilation Data’ £30k
  • Hasan, Md., Chakraborty, A., Cirstea, S., Islam, S. (2023) ‘: QuantoSniff: A Next Generation Cyber Defence using Quantum-Safe Cryptography’ KTN, Innovate UK 12k
  • Chakraborty A., Cristina L (2025) SecuraNext: Next-Gen AI Security Appliance For SMEs And Households (PI), Innovate UK £50k
  • Shahina P., Silvia., C., Ashim C., (2025) UK Biobank Project collaboration with Anglia Ruskin University Vision and Eye Research Institute, CO-I
Selected recent publications

Chakraborty, A., Thota, Y., Luca, C., & van der Linde, I. (2025). Explainable deep learning for neonatal jaundice classification using uncalibrated smartphone images. Machine Learning and Knowledge Extraction, 7(4), Article 136. https://doi.org/10.3390/make7040136

Vallukappully, S., van der Linde, I., & Chakraborty, A. (2025). Early detection and classification of diabetic retinopathy by transfer learning of NASNet-large and ResNet-50 convolutional neural networks. Informatics in Medicine Unlocked, 50, 101688. https://doi.org/10.1016/j.imu.2025.101688

Chakraborty, A., Wilson, G., & Luca, C. (2025). A lightweight classification system for the early detection of diabetic retinopathy. Informatics in Medicine Unlocked, 57, 101655. https://doi.org/10.1016/j.imu.2025.101655

Mathew, M., Chakraborty, A., Dhar, A., & Cirstea, S. (2025, June). Machine learning-based predictive risk assessment for preterm infants: A clinical decision support approach. In Proceedings of the 34th IEEE International Symposium on Industrial Electronics (ISIE 2025). Toronto, Canada: IEEE. https://doi.org/10.1109/ISIE62713.2025.11124804

Yordanov, D., Chakraborty, A., Hasan, M. M., & Cirstea, S. (2024). A framework for optimizing deep learning-based lane detection and steering for autonomous driving. Sensors, 24(24), 8099. https://doi.org/10.3390/s24248099

Chakraborty, A., Hubbard, T., Cirstea, S., (2024) ‘A Deep Transfer Learning Approach for Lung Tumor Detection with Resilience Testing Under Suboptimal Conditions.’ 25th IEEE International Conference on Industrial Technology, Bristol, UK

Hasan, M. M.,. Bitto, A. K., Chakraborty, A.,  Nanwani, R.,  Rahman, M. M., and Hameed, N.(2023)  ‘Net0Chain: An AI-Enabled Climate and Environmental Risks (CER) Framework for Achieving Net-Zero’  15th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), Kuala Lumpur, Malaysia, 2023, pp. 163-168, doi: 10.1109/SKIMA59232.2023.10387335.

Chakraborty, A., Wilson, G., Cristina, L., Biba., M. (2022) 'An Optimised Morphological Image Processing Method suitable for the Early Detection of Diabetic Retinopathy'. In: IEEE 18th International Conference on Intelligent Computer Communication and Processing, Cluj-Napoca, Romania.

Chakraborty, A., Chik, D., Biba, M. and Hossain, M. (2017) 'A decision scheme based on adaptive morphological image processing for mobile detection of early-stage diabetic retinopathy'. In: 11th International Conference (IEEE) on Software, Knowledge, Information Management and Applications (SKIMA).

Recent presentations and conferences

Chakraborty A., (2026) Machines, Media, and Meaning: Rethinking Journalism in the Age of AI, UKBRU conference, London Enterprise Academy, London.

Mathew, M., Chakraborty, A., Dhar, A., & Cirstea, S. (2024). Machine learning-based predictive risk assessment for preterm infants: A clinical decision support approach. 4th MTRC conference, Hughes Hall College, University of Cambridge.

Chakraborty, A., Hubbard, T., Cristea, S. (2023) ‘A deep transfer learning approach for lung tumour detection with resilience testing under suboptimal conditions’. 2nd MTRC annual research conference, Anglia Ruskin University, Cambridge, UK

Hasan, M. M., Chakraborty, A., and Cirstea, S. (2022). 'A next-generation explainable AI-enabled (XAI) expert system for eye disease detection and risks stratification', 1st MTRC annual research conference, Anglia Ruskin University, Chelmsford, UK (poster presentation).

Esteem indicators

2026 – Keynote speaker, Rethinking Journalism in the Age of Artificial Intelligence, on AI’s impact on media and journalism at UKBRU, London.

2024 – Session Chair, AI and Cyber Security Session, IEEE ICFSP Conference, Paris

2024 – Panel Member, Course Validation Event, Open University.

2023 – Keynote Speaker, AI and Security Awareness for Community Journalism, LBPC, London