Senior Research Associate
Academic Division: Manufacturing and Management
Research group: Manufacturing Systems
Email: am2910@cam.ac.uk
Research interests
Dr. Anandarup Mukherjee’s research focuses on digital transformation through the development of modular, interoperable, and intelligent information architectures for complex networked systems, particularly within industrial and healthcare ecosystems. His work integrates digital twins, industrial IoT, machine learning, and low-cost sensing to create scalable, adaptable, and economically viable digital solutions.
A key aspect of his research is democratizing digital transformation, ensuring that even resource-constrained industries and SMEs can access and leverage advanced digital capabilities. He focuses on mapping industrial processes, digital solutions, information flows, logistics, and supply chains to provide a structured approach to digital adoption, interoperability, and automation. His research untangles complex industrial information flows, enabling siloed data to become actionable, interoperable, and valuable for decision-making, predictive insights, and real-time optimization.
His approach to digitalization is multi-layered, considering transformation at both the systems level (processes, workflows, and automation) and the information and architectural level (data-driven intelligence, interoperability, and real-time analytics). By designing digital solutions that align with existing industrial processes, he ensures seamless integration and improved decision support in manufacturing, logistics, and supply chain management.
His work extends beyond manufacturing and industrial automation into healthcare digitalization, predictive maintenance, and UAV-enabled networked intelligence, where he explores novel approaches to improving operational efficiency, resilience, and system intelligence.
By combining cost-effective technological innovations with scalable digital infrastructures, his research aims to bridge the gap between cutting-edge research and real-world applications, making digital transformation accessible, impactful, and inclusive across industries.
Strategic themes
Manufacturing, design and materials
Dr. Mukherjee’s research in manufacturing centers on low-cost, scalable digital solutions for SMEs. His work in Digital Manufacturing on a Shoestring has pioneered approaches to deploying industrial IoT and digital twins using off-the-shelf technologies. He has contributed to methodologies for evaluating the impact of digital solutions, developed modular energy analytics frameworks, and architected predictive maintenance solutions for large-scale industrial operations. His research aligns with the future of smart manufacturing, emphasizing adaptability, resilience, and cost-effectiveness.
Complex, resilient and intelligent systems
Anand’s work on complex systems spans digital twins, networked UAVs, and industrial interoperability. He has led projects on predictive analytics for healthcare, digital infrastructure for hospitals, and anomaly detection in critical industrial networks. By integrating AI-driven insights, edge computing, and service-oriented architectures, his research advances the intelligence and resilience of industrial and healthcare ecosystems.
Teaching activity
Dr. Mukherjee has extensive teaching experience at both undergraduate and postgraduate levels. At the University of Cambridge, he has designed and delivered guest lectures on Industrial IoT, low-cost digital solutions, and digital manufacturing. He has supervised postgraduate research on energy analytics and industrial automation. Previously, at IIT Kharagpur, he co-developed two MOOCs on IoT and Industrial IoT, and he has taught courses on wireless networks, software engineering, and IoT architectures. He continues to mentor students in applied research, focusing on real-world industrial and healthcare challenges.
Other positions
- Vice Chair, IEEE P1954 Standards Committee
- Area Expert, IEEE ComSoc SIG on IoT for e-Health
- Associate Editor, Springer Nature Peer-to-peer Networking and Applications (PPNA)
- Editor, IET Digital Twin and Applications
Biography
Dr. Anandarup Mukherjee is a Senior Research Associate at the Institute for Manufacturing (IfM), University of Cambridge, specializing in industrial digitalization, IoT, and intelligent systems. With over 13 years of research experience, his expertise spans digital twin architectures, predictive analytics, and low-cost automation solutions for manufacturing and healthcare. He has contributed to numerous international research projects, including Digital Manufacturing on a Shoestring, Digital Hospitals.
Dr. Mukherjee has authored over 70 peer-reviewed publications and two textbooks, with an h-index of 22 and i-10 index of 34. His work has earned recognition in Elsevier’s Top 2% Scientists list. In addition to his research, he has led global initiatives such as the Low-Cost Digital Solutions for Industrial Automation (LoDiSA) workshops at Cambridge and has actively engaged with SMEs to implement practical digital transformation strategies.
He holds a Ph.D. in Computer Science and Engineering from IIT Kharagpur, where his thesis, titled “Offloading in UAV Networks,” focused on optimizing data processing and communication efficiency in constrained aerial IoT environments. He also holds a Master’s and Bachelor’s degree in Electronics and Communication Engineering, providing a strong foundation in hardware-software co-design, communication networks, and embedded systems.
Dr. Mukherjee serves as Vice Chair of IEEE P1954, leading standardization efforts for Self-Organizing Spectrum-Agile Unmanned Aerial Vehicle (UAV) Communications since February 2022. Since August 2020, he has also contributed as an Area Expert in IoT Communications and Interoperability for the IEEE ComSoc Special Interest Group (SIG) on IoT for e-Health. He is a Senior Member of IEEE and a Fellow of the Royal Statistical Society (RSS). He holds editorial positions as Editor for IET Digital Twins and Applications and Associate Editor for Springer Nature’s Peer-to-Peer Networking and Applications (PPNA).
His accolades include the IEEE e-Health TC Best Paper Award (2022), the Gandhian Young Technological Innovation Award (2018), and multiple top-cited papers in IEEE Transactions on Parallel and Distributed Systems.His work aims to bridge the gap between cutting-edge research and real-world applications and untangle the complex industrial information flows to make siloed information available for actionable decision-making, predictive insights, and seamless interoperability across digital ecosystems.
Department role and responsibilities