A research enthusiast, pursuing a Doctor of Philosophy (Ph.D.) with a fully-funded scholarship at the School of ICT, University of Calabria (Cosenza, Italy)
and University of College Dublin (UCD), Ireland.
Mr. Babul completed his M.Sc. in Information Engineering at 👨🎓 Huzhou University, China,
and his B.Sc. in Computer Science and Engineering at 👨🎓City University, Bangladesh.
Mr. Babul gained valuable experience as a lecturer at the Micro Institute of Technology (2018–2019)
and later worked as a data scientist at Zhejiang Seating Technology in China for more than two years.
He has always dedicated himself to both academia and industry, with research at the core of his interests.
His constant passion for learning and sharing knowledge helps him maintain a strong technical background,
and he always enjoys immersing himself in new ideas and innovations.
In the broad area of research interest is in the domain of Tiny Machine Learning (TinyML), Applied Deep Learning,
IoT Energy Efficiency, Temporal Convolutional Network, Transformer, Bioinformatics,
and IoT Healthcare.
In his PhD, he worked on two real-world projects. The first, based in Italy, focused on smart building occupancy and indoor prediction,
including multi-step forecasting with Tiny AI models under resource constraints.
The second, in Ireland, involved soil moisture forecasting for smart agriculture using Tiny AI models.
Both projects rely on diverse real datasets.
In particular, he worked with a real-time smart building HVAC dataset from AEI Energia and the ICAR CNR Smart Laboratory.
For more details about these projects, you may visit the project webpage. Dataset, Code, and Papers.
🎓 PhD Research Project: "AI Driven Predictive Approaches in Smart Environment: From Smart Buildings🏢 to Smart Agriculture🌱"
I am working on ML techniques for IoT, Smart Cities/ Smart Buildings/Smart Healthcare with the aim of providing the community with new instruments for optimizing energy consumption in IoT Systems. My future dream project: Applied Bahari Big Data Research HouseI partner with universities and industry to design efficient, deployment-ready AI systems. This tabbed layout is long-text friendly: add paragraphs, nested lists, or references—the design will hold.
Open to collaborations that bridge research and real-world impact: efficient transformers, forecasting, and edge-AI. I’m especially interested in projects with measurable deployment outcomes and transparent evaluation.