Md Babul Islam Ph.D. Scholar, at Unical

About Babul

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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.

Secondly, he also worked on the ISON Project at University College Dublin (UCD), Ireland, which involves Smart Agriculture Systems. You could visit the link below for more details of the project. Dataset and Project Description .

During his Ph.D., his tutors were: Dr. Antonio Guerrieri, Raffaele Gravina, Declan Delaney and Giancarlo Fortino.

🎓 PhD Research Project: "AI Driven Predictive Approaches in Smart Environment: From Smart Buildings🏢 to Smart Agriculture🌱"

🎓 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 House

👨‍💻Babul's Services

📑Journal Reviewer 🎯Technical Committee 🤝Research Collaboration
Journal Reviewer
IEEE Transactions on Human-Machine Systems ACM Reviewer IEEE Access Reviewer MDPI Computers Reviewer The Journal of Supercomputing
Technical Program Committee
PiCom 2025-Japan iEDGE 2025-Croatia PiCom 2024-Philippines Quatic 2023-Portugal EMCEI 2023-Italy
Research Collaboration

What I Collaborate On

I 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.

Focus

  • Time-series forecasting & evaluation
  • Model compression (pruning, quantization)
  • Edge deployment patterns (TinyML/TinyLLM)

Outputs

  • Reproducible baselines, ablations, and toolkits
  • Publications and open datasets when possible
  • Prototype-to-deployment blueprints

Current / Past Collaborations

  • University of Cambridge, United Kingdom (UK) — Deep learing approach in AI Healthcare.
  • University College Dublin (UCD), Ireland — Tiny Transformer-based model for forecasting model towards Smart Healthcare .
  • University of Queensland, Australia — Hybrid deep learing approach in AI Healthcare.
  • Huzhou University, China — Applied ML/DL in various domains such as Twitter sentiment analysis and fraud detection
  • Hamad Bin Khalifa University, Doha, Qatar —Applied DL in security.
  • University of Rome "Roma TRE, Italy —Applied DL in various domain.
  • University of Calabria, Italy —Smart environments & Digital Twins; robust multivariate models, pruning/quantization, edge readiness.
  • ICAR CNR, Italy — TinyML for sensor streams, forecasting, and deployability on constrained devices.
  • North South University, Bangladesh — DL for diseases classification .
  • Rajshahi University of Engineering and Technology (RUET), Rajshahi, Bangladesh — Fault Detection and Classification by ML and DL.

Collaboration Modes

  • Joint research & co-authored papers
  • Shared benchmarks and baselines
  • Edge deployment pilots (Raspberry Pi, MCUs)
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Domains of Work

TinyML & IoT
Pruning/quantization, streaming inference, telemetry, reliability under low-power and low-compute constraints.
Smart Buildings
HVAC control, energy optimization, occupancy-aware forecasting, Digital-Twin workflows with sensor-fault tolerance.
Smart Agriculture
Soil moisture (VWC) forecasting, rain/no-rain analysis, multi-depth transfer learning and generalization.

Future Opportunities

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.

  • Designing lightweight models with strong ablations
  • Establishing clear datasets, metrics, and protocols
  • Field trials on edge hardware
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Some Memories of Babul
MSc Graduation
MSc Graduation
With Professor
Lab

Last news

  • April. 25 |   Glad to announce that "Joined as PC Member at PiCom" , PiCOM 2025 , Top Applied AI and IoT Conference , [Link]
  • Dec. 23 |   Thrilled to share that our Two Paper Accepted and Presented on UPCON in 2023, 10th Edition, IEEE UP SECTION INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS AND COMPUTER ENGINEERING in India . [Accepted and Presented Certificate]
  • Oct. 23 |   Best Papers Awards AIKIIE 2023 in 2023, 1 st Edition, International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics in India . [Best Paper Awards Certificate]

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