RESEARCH & PUBLISHED WORK
I explore how engineering, AI applications and institutional choices can work together to support sustainable infrastructure development.
CURRENT DIRECTION · TSINGHUA · 2026—
My doctoral research direction at Tsinghua explores how environmental infrastructure and digital methods can support smarter, more sustainable and climate-resilient cities. A central question is how the physical systems of a city—water, drainage, energy and green infrastructure—can be better understood and managed through timely information.
My research proposal connects sponge-city approaches with IoT sensing, predictive analysis and digital twins. The proposed work considers system architecture, data flows, governance, privacy, stakeholder roles and the practical conditions for implementation.
The intended pathway moves from literature and comparative cases toward modeling, pilot design and evaluation. I am interested in how these tools can support urban decisions while remaining financially feasible, locally adaptable and useful to the people who operate and depend on infrastructure. The precise dissertation scope will continue to develop through doctoral research.
The physical infrastructure, digital layer and governance of a city need to work together. My research explores these connections, with people’s needs and climate resilience at the centre.
Select a perspective to explore how it fits into the whole system.Water, drainage, energy and green infrastructure form the physical foundation. Sponge-city approaches offer one way to examine stormwater and adaptation, while system analysis asks how the assets interact under changing conditions.
Research question: what infrastructure relationships matter for resilience?IoT sensing, AI-assisted predictive analysis and digital twins can help describe conditions and compare scenarios. Their usefulness depends on the quality of the data, the validity of the model and the ability to communicate uncertainty.
Research question: when do digital methods improve the quality of a decision?A technical idea needs accountable institutions, appropriate privacy controls, financing and people able to use it. This perspective connects analytical results to stakeholder responsibilities and local capacity.
Research question: what enables responsible, locally useful adoption?Conceptual framework for an evolving doctoral research agenda.
MASTER’S THESIS · 2025
My 2025 master’s thesis examined smart-city initiatives and sharing-economy models for energy sustainability in residential complexes in Dhaka, Bangladesh. It considered shared solar photovoltaic systems, IoT-enabled energy management and peer-to-peer energy trading.
The study combined solar-resource and financial-feasibility modeling with 100 household surveys and 15 stakeholder interviews. NPV, IRR and payback analysis supported the assessment of financial viability, while residents’ views and institutional conditions informed the discussion of adoption and governance.
Comparative learning from Songdo and Freiburg helped frame the importance of combining technical design with community participation. The work proposed a context-sensitive framework addressing technology, financing, regulation and public engagement. Its quantitative results are study estimates and scenarios, rather than measured outcomes from an operating community-energy programme.
UNDERGRADUATE THESIS · 2023
My undergraduate thesis applied deep learning to image-based detection of defective electrical insulators. The work explored YOLOv5 object detection, image annotation, model training and evaluation, connecting computer vision with an electrical-engineering problem.
The research archive includes an annotated image dataset, experiment outputs, evaluation charts, a technical thesis and defense materials. It developed my experience in preparing data, interpreting model behavior and examining how AI tools might support infrastructure inspection.
This was an academic research project. Its value for my later work lies in the discipline of testing a technical idea against data, understanding its limitations and considering what would be required for dependable real-world use.
Thesis: Defective Insulator Detection and Recognition Based on Deep Convolutional Neural Network.
CO-AUTHORED PUBLICATIONS
Co-authored engineering work on hydraulic-retarder control, modeling and a self-tuning fuzzy-logic control approach.
Hassan Asad, Matobbar Tanvir Parvez, Rehman Mati Ur, Qureshi Adan.
International Research Journal of Modernization in Engineering Technology and Science, 4(11), November 2022, 2018–2025.
Read publication DOI · 10.56726/IRJMETS31553Co-authored review of additive manufacturing, injection molding and the technical and implementation challenges affecting these processes.
Hassan Asad, Rehman Mati Ur, Matobbar Tanvir Parvez, Areeba Haroon.
International Research Journal of Modernization in Engineering Technology and Science, 4(11), November 2022, 1891–1898.
Read publication DOI · 10.56726/IRJMETS31738APPLIED ANALYTICAL METHODS
My interest in AI extends from engineering research into professional knowledge work. I have developed structured approaches to market monitoring, research, analysis and documentation, including AI-assisted workflows that retain human review.
I am interested in traceable inputs, clear assumptions and useful outputs: tools should help people ask better questions, compare information and communicate uncertainty. This connects my technical background with the day-to-day needs of research and infrastructure-development teams.
Additional academic work has included power-distribution design, voltage regulation, feedback motor control, campus accessibility, service operations, product design and strategic analysis of mobility platforms.
See the professional applicationsLET’S BUILD WHAT COMES NEXT