Data & Design Lab

Welcome to Data and Design lab. Here we blend Data science, machine learning, statistical inference with human-centric design principals to aid policymakers what they do the best – policymaking. We believe evidence-based policymaking can help policy-making easier and efficient. However, we do not think everything can be solved from data-driven knowledge. We, therefore, focus only on certain fields- energy, education, ICT, health- where there is an access to data that can be harnessed. 
Data science- encompasses ideas and thoughts from computational social science, machine learning, statistical learning, social science, behavioral science and many other new disciplines of physical and social sciences. As the world is embracing digital technologies, machines and humans are producing more data that may directly or indirectly give insights about our environment. Policymakers be that from public offices or from private enterprises may use these insights to shape their policies or business goals. Harnessing data efficiently thus can open up new horizons. The developed world has embraced the idea and many countries have taken initiatives to find out the best way to harness data for public good. Developing world should not wait as this may be the cause of new digital divide as data begets information and information begets efficient business and policy. 
Design thinking helps to make understanding of our surroundings better. The researchers believing in Design centric thinking argue that better design can help get a better result, and design flaws may cause a worse result. We believe working with data needs a marriage with Design thinking as we need to think about the consequence of our works before we delve into data-centric research. We believe design thinking, therefore, can play a vital role in public policy. 
With the advent of efficient hardware to store and process a large amount of data, computational mechanisms to learn patterns from large datasets and people’s interest to harness data new possibilities of Data science for social good are engendering. We harness state-of-the-art knowledge from social science, arts, physical science and behavioral science and blend them with our visions of human-centric computing to harness insights from Data. 

Research Focus


Education & ICT


Society & Economy

Sustainable City

Recent News

Dr. Zaber and Dr. Rohman’s article has been printed on The Jarkarta Post titled “Can inflation be better measured with big data?“. An snapshot (JPEG) of the print is available on our website or you can read it on The Jarkarta Post if you are already subscribed.

Update (Apr. 22, 2022): This piece has also been published in the Straits Times (Singapore).

Our paper, in collaboration with AgenCy Lab (Independent University, Bangladesh) and Center of Spatial Information Science (University of Tokyo), titled Understanding the Urban Environment from Satellite Images with New Classification Method—Focusing on Formality and Informality” has been published in Sustainability, MPDI.

Our paper, in collaboration with University of Texas at Arlington, titled “Does Immergence of ICT Focused Institutions Increase the Pace of Urban Development? A Provincial Case Study in Iran Using Data from the Ground and Above” has been accepted for publication in 2022 IEEE Conference on Technologies for Sustainability (SusTech). 

How the arts can help make AI better? Read the latest article from The Daily post using this link.

(image retrieved from link)

Our paper on “Is My Password Strong Enough?: A Study on User Perception in The Developing World” has been accepted in EAI Endorsed Transactions on Creative Technologies.

For more details, please read our paper. 

What are the possible consequences for the economy if the NFT bubble gets bigger and wider? We would like to ask folks around to be a bit more cautious! You can find out OpEd piece that was published in Jakarta Post using this link.

A pdf of the printed version is also available in our library. Download PDF file (189kB) 

(image retrieved from link)

Recent Works

T.I. Tanni, T. Taharat, M.S. Parvez, S.T.A. Rumee, M.I. Zaber, “Is My Password Strong Enough?: A Study on User Perception in The Developing World”, EAI, 2022; DOI: 10.4108/eai.11-2-2022.173452

INTRODUCTION: The first line of defense in the cyber world is strong and difficult to predict passwords. However, users often choose highly predictable passwords based on personal information, dictionary words, birth date, etc.

OBJECTIVES: The primary objective is to ascertain password choice and practices of users of developing countries.

METHODS: Most of the existing studies are done in the developed world and our exhaustive search failed to find similar research in the context of developing countries. Here, we conducted detailed surveybased scrutiny about the password- based security perceptions of Bangladeshi nationals, which include 881 participants, primarily students, and professionals.

RESULTS: Most of the users were found to have bad practices, for example, having personal information(56%), password reuse(69%), having commonly used patterns(81.3%). Students from technical backgrounds fared well compared to non-technical backgrounds as expected. However, some professionals (especially Bankers) surprisingly chose weaker passwords even though dealing with sensitive data.

CONCLUSION: We also make a few recommendations to improve awareness..

Rahman AKMM, Zaber M, Cheng Q, Nayem ABS, Sarker A, Paul O, Shibasaki R. Applying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World. Sensors. 2021; 21(22):7469.

This paper shows the efficacy of a novel urban categorization framework based on deep learning, and a novel categorization method customized for cities in the global south. The proposed categorization method assesses urban space broadly on two dimensions—the states of urbanization and the architectural form of the units observed. This paper shows how the sixteen sub-categories can be used by state-of-the-art deep learning modules (fully convolutional network FCN-8, U-Net, and DeepLabv3+) to categorize formal and informal urban areas in seven urban cities in the developing world—Dhaka, Nairobi, Jakarta, Guangzhou, Mumbai, Cairo, and Lima. Firstly, an expert visually annotated and categorized 50 × 50 km Google Earth images of the cities. Each urban space was divided into four socioeconomic categories: (1) highly informal area; (2) moderately informal area; (3) moderately formal area, and (4) highly formal area. Then, three models mentioned above were used to categorize urban spaces. Image encompassing 70% of the urban space was used to train the models, and the remaining 30% was used for testing and validation of each city. The DeepLabv3+ model can segment the test part with an average accuracy of 90.0% for Dhaka, 91.5% for Nairobi, 94.75% for Jakarta, 82.0% for Guangzhou city, 94.25% for Mumbai, 91.75% for Cairo, and 96.75% for Lima. These results are the best for the DeepLabv3+ model among all. Thus, DeepLabv3+ shows an overall high accuracy level for most of the measuring parameters for all cities, making it highly scalable, readily usable to understand the cities’ current conditions, forecast land use growth, and other computational modeling tasks. Therefore, the proposed categorization method is also suited for real-time socioeconomic comparative analysis among cities, making it an essential tool for the policymakers to plan future sustainable urban spaces.

A. Zaman, S.B. Rabbani, R.R. Haque, M. Zaber. Seasonal, Temporal and Spatial Variation of Particulate Matter Concentration in Bangladesh: A Longitudinal Analysis. TenSYMP 2021.

Particulate matters having diameters of 2.5 micrometers or less (PM2.5) have been linked with life threatening health issues worldwide. Data centric approach to ascertain the patterns in the propagation of PM2.5 materials in the atmosphere of a region can help policy makers take informed decisions to take proper action. In this paper, we analyze and identify seasonal, hourly, and regional patterns of PM2.5 propagation in Bangladesh from 2017 to 2020 using the Berkeley Earth dataset. We observe that the concentration of PM2.5 particles has a nationwide median value of about 50 μgm -3 , which is unhealthy for sensitive individuals. The concentration varies seasonally and diurnally. We observe that the concentrations of PM2.5 in the air is around five times more in winter than in summer. The mean PM2.5 concentration inside Dhaka is significantly worse around 70 μgm -3 , which is 1.25 times than the average concentration throughout Bangladesh. We also observe average concentration dropped during the covid-19 pandemic due to lockdown. Using cross correlation analysis, we observed how spikes in PM2.5 concentration levels in one zone may correspond with peaked concentrations in a different zone a few hours later, indicating that air currents may cause the particles to move in certain directions. Our exploratory analysis serves as the first cross-country data centric study of the state and propagation patterns of PM2.5 particles within Bangladesh and our findings can serve as foundation for further research on the topic. 

Sayed, M. A., & Zaber, M. I. (2020). Just-in-Time Educational Aid to Deliver Instant Help for Students in Developing Countries. Recent Trends in Information Technology and its Application, 3(3).

A well-structured inland waterways system should help Bangladesh fulfill SDG goals. In this study, we employ complex network analysis methods to analyze the river-port network of the country. We ascertain different types of ports based on their importance and placement in the connectivity network. Data regarding port location, vessel routes, and schedules were collected from governmental resources. Using the data, a connectivity network was built for further analysis. Different measures of network analysis are used to categorize the ports and the network model has been identified. These categories should help transportation planners and policymakers to better design the inland waterways network of Bangladesh.

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