Adiba Proma

About Me

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Hi there! I'm Adiba Mahbub Proma and I'm

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I’m a Computer Science PhD student at the University of Rochester, where I am advised by Dr. Ehsan Hoque. I'm interested in developing technology to help people mitigate and adapt to climate change. My research projects include designing technology to promote sustainable consumption and modelling the influence of human interactions in online settings. Previously, I have also worked on designing a framework for risk assessment and early detection of breast cancer for Bangladeshi women.

Research Areas: Human-Computer Interaction, Computational Social Science, Network Science, Machine Learning, Affective Computing
Research Topics: Climate Change, Social Networks, Digital Health

Research

  • Understanding Belief Rigidity in in Temporal Social Networks

    We explore how various attributes and actions of social networks affect the rigidity of one’s beliefs in temporal social networks. [Read]

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  • Influencing human behavior toward eco-friendly choices

    We show that redesiging e-commerce platforms to include explainable environmental impact cues can facilitate sustainable purchasing behavior. [Read]

    With online shopping gaining massive popularity over the past few years, this study identifies a promising opportunity for E-commerce platforms to tackle climate change and other environmental problems. This study investigates a redesign of E-commerce platforms to bridge the existing “attitude-behavior gap” regarding environmental sustainability in online shopping. It introduces a concept design named Sustainable E-commerce with Environmental-impact Rating (SEER) – a way of communicating products’ environmental impact when displaying them on E-commerce platforms. A quasi-randomized case-control experiment with 98 subjects demonstrates the efficacy and user-friendliness of the proposed concept design. The case group using SEER showed significantly more eco-friendly behavior than the control group (p < 0.005) and reported that the components introduced in SEER made finding eco-friendly products more convenient, simultaneously increasing their trust in the labels because of the provided explanation. In addition, SEER has been rated highly in terms of usability, with a System Usability Scale (SUS) score of 79.18. By shaping the behavior of climate-concerned online shoppers, SEER could significantly reduce total carbon emissions of online products, which are currently estimated to exceed 2.88 million tonnes (yearly) in the United States alone.

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  • Curating Benchmark ML Datasets for Natural Disasters

    We develop a web platform where researchers can search for benchmark datasets for natural disasters. [Read] [Check Website]

    With climate change exacerbating the intensity, frequency, and duration of extreme weather events and natural disasters across the world, rapid advancement is required in its management. While the increased data on natural disasters improves the scope of machine learning (ML) for this field, to accelerate research, benchmark datasets can play a key role. Benchmark datasets are preprocessed, curated datasets for training and testing ML algorithms. These datasets provide scope for standard evaluation so that ML communities can quantify their progress and compare models. To facilitate research on natural disasters, we curate a list of existing benchmark datasets for natural disasters, categorizing them according to the disaster management cycle (mitigation, preparedness, responses and recovery). We develop a web platform – NADBenchmarks – where researchers can search for benchmark datasets for natural disasters. Currently, our curation covers the past five years. Our goal is to aid researchers in finding benchmark datasets to train their ML models on and also provide general directions for topics where they can contribute new benchmark datasets.

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Publications and Press

Press Publications and Preprint

Recognition

  • [May, 2023] The CS department highlights our work on the role of technology for climate change! [Read]

  • [Feb, 2023] Our lab's efforts on mentoring undergrad researchers gets featured by the University's News Center! [Read]

  • [2023] Selected for CRAWP Grad Cohort

  • [2022] Selected for CRAWP Grad Cohort

  • [2018] Received grant from ICT division of Ministry of Posts, Telecommunications and Information Technology, Bangladesh to lead a project on early detection and risk assessment of breast cancer

Work Experience

Research Assistant, CMED Health (Breast Cancer Research Division)

  • Developed a questionnaire-based breast cancer risk assessment and early detection algorithm through comprehensive literature review and expert interviews with medical professionals

  • Conducted feasibility analysis to develop web-platform functionalities for breast cancer awareness website [Check website]

Data Intern, Tech for Development Unit, BRAC SDP, BRAC Bangladesh

  • Designed an intelligent system for the field-level operations team to predict loan eligibility using ensemble learning

Teaching Experience

    Graduate Teaching Assistant, University of Rochester

  • CSC 212/412: Human Computer Interaction

  • CSC 200H: Undergraduate Problem Seminar (Focused on Database Systems Research)

  • CSC 261: Database Systems

    Contractual Lecturer, BRAC University

  • CSE 101: Introduction to computing

  • CSE 220: Data Structures

  • CSE 221: Algorithms

  • CSE 341: Microprocessors

  • CSE 420: Compilers

Services

  • Reviewer for ACII 2023 main paper track, and ACII 2023 Late Breaking Results

  • Committee Member for Ph.D Admissions Committee, Department of Computer Science, University of Rochester (Admission cycles of 2022)

Miscellaneous

Writing Experiences

Contact Me

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