Introduction

Hi, I am Saad! I am a PhD candidate in Computer Science at the University of Maryland, College Park, working under the supervision of Dr. Vanessa Frias-Martinez in the Urban Computing Lab.

My research is in general is situated in computational social science, drawing methodologies from machine learning, statistics, and fairness methods. I aim to improve high-stakes decision-making by leveraging behavioral data to build accurate and trustworthy predictive systems.

My dissertation focuses on “Auditing the accuracy and fairness of COVID-19 forecasting and modeling tasks with mobility and contextual data”, where I develop frameworks to evaluate prediction models and mobility datasets for potential biases and performance disparities across different social determinants. Through my research, I aim to ensure that data-driven urban computing applications serve all communities equitably while maintaining high predictive performance. My research has led to 13 peer-reviewed publications (7 journal articles, 2 conference papers, 4 workshop papers) in venues such as PLOS One, EMNLP, International Journal of Social Research Methodology, ACM SIGSPATIAL, Computers Environment and Urban Systems, ACM JCSS, EPJ Data Science.

I completed my M.S. in Computer Science from UMD (2023) and my BSc in Computer Science and Engineering from Bangladesh University of Engineering and Technology (BUET) in 2017.

Recent News

• February 2026 New journal article "Fairness Correction in COVID-19 Predictive Models Using Demographic Optimization" published in Online Journal of Public Health Informatics.
• November 2025 Our paper ProcVQA is live at EMNLP '25. Paper | Code
• November 2025 Traveling to Minneapolis, MN for ACM SIGSPATIAL '25.
• September 2025 Two papers accepted: "Engaging underserved communities in smartphone-based research" at International Journal of Social Research Methodology, and "Beyond Mode Detection: Reconstructing Transit Itineraries" at the ACM SIGSPATIAL 2025 Workshop.
• August 2025 Paper "ProcVQA: Benchmarking Vision-Language Models" accepted at EMNLP Findings '25. Code available via the link.
• February 2025 Paper on Auditing fairness of US COVID-19 Forecast Hub models accepted at PLOS One.
• October 2024 Presented our work on disparate recovery trajectories of small business restaurants across the USA at NetMob 2024.

Research Focus

Model: I design accurate predictors that leverage contextual and behavioral signals to improve forecasting in high-stakes settings. Through systematic evaluation, I determine when and how mobility and social media data enhance predictions across public health, crime, and related domains. [EPJ Data Science ‘22, JCSS ‘23, JMIR ‘23]

Mitigate: I develop comprehensive audit frameworks that uncover bias and failure modes in both data and models. By separating data-driven from model-driven failures, I create targeted fairness strategies that address disparities across demographics and geographies in predictive systems. [CEUS ‘23, JCSS ‘23, PLoS One ‘25, EMNLP Findings ‘25]

Measure: I build ethically-aware data collection toolkits that empower communities to gather and analyze their own mobility and experiential data. Through participatory design that prioritizes consent and transparency, I enable equitable decision-making in public transit planning. [COMPASS ‘23, IJSRM ‘25]

This research aims to enable efficient and equitable data-driven policy and planning, with direct applications in:

• Public Health Response
• Urban Planning
• Economic Recovery
• Transportation Equity