CV

Education

• Ph.D in Computer Science, University of Maryland College Park, 2020 - Present
  • Advisor: Dr. Vanessa Frias-Martinez
  • Research Focus: Urban Computing, Human Mobility, and Data Science for Social Good
• M.S. in Computer Science, University of Maryland College Park, 2020 - 2023
• B.Sc. in Computer Science and Engineering, Bangladesh University of Engineering and Technology, 2013 - 2017

Professional Experience

• Graduate Research Assistant (August 2020 - Present)

  • University of Maryland College Park
  • Urban Computing Lab
  • Research areas:
    • Auditing fairness and accuracy of urban predictors based on mobility and contextual data
    • Developing predictive models for COVID-19 cases using mobility data
    • Investigating business resilience patterns using urban embeddings
    • BALTO: Developing privacy-respectful multimodal data collection tools

• Graduate Research Assistant (January 2020 - July 2020)

  • North Carolina State University
  • Database Group
  • Project: Drug Repurposing using Knowledge Graphs
    • Applied graph embedding algorithms for drug repurposing hypothesis generation

• Graduate Teaching Assistant (August 2019 - December 2019)

  • North Carolina State University
  • Course: CSC-505 (Design and Analysis of Algorithms)

• Lecturer (October 2017 - July 2019)

  • East Delta University
  • School of Science Engineering and Technology

Skills

• Programming Languages
  • Python
  • Java
  • C/C++
  • JavaScript
  • Kotlin
• Technologies and Tools
  • PyTorch
  • Tensorflow
  • Hadoop
  • NodeJS
  • D3
  • Tableau
  • PostgreSQL

Publications

Fairness Correction in COVID-19 Predictive Models Using Demographic Optimization: Algorithm Development and Validation Study

Awasthi, N., Abrar, S., Smolyak, D., & Frias-Martinez, V. (2026). Fairness Correction in COVID-19 Predictive Models Using Demographic Optimization: Algorithm Development and Validation Study. Online Journal of Public Health Informatics, 18, e78235. https://doi.org/10.2196/78235

Fairness and Equity in AI for Societal Decision Making

Abrar, S. M., & Frias-Martinez, V. (2026). Fairness and Equity in AI for Societal Decision Making. In Encyclopedia of GIS (Eds. Shekhar, Xiong, Zhou, and Xie). (In Press)

Evaluating VLMs as Accessibility Bridges for Process Visualizations

Zinat, K. T., Abrar, S. M., Duppala, S., Sakhamuri, S. N., & Liu, Z. (2025). Evaluating VLMs as Accessibility Bridges for Process Visualizations. CVPR 2025 Workshop on the VizWiz Grand Challenge.

Auditing the fairness of the US COVID-19 forecast hub’s case prediction models

Abrar, S. M., Awasthi, N., Smolyak, D., Sigalo, N., & Martinez, V. F. (2025). Auditing the fairness of the US COVID-19 forecast hub case prediction models. PloS one, 20(4), e0319383.

Engaging underserved communities in smartphone-based research: comparing mailings, advertisements, and in-person recruitment strategies

Antoun, C., Frías-Martínez, V., Garove, A., Awasthi, N., & Abrar, S. M. (2025). Engaging underserved communities in smartphone-based research: comparing mailings, advertisements, and in-person recruitment strategies. International Journal of Social Research Methodology, 1-21.

ProcVQA: Benchmarking the Effects of Structural Properties in Mined Process Visualizations on Vision–Language Model Performance

Zinat, K. T.*, Abrar, S. M.*, Saha, S., Sakhamuri, S., Duppala, S., & Liu, Z. (2025). ProcVQA: Benchmarking the Effects of Structural Properties in Mined Process Visualizations on Vision–Language Model Performance. In Findings of the Association for Computational Linguistics: EMNLP 2025. (* equal contribution)

Beyond Mode Detection: Reconstructing Detailed Transit Itineraries from Crowdsourced GPS Trajectories

Jagtap, O., Awasthi, N., Jain, M., Abrar, S. M., & Frías-Martínez, V. (2025). Beyond Mode Detection: Reconstructing Detailed Transit Itineraries from Crowdsourced GPS Trajectories. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Spatial Intelligence for Smart and Connected Communities (pp. 35-38).

Analysis of performance improvements and bias associated with the use of human mobility data in covid-19 case prediction models

Abrar, S.M., Awasthi, N., Smolyak, D., & Frias-Martinez, V. (2023). Analysis of performance improvements and bias associated with the use of human mobility data in COVID-19 case prediction models. ACM Journal on Computing and Sustainable Societies, 1(2), 1-36.

Using COVID-19 vaccine attitudes on Twitter to improve vaccine uptake forecast models in the United States: infodemiology study of Tweets

Sigalo, N., Awasthi, N., Abrar, S. M., & Frias-Martinez, V. (2023). Using COVID-19 vaccine attitudes on Twitter to improve vaccine uptake forecast models in the United States: infodemiology study of Tweets. JMIR infodemiology, 3(1), e43703.

Systematic analysis of the effectiveness of adding human mobility data to COVID-19 case prediction linear models

Abrar, S.M. *, Awasthi, N. *, Smolyak, D. *, & Frias-Martinez, V. Systematic analysis of the effectiveness of adding human mobility data to COVID-19 case prediction linear models. ACM Compass 2023.

The BALTO Toolkit - A New Approach to Ethical and Sustainable Data Collection for Equitable Public Transit

Abrar, S.M.*</b>, Awasthi, N.*, Park, S. & Vitak, J & Frias-Martinez, V. (2022). The BALTO Toolkit - A New Approach to Ethical and Sustainable Data Collection for Equitable Public Transit.

COVID-19’s Unequal Toll: An assessment of small business impact disparities with respect to ethnorace in metropolitan areas in the US using mobility data

Abrar, S. M., Zinat, K. T., Awasthi, N., & Frias-Martinez, V. COVID-19's Unequal Toll: An assessment of small business impact disparities with respect to ethnorace in metropolitan areas in the US using mobility data. Equitable Accessibility and Sustainable Mobility Workshop 2023.

Auditing the fairness of place-based crime prediction models implemented with deep learning approaches

Wu, J., Abrar, S. M., Awasthi, N., & Frias-Martinez, V. (2023). Auditing the fairness of place-based crime prediction models implemented with deep learning approaches. Computers, Environment and Urban Systems, 102, 101967.

Enhancing short-term crime prediction with human mobility flows and deep learning architectures

Wu, J., Abrar, S.M., Awasthi, N., Frias-Martinez, E. & Frias-Martinez, V. (2022). Enhancing short-term crime prediction with human mobility flows and deep learning architectures. EPJ Data Science, 11(1), p.53.

Learning Drug-Disease-Target Embedding (DDTE) from knowledge graphs to inform drug repurposing hypotheses

Moon, C., Jin, C., Dong, X., Abrar, S., Zheng, W., Chirkova, R. Y., & Tropsha, A. (2021). Learning Drug-Disease-Target Embedding (DDTE) from knowledge graphs to inform drug repurposing hypotheses. Journal of biomedical informatics, 119, 103838.

Selected Projects

• Resume Builder Bot
  • Bot for mining data from LinkedIn, Github, and DBLP profiles
• Object Classification using CNNs
  • Implemented on CalTech-256 Dataset
• Bike Data Visualization
  • Explored biking trends in Richmond
• Image Data Tile Optimization
  • Developed efficient image compression techniques

Awards and Recognition

• CS Summer Research Fellowship (2021)
• UMIACS Award (Spring 2022, Summer 2022)
• Undergraduate University Merit Scholarship (2013)

Professional Service

• Reviewer for:
  • KDD (2021, 2022)
  • WSDM 2022
  • ACM Compass 2023
  • EPJ Data Science
  • ACM JCSS
  • PLOS One
  • IEEE Transactions on Computational Social Systems

Selected Coursework

• Big Data Infrastructure
• Causal Inference and Evaluation Methods
• Advanced Numerical Optimization
• Foundations of Deep Learning
• Deep Learning
• Data Visualization
• Interactive Data Analytics
• Automated Learning and Data Analysis
• Software Engineering