Graduate Research Assistant (August 2020 - Present)
Graduate Research Assistant (January 2020 - July 2020)
Graduate Teaching Assistant (August 2019 - December 2019)
Lecturer (October 2017 - July 2019)
Antoun, C., Frías-Martínez, V., Garove, A., Awasthi, N., & Abrar, S. M. (2026). The benefits of in situ reporting: Experimental evidence from in-the-moment surveys of public transit riders. Behavior Research Methods, 58(7), 174. https://doi.org/10.3758/s13428-026-03022-z
Smolyak, D., Abrar, S. M., Awasthi, N., & Frias-Martinez, V. (2026). Assessing the impact of case correction methods on the fairness of COVID-19 predictive models. EPJ Data Science. https://doi.org/10.1140/epjds/s13688-026-00656-z
Awasthi, N., Abrar, S. M., Smolyak, D., & Frias-Martinez, V. (2026). From "I have nothing to hide" to "Its stalking": Americans' comfort sharing individual mobility features. Big Data & Society, 13(1). https://doi.org/10.1177/20539517261429203
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
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)
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.
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.
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.
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)
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).
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.
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.
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.
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.
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.
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.
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.
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.