Hello! I am a Data Science Engineer at NVIDIA who recently graduated from Texas A&M University with a BS in Computer Science and a minor in Statistics. I was an undergraduate researcher in the Data Integration, Visualization, and Exploration (DIVE) Laboratory at Texas A&M, and my research interests include deep learning and network science. Aside from my time with computers, I enjoy volunteering for charitable organizations.



Liu, M., Yan, K., Oztekin, B., & Ji, S. (2021). GraphEBM: Molecular Graph Generation with Energy-based Models. EBM Workshop at ICLR.

Liu, Y., Wang, L., Liu, M., Zhang, X., Oztekin, B., & Ji, S. (2021). Spherical Message Passing for 3D Graph Networks. International Conference on Learning Representations (ICLR).

Liu, M., Luo, Y., Wang, L., Xie, Y., Yuan, H., Gui, S., Yu, H., Xu, Z., Zhang, J., Liu, Y., Yan, K., Liu, H., Fu, C., Oztekin, B., Zhang, X., & Ji, S. (2021). DIG: A Turnkey Library for Diving into Graph Deep Learning Research. Journal of Machine Learning Research.

Yuan, F., Esmalian, A., Oztekin, B., & Mostafavi, A. (2021). Unveiling spatial patterns of disaster impacts and recovery using credit card transaction variances. ArXiv:2101.10090 [Physics, q-Fin].

Li, Q., Yang, Y., Wang, W., Lee, S., Xiao, X., Gao, X., Oztekin, B., Fan, C., & Mostafavi, A. (2021). Unraveling the Dynamic Importance of County-level Features in Trajectory of COVID-19. Nature Scientific Reports.

Fan, C., Lee, S., Yang, Y., Oztekin, B., Li, Q., & Mostafavi, A. (2020). Effects of population co-location reduction on cross-county transmission risk of COVID-19 in the United States. Applied Network Science, 6(1), 1–18.

Fan, C., Esparza, M., Dargin, J., Wu, F., Oztekin, B., & Mostafavi, A. (2020). Spatial biases in crowdsourced data: Social media content attention concentrates on populous areas in disasters. Computers, Environment and Urban Systems, 83, 101514.


COVID-AI at UrbanResilience.AI Lab

I made use of various large datasets to create analyses of COVID-19 and the changes it has caused in urban patterns. I also developed the COVID-AI website for the UrbanResilience.AI Lab and co-authored the Cross-County Transmission Risk study.

In anticipation of compounding risks from hurricanes and COVID-19, I created an interactive map which has been featured in a news article by The Conversation.

Visa Hackathon - Egora

Egora (electronic agora) is a multisided platform that helps consumers and small merchants manage loyalty programs and drive transactions by providing social incentives to consumers.

College Easy

College Easy is a Django application hosted on Heroku designed for managing the hectic and multifaceted college application process.

COVID Over Time

COVID Over Time plots the growth of the Coronavirus Disease over time using data provided by The New York Times.