Bora Oztekin

About

Hello! I am a Senior Cloud Software Engineer at NVIDIA, where I work as a distributed systems engineer on cloud inference platforms. I am currently pursuing an MS in Computer Science with AI specialization at Stanford University. I graduated from Texas A&M University, where I was an undergraduate researcher in the Data Integration, Visualization, and Exploration (DIVE) Laboratory, with a BS in Computer Science and a minor in Statistics. My research interests include deep learning and network science.

Recent

• September 2024: I started my MS in Computer Science with AI specialization at Stanford University
• August 2022: I started my role as a Data Science Engineer at NVIDIA
• May 2022: I graduated from Texas A&M University with a BS in Computer Science and a minor in Statistics
• August 2021: I will be returning to NVIDIA as a Data Science Engineer in 2022
• March 2021: I was nominated by Texas A&M for the 2021 Barry Goldwater Scholarship
• January 2021: I am thrilled to be joining NVIDIA as a Software Intern in Summer 2021
• October 2020: I received the Gathright Dean’s Excellence Award from the College of Engineering
• August 2020: I joined the Data Integration, Visualization, and Exploration (DIVE) Lab, led by Dr. Shuiwang Ji
• October 2019: Our disaster response and informatics research was recognized by the College of Engineering

Publications

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.

Projects

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.