Fereshteh Razmi

About

I am a postdoctoral researcher at Emory University, where I apply my skills and knowledge in machine learning and data science to tackle complex challenges. I have a Ph.D. in Computer Science from Emory University, where my research focused on adversarial machine learning, generative AI, and large-scale data analysis. 

I also have valuable industry experience as a data science intern at Walmart Labs, contributing to the optimization of e-commerce platforms and enhancing the customer experience. Moreover, I hold an MSc degree and a BSc degree  in Software Engineering, both from Sharif University of Technology. 

My portfolio includes multiple publications in the fields of machine learning and data science, and I'm highly proficient in Python and various programming languages. My passion lies in advancing data-driven solutions, with a core skill set encompassing data analysis, machine learning, deep learning, and statistical modeling. I'm constantly eager to acquire new techniques and tools and collaborate with fellow researchers and practitioners in the field.

Publications

Razmi, Fereshteh, and Li Xiong. "Classification auto-encoder based detector against diverse data poisoning attacks." In IFIP Annual Conference on Data and Applications Security and Privacy, pp. 263-281. Cham: Springer Nature Switzerland, 2023.

Razmi, Fereshteh, Jian Lou, Yuan Hong, and Li Xiong. "Interpretation Attacks and Defenses on Predictive Models Using Electronic Health Records." In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 446-461. Cham: Springer Nature Switzerland, 2023.

Amrollahi, Fatemeh, Supreeth P. Shashikumar, Fereshteh Razmi, and Shamim Nemati. "Contextual Embeddings from Clinical Notes Improves Prediction of Sepsis." In AMIA Annual Symposium Proceedings, vol. 2020, p. 197. American Medical Informatics Association, 2020.

Nemati, Shamim, Andre Holder, Fereshteh Razmi, Matthew D. Stanley, Gari D. Clifford, and Timothy G. Buchman. "An interpretable machine learning model for accurate prediction of sepsis in the ICU." Critical care medicine 46, no. 4 (2018): 547.

Holder, Andre L., Elizabeth Overton, Peter Lyu, Jordan A. Kempker, Shamim Nemati, Fereshteh Razmi, Greg S. Martin, Timothy G. Buchman, and David J. Murphy. "Serial daily organ failure assessment beyond ICU day 5 does not independently add precision to ICU risk-of-death prediction." Critical care medicine 45, no. 12 (2017): 2014.

Nemati, S., Stanley, M. D., ​Razmi F​., Buchman T. G. & Lehman L. (presented at ICCAI July 2017). Adherence to Individualized Fluid and Vasopressor Dosing Recommendation is Associated with Mortality Reduction in Sepsis: A Machine Learning Approach abstract to appear in Journal of Critical Care. ​ICCAI.

Contact info: 

frazmim at emory edu