Mustafa Hajij

Mustafa Hajij is an assistant professor at the MSDS program at University of San Francisco. He received his masters in Computer Science, PhD in Mathematics from Louisiana State University and postdoctoral training at the computer science departments at University of South Florida and Ohio State University. Before joining  MSDS program he was an assistant professor at the department of Mathematics and Computer Science at Santa Clara University. Prior to SCU, he spent a year as an AI research scientist at KLA Corporation.

• Deep Learning
• Algorithms

• Graph Neural Networks
• Topological Deep Learning
• Topological Data Analysis

• Louisiana State University, PhD in Mathematics.
• Louisiana State University, MS in Computer Science.
• Jordan University for Science and Technology, MS in Mathematics.
• Damascus University, BS in Mathematics.

• Assistant Professor of Data Science, Department of Mathematics and Computer Science, Santa Clara University. 
• Computer Science Researcher, Topology, Geometry, and Data Analysis group (TGDA@OSU), Department of Computer Science and Engineering, Ohio State University. 
• NSF Postdoctoral Scholar, Department of Computer Science and Engineering, University of South Florida. 
• Postdoctoral Scholar, Department of Mathematics and Statistics, University of South Florida. 

• DMS-2134231, Dec 2021- Nov 2024 Title: A Unifying Deep Learning Framework Using Cell Complex Neural Networks. Total amount $547,626.

• Roddenberry, T. Mitchell, Michael T. Schaub, and Mustafa Hajij. "Signal processing on cell complexes." ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022.
• Hajij, Mustafa, et al. "Normalizing Flow for Synthetic Medical Images Generation." 2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT). IEEE, 2022.
• Hajij, M., Jonoska, N., Kukushkin, D. and Saito, M. Graph Based Analysis for Gene Segment Organization In a Scrambled Genome. Journal of Theoretical Biology. 2020.
• Hajij, Mustafa, et al. "Visual detection of structural changes in time-varying graphs using persistent homology." 2018 ieee pacific visualization symposium (pacificvis). IEEE, 2018.